diff --git a/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb b/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb index 8451404..2aa4e94 100644 --- a/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb +++ b/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyME6RAicQ3TvPEi7ALaR+1f", + "authorship_tag": "ABX9TyMb1IjnGqP8ABGWnomNxjAN", "include_colab_link": true }, "kernelspec": { @@ -29,7 +29,9 @@ { "cell_type": "markdown", "source": [ - "Examples how to use ALIGNN-FF (https://doi.org/10.1039/D2DD00096B) Pre-trained model" + "Examples how to use ALIGNN-FF (https://doi.org/10.1039/D2DD00096B) Pre-trained model.\n", + "\n", + "An example of training ALIGNN-FF from scratch for 307K dataset is given [here](https://github.com/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/Train_ALIGNNFF_JARVIS_DFT_DB_307k.ipynb)" ], "metadata": { "id": "b9ppLFUUhh6g" @@ -47,7 +49,7 @@ "base_uri": "https://localhost:8080/" }, "id": "WG8GDWY15Oan", - "outputId": "9bb02c9b-cdb3-4ba6-de5f-9936aa8135a9" + "outputId": "ab9e65dd-81f8-4df0-bae1-d60c4ce64cd5" }, "execution_count": 1, "outputs": [ @@ -59,7 +61,7 @@ "📦 Installing...\n", "📌 Adjusting configuration...\n", "🩹 Patching environment...\n", - "⏲ Done in 0:00:19\n", + "⏲ Done in 0:00:16\n", "🔁 Restarting kernel...\n" ] } @@ -82,7 +84,7 @@ "base_uri": "https://localhost:8080/" }, "id": "XhPIzL-s5EXn", - "outputId": "3a7d5e22-113c-491d-9d36-348987aef977" + "outputId": "8ace9b45-3358-4dbb-d2b4-c50bdb64bc47" }, "outputs": [ { @@ -165,7 +167,7 @@ " importlib-resources-6.4.0 | pyhd8ed1ab_0 9 KB conda-forge\n", " importlib_metadata-8.0.0 | hd8ed1ab_0 9 KB conda-forge\n", " importlib_resources-6.4.0 | pyhd8ed1ab_0 32 KB conda-forge\n", - " inflect-7.3.0 | pyhd8ed1ab_0 37 KB conda-forge\n", + " inflect-7.3.1 | pyhd8ed1ab_0 37 KB conda-forge\n", " itsdangerous-2.2.0 | pyhd8ed1ab_0 19 KB conda-forge\n", " jarvis-tools-2024.5.10 | pyhd8ed1ab_0 3.8 MB conda-forge\n", " jinja2-3.1.4 | pyhd8ed1ab_0 109 KB conda-forge\n", @@ -216,14 +218,14 @@ " markdown-3.6 | pyhd8ed1ab_0 76 KB conda-forge\n", " markdown-it-py-3.0.0 | pyhd8ed1ab_0 63 KB conda-forge\n", " markupsafe-2.1.5 | py310h2372a71_0 24 KB conda-forge\n", - " matplotlib-base-3.8.4 | py310hef631a5_2 6.5 MB conda-forge\n", + " matplotlib-base-3.9.0 | py310h0b1de36_0 6.6 MB conda-forge\n", " mccabe-0.7.0 | pyhd8ed1ab_0 11 KB conda-forge\n", " mdurl-0.1.2 | pyhd8ed1ab_0 14 KB conda-forge\n", " mergedeep-1.3.4 | pyhd8ed1ab_0 9 KB conda-forge\n", " metis-5.1.1 | h59595ed_2 3.7 MB conda-forge\n", " mkdocs-1.6.0 | pyhd8ed1ab_0 3.4 MB conda-forge\n", " mkdocs-get-deps-0.2.0 | pyhd8ed1ab_0 14 KB conda-forge\n", - " mkdocs-material-9.5.27 | pyhd8ed1ab_0 4.8 MB conda-forge\n", + " mkdocs-material-9.5.28 | pyhd8ed1ab_0 4.8 MB conda-forge\n", " mkdocs-material-extensions-1.3.1| pyhd8ed1ab_0 16 KB conda-forge\n", " mkl-2023.2.0 | h84fe81f_50496 156.8 MB conda-forge\n", " ml_dtypes-0.3.2 | py310hcc13569_0 160 KB conda-forge\n", @@ -249,8 +251,8 @@ " psutil-6.0.0 | py310hc51659f_0 363 KB conda-forge\n", " pthread-stubs-0.4 | h36c2ea0_1001 5 KB conda-forge\n", " pycodestyle-2.12.0 | pyhd8ed1ab_0 34 KB conda-forge\n", - " pydantic-2.7.4 | pyhd8ed1ab_0 275 KB conda-forge\n", - " pydantic-core-2.18.4 | py310he421c4c_0 1.5 MB conda-forge\n", + " pydantic-2.8.0 | pyhd8ed1ab_0 284 KB conda-forge\n", + " pydantic-core-2.20.0 | py310he421c4c_0 1.6 MB conda-forge\n", " pydantic-settings-2.3.4 | pyhd8ed1ab_0 25 KB conda-forge\n", " pydocstyle-6.3.0 | pyhd8ed1ab_0 39 KB conda-forge\n", " pyflakes-3.2.0 | pyhd8ed1ab_0 57 KB conda-forge\n", @@ -260,7 +262,7 @@ " python-dateutil-2.9.0 | pyhd8ed1ab_0 218 KB conda-forge\n", " python-dotenv-1.0.1 | pyhd8ed1ab_0 24 KB conda-forge\n", " python-flatbuffers-24.3.25 | pyh59ac667_0 34 KB conda-forge\n", - " python-lmdb-1.4.1 | py310hdf73078_1 131 KB conda-forge\n", + " python-lmdb-1.5.1 | py310h6a71df1_0 130 KB conda-forge\n", " python-tzdata-2024.1 | pyhd8ed1ab_0 141 KB conda-forge\n", " pytorch-2.3.1 |cpu_mkl_py310h75865b9_100 28.9 MB conda-forge\n", " pytorch-cuda-12.4 | hc786d27_6 7 KB pytorch\n", @@ -268,14 +270,15 @@ " pytz-2024.1 | pyhd8ed1ab_0 184 KB conda-forge\n", " pyyaml-6.0.1 | py310h2372a71_1 167 KB conda-forge\n", " pyyaml-env-tag-0.1 | pyhd8ed1ab_0 7 KB conda-forge\n", + " qhull-2020.2 | h4bd325d_2 1.9 MB conda-forge\n", " re2-2023.09.01 | h7f4b329_2 26 KB conda-forge\n", " regex-2024.5.15 | py310hc51659f_0 340 KB conda-forge\n", " rich-13.7.1 | pyhd8ed1ab_0 180 KB conda-forge\n", - " scikit-learn-1.5.0 | py310h981052a_1 8.9 MB conda-forge\n", + " scikit-learn-1.5.1 | py310h146d792_0 8.8 MB conda-forge\n", " scipy-1.14.0 | py310h93e2701_0 16.1 MB conda-forge\n", " six-1.16.0 | pyh6c4a22f_0 14 KB conda-forge\n", " sleef-3.5.1 | h9b69904_2 1.5 MB conda-forge\n", - " snappy-1.2.0 | hdb0a2a9_1 41 KB conda-forge\n", + " snappy-1.2.1 | ha2e4443_0 41 KB conda-forge\n", " snowballstemmer-2.2.0 | pyhd8ed1ab_0 57 KB conda-forge\n", " spglib-2.4.0 | py310h813e3b9_2 631 KB conda-forge\n", " sympy-1.12.1 | pypyh2585a3b_103 4.1 MB conda-forge\n", @@ -358,7 +361,7 @@ " importlib-resourc~ conda-forge/noarch::importlib-resources-6.4.0-pyhd8ed1ab_0 \n", " importlib_metadata conda-forge/noarch::importlib_metadata-8.0.0-hd8ed1ab_0 \n", " importlib_resourc~ conda-forge/noarch::importlib_resources-6.4.0-pyhd8ed1ab_0 \n", - " inflect conda-forge/noarch::inflect-7.3.0-pyhd8ed1ab_0 \n", + " inflect conda-forge/noarch::inflect-7.3.1-pyhd8ed1ab_0 \n", " itsdangerous conda-forge/noarch::itsdangerous-2.2.0-pyhd8ed1ab_0 \n", " jarvis-tools conda-forge/noarch::jarvis-tools-2024.5.10-pyhd8ed1ab_0 \n", " jinja2 conda-forge/noarch::jinja2-3.1.4-pyhd8ed1ab_0 \n", @@ -407,14 +410,14 @@ " markdown conda-forge/noarch::markdown-3.6-pyhd8ed1ab_0 \n", " markdown-it-py conda-forge/noarch::markdown-it-py-3.0.0-pyhd8ed1ab_0 \n", " markupsafe conda-forge/linux-64::markupsafe-2.1.5-py310h2372a71_0 \n", - " matplotlib-base conda-forge/linux-64::matplotlib-base-3.8.4-py310hef631a5_2 \n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.9.0-py310h0b1de36_0 \n", " mccabe conda-forge/noarch::mccabe-0.7.0-pyhd8ed1ab_0 \n", " mdurl conda-forge/noarch::mdurl-0.1.2-pyhd8ed1ab_0 \n", " mergedeep conda-forge/noarch::mergedeep-1.3.4-pyhd8ed1ab_0 \n", " metis conda-forge/linux-64::metis-5.1.1-h59595ed_2 \n", " mkdocs conda-forge/noarch::mkdocs-1.6.0-pyhd8ed1ab_0 \n", " mkdocs-get-deps conda-forge/noarch::mkdocs-get-deps-0.2.0-pyhd8ed1ab_0 \n", - " mkdocs-material conda-forge/noarch::mkdocs-material-9.5.27-pyhd8ed1ab_0 \n", + " mkdocs-material conda-forge/noarch::mkdocs-material-9.5.28-pyhd8ed1ab_0 \n", " mkdocs-material-e~ conda-forge/noarch::mkdocs-material-extensions-1.3.1-pyhd8ed1ab_0 \n", " mkl conda-forge/linux-64::mkl-2023.2.0-h84fe81f_50496 \n", " ml_dtypes conda-forge/linux-64::ml_dtypes-0.3.2-py310hcc13569_0 \n", @@ -439,8 +442,8 @@ " psutil conda-forge/linux-64::psutil-6.0.0-py310hc51659f_0 \n", " pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001 \n", " pycodestyle conda-forge/noarch::pycodestyle-2.12.0-pyhd8ed1ab_0 \n", - " pydantic conda-forge/noarch::pydantic-2.7.4-pyhd8ed1ab_0 \n", - " pydantic-core conda-forge/linux-64::pydantic-core-2.18.4-py310he421c4c_0 \n", + " pydantic conda-forge/noarch::pydantic-2.8.0-pyhd8ed1ab_0 \n", + " pydantic-core conda-forge/linux-64::pydantic-core-2.20.0-py310he421c4c_0 \n", " pydantic-settings conda-forge/noarch::pydantic-settings-2.3.4-pyhd8ed1ab_0 \n", " pydocstyle conda-forge/noarch::pydocstyle-6.3.0-pyhd8ed1ab_0 \n", " pyflakes conda-forge/noarch::pyflakes-3.2.0-pyhd8ed1ab_0 \n", @@ -450,7 +453,7 @@ " python-dateutil conda-forge/noarch::python-dateutil-2.9.0-pyhd8ed1ab_0 \n", " python-dotenv conda-forge/noarch::python-dotenv-1.0.1-pyhd8ed1ab_0 \n", " python-flatbuffers conda-forge/noarch::python-flatbuffers-24.3.25-pyh59ac667_0 \n", - " python-lmdb conda-forge/linux-64::python-lmdb-1.4.1-py310hdf73078_1 \n", + " python-lmdb conda-forge/linux-64::python-lmdb-1.5.1-py310h6a71df1_0 \n", " python-tzdata conda-forge/noarch::python-tzdata-2024.1-pyhd8ed1ab_0 \n", " pytorch conda-forge/linux-64::pytorch-2.3.1-cpu_mkl_py310h75865b9_100 \n", " pytorch-cuda pytorch/linux-64::pytorch-cuda-12.4-hc786d27_6 \n", @@ -458,14 +461,15 @@ " pytz conda-forge/noarch::pytz-2024.1-pyhd8ed1ab_0 \n", " pyyaml conda-forge/linux-64::pyyaml-6.0.1-py310h2372a71_1 \n", " pyyaml-env-tag conda-forge/noarch::pyyaml-env-tag-0.1-pyhd8ed1ab_0 \n", + " qhull conda-forge/linux-64::qhull-2020.2-h4bd325d_2 \n", " re2 conda-forge/linux-64::re2-2023.09.01-h7f4b329_2 \n", " regex conda-forge/linux-64::regex-2024.5.15-py310hc51659f_0 \n", " rich conda-forge/noarch::rich-13.7.1-pyhd8ed1ab_0 \n", - " scikit-learn conda-forge/linux-64::scikit-learn-1.5.0-py310h981052a_1 \n", + " scikit-learn conda-forge/linux-64::scikit-learn-1.5.1-py310h146d792_0 \n", " scipy conda-forge/linux-64::scipy-1.14.0-py310h93e2701_0 \n", " six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n", " sleef conda-forge/linux-64::sleef-3.5.1-h9b69904_2 \n", - " snappy conda-forge/linux-64::snappy-1.2.0-hdb0a2a9_1 \n", + " snappy conda-forge/linux-64::snappy-1.2.1-ha2e4443_0 \n", " snowballstemmer conda-forge/noarch::snowballstemmer-2.2.0-pyhd8ed1ab_0 \n", " spglib conda-forge/linux-64::spglib-2.4.0-py310h813e3b9_2 \n", " sympy conda-forge/noarch::sympy-1.12.1-pypyh2585a3b_103 \n", @@ -515,8 +519,8 @@ "Preparing transaction: ...working... done\n", "Verifying transaction: ...working... done\n", "Executing transaction: ...working... done\n", - "CPU times: user 2.84 s, sys: 345 ms, total: 3.18 s\n", - "Wall time: 6min 31s\n" + "CPU times: user 2.03 s, sys: 262 ms, total: 2.29 s\n", + "Wall time: 5min 53s\n" ] } ], @@ -568,7 +572,7 @@ "base_uri": "https://localhost:8080/" }, "id": "qdyraGtI5G0l", - "outputId": "6c5c2e34-2815-47cc-9d45-c08f3777a93e" + "outputId": "8f961ccf-1826-4ffd-f112-33cb19cc6769" }, "execution_count": 2, "outputs": [ @@ -642,7 +646,7 @@ "metadata": { "id": "5AToeQIv6STA" }, - "execution_count": 3, + "execution_count": null, "outputs": [] }, { @@ -660,7 +664,7 @@ "metadata": { "id": "88GqbPa56ZJq" }, - "execution_count": 4, + "execution_count": null, "outputs": [] }, { @@ -3871,11 +3875,1090 @@ } ] }, + { + "cell_type": "code", + "source": [ + "!pip install -q git+https://github.com/usnistgov/intermat.git@develop" + ], + "metadata": { + "id": "U5Exr15t87Ms", + "outputId": "ac689a20-4e4f-4970-9349-9a00e1d61d7e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for intermat (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.jsonutils import dumpjson,loadjson\n", + "from jarvis.db.jsonutils import dumpjson\n", + "import plotly.graph_objects as go\n", + "\n", + "config = {'calculator_method': 'alignn_ff',\n", + " 'disp_intvl': 0.1,\n", + " 'dataset': 'dft_3d',\n", + " 'film_index': '1_1_0',\n", + " 'film_jid': 'JVASP-1002',\n", + " 'substrate_index': '1_1_0',\n", + " 'substrate_jid': 'JVASP-1002'}\n", + "\n", + "dumpjson(data=config,filename='config_example2.json')\n", + "!run_intermat.py --config_file config_example2.json >out2\n", + "\n", + "res=loadjson('intermat_results.json')" + ], + "metadata": { + "id": "OW5IpLScIgTY", + "outputId": "a11437e2-3d0e-4dfd-960c-d449049f0e4d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "100% 40.8M/40.8M [00:02<00:00, 17.6MiB/s]\n", + "/usr/local/lib/python3.10/site-packages/intermat/generate.py:52: RuntimeWarning: invalid value encountered in scalar divide\n", + " strain_x = (\n", + "/usr/local/lib/python3.10/site-packages/intermat/generate.py:55: RuntimeWarning: invalid value encountered in scalar divide\n", + " strain_y = (\n", + " 0% 0/100 [00:00" + ], + "image/png": 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KfiBH//nZnFmfZsl+rllbI/LcrQyc3ekJ73gzXbfIY70sD+Is3wdrOjNPzm4uZjppHJXjaU8GOGamQhChVXD//Lu/Nvk6UYUgwzvWCxsLqmqR31tnScoyILw1rf6qoHURqF4IWplpl7ca8/i4kUVg27btg9N/WnF//9f/jf4Wuvvdf//Trv/+9//yP9vf/vLHTt/NdZ+9/n57++bV8D+3hV4nAn/+3V+3P/39D6e/7tc/f7h99dEvp7/OSo48yMyTdj7+4oftp28/HfJnZTO6CGxbp5OBqCavhT5t5IBa8XSg96sBJwTHjVyPKxSBI6rP5dGvvCKKwLYlfE1wNLxafFvVAzzKqJ8RaFUIuI31f12VzUXF10tRRWDbEpaBUVYrAlUCXMnoHxZsUQhWOh2wschDuXpZdDZTlYGR4eVlVa9rpp9kf8neB/xqhaDC7m7FInDkMx+dJytsLjL8LaRUZWCUFcO7bWMDfFSF4b9tscd5VQpBZCmzsehv1TnaWoYisG0dysDoYS68+4wK8MxtPrIItPrf37ZaJwRZeSDuY14/lKUIbFuik4FRDw/h3U+A38lQBFp+HYXgHet8v+ybi8wnjZmKwLYlKgNHCO8x2QOcVaYi0PLrzVYIbCxyqzS3e73qylYEti1JGTgSXj/xO1alAPeQsQi0/LoZC8HIXd3q6/sMm4t9MhaBbUtSBkZQBB7KfD2yHe1lLgItZSwEe9lY1LBq+cpaBLatcRk4MsRHhZfzjlz30W2+9bFelSKQ9V8prPTXPG+lCDxW4W8qzaLXPFniZEB4nybAz6u2U85aCDJbaT33NmLOHtlcZDlpzL6xKFcGhLeeiu/6Mh/njfjzKhaCEevMxqKtVeZ59iKwbQXLAIyw+m/4W/3zXzPqN/atIuK3o1bV4reXPqdcGTiyeAS4nSPXv+qvIW3xQOwd4B5/XtUicGSdeRi1Y85e1ypTPedJ0zJw5HfRC3Ac4R1jVCHIWAS+/PzHpl8vA7lpY9TG4shzqYfshaDcycBRAnxehVOB1g+f7AFu+fWrngjcZ3MRw3y9TeaMlS0DAnxO5vBmafIX2QvB6FcRo2RbB/dlzk8FFTYWvWR9/ZiiDIy6yQJ858h1WDm825a3ELT6epl3LCPYXBxnru6XsRCkKANHCfAYrvOdbIVAEXiazcU4ozYWR509Xer5My/ZCkHzMnD04gvwGKt//rOyFAJFoD1/U6m/o0VgplPG+zIVgtInA9smwL0J72PRhUAReNnR9ecU7HbmaBtZCkGqMiDAfVUIb4sfGhvx19miCkGlItDiPmT+IcKLCrnKwMbiugyFIFUZGEmAXya8zxu9s65UBDKwuejH/GwvuhBMUwYE+HnC28eoACsCucnX8yI2FhVOlXo4Oiu6lIEzN2HkrnKVAI/8id9VTgXui270t1IEbmdzcd0qc/Ni5L+iGfnzSNOcDGybHyZsxSDcr2ch8K8LHmdz0Vb2v0o4g6hC8EGTP7Wxn7799PCvI/3bX/64+9dirhDiCqof63310S+nf1Xpn/7+hwe/qlQRuFsXEb+T3iw570wRWPGU8aLFLNm2x/PkOVOdDHCe8J7T8oSgchHI9AuKzqxLu1qijD4hSFsGBJgzIh9GWX6GoPqJALVFbiyqnzJejMxwtzIwy81YiVOBdqIfxNF//kxsLog0KstpTwbOEmBWpQg8pqyOZf62NSLTqcuAAI8THd4ZT5IiHsqzFoHo9RGdj0rOXquMcz/Dz8D0znbqMnCWAN9mxvBmMevDuaKz69Q8IVrPeZK+DAgw1Y0qBIoH0TJsLKJPkXrrlfOuZWD2mzKDDOFdQe8HdaYikOFI9RqbC2bQI+/pTwZaEODcepXGbA+lXg/sTEWAddlYjNU69yXKgEXSh/CO1zrAKxWBLCeNNhdk0TL/JcpACwL8kOsRp1WAVyoCLSmx7ZkncVrNgTJlQICZydkAKwKxPPzeaXEtWs33nqdH2V473tdiHnQvA1mO9rZNgC8yhXdlRwOsCJzXYv2aJ8ykzMnAtgnwjDKVxQh7H+zZi0Dm3RPt2VjkcXY2lCoDnLdaeCs8nG4NcfYiMEK28mhzQSZnZsSSZUCAyealECsC7VUqtRmZozkdnRXlyoAAHye8uV0LsSKQm1wd13KeZzs1inRkZgwpAxlv0moBbvV5lbG+3g+xItCX9XzMavPzosJrx4u9s6PcycC2CfAsMpbEDC4hVgTqWOnhaGNRx54ZUrIMtLJKgIW3HkXgutYlstW6XmWeUMuts2TpMrBtAgytVDpC5Rgbi3l9EP0NHPXTt59uH3/xQ5OvpRC8rHJ4v/z8x+2b7z6J/jZYgFkSwyvH84adDLhZQFaVyy60UPo1gQDXpRzCmsztnEqXAcYQXrLpUSatc24x68/GlC8DAswtZg1wFq4vtzCv8yr7A4SMMVN47z+w/EDheTMWgJY/mMwYEa8cZ5wlQ8vA2zevts9ef9/86wowe80Y5hFmLACMMdPG4r73M1F1njgZ4Kpe4c32w4OKwfOyFgCbCzKqOk+mKQMC3NasLf4lVYPcWtYCQE3mSf55Mk0Z2DaFgLYqBbkFBeAds6SdnkUg2ynjc7LPk+FloNfRHu2s2uKfkz3IR3j4P08hoJeM82Sqk4FtE+DsKjX5ayr/wNBsBcDmIjcbi5dlKQbTlQHOEd79soT5mtkKwEg2F8eZJftFzpKQMtC7zQvwMcJ7XpZioAC0Y57kNMMp43NGz5JpTwYEmGijw6wAkIWNRVsjXk1OWwbYZ0R4Z2/yz+lVDBSAMT83YHNBJj3mydRlQIBvo8WPdTbICkAM8+Q25slYrYpBWBkY9VPAAkxmtwZZAaCCUUVg5VPG55wpBlOfDPAyLT4PD/z8bC6oYu88Kf8rjG/hgfe0kddFk6cn6yueOVvbEmUAoBUPvcdck/pCy8DINm+xPuR6wHHyE8cpUB9LnQwIMEBb5uoclioD3BkdXk2eEUavMw9B12Amy5WB1Rfv6p8fWpInZhFeBiJ2jQIMcE7EHHXK2E94GWAcJQjakytmsGwZWC3AUZ9Xk2ck622M1ebnCpYtA9tmQQNtrDRLVvqsK0lRBrT5voQX+pOzvjwn+lr+dxMIMNCKeUJVKU4GmJMmTwTrDvZTBgBgcWnKgDYPwFM8H/pLUwYAgBjKAF1o8kSy/mAfZQAAFpeqDGjzANznuTBGqjLAHIQXoJbl/9EhyOSz198/+p8pV8e8ffPqyesJK9i79pUBCPZSaO///ysGwDVnym+6MqDNM7sz61sxYCXW+MtaPS/TlQFqE96n9Si4739N1x7W0GOeKAPQyegTLqcGjzlpZBa917EyAA1lefAoBlDfyHmSsgxo81SSfa0qBlS06lqNmif+nQGaWTW85GZdUkXkxiLlycC2PQ5w9t3XqlYftNZlDffXqXuW0+qzJFraMvA+Yc5BYGv77PX3y99DG408Vl+LmZQpA/cpBmMJLDMzT8YyT54WvfZKloH7tPw+BPZlVdea04HrFIP2rLUaypeB9wnzcUIL75glx5kl+2RYX9OVgfuE+XkCe1z19eR0YB8nkC+znmqbugzcpxjcEVg4zzwxS1rJsn6WKQP3rdbyhbat2dcL+6xUDMySeS1ZBt5ngbMirwracz2pyr9ACAABMp0kKQOwQ6bwtjDb5wGOUQYAYLBsRVwZgBtlC28rs34u4HbKAAAMlLGAKwNwg4zhbWn2zwc8TxkAgMUpAwAwSNZTOGUAXpA1vK2t8jmBx5QBABggc+FWBuAZmcPbw2qfF7ijDABAZ9mLtjIAV2QPby+rfm5YmTIAAB1VKNh+hfG2bd9890n0t9DVl5//GP0tlFMhvORjllDVsmVg9tDed/msgsytPnv9/fb2zavob6OMVeaJWTKv5crAKqF9iiBDW6vOE7PkdlVOGZcpA6uG9imC/Lwq4e3N6cB15smd+9fBPKlt+jIgtNcpBbCPeXKdefJYpY3FtGVAaG8nxO9UCu8ITgfumCe3M09qmq4MCO1xQgwPmSfHrT5Pqm0spikDQtvOqiGuFl76MU/aWXWeVFO+DAhtP0LMtq31qsA86cc8ya1sGRDacYSYmZklY60wTyqeMpYrA4IbZ+a/RlQxvCPNeDpglsRaoRRUUqYMCG4ugkxVZkkus82SqhuL9GVAcHObIchVwzta9dMBsyS3GWZJZWnLgODWIshkZZbUUnmWVN5YpCsDgltbtSBXDm+ESqcDZklt1WZJdSnKgNDOR5CJYp7Mpcosqb6xCC0DGUL79c8fRn8LQ3z10S8hf26VIFNf9DwxS/oyS/oKKQPRod22dYJ7cfm8gsxsoueJWTKWWdLHb6O/gQirhTeT6MHNXKynONFz1L1va3gZiL6B0Qs42uqfH1paPU/Rnz/6eXJflR+svWZoGch041YmwO9UD/Boma5X9DqKzhG0tNRrAuGFOUQXAd6JnqvWQhvDykD0DYtesNlEX4/o9QBnROcnm+jrkWWeZDo522upkwEeEmD2yDLootdNdG6ghyFlQHjJLMtDjpdFzxKui56z1sY53ctA9A2KXqDZRV+f6PUBe0TnJbvo65NhnlTdXHhNgADzogwDLnqdROcEeupaBoSXKjI87LguepZwu+i5a60c060MRN+Q6AVZTfT1il4v8JzofFQTfb2i50nFzYXXBPzD6gHmadGDLXpdROcCRuhSBoSXiqIfejwWPUs4LnoOWzv7NC8D0TcgegFWF339otcP3Bedh+qir1/kPKm2ufCagEdWDjB5RK+D6BzASE3LgPBSXbU231vU9YieJbQTPZetpds0KwPRFzx6wc0m+npGryfWFr3+ZxN9PaPmSaXNhdcEXLVqgIkVfd+j1z1EaFIGhJeZVGrzPUVch+hZQj/Rc9raet7pMhB9gaMX2Oyir2/0+mIt0et9dtHXN2KeVNlceE3Ai1YMcLSPv/gh+lsYLvo+R69z6OHWWXKqDAgvs6rS5ntZ/fPTT/Tcjn5uZXW4DERf0OgFtZro6x293ugr+v5Gr+/VRF/v6PWWkdcE3EyA6SH6vkava+ZX4aTtUBkQXlZQIcA9rPq5GSt6jkc/x7LZXQaiL2D0Alpd9PWPXn+0FX0/o9fz6qKvf/T6y8RrAnYT4L5W+ZsE0fcxeh2zluwnbrvKgPCymuwBbm21z0u86Lke/VzL4uYyEH3BohcMD0Xfj+j1OMLMJwTR9y96/fJQ9P2IXo8ZeE3AYdEBpqbowWvdEiXzyVuJMiC8PGXUQyVzgFta5XOSkzkfK30ZsEByc3/Yw6kAz4m8P9FrM1r6MkB+AkwFigAZZD2BS10GhBfGGDGgFDduYXMRI20ZUARqmT3AWds8tzFPanG/2tjzN5LSlgHqEWCuidxxWZfssermImUZEF72Wvl476yMgwk8B8ZKVwYsgNpmvn8emvXMvB5XEHX/VtxcpCsD1CfA3Bd1XxQBMsu2uUhVBoQXxso2kOB9Nhdj/ObXX3/9NfqbAADipDoZAADGUwYAYHHKAAAsThkAgMUpAwCwOGUAABanDADA4pQBAFicMgAAi/s/Q/ebwjdzGNIAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "seperations=np.arange(0.05,5,.1)\n", + "from intermat.generate import InterfaceCombi\n", + "from intermat.config import IntermatConfig\n", + "config = IntermatConfig()\n", + "x = InterfaceCombi(\n", + " film_indices=[[1,1,0]],\n", + " subs_indices=[[1,1,0]],\n", + " film_ids=['JVASP-1002'],\n", + " subs_ids=['JVASP-1002'],\n", + " disp_intvl=0.0,\n", + " seperations=seperations,\n", + ")\n", + "wads = x.calculate_wad(method=\"alignn_ff\",extra_params=config.dict())" + ], + "metadata": { + "id": "am9kprPkKXTC", + "outputId": "33518b74-fa67-4146-a0d2-dc56bb3e6b79", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 3D dataset 76k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n", + "Loading the zipfile...\n", + "Loading completed.\n", + "len generated 50\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\r 0%| | 0/50 [00:00" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, { "cell_type": "code", "source": [], "metadata": { - "id": "U5Exr15t87Ms" + "id": "bOPh8__bM0Jp" }, "execution_count": null, "outputs": [] diff --git a/jarvis-tools-notebooks/Basic_ML.ipynb b/jarvis-tools-notebooks/Basic_ML.ipynb new file mode 100644 index 0000000..f24e4d7 --- /dev/null +++ b/jarvis-tools-notebooks/Basic_ML.ipynb @@ -0,0 +1,2116 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyP0vNNwxEHjnSZo16c0vh4k", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Introduction\n", + "\n", + "The mechanical properties of a material affect how it behaves as it is loaded. The elastic modulus of the material affects how much it deflects under a load, and the strength of the material determines the stresses that it can withstand before it fails. The ductility of a material also plays a significant role in determining when a material will break as it is loaded beyond its elastic limit. Because every mechanical system is subjected to loads during operation, it is important to understand how the materials that make up those mechanical systems behave. There are a lot of parameters to affect mechanical properties such as microstrucre, heat treatment, process method, composition etc.\n", + "\n", + "Composition and heat treatment are most important parameters. They give information about mechanical properties of materials in many engineering applications. Many tests are carried out to examine the mechanical effects of these properties. Sometimes these tests are disadvantageous in terms of time and money. Machine learning methods have proven to be successful in the prediction of a large number of material properties. ML studies have established the unequivocal potential of this emerging discipline in accelerating discovery and design of new/improved materials. However, there still does not exist a standardized set of protocols for exploring this approach in a systematic manner on many potential applications, and thus, establishing the composition-processing-structure-property relationships still remains an arduous task.\n", + "\n", + "#### DataSet\n", + "#### Reference:\n", + "\n", + "1. https://link.springer.com/article/10.1186/2193-9772-3-8\n", + "\n", + "2. https://www.kaggle.com/code/emrzcn/prediction-of-mechanical-properties-of-steels/notebook\n", + "\n", + "Fatigue Dataset for Steel from National Institute of Material Science (NIMS) MatNavi was used in this work, which is one of the largest databases in the world with details on composition, mill product (upstream) features and subsequent processing (heat treatment) parameters. The database comprises carbon and low-alloy steels, carburizing steels and spring steels. Fatigue life data, which pertain to rotating bending fatigue tests at room temperature conditions, was the target property for which we aimed to construct predictive models in the current study. The features in the dataset can be categorized into the following:\n", + "\n", + "* Chemical composition - %C, %Si, %Mn, %P, %S, %Ni, %Cr, %Cu, %Mo (all in wt. %)\n", + "* Upstream processing details - ingot size, reduction ratio, non-metallic inclusionsAgrawal et al.\n", + "* Heat treatment conditions - temperature, time and other process conditions for normalizing, through-hardening, carburizing-quenching and tempering processes\n", + "* Mechanical properties - YS, UTS, %EL, %RA, hardness, Charpy impact value (J/cm2), fatigue strength.[3]\n", + "\n", + "The data used in this work has 437 instances/rows, **25 features/columns (composition and processing parameters)**, and **1 target property (fatigue strength).** The 437 data instances include 371 carbon and low alloy steels, 48 carburizing steels, and 18 spring steels. This data pertains to various heats of each grade of steel and different processing conditions.\n", + "\n", + "\n", + "## Variable Description\n", + "\n", + "* C % - Carbon\n", + "* Si % - Silicon\n", + "* Mn % - Manganese\n", + "* P % - Phosphorus\n", + "* S % - Sulphur\n", + "* Ni % - Nickel\n", + "* Cr % - Chromium\n", + "* Cu % - Copper\n", + "* Mo % - Molybdenum\n", + "* NT - Normalizing Temperature\n", + "* THT - Through Hardening Temperature\n", + "* THt - Through Hardening Time\n", + "* THQCr - Cooling Rate for Through Hardening\n", + "* CT - Carburization Temperature\n", + "* Ct - Carburization Time\n", + "* DT - Diffusion Temperature\n", + "* Dt - Diffusion time\n", + "* QmT - Quenching Media Temperature (for Carburization)\n", + "* TT - Tempering Temperature\n", + "* Tt - Tempering Time\n", + "* TCr - Cooling Rate for Tempering\n", + "* RedRatio - Reduction Ratio (Ingot to Bar)\n", + "* dA - Area Proportion of Inclusions Deformed by Plastic Work\n", + "* dB -Area Proportion of Inclusions Occurring in Discontinuous Array\n", + "* dC -Area Proportion of Isolated Inclusions\n", + "* Fatigue - Rotating Bending Fatigue Strength (107 Cycles)\n", + "\n", + "\n", + "Email: kamal.choudhary@nist.gov" + ], + "metadata": { + "id": "FI4N714HspRU" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 461 + }, + "id": "F34AQ65lr9MF", + "outputId": "8067f820-003b-4bda-dc26-c95432f41975" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-07-08 20:14:01-- https://static-content.springer.com/esm/art%3A10.1186%2F2193-9772-3-8/MediaObjects/40192_2013_16_MOESM1_ESM.xlsx\n", + "Resolving static-content.springer.com (static-content.springer.com)... 151.101.0.95, 151.101.64.95, 151.101.128.95, ...\n", + "Connecting to static-content.springer.com (static-content.springer.com)|151.101.0.95|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 91132 (89K) [application/octet-stream]\n", + "Saving to: ‘40192_2013_16_MOESM1_ESM.xlsx’\n", + "\n", + "\r 40192_201 0%[ ] 0 --.-KB/s \r40192_2013_16_MOESM 100%[===================>] 89.00K --.-KB/s in 0.004s \n", + "\n", + "2024-07-08 20:14:01 (19.6 MB/s) - ‘40192_2013_16_MOESM1_ESM.xlsx’ saved [91132/91132]\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Unknown extension is not supported and will be removed\n", + " warn(msg)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Sl. No. NT THT THt THQCr CT Ct DT Dt QmT ... S Ni \\\n", + "0 1 885 30 0 0 30 0.0 30.0 0.0 30 ... 0.022 0.01 \n", + "1 2 885 30 0 0 30 0.0 30.0 0.0 30 ... 0.017 0.08 \n", + "2 3 885 30 0 0 30 0.0 30.0 0.0 30 ... 0.015 0.02 \n", + "3 4 885 30 0 0 30 0.0 30.0 0.0 30 ... 0.024 0.01 \n", + "4 5 885 30 0 0 30 0.0 30.0 0.0 30 ... 0.022 0.01 \n", + "\n", + " Cr Cu Mo RedRatio dA dB dC Fatigue \n", + "0 0.02 0.01 0.0 825 0.07 0.02 0.04 232 \n", + "1 0.12 0.08 0.0 610 0.11 0.00 0.04 235 \n", + "2 0.03 0.01 0.0 1270 0.07 0.02 0.00 235 \n", + "3 0.02 0.01 0.0 1740 0.06 0.00 0.00 241 \n", + "4 0.02 0.02 0.0 825 0.04 0.02 0.00 225 \n", + "\n", + "[5 rows x 27 columns]" + ], + "text/html": [ + "\n", + "
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No. 437 non-null int64 \n", + " 1 NT 437 non-null int64 \n", + " 2 THT 437 non-null int64 \n", + " 3 THt 437 non-null int64 \n", + " 4 THQCr 437 non-null int64 \n", + " 5 CT 437 non-null int64 \n", + " 6 Ct 437 non-null float64\n", + " 7 DT 437 non-null float64\n", + " 8 Dt 437 non-null float64\n", + " 9 QmT 437 non-null int64 \n", + " 10 TT 437 non-null int64 \n", + " 11 Tt 437 non-null int64 \n", + " 12 TCr 437 non-null float64\n", + " 13 C 437 non-null float64\n", + " 14 Si 437 non-null float64\n", + " 15 Mn 437 non-null float64\n", + " 16 P 437 non-null float64\n", + " 17 S 437 non-null float64\n", + " 18 Ni 437 non-null float64\n", + " 19 Cr 437 non-null float64\n", + " 20 Cu 437 non-null float64\n", + " 21 Mo 437 non-null float64\n", + " 22 RedRatio 437 non-null int64 \n", + " 23 dA 437 non-null float64\n", + " 24 dB 437 non-null float64\n", + " 25 dC 437 non-null float64\n", + " 26 Fatigue 437 non-null int64 \n", + "dtypes: float64(16), int64(11)\n", + "memory usage: 92.3 KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.describe()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "id": "lq0rprLVsY3o", + "outputId": "442db315-fe62-4ced-ff7c-c83cb9d13765" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Sl. No. NT THT THt THQCr CT \\\n", + "count 437.000000 437.000000 437.000000 437.000000 437.000000 437.000000 \n", + "mean 219.000000 872.299771 737.643021 25.949657 10.654462 128.855835 \n", + "std 126.295289 26.212073 280.036541 10.263824 7.841437 281.743539 \n", + "min 1.000000 825.000000 30.000000 0.000000 0.000000 30.000000 \n", + "25% 110.000000 865.000000 845.000000 30.000000 8.000000 30.000000 \n", + "50% 219.000000 870.000000 845.000000 30.000000 8.000000 30.000000 \n", + "75% 328.000000 870.000000 855.000000 30.000000 8.000000 30.000000 \n", + "max 437.000000 930.000000 865.000000 30.000000 24.000000 930.000000 \n", + "\n", + " Ct DT Dt QmT ... S \\\n", + "count 437.000000 437.000000 437.000000 437.000000 ... 437.000000 \n", + "mean 40.502059 123.699844 4.843936 35.491991 ... 0.014611 \n", + "std 126.924697 267.128933 15.700076 19.419277 ... 0.006145 \n", + "min 0.000000 30.000000 0.000000 30.000000 ... 0.003000 \n", + "25% 0.000000 30.000000 0.000000 30.000000 ... 0.010000 \n", + "50% 0.000000 30.000000 0.000000 30.000000 ... 0.015000 \n", + "75% 0.000000 30.000000 0.000000 30.000000 ... 0.019000 \n", + "max 540.000000 903.333000 70.200000 140.000000 ... 0.030000 \n", + "\n", + " Ni Cr Cu Mo RedRatio \\\n", + "count 437.000000 437.000000 437.000000 437.000000 437.000000 \n", + "mean 0.517048 0.570458 0.067780 0.069794 923.629291 \n", + "std 0.852976 0.411769 0.049161 0.088124 576.617020 \n", + "min 0.010000 0.010000 0.010000 0.000000 240.000000 \n", + "25% 0.020000 0.120000 0.020000 0.000000 590.000000 \n", + "50% 0.060000 0.710000 0.060000 0.000000 740.000000 \n", + "75% 0.460000 0.980000 0.100000 0.170000 1228.000000 \n", + "max 2.780000 1.170000 0.260000 0.240000 5530.000000 \n", + "\n", + " dA dB dC Fatigue \n", + "count 437.000000 437.000000 437.000000 437.000000 \n", + "mean 0.047181 0.003391 0.007712 552.903890 \n", + "std 0.031093 0.008240 0.010418 186.630528 \n", + "min 0.000000 0.000000 0.000000 225.000000 \n", + "25% 0.020000 0.000000 0.000000 448.000000 \n", + "50% 0.040000 0.000000 0.000000 505.000000 \n", + "75% 0.070000 0.000000 0.010000 578.000000 \n", + "max 0.130000 0.050000 0.058000 1190.000000 \n", + "\n", + "[8 rows x 27 columns]" + ], + "text/html": [ + "\n", + "
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Sl. No.NTTHTTHtTHQCrCTCtDTDtQmT...SNiCrCuMoRedRatiodAdBdCFatigue
count437.000000437.000000437.000000437.000000437.000000437.000000437.000000437.000000437.000000437.000000...437.000000437.000000437.000000437.000000437.000000437.000000437.000000437.000000437.000000437.000000
mean219.000000872.299771737.64302125.94965710.654462128.85583540.502059123.6998444.84393635.491991...0.0146110.5170480.5704580.0677800.069794923.6292910.0471810.0033910.007712552.903890
std126.29528926.212073280.03654110.2638247.841437281.743539126.924697267.12893315.70007619.419277...0.0061450.8529760.4117690.0491610.088124576.6170200.0310930.0082400.010418186.630528
min1.000000825.00000030.0000000.0000000.00000030.0000000.00000030.0000000.00000030.000000...0.0030000.0100000.0100000.0100000.000000240.0000000.0000000.0000000.000000225.000000
25%110.000000865.000000845.00000030.0000008.00000030.0000000.00000030.0000000.00000030.000000...0.0100000.0200000.1200000.0200000.000000590.0000000.0200000.0000000.000000448.000000
50%219.000000870.000000845.00000030.0000008.00000030.0000000.00000030.0000000.00000030.000000...0.0150000.0600000.7100000.0600000.000000740.0000000.0400000.0000000.000000505.000000
75%328.000000870.000000855.00000030.0000008.00000030.0000000.00000030.0000000.00000030.000000...0.0190000.4600000.9800000.1000000.1700001228.0000000.0700000.0000000.010000578.000000
max437.000000930.000000865.00000030.00000024.000000930.000000540.000000903.33300070.200000140.000000...0.0300002.7800001.1700000.2600000.2400005530.0000000.1300000.0500000.0580001190.000000
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Another way of plotting data distribution\n", + "df.plot(kind=\"density\", layout=(6,5),subplots=True,sharex=False, sharey=False, figsize=(15,15));\n", + "\n", + "plt.tight_layout()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "7PDkSR2bnR27", + "outputId": "c8162dbe-8f3c-448c-82d5-874d60e78a16" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Just chemical composition plot\n", + "df_1 = df[[\"C\", \"Si\", \"Mn\", \"P\", \"S\", \"Ni\", \"Cu\", \"Cr\", \"Mo\"]]\n", + "df_1.plot(kind=\"density\", layout=(6,5),subplots=True,sharex=False, sharey=False, figsize=(15,15));\n", + "plt.suptitle(\"Chemical Composition Distribution\",\n", + " x=0.5, y=1.05, ha='center', fontsize='xx-large');\n", + "plt.tight_layout()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 562 + }, + "id": "RNWBvQIilvN_", + "outputId": "3287aa21-6cd5-4078-f38e-9cda5625861a" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_2 = df[['NT', 'THT', 'THt', 'THQCr', 'CT', 'Ct', 'DT', 'Dt', 'QmT',\n", + " 'TT', 'Tt', 'TCr']]\n", + "df_2.hist(figsize=(10, 10));\n", + "plt.suptitle(\"Heat Treatment Conditions - Temperature, Time and Other Process(normalizing,tempering etc.)\",\n", + " x=0.5, y=1.05, ha='center', fontsize='xx-large');\n", + "plt.tight_layout();" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 642 + }, + "id": "3ahPKIUYlvLF", + "outputId": "cec409fd-f23a-4a64-d4d1-c7de3496c00f" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "# No clear trends from scatter plot, need ML\n", + "from sklearn.metrics import r2_score\n", + "plt.plot(df['RedRatio'],df['Fatigue'],'o')\n", + "plt.xlabel('RedRatio')\n", + "plt.ylabel('Fatigue')\n", + "plt.title('RedRatio vs Fatigue')\n", + "print('Correlation Coefficient: ',r2_score(df['RedRatio'],df['Fatigue']))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 518 + }, + "id": "HM7iwxSCn57q", + "outputId": "93a5795e-6704-4aa7-9cc1-91f0dc377732" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Correlation Coefficient: -0.6662823683853338\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sb\n", + "corr = df.corr()\n", + "sb.heatmap(corr, cmap=\"Blues\", annot=False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 554 + }, + "id": "sezdy6fynmin", + "outputId": "b0d7606f-e2b5-44f3-ed54-8ef01ddf40db" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Principal Component Analysis (PCA)" + ], + "metadata": { + "id": "z0rgMYAKKxV8" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "df = pd.read_excel('40192_2013_16_MOESM1_ESM.xlsx')\n", + "df.drop(columns=['Sl. No.'], inplace = True)\n", + "X = df.drop([\"Fatigue\"], axis = 1)\n", + "scaler = StandardScaler()\n", + "X_scaled = scaler.fit_transform(X)\n", + "\n", + "pca = PCA() # Reduce to 20 principal components\n", + "X_pca = pca.fit_transform(X_scaled)\n", + "exp_var_pca = pca.explained_variance_ratio_\n", + "#\n", + "# Cumulative sum of eigenvalues; This will be used to create step plot\n", + "# for visualizing the variance explained by each principal component.\n", + "#\n", + "cum_sum_eigenvalues = np.cumsum(exp_var_pca)\n", + "#\n", + "# Create the visualization plot\n", + "#\n", + "plt.bar(range(0,len(exp_var_pca)), exp_var_pca, alpha=0.5, align='center', label='Individual explained variance')\n", + "plt.step(range(0,len(cum_sum_eigenvalues)), cum_sum_eigenvalues, where='mid',label='Cumulative explained variance')\n", + "plt.ylabel('Explained variance ratio')\n", + "plt.xlabel('Principal component index')\n", + "plt.legend(loc='best')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "# ##################\n", + "# https://vitalflux.com/pca-explained-variance-concept-python-example/\n", + "# # Apply PCA\n", + "# pca = PCA(n_components=20) # Reduce to 2 principal components\n", + "# X_pca = pca.fit_transform(X_scaled)\n", + "\n", + "# # Explained variance\n", + "# explained_variance = pca.explained_variance_ratio_\n", + "# print(\"Explained variance by each component:\", explained_variance)\n", + "\n", + "# # Plot the PCA-transformed data\n", + "# plt.figure(figsize=(10, 6))\n", + "# plt.plot(explained_variance,'-o')\n", + "# plt.axhline(y=0.05, color='r', linestyle='--')\n", + "# plt.xlabel('Component')\n", + "# plt.ylabel('Explained Variance')\n", + "# plt.show()\n" + ], + "metadata": { + "id": "KZdBRdfXEzhx", + "outputId": "e80c6ccd-da8c-4c86-ff6f-7f57e9623797", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 515 + } + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Unknown extension is not supported and will be removed\n", + " warn(msg)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_pca[:, 0], X_pca[:, 1], color='r')\n", + "plt.title('PCA Transformed Data')\n", + "plt.xlabel('Principal Component 1')\n", + "plt.ylabel('Principal Component 2')\n", + "plt.show()" + ], + "metadata": { + "id": "_8mZFw30GHJy", + "outputId": "bababfca-3cf1-4bb4-b7bf-52df854bdb1c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 481 + } + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Modeling\n", + "1. Linear Regression\n", + "2. RandomForest\n", + "3. GBM\n", + "4. MLP\n", + "\n" + ], + "metadata": { + "id": "mRUTJuE2oO90" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Linear Regression" + ], + "metadata": { + "id": "D89w_bVvsi4N" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n", + "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", + "df = pd.read_excel('40192_2013_16_MOESM1_ESM.xlsx')\n", + "df.drop(columns=['Sl. No.'], inplace = True)\n", + "X = df.drop([\"Fatigue\"], axis = 1)\n", + "y = df[\"Fatigue\"]\n", + "# 80 % training-20% testing\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state= 42)\n", + "# Linear regression from sklearn package\n", + "lm = LinearRegression()\n", + "pred = lm.fit(X_train, y_train).predict(X_test)\n", + "print(\"MAE: \", mean_absolute_error(y_test, pred))\n", + "print(\"R2: \", r2_score(y_test, pred))\n", + "# data on x = y i.e. 45 degree line represent good agreement\n", + "plt.plot(y_test, pred, 'o')\n", + "plt.xlabel('Actual')\n", + "plt.ylabel('Predicted')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 556 + }, + "id": "8W_OkwQAnmfB", + "outputId": "3d14c36f-9dc4-4515-87f2-92518d41eef2" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MAE: 24.866119152828517\n", + "R2: 0.9749818499365817\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:329: UserWarning: Unknown extension is not supported and will be removed\n", + " warn(msg)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted')" + ] + }, + "metadata": {}, + "execution_count": 13 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### RandomForest" + ], + "metadata": { + "id": "RLhxfHwdsgHp" + } + }, + { + "cell_type": "code", + "source": [ + "# Comapre with RandomForest Model\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "rf = RandomForestRegressor()\n", + "model = rf.fit(X_train, y_train)\n", + "pred = model.predict(X_test)\n", + "print(\"MAE: \", mean_absolute_error(y_test, pred))\n", + "print(\"R2: \", r2_score(y_test, pred))\n", + "# data on x = y i.e. 45 degree line represent good agreement\n", + "plt.plot(y_test, pred, 'o')\n", + "plt.xlabel('Actual')\n", + "plt.ylabel('Predicted')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "FZZjfKwBnmcV", + "outputId": "f22cc80d-0c51-48df-8723-1b1cb64176ca" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MAE: 19.27443181818182\n", + "R2: 0.9854470927690341\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted')" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Feature Importance plot" + ], + "metadata": { + "id": "hOGztzM6sba0" + } + }, + { + "cell_type": "code", + "source": [ + "plt.rcParams.update({'font.size': 12})\n", + "Importance = pd.DataFrame({\"Importance\": model.feature_importances_*100},\n", + " index = X_train.columns)\n", + "\n", + "Importance.sort_values(by = \"Importance\",\n", + " axis = 0,\n", + " ascending = True).plot(kind =\"barh\", color = \"r\");\n", + "\n", + "plt.xlabel(\"Variable Significance Levels\");" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 457 + }, + "id": "09-fWbnUsCtg", + "outputId": "9df4826f-91a8-4140-85b3-dd3d80e5398e" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Gradient boosting" + ], + "metadata": { + "id": "Drg3QTiKIgxg" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.ensemble import GradientBoostingRegressor\n", + "gbm_model = GradientBoostingRegressor()\n", + "model = gbm_model.fit(X_train, y_train)\n", + "pred = gbm_model.predict(X_test)\n", + "print(\"MAE: \", mean_absolute_error(y_test, pred))\n", + "print(\"R2: \", r2_score(y_test, pred))\n", + "# data on x = y i.e. 45 degree line represent good agreement\n", + "plt.plot(y_test, pred, 'o')\n", + "plt.xlabel('Actual')\n", + "plt.ylabel('Predicted')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 509 + }, + "id": "5WwKXRPHsnhM", + "outputId": "ab33547d-2497-4688-c754-2358b14e6cbd" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MAE: 15.651200368803154\n", + "R2: 0.9894806802937289\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted')" + ] + }, + "metadata": {}, + "execution_count": 16 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+WIMGDWqMrwkAAIJQSIan5cuX6/jx49qyZYt69+4tSZo6daoqKir017/+VceOHVO7du300EMPqV27dvrkk08UGxsrSerRo4emTJmiDz74QMOGDZMkZWdna+XKlVq8eLHuv/9+SdJtt92m5ORk/fa3v9XWrVsb54sCAICgE5LTdsXFxZKkzp07ux232WwKCwtTRESEiouLtWnTJk2YMMEVnKTKUNS6dWu9+eabrmOrVq1SeHi4pk6d6joWFRWlSZMmadu2bdq/f389fyMAABAqQjI8XXXVVZKkSZMmKTc3V/v379cbb7yhzMxMzZw5UzExMfryyy9VXl6u/v37u703IiJCKSkpysnJcR3LyclRr1693EKWJKWlpUmqnP7zpqysTMXFxW5/AACg6QrJ8JSRkaH58+dr06ZN6tu3r7p166axY8dqxowZevrppyVJdrtdUuVo1NlsNpsOHTrkem232722k+TW9myLFi2S1Wp1/Z177rl+fTcAABDcQjI8SZW1S1deeaWWLVumt956SxMnTtTChQv17LPPSpJOnTolSYqMjPR4b1RUlOu8s623dlWvVZ3Zs2erqKjI9ccUHwAATVtIFoyvXLlSU6dO1Z49e9S1a1dJ0o033qiKigrNmjVL48aNU3R0tKTKabWzlZaWus5LUnR0tNd2zvPeREZGVhu8AABA0xSSI0/PP/+8+vbt6wpOTqNGjdLJkyeVk5PjmnJzTt9VZbfb1aVLF9drm83mtZ0kt7YAAKB5C8nw9P3338vhcHgcP3PmjCSpvLxcycnJatGihT7//HO3NqdPn1Zubq5SUlJcx1JSUrRnzx6PYu8dO3a4zgMAAEghGp569eqlnJwc7dmzx+3466+/rrCwMPXp00dWq1VDhgxRVlaWTpw44WqzfPlylZSUaMyYMa5jo0ePlsPh0LJly1zHysrK9PLLLys9PZ0icAAA4BKSNU8PPPCA1q9fryuuuEJ333232rdvr7Vr12r9+vWaPHmya5ptwYIFGjhwoAYPHqypU6fqwIEDevLJJzVs2DBlZGS4rpeenq4xY8Zo9uzZOnLkiHr27KlXX31VBQUFeumllxrrawIAgCBkMQzDaOxO1EV2drYef/xx5eTk6OjRo0pISNCvfvUr/fa3v1WLFv/NhFu2bNGsWbO0c+dOtWnTRjfffLMWLVqkNm3auF2vtLRUjz76qLKysnTs2DH16dNH8+fP17XXXmuqX8XFxbJarSoqKvJYNwoAAAQnM7/fIRueghXhCQD+y1FhKDu/UEdOlKpTmyilJcQpPMzS2N0CPJj5/Q7JaTsAQPDbkGfX3DW7ZC8qdR2zWaM0Z2SSMpI9FyYGQkVIFowDAILbhjy7pmftdAtOknS4qFTTs3ZqQ57n8jBAqCA8AQACylFhaO6aXaquJsR5bO6aXXJUUDWC0GRq2m7ixImmP8BisfDEGgA0I9n5hR4jTlUZkuxFpcrOL9SAxPYN1zEgQEyFp48++kgWi3uh38mTJ/XDDz9Iktq1aydJOnbsmCSpY8eOiomJCUQ/AQAh4sgJ78GpLu2AYGNq2q6goED5+fmuv3Xr1qlly5Z66KGHdOTIER09elRHjx7VkSNHNHv2bEVERGjdunX11XcAQBDq1CYqoO2AYOPXUgXXXHONzjvvPL3wwgvVnp8yZYry8/O1efPmOncw1LBUAYDmzlFhaNATH+lwUWm1dU8WSfHWKG2ZdTXLFiBomPn99qtgfPv27UpNTfV6vm/fvtq+fbs/HwEACDHhYRbNGZkkqTIoVeV8PWdkEsEJIcuv8BQXF6f169d7Pf/++++rbdu2/nwEACAEZSTblDkhVfFW96m5eGuUMiekss4TQppfi2ROmzZNjz32mG644QbNmDFDPXv2lCTt3btXzzzzjNavX6+5c+cGpKMAgNCSkWzT0KR4VhhHk+P39iyPPvqoFi9erDNnzrgdb9Gihe6//34tWLDArw6GGmqeAAAIPQ2+t92PP/6oTZs26bvvvpMkde/eXUOGDFGHDh38vXTIITwBABB6Gnxvuw4dOmjcuHGBuBQAAEBQ8zs8ORwO/e1vf9PHH3+sI0eOaN68ebr44otVVFSkDz/8UJdffrk6d+4ciL4CQJPjqDC0fd9RbfvPj5IsGpDYXped1566ICCI+TVtd/z4cWVkZCg7O1utW7fWTz/9pE2bNunqq6+Ww+FQ9+7dddttt2nhwoWB7HNQY9oOgK825Nn14Ntf6vhJ95rRtq1a6n9uvJgn0oAG1GDrPD344IP66quvtHHjRv3nP/9R1RwWHh6u0aNH6/333/fnIwCgSdqQZ9cdWTs9gpMkHT95Rndk7dSGPHsj9AxAbfwKT++8845mzJihoUOHeux5J0m9evVSQUGBPx8BAE2Oo8LQ4+99VWu7uWt2yVHh9zM9AALMr/BUVFSkhIQEr+fPnDmj8vJyfz4CAJqc7PxCHS4uq7WdvahU2fmFDdAjAGb4FZ4SExO1c+dOr+c/+OADJSUl+fMRANDkHDlRWi9tATQMv8LT5MmT9Ze//EVvvPGGq97JYrGorKxMDz/8sDZs2KBp06YFpKMA0FR0ahNVe6M6tAXQMPxaquCee+7RV199pXHjxrn2sBs/fryOHj2q8vJyTZs2TZMmTQpEPwGgyUhLiFN8bGStU3c2a+V2JgCCS0BWGN+yZYtWrVqlvXv3qqKiQomJibr55pt15ZVXBqKPIYWlCgD4wvm0XU2WsIEu0GAafHsW/BfhCYCvvK3z1K5VSy1inSegQTXY9izh4eFavny5xo8fX+35N954Q+PHj5fD4fDnYwCgScpItmloUjwrjAMhxq/wVNuglcPhqHb9JwAIRY4KQ9n5hTpyolSd2lTWI/kbcsLDLLr8/A66/Pzmt5E6EKr83tvOWzgqLi7Wxo0b1aED/4UAIPRtyLNr7ppdshf9d+kAmzVKc0YmMb0GNDOmlyqYO3euwsPDFR4eLovFogkTJrheV/1r166dli9frrFjx9ZHvwGgwWzIs2t61k634CRJh4tKNZ1tVIBmx/TIU1pamu68804ZhqHnn39eQ4cOVa9evdzaWCwWxcTEqF+/frrxxhsD1lkAaGiOCkNz1+xSdUUKhiSLKrdRGZoUT50S0EyYDk/Dhw/X8OHDJUk//fSTpk2bpssuuyzgHQOAYJCdX+gx4lSVof9uozIgsX3DdQxAo/Gr5unll18OVD8AICj5uj0K26gAzYdf27M888wzuvbaa72eHz58uDIzM/35CABoVL5uj8I2KkDz4Vd4evHFF2vc+DcpKUnLli3z5yMAoFGlJcTJZo2St2omi9hGBWhu/ApP+/bt00UXXeT1/IUXXqh9+/b58xEA0KjCwyyaM7LyfySeHaCcr+eMTKJYHGhG/ApPEREROnz4sNfzdrtdYWF+fQQANLqMZJsyJ6Qq3uo+NRdvjVIm+88BzY5fe9tdd911+vrrr/Wvf/1Lbdq0cTtXVFSklJQUXXDBBdqwYYPfHQ0V7G0HNF31scI4gODQYHvbzZkzR4MHD1ZKSoruvfde9e7dW5KUl5enP/7xj7Lb7Xrttdf8+QgACBrhYRaWIwDgX3hKT0/XmjVrNG3aNN1zzz2urVoMw1BCQoLee+89DRgwICAdBQAACAZ+7203dOhQffPNN8rJyXEVhycmJio1NZVNgQEAQJPjd3iSpLCwMPXr10/9+vULxOUAAACClqnw9I9//EOSdOWVV7q9ro2zPQAAQKgz9bRdWFiYLBaLTp06pYiICNdrbwzDkMVikcPhCEhnQwFP2wEAEHrq7Wm7jz/+WFLl+k5VXwMAADQXfq3zBE+MPAEAEHrM/H6z/DcAAIAJpqbtJk6caPoDLBaLXnrpJdPvAwAACEamwtNHH33kUSB+8uRJ/fDDD5Kkdu3aSZKOHTsmSerYsaNiYmIC0U8AAICgYGrarqCgQPn5+a6/devWqWXLlnrooYd05MgRHT16VEePHtWRI0c0e/ZsRUREaN26dfXVdwAAgAbnV8H4Nddco/POO08vvPBCteenTJmi/Px8bd68uc4dDDUUjAMAEHoarGB8+/btSk1N9Xq+b9++2r59uz8fAQAAEFT8Ck9xcXFav3691/Pvv/++2rZt689HAAAABBW/wtO0adO0du1a3XDDDdq8ebMKCgpUUFCgTZs2adSoUVq/fr3uuOOOQPUVAACg0fm1MfAjjzyisrIyLV68WGvXrnW/cIsWevDBB/XII4/41UEAAIBgEpAVxn/88Udt2rRJ3333nSSpe/fuGjJkiDp06OB3B0MNBeMAAISeetvbzpsOHTpo3LhxgbgUAABAUPN7exaHw6GVK1dq2rRp+sUvfqEvv/xSklRUVKS3335b33//vd+dBAAACBZ+hafjx4/r8ssv1/jx4/X666/rvffec6023rp1a82cOVN/+tOfAtJRAACAYOBXeHrwwQf11VdfaePGjfrPf/6jquVT4eHhGj16tN5//32/OwkAABAs/ApP77zzjmbMmKGhQ4d67HknSb169VJBQYE/HwEAABBU/ApPRUVFSkhI8Hr+zJkzKi8v9+cjAAAAgopf4SkxMVE7d+70ev6DDz5QUlKSPx8BAAAQVPwKT5MnT9Zf/vIXvfHGG656J4vForKyMj388MPasGGDpk2bFpCOAoA3jgpD2/Yd1bu5B7Vt31E5Kvxevg4AvPJrnad77rlHX331lcaNG+faw278+PE6evSoysvLNW3aNE2aNCkQ/QSAam3Is2vuml2yF5W6jsXFROjnKV00NCleaQlxCg/zrMkEgLrya+TJYrHohRde0D/+8Q/ddtttGj58uFJSUjR16lR98sknyszMDFQ/q7Vz506NGjVKcXFxatWqlZKTk/XnP//Zrc3WrVs1aNAgtWrVSvHx8Zo5c6ZKSko8rlVWVqZZs2apS5cuio6OVnp6ujZt2lSv/Qfgnw15dk3P2ukWnCSp8KfT+ss/CzTuhe0a9MRH2pBnb6QeAmiK6jzydPLkSU2YMEE33XSTbr31Vg0aNCiQ/arVBx98oJEjR6pv37569NFH1bp1a+3bt08HDhxwtcnNzdU111yjiy66SE899ZQOHDigP/zhD9q7d6/Wr1/vdr3bb79dq1at0r333qvzzz9fr7zyiq677jp9/PHHDf7dANTOUWFo7ppdqm2Czl5UqulZO5U5IVUZybYG6RuAps2vve1iY2P15JNPasqUKYHsU62Ki4vVq1cvDRw4UKtWrVJYWPUDaNddd51yc3P19ddfu/apefHFFzVlyhRt3LhRw4YNkyRlZ2crPT1dixcv1v333y9JKi0tVXJysjp16qStW7ea6ht72wH1b9u+oxr3wnaf2lokxVujtGXW1UzhAaiWmd9vv6btBg0apG3btvlziTp57bXX9P3332vBggUKCwvTTz/9pIqKCrc2xcXF2rRpkyZMmOD2j3DbbbepdevWevPNN13HVq1apfDwcE2dOtV1LCoqSpMmTdK2bdu0f//++v9SQDPkT6H3kROltTf6P4YqR6Cy8wvr0EsAcOdXeHr22Wf16aef6pFHHnGbLqtvmzdvVmxsrA4ePKgLLrhArVu3VmxsrKZPn67S0sr/Qv3yyy9VXl6u/v37u703IiJCKSkpysnJcR3LyclRr169PJJmWlqapMrpP2/KyspUXFzs9gegdhvy7Br0xEca98J23bMy13R9Uqc2UaY/00zgAgBv/ApPl1xyiQ4cOKBFixape/fuioyMVGxsrNuf1WoNVF9d9u7dq/Lyct1www269tpr9dZbb2nixIlasmSJfv3rX0uS7PbK/wK22TxrHGw2mw4dOuR6bbfbvbaT5Nb2bIsWLZLVanX9nXvuuX59N6A58Fboffj/6pN8CVBpCXGyWaNkZhKuLoELAM7m11IFo0ePDlQ/TCkpKdHJkyd1xx13uJ6uu/HGG3X69GktXbpU8+bN06lTpyRJkZGRHu+PiopynZekU6dOeW3nPO/N7Nmz9Zvf/Mb1uri4mAAF1KCmQm9DlfVJc9fs0tCk+Brrk8LDLJozMknTs3bK8n/v9cZZ85SWEOdX3wFAqmN4Ki0t1bvvvqsLLrhA7du31/XXX1/tyE19iY6OliSNGzfO7fj48eO1dOlSbdu2Ta1atZJUOa12ttLSUtc1nNfz1q7q51UnMjKy2uAFNFeOCkPZ+YU6cqJUndpEeayzlJ1f6DHiVFXV+qQBie1r/KyMZJsyJ6R6rPNUlfOT54xMolgcQECYDk9HjhzRwIEDlZ+fL8MwZLFY1KpVK61evVpDhgypjz566NKli7766it17tzZ7XinTp0kSceOHVNiYqKk/07fVWW329WlSxfXa5vNpoMHD1bbzvl5AGpX3YKVNmuU5oxMUkayTY4KQ//85kefruVrfVJGsk1Dk+KVnV+ozbsOa3XuQRX+dMZ1Pr7K5wNAIJgOT/Pnz1dBQYHuu+8+XX311frmm280f/58TZs2Tfv27auPPnro16+fNm3a5CoYd3LWJnXs2FHJyclq0aKFPv/8c918882uNqdPn1Zubq7bsZSUFH388ccqLi52KxrfsWOH6zyAmjnrmM6ePnPWMU29MkHv/cte46hTVWbqk8LDLBqQ2F4DEtvroRFJNY58AYC/TBeMf/DBB7rtttv0hz/8Qdddd51mzpypZ599VgUFBfrf//3f+uijB2fweemll9yOv/jii2rRooWuuuoqWa1WDRkyRFlZWTpx4oSrzfLly1VSUqIxY8a4jo0ePVoOh0PLli1zHSsrK9PLL7+s9PR0apiAWtRWx2RIWvqPfJ+Ck0WVo1V1rU9yBqkbUs7RgMT2BCcAAWd65Om7777TrFmz3I4NGjRIhmHo+++/dxsJqi99+/bVxIkT9Ze//EXl5eUaPHiwPvnkE/3tb3/T7NmzXdNsCxYs0MCBAzV48GBNnTpVBw4c0JNPPqlhw4YpIyPDdb309HSNGTNGs2fP1pEjR9SzZ0+9+uqrKigo8AhoADzVVsfkK+qTAIQC0+GprKzM9RSak/N1eXl5YHrlgyVLlqhbt256+eWXtXr1anXv3l1PP/207r33Xleb1NRUbd68WbNmzdJ9992nNm3aaNKkSVq0aJHH9f7617/q0Ucf1fLly3Xs2DH16dNHa9eu1ZVXXtlg3wkIVYFaP4n6JAChwPT2LGFhYfrd737nNnJTVFSka665RpmZmbr00ks93pOamup/T0ME27OgOTKzVYo3w5M769nx/RhxAtAozPx+1yk8WSye/+XmfPKuumMOh8PMR4Q0whOaI0eFoUFPfOT31N0SNu8F0EjM/H6bnrZ7+eWX69wxAE1TeJhFj45I0p2v7azzNXxdHBMAGpvp8PSrX/2qPvoBoIHUtohlXVmjW/r1fjOLYwJAY/JrexYAoaW2RSz9se0/vi1+WRs27wUQ7PzaGBhA6AjEZrw1C8xUG5v3Agh2hCegGahtEUupst7IUWHq+RE36X5uuuvv4pgA0FAIT0AzYGYz3roKq+YpXF+xOCaAUELNE9AM+FpH5E+90Y8/ldX5vSyOCSCUEJ6AZsDXOqLa2tX0pJ7ZWqX7hpyvHh1i2LwXQMghPAHNQFpCnGzWKB0uKq227smiytGfmuqNqntSr210S/368h66++rza/0Mp0A93QcAjYWaJ6CZuLl/V6+hxlDN9UbentQ7fuqMnt68V/1+t0mbdh3WnJFJkrw/d3ffkPO1ZdbVBCcAIY3wBDRxG/Ls6ve7TfrTh994bdO2lfcFLmt6Us/p+Mkzmp5Vubp45oRUxVvdp/Bs1igtmZCqe4b0YnoOQMgzvbcdasbedggGztqkTbsO6y//LKi1vTPOZFazt5yZTX9t1ihtmXW1JNXLKuYAUF/qdW87AI2rtu1VqqtNqo0h73vLmXkCr+r2KmyxAqCpIjwBIaS27VWctUl1GU72trec2afo2F4FQFNHzRMQImrbXuX9fx+qtTbJF2eHH+dTdL5iexUATR3hCQgBvmyv8si7eaam6rw5O/yEh1lcT9HVhO1VADQXhCcgBPiyvUrhT2f8+oyawk9Gsk1LJqR6fSqP7VUANCeEJyAENFQdUU3hJyPZpi8eGar7hvRS22j3EBVvjar2ST0AaIooGAdCgK91RK0jw1VS5jB9/bbRLfU/N11ca/gJD7PoniHn6+6re7IUAYBmi/AEhABft1e5pf+5+uOHe01f/7lbU3V5zw4+tw8Ps7AUAYBmi2k7IARULdo+e3zH+frREUm6tEecWkWE+3xdZ53TZecRhADAV4w8ASEiI9mmzAmpHus8xVujNOoSm+avM7cwJkXeAFA3hCcghGQk2zQ0Kd6t3ujYT2W667WcWtd3CrNIFVUaxVdZXBMA4DvCExBiqtYbOSoMDXrioxqDk0XSPdecrzt/1lNffHuMIm8A8BPhCQhhta3/JFWuAfXHD/fqQlsbRpkAIAAoGAdCmJn1n+au2SVHhe+btzgqDG3bd1Tv5h7Utn1HTb0XAJoyRp6AEGZmH7nqNv31prYNiAGgOSM8AUHCUWFUu/Bk1eMdWkdKhvTjT2Xq1CZK/bq3U9tWLXX8pG9bs/gyUuXcgPjscSbnBsSsJA6guSM8AUGgupGe+NhIXdqjnf6x90cVnSqv9n3tWrXQmXLfp9NqG6mqbQNiiyqn/4YmxVNsDqDZouYJaGTOkZ6zC78PF5dpzb8Pew1OknTsZLl+Ou3bdiztYyLUr3u7Gtv4sgGxc/oPAJorwhPQiGoa6Qm0oz+d1uDFH2tDnt1rG18L0Btqo2IACEaEJ6AR+bLUQCA565a8BShfC9DNFKoDQFNDeAIa0aZdhxv085wjXN6WLXBuQOytmsm5F15aQlx9dREAgh7hCWgkjgpD7+QeavDPraluyZcNiNkLD0BzR3gCGkl2fqEKfzrdaJ/vrW7JuQFxvNV9ai7eGsUyBQAglioAGk1jF13XVLdU3QbE7IUHAJUIT0Ajaayia4sqR5Fqq1uqugExAOC/mLYDGklaQpziYxs2QFG3BAD+IzwBDeDsTXZPl1coO79QfbtZ6+0zp1yRIBt1SwAQcEzbAfWsuq1XwixSNSsFBETVDXwfHH4RdUsAEGCEJ6AeedtkN9DB6dERF6lDm0iPgETdEgAEHuEJqCcNsfWKs/j79ssTGFECgAZCzRNQDxwVhl75Z369br1C8TcANA5GnoAAq67GqT7EV6ltAgA0HMITEEDeapwCJS6mpR69vrfiYyn+BoDGQngCAqQhapx+d8PFuq4PI00A0JgIT4AXjgrD1GP+2fmF9T5V1y4mol6vDwCoHeEJqEZ1dUu2WmqMzOxVV9d1nhp7PzwAAE/bAR6cdUtnjyIdLirV9Kyd2pBnr/Z9vu5V94uULnr112l6+LqLTPetsfbDAwD8FyNPQBU11S0ZqlweYO6aXRqaFO8xhZeWECebNUqHi0prrHtanXtIq3MPqW10S5/75etmvgCA+sfIE1BFbXVLhiR7Uale+We+a586R5X5t7GXdvO5YPz4qTOm+sZ6TgAQHBh5AqrwtaZo/rrdrv+/zRqlUZfY9N6/7PVSMB4fG6nHR/VmPScACBKEJ6CKutQU2YtKtfQf+fXQG+m+Ib1099U9GXECgCBCeAKq8LVuKdDaRrd0m8ar7ck+AEDjITwBVYSHWTRnZJKmZ+2URWqwAPXc+FSFhVl8XlMKANB4CE/AWTKSbcqckNog+9M5n6K7LLE9YQkAQgThCU3O2SuD9+veTl98e6zaUR1vq4hnJNs0NCnede7HE2VuReJ1cfZIljMq8RQdAIQWwhOalOpWBj97NW9nPZGkGlcRDw+zaEBie0mVIevFLfmma6GcI0uPjkjS/HXunxVPXRMAhCSLYRgNWRfb5BUXF8tqtaqoqEixsbGN3Z1mxbkyeG3/ga6plsk5/pM5IdVt5KlTmygd+6lMd72WI9Xwfm/Xyki2md4rDwDQcMz8fhOeAozw1DgcFYYGPfFRQGqULJKsrVoqqkW4Dhe7j0pd3ydeb+08qMKf3J+Mq26dJ56YA4DQYeb3m2k7NAm1rQxuhiHp+MkzktxXALcXleqFTwvcjsXFROjREUm6ro9Nv824iJElAGgGmsz2LAsWLJDFYlFycrLHua1bt2rQoEFq1aqV4uPjNXPmTJWUlHi0Kysr06xZs9SlSxdFR0crPT1dmzZtaojuw0++rgweaMd+Oq27XqvcLNhZI3VDyjkawNNzANBkNYnwdODAAS1cuFAxMTEe53Jzc3XNNdfo5MmTeuqppzR58mQtW7ZMY8aM8Wh7++2366mnntKtt96qP/3pTwoPD9d1112nLVu2NMTXgB/qsjJ4IDjnvOeu2eW2xx0AoOlqEjVPY8eO1Q8//CCHw6Eff/xReXl5rnPXXXedcnNz9fXXX7vmMF988UVNmTJFGzdu1LBhwyRJ2dnZSk9P1+LFi3X//fdLkkpLS5WcnKxOnTpp69atPvWFmqfG4ax5auiVwat6fcplrqfzAAChxczvd8iPPP3jH//QqlWr9Mc//tHjXHFxsTZt2qQJEya4/UPcdtttat26td58803XsVWrVik8PFxTp051HYuKitKkSZO0bds27d+/v16/B/wTHmbRqEtsjRacpMabOgQANKyQDk8Oh0MzZszQ5MmTdfHFF3uc//LLL1VeXq7+/fu7HY+IiFBKSopycnJcx3JyctSrVy+PtJmWliapcvqvOmVlZSouLnb7Q8PbkGfXsgBtzts6soXqUq3UWFOHAICGFdLhacmSJfr22281f/78as/b7XZJks3m+ai4zWbToUOH3Np6ayfJrW1VixYtktVqdf2de+65pr8H/OOoMDR3za6AjDq1ahmm39/UR5J8DlAWVS5LkJYQF4AeAACCXciGp6NHj+qxxx7To48+qo4dO1bb5tSpU5KkyMhIj3NRUVGu88623tpVvdbZZs+eraKiItcf03sNL5DLFJw8U6GwsMqFLeOttY8kscUKADQ/IbvO0yOPPKK4uDjNmDHDa5vo6GhJlVNrZystLXWdd7b11q7qtc4WGRlZbehC/Tl7pe5Dx6sPtnU1d80ubZl1dTUrjJ9mixUAQGiGp71792rZsmX64x//6DadVlpaqjNnzqigoECxsbGuKTfn9F1VdrtdXbp0cb222Ww6ePBgte0kubVF46lu7zpLgAd87EWlys4v1IDE9h5Pz12bHM9CmADQzIXktN3BgwdVUVGhmTNnKiEhwfW3Y8cO7dmzRwkJCZo3b56Sk5PVokULff75527vP336tHJzc5WSkuI6lpKSoj179ngUfO/YscN1Ho3LuXfd2VN09bHYhrcn51gIEwAQkuEpOTlZq1ev9vjr3bu3unXrptWrV2vSpEmyWq0aMmSIsrKydOLECdf7ly9frpKSEreFMkePHi2Hw6Fly5a5jpWVlenll19Weno6heCNLJBF4b7gyTkAgDchOW3XoUMH/fznP/c47lzrqeq5BQsWaODAgRo8eLCmTp2qAwcO6Mknn9SwYcOUkZHhapeenq4xY8Zo9uzZOnLkiHr27KlXX31VBQUFeumll+r5G6E2gSwKrw1PzgEAahKSI09mpKamavPmzYqOjtZ9992nZcuWadKkSVq1apVH27/+9a+69957tXz5cs2cOVNnzpzR2rVrdeWVVzZCz1GVvwtQ+jq7ZhFPzgEAatYktmcJJmzPUj+27TuqcS9sN/2+u3/WU5f37KB+3dsp85Nv9PTmvV7btmvVUotuvJgn5wCgGTLz+x2S03ZoftIS4mSzRvm8d51FlcsI3De0l2sU6Z4hvXRBfBuPp/XaRrfUry/vobuvPp8RJwBArQhPCAnhYRbNGZmk6Vk7ZZFqDFA1LVyZkWzzWL+J5QYAAGYwbRdgTNvVr+rWeQqzSBVV/lNsY+FKAIBJTNshpJ29gnjVkaHqRo76dW+nL749xkgSAKBBEJ4QVKobWTp7JMm5UGVVZ78GAKC+NPmlChA6vK0gfrioVNOzdmpDnuc2OwAANDTCE4JCTSuIO4/NXbNLjgpK9AAAjYtpOwSF2lYQN1S5Ye/2/xxVmMVCfRMAoNEQnhAUfF1B/K4VO3X81BnXa56sAwA0NKbtUO8cFYa27Tuqd3MPatu+o9VOvfm6EW/V4CRRDwUAaHiMPKFe+fL0nGR+BXEnQ5WLYs5ds0tDk+KZwgMA1DtGnlBvzDw951xBXPrvCuG+ctZDZecX+tljAABqR3hCvajL03MZyTZlTkhVvNV9Cq9tq5Y+faavdVMAAPiDaTvUC1+fnsvOL3Rb4LK6FcQrKgzd+tKOWj/T17opAAD8QXhCvfB1FKi6dmevIO6oMGqsh7JIirdWLlsAAEB9Y9oO9cLXUSBf2tVUD+V8PWdkEsXiAIAGQXhCvXA+PectzlhU+dSdr6NF3uqh4q1RypyQyjpPAIAGw7Qd6oVztGh61k5ZJLfptrqOFlVXD8UK4wCAhmYxDIPNwgKouLhYVqtVRUVFio2NbezuNDpf13kCAKAxmfn9ZuQJ9YrRIgBAU0N4Qr07++k5AABCGQXjAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBAACYQHgCAAAwgfAEAABgAuEJAADAhBaN3QHUjaPCUHZ+oY6cKFWnNlFKS4hTeJilsbsFAECTR3gKQRvy7Jq7ZpfsRaWuYzZrlOaMTFJGsq0RewYAQNPHtF2I2ZBn1/SsnW7BSZIOF5VqetZObcizN1LPAABoHghPIcRRYWjuml0yqjnnPDZ3zS45KqprAQAAAoHwFEKy8ws9RpyqMiTZi0qVnV/YcJ0CAKCZITyFCEeFoX9+84NPbY+c8B6wAACAfygYDwHVFYjXpFObqHruEQAAzRfhKcg5C8R9qWKySIq3Vi5bAAAA6gfTdkGspgLxszlXeJozMon1ngAAqEeMPAWx2grEq4pnnScAABoE4SmI+Vr4fffPeuq+ob0YcQIAoAEwbRfEfC38vrxnB4ITAAANhPAUxNIS4mSzRslbLLKoclsWCsQBAGg4hKcgFh5m0ZyRSZLkEaAoEAcAoHEQnoJcRrJNmRNSFW91n8KLt0Ypc0IqBeIAADQwCsZDQEayTUOT4pWdX6gjJ0rVqU3lVB0jTgAANDzCU4gID7NoQGL7xu4GAADNHtN2AAAAJhCeAAAATAjJ8PTZZ5/p7rvvVu/evRUTE6Nu3brp5ptv1p49ezza7t69WxkZGWrdurXi4uL0y1/+Uj/88INHu4qKCv3+979XQkKCoqKi1KdPH73++usN8XUAAEAICcmapyeeeEL//Oc/NWbMGPXp00eHDx/Ws88+q9TUVG3fvl3JycmSpAMHDujKK6+U1WrVwoULVVJSoj/84Q/68ssvlZ2drYiICNc1H374Yf3P//yPpkyZoksvvVTvvvuuxo8fL4vForFjxzbWVwUAAEHGYhiGL/vOBpWtW7eqf//+buFn7969uvjiizV69GhlZWVJku6880698sor+vrrr9WtWzdJ0ubNmzV06FAtXbpUU6dOlSQdPHhQCQkJmjp1qp599llJkmEYGjx4sPLz81VQUKDw8HCf+lZcXCyr1aqioiLFxsYG8msDAIB6Yub3OySn7QYOHOgWnCTp/PPPV+/evbV7927XsbfeekvXX3+9KzhJ0pAhQ9SrVy+9+eabrmPvvvuuzpw5ozvvvNN1zGKxaPr06Tpw4IC2bdtWj98GAACEkpAMT9UxDEPff/+9OnToIKlyNOnIkSPq37+/R9u0tDTl5OS4Xufk5CgmJkYXXXSRRzvneW/KyspUXFzs9gcAAJquJhOeVqxYoYMHD+qWW26RJNntdkmSzea5ArfNZlNhYaHKyspcbTt37iyLxeLRTpIOHTrk9XMXLVokq9Xq+jv33HMD8n0AAEBwahLh6euvv9Zdd92lAQMG6Fe/+pUk6dSpU5KkyMhIj/ZRUVFubU6dOuVTu+rMnj1bRUVFrr/9+/f792UAAEBQC8mn7ao6fPiwRowYIavVqlWrVrkKu6OjoyXJNbpUVWlpqVub6Ohon9pVJzIy0i14Oevvmb4DACB0OH+3fXmOLqTDU1FRkYYPH67jx4/r008/VZcuXVznnFNuzum7qux2u+Li4lyhx2az6eOPP5ZhGG5Td873Vr1ubU6cOCFJTN8BABCCTpw4IavVWmObkA1PpaWlGjlypPbs2aPNmzcrKSnJ7fw555yjjh076vPPP/d4b3Z2tlJSUlyvU1JS9OKLL2r37t1u19mxY4frvK+6dOmi/fv3q02bNh41VMXFxTr33HO1f/9+ljEIEtyT4MR9CT7ck+DEfQkcwzB04sQJnwZMQjI8ORwO3XLLLdq2bZveffddDRgwoNp2N910k1599VXt37/fNRL04Ycfas+ePbrvvvtc7W644Qbdd999ev75593WeVqyZInOOeccDRw40Oe+hYWFqWvXrjW2iY2N5T/kQYZ7Epy4L8GHexKcuC+BUduIk1NIhqf/9//+n9577z2NHDlShYWFrkUxnSZMmCBJeuihh/S3v/1NP/vZz3TPPfeopKREixcv1sUXX6xf//rXrvZdu3bVvffeq8WLF+vMmTO69NJL9c477+jTTz/VihUrfF4gEwAANH0hucL4VVddpb///e9ez1f9Sl999ZV+85vfaMuWLYqIiNCIESP05JNPqnPnzm7vqaio0BNPPKGlS5fKbrfr/PPP1+zZs3XrrbcGrN+sPh58uCfBifsSfLgnwYn70jhCcuTpk08+8blt7969tXHjxlrbhYWFafbs2Zo9e7YfPatZZGSk5syZU+2yCGgc3JPgxH0JPtyT4MR9aRwhOfIEAADQWJrEIpkAAAANhfAEAABgAuEJAADABMITAACACYSnOvjss8909913q3fv3oqJiVG3bt108803a8+ePR5td+/erYyMDLVu3VpxcXH65S9/qR9++MGjXUVFhX7/+98rISFBUVFR6tOnj15//fWG+DpN1oIFC2SxWJScnOxxbuvWrRo0aJBatWql+Ph4zZw5UyUlJR7tysrKNGvWLHXp0kXR0dFKT0/Xpk2bGqL7TcrOnTs1atQoxcXFqVWrVkpOTtaf//xntzbck4a1d+9ejR07Vl27dlWrVq104YUXat68eTp58qRbO+5L/SgpKdGcOXOUkZGhuLg4WSwWvfLKK9W2rY/fEV+vCS8MmHbTTTcZ8fHxxowZM4wXXnjBmD9/vtG5c2cjJibG+PLLL13t9u/fb3To0MFITEw0/vSnPxkLFiww2rVrZ1xyySVGWVmZ2zUffPBBQ5IxZcoUY9myZcaIESMMScbrr7/e0F+vSdi/f7/RqlUrIyYmxujdu7fbuZycHCMqKsro27evkZmZaTz88MNGZGSkkZGR4XGdsWPHGi1atDDuv/9+Y+nSpcaAAQOMFi1aGJ9++mlDfZWQt3HjRiMiIsJIT083nnrqKWPZsmXGrFmzjAceeMDVhnvSsL777jujbdu2Rvfu3Y1FixYZS5cuNW6//XZDkjFq1ChXO+5L/cnPzzckGd26dTOuuuoqQ5Lx8ssve7Srj98RM9dE9QhPdfDPf/7T4z9ge/bsMSIjI41bb73VdWz69OlGdHS08e2337qObdq0yZBkLF261HXswIEDRsuWLY277rrLdayiosK44oorjK5duxrl5eX1+G2apltuucW4+uqrjcGDB3uEp+HDhxs2m80oKipyHXvhhRcMScbGjRtdx3bs2GFIMhYvXuw6durUKSMxMdEYMGBA/X+JJqCoqMjo3Lmz8Ytf/MJwOBxe23FPGtaCBQsMSUZeXp7b8dtuu82QZBQWFhqGwX2pT6WlpYbdbjcMwzA+++wzr+GpPn5HfL0mvCM8BVBqaqqRmprqet2pUydjzJgxHu169eplXHPNNa7Xzz33nCHJ+Oqrr9zavfbaa4Yk/pebSX//+9+N8PBw49///rdHeCoqKjJatGjhNuphGIZRVlZmtG7d2pg0aZLr2AMPPGCEh4e7/XAYhmEsXLjQkGR899139ftFmoDMzExDkrFr1y7DMAyjpKTEI0RxTxrerFmzDEnGDz/84HE8LCzMKCkp4b40oJrCU338jvh6TXhHzVOAGIah77//Xh06dJAkHTx4UEeOHFH//v092qalpSknJ8f1OicnRzExMbrooos82jnPwzcOh0MzZszQ5MmTdfHFF3uc//LLL1VeXu5xXyIiIpSSkuJxX3r16uWx5YHzvuTm5gb+CzQxmzdvVmxsrA4ePKgLLrhArVu3VmxsrKZPn67S0lJJ3JPGcNVVV0mSJk2apNzcXO3fv19vvPGGMjMzNXPmTMXExHBfgkB9/I6YuSa8IzwFyIoVK3Tw4EHdcsstkiS73S5JstlsHm1tNpsKCwtVVlbmatu5c2dZLBaPdpJ06NCh+ux6k7JkyRJ9++23mj9/frXna7svVf+t7Xa713YS98UXe/fuVXl5uW644QZde+21euuttzRx4kQtWbLEtTk396ThZWRkaP78+dq0aZP69u2rbt26aezYsZoxY4aefvppSdyXYFAfvyNmrgnvQnJvu2Dz9ddf66677tKAAQP0q1/9SpJ06tQpSap2v6GoqChXm8jISNf/W1M71O7o0aN67LHH9Oijj6pjx47VtqntvlT9t+a++K+kpEQnT57UHXfc4Xq67sYbb9Tp06e1dOlSzZs3j3vSSHr06KErr7xSN910k9q3b69169Zp4cKFio+P19133819CQL18Tti5prwjvDkp8OHD2vEiBGyWq1atWqVwsPDJUnR0dGSVG2Cd05XONtER0f71A41e+SRRxQXF6cZM2Z4bVPbfan6b8198Z/z32jcuHFux8ePH6+lS5dq27ZtatWqlSTuSUNauXKlpk6dqj179qhr166SKkNtRUWFZs2apXHjxvF/K0GgPn5HzFwT3jFt54eioiINHz5cx48f14YNG9SlSxfXOeeQqHOItCq73a64uDhXsrfZbDp8+LCMs/Zodr636nVRvb1792rZsmWaOXOmDh06pIKCAhUUFKi0tFRnzpxRQUGBCgsLa70vZ99Db+0k7osvnP9GnTt3djveqVMnSdKxY8e4J43g+eefV9++fV3ByWnUqFE6efKkcnJyuC9BoD5+R8xcE94RnuqotLRUI0eO1J49e7R27VolJSW5nT/nnHPUsWNHff755x7vzc7OVkpKiut1SkqKTp48qd27d7u127Fjh+s8anbw4EFVVFRo5syZSkhIcP3t2LFDe/bsUUJCgubNm6fk5GS1aNHC476cPn1aubm5Hvdlz549Ki4udmvLffFdv379JFXen6qc9RcdO3bknjSC77//Xg6Hw+P4mTNnJEnl5eXclyBQH78jZq6JGjTy034hqby83Bg1apTRokULY926dV7b3XHHHUZ0dLTbY7qbN282JBmZmZmuY/v37/e6Psc555zDOk8++OGHH4zVq1d7/PXu3dvo1q2bsXr1auPf//63YRiGkZGRYdhsNqO4uNj1/hdffNGQZKxfv951bPv27R5r15SWlho9e/Y00tPTG+7LhbCdO3cakozx48e7HR83bpzRokUL4+DBg4ZhcE8a2vXXX29EREQY//u//+t2/Oc//7kRFhbGfWlgNS1VUB+/I75eE94RnurgnnvuMSQZI0eONJYvX+7x5/Tdd98Z7du3NxITE40///nPxsKFC4127doZF198sVFaWup2zQceeMCQZEydOtV44YUXXCvDrlixoqG/XpNS3SKZX3zxhREZGem2anJUVJQxbNgwj/ePGTPGtdbN0qVLjYEDBxotWrQw/v73vzfUVwh5EydONCQZN998s/Hcc88ZY8aMMSQZs2fPdrXhnjQs51ponTp1MubNm2c899xzxvDhww1JxuTJk13tuC/165lnnjHmz59vTJ8+3ZBk3Hjjjcb8+fON+fPnG8ePHzcMo35+R8xcE9UjPNXB4MGDDUle/6rKy8szhg0bZrRq1cpo27atceuttxqHDx/2uKbD4TAWLlxodO/e3YiIiDB69+5tZGVlNdRXarKqC0+GYRiffvqpMXDgQCMqKsro2LGjcdddd7n9r2unU6dOGffff78RHx9vREZGGpdeeqmxYcOGhuh6k3H69Gnj8ccfN7p37260bNnS6Nmzp/H00097tOOeNKwdO3YYw4cPN+Lj442WLVsavXr1MhYsWGCcOXPGrR33pf50797d6+9Ifn6+q119/I74ek1Uz2IYZ1WXAQAAwCsKxgEAAEwgPAEAAJhAeAIAADCB8AQAAGAC4QkAAMAEwhMAAIAJhCcAAAATCE8AAAAmEJ4AAABMIDwBQJCwWCx6/PHHG7sbAGpBeALQZD3//POyWCxKT0+v0/sPHTqkxx9/XLm5uYHtGICQRngC0GStWLFCPXr0UHZ2tr755hvT7z906JDmzp1LeALghvAEoEnKz8/X1q1b9dRTT6ljx45asWJFY3cJQBNBeALQJK1YsULt2rXTiBEjNHr06GrD0/Hjx3XfffepR48eioyMVNeuXXXbbbfpxx9/1CeffKJLL71UkvTrX/9aFotFFotFr7zyiiSpR48euv322z2uedVVV+mqq65yvT59+rQee+wx9evXT1arVTExMbriiiv08ccf18fXBtAAWjR2BwCgPqxYsUI33nijIiIiNG7cOGVmZuqzzz5zBaKSkhJdccUV2r17tyZOnKjU1FT9+OOPeu+993TgwAFddNFFmjdvnh577DFNnTpVV1xxhSRp4MCBpvpRXFysF198UePGjdOUKVN04sQJvfTSS7r22muVnZ2tlJSUQH91APWM8ASgyfniiy/09ddf65lnnpEkDRo0SF27dtWKFStc4Wnx4sXKy8vT22+/rV/84heu9z7yyCMyDEMWi0XDhw/XY489pgEDBmjChAl16ku7du1UUFCgiIgI17EpU6bowgsv1DPPPKOXXnrJj28KoDEwbQegyVmxYoU6d+6sn/3sZ5IqlwC45ZZbtHLlSjkcDknSW2+9pUsuucQtODlZLJaA9SU8PNwVnCoqKlRYWKjy8nL1799fO3fuDNjnAGg4hCcATYrD4dDKlSv1s5/9TPn5+frmm2/0zTffKD09Xd9//70+/PBDSdK+ffuUnJzcIH169dVX1adPH0VFRal9+/bq2LGj1q1bp6Kiogb5fACBxbQdgCblo48+kt1u18qVK7Vy5UqP8ytWrNCwYcP8/hxvo1MOh0Ph4eGu11lZWbr99tv185//XA888IA6deqk8PBwLVq0SPv27fO7HwAaHuEJQJOyYsUKderUSc8995zHubffflurV6/WkiVLlJiYqLy8vBqvVdP0Xbt27XT8+HGP499++63OO+881+tVq1bpvPPO09tvv+12vTlz5vjwbQAEI8ITgCbj1KlTevvttzVmzBiNHj3a43yXLl30+uuv67333tNNN92kefPmafXq1R51T86C8ZiYGEmqNiQlJibq008/1enTp101TWvXrtX+/fvdwpNzFMp5TUnasWOHtm3bpm7dugXkewNoWIQnAE3Ge++9pxMnTmjUqFHVnr/ssstcC2a+9tprWrVqlcaMGaOJEyeqX79+Kiws1HvvvaclS5bokksuUWJiotq2baslS5aoTZs2iomJUXp6uhISEjR58mStWrVKGRkZuvnmm7Vv3z5lZWUpMTHR7TOvv/561xN9I0aMUH5+vpYsWaKkpCSVlJQ0xD8LgACjYBxAk7FixQpFRUVp6NCh1Z4PCwvTiBEjtGHDBpWVlenTTz/V9OnT9f7772vmzJl6/vnndcEFF6hr166SpJYtW+rVV19VeHi47rjjDo0bN05///vfJUnXXnutnnzySe3Zs0f33nuvtm3bprVr17re63T77bdr4cKF+te//qWZM2dq48aNysrKUv/+/ev3HwNAvbEYhmE0dicAAABCBSNPAAAAJhCeAAAATCA8AQAAmEB4AgAAMIHwBAAAYALhCQAAwATCEwAAgAmEJwAAABMITwAAACYQngAAAEwgPAEAAJhAeAIAADDh/wMfk8XrMhthHgAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Note: there are only a few models that allow feature importance plot" + ], + "metadata": { + "id": "CPgcbYxds7o1" + } + }, + { + "cell_type": "code", + "source": [ + "plt.rcParams.update({'font.size': 12})\n", + "Importance = pd.DataFrame({\"Importance\": gbm_model.feature_importances_*100},\n", + " index = X_train.columns)\n", + "\n", + "Importance.sort_values(by = \"Importance\",\n", + " axis = 0,\n", + " ascending = True).plot(kind =\"barh\", color = \"r\");\n", + "\n", + "plt.xlabel(\"Variable Significance Levels\");" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 457 + }, + "id": "gt55TD1Hsnff", + "outputId": "7b367246-6b2e-49e8-ba66-8a400beca3be" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "In the above models, we used the default values of the model parameters, now lets perfrom hyperparameter tuning. This requires a range of values of the model parameters." + ], + "metadata": { + "id": "GakyBwUBtKBy" + } + }, + { + "cell_type": "code", + "source": [ + "#model tuning\n", + "\n", + "%%time\n", + "gbm_params = {\n", + " 'learning_rate': [0.001, 0.01, 0.1, 0.2],\n", + " 'max_depth': [3, 5, 8,50,100],\n", + " 'n_estimators': [200, 500, 1000, 2000],\n", + " 'subsample': [1,0.5,0.75],\n", + "}\n", + "\n", + "gbm = GradientBoostingRegressor()\n", + "# 5-fold cross-validation\n", + "gbm_cv_model = GridSearchCV(gbm, gbm_params, cv = 5, n_jobs = -1, verbose = 2)\n", + "gbm_cv_model.fit(X_train, y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "id": "nYgViaP5sna-", + "outputId": "f26462c9-d047-43dd-bbbe-0622f4dd5019" + }, + "execution_count": 54, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Fitting 5 folds for each of 240 candidates, totalling 1200 fits\n", + "CPU times: user 18.7 s, sys: 3.01 s, total: 21.7 s\n", + "Wall time: 23min 58s\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=5, estimator=GradientBoostingRegressor(), n_jobs=-1,\n", + " param_grid={'learning_rate': [0.001, 0.01, 0.1, 0.2],\n", + " 'max_depth': [3, 5, 8, 50, 100],\n", + " 'n_estimators': [200, 500, 1000, 2000],\n", + " 'subsample': [1, 0.5, 0.75]},\n", + " verbose=2)" + ], + "text/html": [ + "
GridSearchCV(cv=5, estimator=GradientBoostingRegressor(), n_jobs=-1,\n",
+              "             param_grid={'learning_rate': [0.001, 0.01, 0.1, 0.2],\n",
+              "                         'max_depth': [3, 5, 8, 50, 100],\n",
+              "                         'n_estimators': [200, 500, 1000, 2000],\n",
+              "                         'subsample': [1, 0.5, 0.75]},\n",
+              "             verbose=2)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 54 + } + ] + }, + { + "cell_type": "code", + "source": [ + "gbm_cv_model.best_params_" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oYHzrqdAtbVB", + "outputId": "18b44646-b5e2-4000-e3b3-8219fa0cf688" + }, + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'learning_rate': 0.01,\n", + " 'max_depth': 3,\n", + " 'n_estimators': 2000,\n", + " 'subsample': 0.75}" + ] + }, + "metadata": {}, + "execution_count": 55 + } + ] + }, + { + "cell_type": "code", + "source": [ + "gbm_tuned = GradientBoostingRegressor(learning_rate = 0.01,\n", + " max_depth = 3,\n", + " n_estimators = 2000,\n", + " subsample = 0.75)\n", + "model = gbm_tuned.fit(X_train, y_train)\n", + "pred = gbm_tuned.predict(X_test)\n", + "print(\"MAE: \", mean_absolute_error(y_test, pred))\n", + "print(\"R2: \", r2_score(y_test, pred))\n", + "# data on x = y i.e. 45 degree line represent good agreement\n", + "plt.plot(y_test, pred, 'o')\n", + "plt.xlabel('Actual')\n", + "plt.ylabel('Predicted')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 509 + }, + "id": "cncSEXuztbR1", + "outputId": "33578fca-72cf-485e-9b4c-b897ad75207f" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MAE: 15.310697316808898\n", + "R2: 0.990289538967789\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted')" + ] + }, + "metadata": {}, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Multi-layer Perceptron/ Neural Network\n", + "Note that for a more involved training, we should use [PyTorch](https://pytorch.org/) instead of Scikit-learn package" + ], + "metadata": { + "id": "Wo8LPebpKS2G" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.neural_network import MLPRegressor\n", + "mlp_model = MLPRegressor(hidden_layer_sizes=(300), max_iter=1000,learning_rate_init=1e-3,batch_size=40)\n", + "model = mlp_model.fit(X_train, y_train)\n", + "pred = mlp_model.predict(X_test)\n", + "print(\"MAE: \", mean_absolute_error(y_test, pred))\n", + "print(\"R2: \", r2_score(y_test, pred))\n", + "# data on x = y i.e. 45 degree line represent good agreement\n", + "plt.plot(y_test, pred, 'o')\n", + "plt.xlabel('Actual')\n", + "plt.ylabel('Predicted')" + ], + "metadata": { + "id": "m5rRwbkuIzc1", + "outputId": "0bfe2733-7986-47a8-b826-cb09bfc75abb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 509 + } + }, + "execution_count": 55, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MAE: 42.46478517119089\n", + "R2: 0.9341777309261525\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted')" + ] + }, + "metadata": {}, + "execution_count": 55 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "WFvdBbPnIzZY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "7HaVAnpAInff" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The above is an example of tabular data. For more examples, say image classification/segmentation, atomic structure dataset machine learning etc., feel free to see https://github.com/knc6/jarvis-tools-notebooks?tab=readme-ov-file#artificial-intelligencemachine-learning\n", + "\n", + "For looing into AI benchmarks see: https://pages.nist.gov/jarvis_leaderboard/AI/" + ], + "metadata": { + "id": "qTwkYDncux3w" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "14y5eSZmtbPL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "KGpP1twctbKS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "YrNFaTNLtbIE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "oTN36_r_snXk" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/jarvis-tools-notebooks/Basic_UQ.ipynb b/jarvis-tools-notebooks/Basic_UQ.ipynb new file mode 100644 index 0000000..fa1ce71 --- /dev/null +++ b/jarvis-tools-notebooks/Basic_UQ.ipynb @@ -0,0 +1,1826 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyO9RKOe4azE8MZl5VTDQEZf", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "1. This code uses the Monte Carlo method to estimate the value of π (pi) by simulating random points in a unit square and determining how many fall inside a quarter circle." + ], + "metadata": { + "id": "nJK_YaX7LnoA" + } + }, + { + "cell_type": "markdown", + "source": [ + "Estimating Pi (3.14159265358979323846264338327950288419716939937510)\n", + "### https://www.geeksforgeeks.org/estimating-value-pi-using-monte-carlo/" + ], + "metadata": { + "id": "02GgsMtWL9pf" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "# Number of random points\n", + "n_points = 1000000\n", + "\n", + "# Generate random points\n", + "x = np.random.uniform(0, 1, n_points)\n", + "y = np.random.uniform(0, 1, n_points)\n", + "\n", + "# Check if points are inside the quarter circle\n", + "inside_circle = (x**2 + y**2) <= 1\n", + "\n", + "# Uses the ratio of points inside the quarter circle to the total number of points to estimate π.\n", + "\n", + "pi_estimate = 4 * np.sum(inside_circle) / n_points\n", + "print(f\"Estimated value of pi: {pi_estimate}\")\n" + ], + "metadata": { + "id": "Oc5JQdnPL3bs", + "outputId": "8d0bee71-08bc-4890-be88-2b31ea105b42", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Estimated value of pi: 3.140376\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "2. This code performs a Monte Carlo simulation to evaluate the distribution of the output of a quadratic function given normally distributed input samples. It then visualizes the distribution of the output using a histogram and calculates the mean and standard deviation of the output." + ], + "metadata": { + "id": "YtMK7TpYdEyB" + } + }, + { + "cell_type": "code", + "source": [ + "#\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "# Define the function\n", + "def f(x):\n", + " return x**2 + 2*x + 1\n", + "\n", + "# Number of samples\n", + "N = 10000\n", + "\n", + "# Generate random samples for input variable x (assuming normal distribution)\n", + "mean_x = 5\n", + "std_x = 1\n", + "x_samples = np.random.normal(mean_x, std_x, N)\n", + "\n", + "# Compute the output for each sample\n", + "y_samples = f(x_samples)\n", + "\n", + "# Plot the histogram of the output\n", + "plt.hist(y_samples, bins=50, density=True, alpha=0.7, color='blue')\n", + "plt.title('Monte Carlo Simulation: Histogram of Output')\n", + "plt.xlabel('Output')\n", + "plt.ylabel('Probability Density')\n", + "plt.show()\n", + "\n", + "# Calculate mean and standard deviation of the output\n", + "mean_y = np.mean(y_samples)\n", + "std_y = np.std(y_samples)\n", + "print(f'Mean of output: {mean_y:.2f}')\n", + "print(f'Standard deviation of output: {std_y:.2f}')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 507 + }, + "id": "rqB_vwBJX-DL", + "outputId": "a7056897-8027-4d83-a8ab-894d9a924abd" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean of output: 37.27\n", + "Standard deviation of output: 12.16\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "3. This code performs a Monte Carlo simulation to estimate the probability of failure of a mechanical component under load, considering the randomness in both the load and the material strength." + ], + "metadata": { + "id": "GHkuv6sMdQ5U" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "# Define parameters\n", + "n_simulations = 10000\n", + "mean_load = 500\n", + "std_load = 50\n", + "mean_strength = 600\n", + "std_strength = 50\n", + "\n", + "# Generate random samples\n", + "load_samples = np.random.normal(mean_load, std_load, n_simulations)\n", + "strength_samples = np.random.normal(mean_strength, std_strength, n_simulations)\n", + "\n", + "# Determine failure\n", + "failures = load_samples > strength_samples\n", + "\n", + "# Estimate probability of failure\n", + "probability_of_failure = np.mean(failures)\n", + "print(f\"Estimated Probability of Failure: {probability_of_failure:.2f}\")\n" + ], + "metadata": { + "id": "lu_HytP0LlJX", + "outputId": "ff9b8f74-2fea-4fc5-abcb-61978749d07a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Estimated Probability of Failure: 0.07\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Aa-d4sWFeRcx" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "source": [ + "4. This code uses Latin Hypercube Sampling (LHS) to generate samples for two input variables, X1 and X2 , uniformly distributed between 0 and 1. LHS is a statistical method that ensures each sample is representative of the entire range of each input variable." + ], + "metadata": { + "id": "2QSBQUqNfo-l" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q pyDOE" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bOSK-nWBeTuH", + "outputId": "654e7ef5-358a-4e6d-9375-c2cfafe0592a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for pyDOE (setup.py) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Generating 4 samples for two input variables \\( X_1 \\) and \\( X_2 \\) uniformly distributed between 0 and 1.\n", + "import numpy as np\n", + "from pyDOE import lhs\n", + "\n", + "# Number of samples\n", + "n_samples = 4\n", + "\n", + "# Generate LHS samples\n", + "lhs_samples = lhs(2, samples=n_samples)\n", + "\n", + "# Scale samples to desired range [0, 1]\n", + "lhs_samples_scaled = lhs_samples * (1 - 0) + 0\n", + "\n", + "print(\"LHS Samples:\")\n", + "print(lhs_samples_scaled)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RBRkuvSUYdVs", + "outputId": "424b1d6b-c972-4538-9be4-5d4d041bd606" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "LHS Samples:\n", + "[[0.24406955 0.12474267]\n", + " [0.93714431 0.55290151]\n", + " [0.43227837 0.81390031]\n", + " [0.71200331 0.46465837]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Each row represents a sample, and each column corresponds to one of the input variables\n", + "X1 and X2. This ensures that the entire range of possible values for each variable is covered more uniformly than with simple random sampling." + ], + "metadata": { + "id": "MfNaXeJsf1_l" + } + }, + { + "cell_type": "markdown", + "source": [ + "5. This code demonstrates the use of Latin Hypercube Sampling (LHS) to generate input samples for a given function, compute the corresponding outputs, and analyze the distribution of these outputs through a histogram. It also calculates the mean and standard deviation of the outputs." + ], + "metadata": { + "id": "wFev_-WjdZ19" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pyDOE import lhs\n", + "\n", + "# Define the function\n", + "def f(x):\n", + " return x**2 + 2*x + 1\n", + "\n", + "# Number of samples\n", + "N = 1000\n", + "\n", + "# Generate Latin Hypercube samples for input variable x\n", + "lhs_samples = lhs(1, samples=N)\n", + "x_samples = 10 * lhs_samples[:, 0] # Scale to desired range [0, 10]\n", + "\n", + "# Compute the output for each sample\n", + "y_samples = f(x_samples)\n", + "\n", + "# Plot the histogram of the output\n", + "plt.hist(y_samples, bins=50, density=True, alpha=0.7, color='green')\n", + "plt.title('Latin Hypercube Sampling: Histogram of Output')\n", + "plt.xlabel('Output')\n", + "plt.ylabel('Probability Density')\n", + "plt.show()\n", + "\n", + "# Calculate mean and standard deviation of the output\n", + "mean_y = np.mean(y_samples)\n", + "std_y = np.std(y_samples)\n", + "print(f'Mean of output: {mean_y:.2f}')\n", + "print(f'Standard deviation of output: {std_y:.2f}')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 508 + }, + "id": "XlyQ2K75ehwU", + "outputId": "55cd1d01-97ce-4635-a3d7-9449984f575b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean of output: 44.33\n", + "Standard deviation of output: 35.44\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + " 6. This code performs Polynomial Chaos Expansion (PCE) using a polynomial regression model to estimate the probability of failure of a mechanical component under random load and material strength conditions. The steps include generating random samples, creating polynomial features, fitting a linear regression model, and evaluating the model on new samples." + ], + "metadata": { + "id": "hNqQ7xLBYxpI" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from scipy.stats import norm\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", + "# Define parameters\n", + "n_samples = 1000\n", + "mean_load, std_load = 500, 50\n", + "mean_strength, std_strength = 600, 50\n", + "\n", + "# Generate random samples\n", + "L_samples = np.random.normal(mean_load, std_load, n_samples)\n", + "S_samples = np.random.normal(mean_strength, std_strength, n_samples)\n", + "failure_samples = (L_samples > S_samples).astype(int)\n", + "\n", + "# Create polynomial features\n", + "poly = PolynomialFeatures(degree=3)\n", + "X = np.vstack((L_samples, S_samples)).T\n", + "X_poly = poly.fit_transform(X)\n", + "\n", + "# Fit PCE model using linear regression\n", + "model = LinearRegression()\n", + "model.fit(X_poly, failure_samples)\n", + "coefficients = model.coef_\n", + "\n", + "# Function to evaluate PCE model\n", + "def evaluate_pce(L, S, model, poly):\n", + " X = np.vstack((L, S)).T\n", + " X_poly = poly.transform(X)\n", + " return model.predict(X_poly)\n", + "\n", + "# Evaluate PCE model on new samples\n", + "new_L_samples = np.random.normal(mean_load, std_load, n_samples)\n", + "new_S_samples = np.random.normal(mean_strength, std_strength, n_samples)\n", + "predicted_failures = evaluate_pce(new_L_samples, new_S_samples, model, poly)\n", + "\n", + "# Estimate probability of failure\n", + "probability_of_failure = np.mean(predicted_failures)\n", + "print(f\"Estimated Probability of Failure: {probability_of_failure:.2f}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZgBcmy8cY5x8", + "outputId": "ecb29347-831d-4009-8971-323dcadda191" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Estimated Probability of Failure: 0.07\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + " pip install bayesian-optimization" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LdID7_aZbCYa", + "outputId": "6f8aa107-bfc6-418e-ce2a-39cc8733905f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting bayesian-optimization\n", + " Downloading bayesian_optimization-1.5.1-py3-none-any.whl (28 kB)\n", + "Collecting colorama<0.5.0,>=0.4.6 (from bayesian-optimization)\n", + " Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n", + "Requirement already satisfied: numpy>=1.25 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (1.25.2)\n", + "Requirement already satisfied: scikit-learn<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (1.2.2)\n", + "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bayesian-optimization) (1.11.4)\n", + "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<2.0.0,>=1.0.0->bayesian-optimization) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn<2.0.0,>=1.0.0->bayesian-optimization) (3.5.0)\n", + "Installing collected packages: colorama, bayesian-optimization\n", + "Successfully installed bayesian-optimization-1.5.1 colorama-0.4.6\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "7. This code uses Bayesian Optimization to find the maximum value of a given function within a specified range. The function to be optimized is ( -sin(3x) - x^2 + 0.7x ). The code initializes the Bayesian optimizer, performs the optimization, and then visualizes the results.\n" + ], + "metadata": { + "id": "wxmKJ3_ideWQ" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from bayes_opt import BayesianOptimization\n", + "\n", + "# Define the function to minimize\n", + "def f(x):\n", + " return -np.sin(3*x) - x**2 + 0.7*x\n", + "\n", + "# Define the bounds of the search space\n", + "pbounds = {'x': (0, 2)}\n", + "\n", + "# Initialize the optimizer\n", + "optimizer = BayesianOptimization(f=f, pbounds=pbounds, verbose=2, random_state=1)\n", + "\n", + "# Perform the optimization\n", + "optimizer.maximize(init_points=2, n_iter=10)\n", + "\n", + "# Extract the results\n", + "x = np.linspace(0, 2, 1000)\n", + "y = f(x)\n", + "max_x = optimizer.max['params']['x']\n", + "max_y = f(max_x)\n", + "\n", + "# Plot the results\n", + "plt.plot(x, y, label='Function')\n", + "plt.scatter([max_x], [max_y], color='red', zorder=5, label='Maximum')\n", + "plt.title('Bayesian Optimization')\n", + "plt.xlabel('x')\n", + "plt.ylabel('f(x)')\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "print(f'Optimal value: x = {max_x:.2f}, f(x) = {max_y:.2f}')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 761 + }, + "id": "LL-eoPlvbCVz", + "outputId": "254a112b-c387-45c6-af71-29cb50ace1d6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "| iter | target | x |\n", + "-------------------------------------\n", + "| \u001b[39m1 \u001b[39m | \u001b[39m-0.7086 \u001b[39m | \u001b[39m0.834 \u001b[39m |\n", + "| \u001b[35m2 \u001b[39m | \u001b[35m-0.1423 \u001b[39m | \u001b[35m1.441 \u001b[39m |\n", + "| \u001b[35m3 \u001b[39m | \u001b[35m-0.1391 \u001b[39m | \u001b[35m1.438 \u001b[39m |\n", + "| \u001b[35m4 \u001b[39m | \u001b[35m0.0 \u001b[39m | \u001b[35m0.0 \u001b[39m |\n", + "| \u001b[39m5 \u001b[39m | \u001b[39m-0.5841 \u001b[39m | \u001b[39m0.2576 \u001b[39m |\n", + "| \u001b[39m6 \u001b[39m | \u001b[39m-2.321 \u001b[39m | \u001b[39m2.0 \u001b[39m |\n", + "| \u001b[39m7 \u001b[39m | \u001b[39m-0.2313 \u001b[39m | \u001b[39m1.137 \u001b[39m |\n", + "| \u001b[39m8 \u001b[39m | \u001b[39m-0.1006 \u001b[39m | \u001b[39m1.277 \u001b[39m |\n", + "| \u001b[39m9 \u001b[39m | \u001b[39m-0.1193 \u001b[39m | \u001b[39m0.05099 \u001b[39m |\n", + "| \u001b[39m10 \u001b[39m | \u001b[39m-0.1193 \u001b[39m | \u001b[39m0.05099 \u001b[39m |\n", + "| \u001b[39m11 \u001b[39m | \u001b[39m-0.1193 \u001b[39m | \u001b[39m0.05099 \u001b[39m |\n", + "| \u001b[39m12 \u001b[39m | \u001b[39m-0.1193 \u001b[39m | \u001b[39m0.05099 \u001b[39m |\n", + "=====================================\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Optimal value: x = 0.00, f(x) = 0.00\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "8. Gaussian Process Regression (GPR): Suppose we want to model the relationship between the load applied to a mechanical system and its resulting displacement, given a set of noisy measurements. This code demonstrates how to use Gaussian Process Regression (GPR) to model a function based on training data and predict its behavior over a range of input values. It visualizes the predictions along with the 95% confidence interval, showing the uncertainty in the predictions." + ], + "metadata": { + "id": "p8XZlhh7baMt" + } + }, + { + "cell_type": "markdown", + "source": [ + "Different types of Kernel functions\n", + "\n", + "For each kernel, the script generates samples from the GP prior using gp.sample_y(X, n_samples=3), which gives us an idea of the kinds of functions that each kernel can model." + ], + "metadata": { + "id": "xpmZoXTDRPor" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.gaussian_process import GaussianProcessRegressor\n", + "from sklearn.gaussian_process.kernels import RBF, Matern, RationalQuadratic, ExpSineSquared, DotProduct, ConstantKernel as C\n", + "# Define a range of input points\n", + "X = np.linspace(0, 10, 100).reshape(-1, 1)\n", + "# Define kernels\n", + "kernels = [\n", + " C(1.0, (1e-3, 1e3)) * RBF(length_scale=1),\n", + " C(1.0, (1e-3, 1e3)) * Matern(length_scale=1, nu=1.5),\n", + " C(1.0, (1e-3, 1e3)) * RationalQuadratic(length_scale=1, alpha=0.5),\n", + " C(1.0, (1e-3, 1e3)) * ExpSineSquared(length_scale=1, periodicity=3),\n", + " DotProduct() + C(1.0)\n", + "]\n", + "\n", + "kernel_names = [\n", + " \"RBF\",\n", + " \"Matern\",\n", + " \"Rational Quadratic\",\n", + " \"Periodic\",\n", + " \"Dot Product\"\n", + "]\n", + "\n", + "# Generate samples from each kernel\n", + "plt.figure(figsize=(12, 8))\n", + "for kernel, name in zip(kernels, kernel_names):\n", + " gp = GaussianProcessRegressor(kernel=kernel)\n", + " y_samples = gp.sample_y(X, n_samples=3)\n", + " plt.subplot(3, 2, kernel_names.index(name) + 1)\n", + " for i in range(y_samples.shape[1]):\n", + " plt.plot(X, y_samples[:, i], label=f'Sample {i+1}')\n", + " plt.title(name)\n", + " plt.xlabel('$x$')\n", + " plt.ylabel('$f(x)$')\n", + " plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 807 + }, + "id": "IYTI8Vu4RAA-", + "outputId": "4f00f9bb-eef0-4e9e-dc60-ae24d2112114" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html" + ], + "metadata": { + "id": "DbbiL9EyRpr8" + } + }, + { + "cell_type": "code", + "source": [ + "Suppose we have a few measurement points and we want to develop a model for future prediction with uncertainty estimates\n", + "The ground truth function is assumed as xsin(x) [Note: this is not always known for real world scenarios]\n" + ], + "metadata": { + "id": "XKbfnqJnbCTI" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "X = np.linspace(start=0, stop=10, num=1_000).reshape(-1, 1)\n", + "y = np.squeeze(X * np.sin(X))\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(X, y, label=r\"$f(x) = x \\sin(x)$\", linestyle=\"dotted\")\n", + "plt.legend()\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"$f(x)$\")\n", + "_ = plt.title(\"True generative process\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "BI3l9xFPSZFv", + "outputId": "5bca66f1-d35e-4928-81da-69dd53a5daff" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now we take six random values on the curve as experimental measurements and from their try to develop a model that should resemble the ground trutch function in addition to giving uncertainty esimates." + ], + "metadata": { + "id": "L29G2RVbSjJH" + } + }, + { + "cell_type": "code", + "source": [ + "rng = np.random.RandomState(1)\n", + "training_indices = rng.choice(np.arange(y.size), size=6, replace=False)\n", + "X_train, y_train = X[training_indices], y[training_indices]" + ], + "metadata": { + "id": "b0BktJo6SZDV" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train, y_train" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7aiZHS8rS08K", + "outputId": "845fc261-6c8d-4cbd-fa4c-18e17ad9e101" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([[5.07507508],\n", + " [8.18818819],\n", + " [4.52452452],\n", + " [3.68368368],\n", + " [2.42242242],\n", + " [9.2992993 ]]),\n", + " array([-4.74492726, 7.73514288, -4.44491693, -1.90051689, 1.5957965 ,\n", + " 1.16380401]))" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.gaussian_process import GaussianProcessRegressor\n", + "from sklearn.gaussian_process.kernels import RBF\n", + "\n", + "kernel = 1 * RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e2))\n", + "gaussian_process = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)\n", + "gaussian_process.fit(X_train, y_train)\n", + "gaussian_process.kernel_" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DXcgDhiCS05y", + "outputId": "2e617827-9939-49ac-d105-7dd183e344ab" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5.02**2 * RBF(length_scale=1.43)" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Mean and Standard deviation predictor" + ], + "metadata": { + "id": "EEo74dKMS991" + } + }, + { + "cell_type": "code", + "source": [ + "mean_prediction, std_prediction = gaussian_process.predict(X, return_std=True)\n", + "\n", + "plt.plot(X, y, label=r\"$f(x) = x \\sin(x)$\", linestyle=\"dotted\")\n", + "plt.scatter(X_train, y_train, label=\"Observations\")\n", + "plt.plot(X, mean_prediction, label=\"Mean prediction\")\n", + "plt.fill_between(\n", + " X.ravel(),\n", + " mean_prediction - 1.96 * std_prediction,\n", + " mean_prediction + 1.96 * std_prediction,\n", + " alpha=0.5,\n", + " label=r\"95% confidence interval\",\n", + ")\n", + "plt.legend()\n", + "plt.xlabel(\"$x$\")\n", + "plt.ylabel(\"$f(x)$\")\n", + "_ = plt.title(\"Gaussian process regression on noise-free dataset\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "1QuuOD_cS03m", + "outputId": "83bb887c-9628-4b41-b3d4-5fbb9ddad64f" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "E0N-S0-aSZA8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "9. Prediction Intervals: https://developer.ibm.com/articles/prediction-intervals-explained-a-lightgbm-tutorial/" + ], + "metadata": { + "id": "ZecFSjhxbt1e" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import fetch_california_housing\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "def sklearn_to_df(data_loader):\n", + " X_data = data_loader.data\n", + " X_columns = data_loader.feature_names\n", + " x = pd.DataFrame(X_data, columns=X_columns)\n", + "\n", + " y_data = data_loader.target\n", + " y = pd.Series(y_data, name='target')\n", + "\n", + " return x, y\n", + "x, y = sklearn_to_df(fetch_california_housing())\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(\n", + " x, y, test_size=0.2, random_state=42)\n", + "\n", + "import lightgbm as lgb\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import r2_score\n", + "#from data_loader import x_train, x_test, y_train, y_test\n", + "\n", + "regressor = lgb.LGBMRegressor()\n", + "regressor.fit(x_train, y_train)\n", + "regressor_pred = regressor.predict(x_test)\n", + "\n", + "lower = lgb.LGBMRegressor(objective = 'quantile', alpha = 1 - 0.95)\n", + "lower.fit(x_train, y_train)\n", + "lower_pred = lower.predict(x_test)\n", + "\n", + "\n", + "upper = lgb.LGBMRegressor(objective = 'quantile', alpha = 0.95)\n", + "upper.fit(x_train, y_train)\n", + "upper_pred = upper.predict(x_test)\n", + "\n", + "score = r2_score(y_test, regressor_pred)\n", + "print('score',score)\n", + "\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "plt.scatter(x_test.MedInc, lower_pred, color='limegreen', marker='o', label='lower', lw=0.5, alpha=0.5)\n", + "plt.scatter(x_test.MedInc, regressor_pred, color='aqua', marker='x', label='pred', alpha=0.7)\n", + "plt.scatter(x_test.MedInc, upper_pred, color='dodgerblue', marker='o', label='upper', lw=0.5, alpha=0.5)\n", + "plt.plot(sorted(x_test.MedInc), sorted(lower_pred), color='black')\n", + "plt.plot(sorted(x_test.MedInc), sorted(regressor_pred), color='red')\n", + "plt.plot(sorted(x_test.MedInc), sorted(upper_pred), color='black')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 811 + }, + "id": "167MHhdLbCQ1", + "outputId": "2bb1084c-f1eb-4ba8-cd8a-a4dce3221c88" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002669 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 16512, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 2.071947\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002575 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 16512, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 0.665000\n", + "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002406 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 1838\n", + "[LightGBM] [Info] Number of data points in the train set: 16512, number of used features: 8\n", + "[LightGBM] [Info] Start training from score 4.945349\n", + "score 0.8360449251645318\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Additonal metrics calculation for uncertainty such as Sharpness" + ], + "metadata": { + "id": "N0RgA2GwTPyb" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q uncertainty-toolbox" + ], + "metadata": { + "id": "vKmfqA0WbCOG" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "10. Uncertainty Metrics: This example computes metrics for a vector of predicted values (predictions) and associated uncertainties (predictions_std, a vector of standard deviations), taken with respect to a corresponding set of ground truth values y." + ], + "metadata": { + "id": "k20c0eQqdsaT" + } + }, + { + "cell_type": "code", + "source": [ + "import uncertainty_toolbox as uct\n", + "\n", + "# Load an example dataset of 100 predictions, uncertainties, and ground truth values\n", + "predictions, predictions_std, y, x = uct.data.synthetic_sine_heteroscedastic(100)\n", + "\n", + "# Compute all uncertainty metrics\n", + "metrics = uct.metrics.get_all_metrics(predictions, predictions_std, y)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eiErsqezbCL1", + "outputId": "68015fb0-b4b3-44a1-e734-de4aa9f35082" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " (1/n) Calculating accuracy metrics\n", + " (2/n) Calculating average calibration metrics\n", + " (3/n) Calculating adversarial group calibration metrics\n", + " [1/2] for mean absolute calibration error\n", + "Measuring adversarial group calibration by spanning group size between 0.0 and 1.0, in 10 intervals\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 10/10 [00:02<00:00, 3.68it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " [2/2] for root mean squared calibration error\n", + "Measuring adversarial group calibration by spanning group size between 0.0 and 1.0, in 10 intervals\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 10/10 [00:05<00:00, 1.80it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " (4/n) Calculating sharpness metrics\n", + " (n/n) Calculating proper scoring rule metrics\n", + "**Finished Calculating All Metrics**\n", + "\n", + "\n", + "===================== Accuracy Metrics =====================\n", + " MAE 0.309\n", + " RMSE 0.444\n", + " MDAE 0.187\n", + " MARPD 57.920\n", + " R2 0.737\n", + " Correlation 0.860\n", + "=============== Average Calibration Metrics ================\n", + " Root-mean-squared Calibration Error 0.036\n", + " Mean-absolute Calibration Error 0.031\n", + " Miscalibration Area 0.031\n", + "========== Adversarial Group Calibration Metrics ===========\n", + " Mean-absolute Adversarial Group Calibration Error\n", + " Group Size: 0.11 -- Calibration Error: 0.163\n", + " Group Size: 0.56 -- Calibration Error: 0.066\n", + " Group Size: 1.00 -- Calibration Error: 0.031\n", + " Root-mean-squared Adversarial Group Calibration Error\n", + " Group Size: 0.11 -- Calibration Error: 0.192\n", + " Group Size: 0.56 -- Calibration Error: 0.080\n", + " Group Size: 1.00 -- Calibration Error: 0.036\n", + "==================== Sharpness Metrics =====================\n", + " Sharpness 0.512\n", + "=================== Scoring Rule Metrics ===================\n", + " Negative-log-likelihood 0.194\n", + " CRPS 0.217\n", + " Check Score 0.109\n", + " Interval Score 1.059\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "4wcoZYzxcoNK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "16QZYPCrcoKJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ghXiErFacoHU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "0-R04tFYbCI4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "z8_nvX2GbCGu" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Extras" + ], + "metadata": { + "id": "s819zjhbcsN_" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q chaospy" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2Xg5eBtKelUh", + "outputId": "47242c36-fc8b-478a-f2b5-cc791a41f4c1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m254.0/254.0 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m148.1/148.1 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Example Sobol Sensitivity Analysis: This example demonstrates how to perform Sobol sensitivity analysis to determine the sensitivity of the output with respect to input variables." + ], + "metadata": { + "id": "4WWXC0CYfAuW" + } + }, + { + "cell_type": "code", + "source": [ + "pip install -q SALib" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k94E31SDfG2y", + "outputId": "78b89653-1cc0-44e5-cb53-44f1a91be4ba" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m778.8/778.8 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import SALib\n", + "from SALib.sample import saltelli\n", + "from SALib.analyze import sobol\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Define the function\n", + "def f(x):\n", + " return x[:, 0]**2 + x[:, 1] + x[:, 2]*x[:, 3]\n", + "\n", + "# Define the problem\n", + "problem = {\n", + " 'num_vars': 4,\n", + " 'names': ['x1', 'x2', 'x3', 'x4'],\n", + " 'bounds': [[-1, 1], [-1, 1], [-1, 1], [-1, 1]]\n", + "}\n", + "\n", + "# Generate samples\n", + "param_values = saltelli.sample(problem, 1000)\n", + "\n", + "# Evaluate the function\n", + "Y = f(param_values)\n", + "\n", + "# Perform Sobol sensitivity analysis\n", + "Si = sobol.analyze(problem, Y)\n", + "\n", + "# Print the first-order, second-order and total-order indices\n", + "print(\"First-order indices:\", Si['S1'])\n", + "print(\"Second-order indices:\", Si['S2'])\n", + "print(\"Total-order indices:\", Si['ST'])\n", + "\n", + "# Plot the results\n", + "fig, ax = plt.subplots()\n", + "ax.bar(problem['names'], Si['S1'], yerr=Si['S1_conf'])\n", + "ax.set_title('Sobol Sensitivity Analysis')\n", + "ax.set_ylabel('First-order sensitivity index')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 686 + }, + "id": "krxsnDRwexOJ", + "outputId": "2e877c29-c536-49be-a389-815bf66aa73e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":19: DeprecationWarning: `salib.sample.saltelli` will be removed in SALib 1.5.1 Please use `salib.sample.sobol`\n", + " param_values = saltelli.sample(problem, 1000)\n", + "/usr/local/lib/python3.10/dist-packages/SALib/sample/saltelli.py:110: UserWarning: \n", + " Convergence properties of the Sobol' sequence is only valid if\n", + " `N` (1000) is equal to `2^n`.\n", + " \n", + " warnings.warn(msg)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "First-order indices: [ 0.17083529 0.62419573 -0.00323803 0.00170401]\n", + "Second-order indices: [[ nan -0.00519169 -0.0033711 -0.00125131]\n", + " [ nan nan 0.00406611 0.0014575 ]\n", + " [ nan nan nan 0.21252934]\n", + " [ nan nan nan nan]]\n", + "Total-order indices: [0.16819911 0.62563663 0.20859542 0.20646136]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Example 5: Bootstrapping\n", + "Description: This example demonstrates how to use bootstrapping to estimate the uncertainty in the mean of a dataset." + ], + "metadata": { + "id": "MqqX65vSfR1p" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Generate synthetic data\n", + "np.random.seed(0)\n", + "data = np.random.normal(loc=0, scale=1, size=1000)\n", + "\n", + "# Number of bootstrap samples\n", + "B = 10000\n", + "bootstrap_means = np.zeros(B)\n", + "\n", + "# Perform bootstrapping\n", + "for i in range(B):\n", + " bootstrap_sample = np.random.choice(data, size=len(data), replace=True)\n", + " bootstrap_means[i] = np.mean(bootstrap_sample)\n", + "\n", + "# Calculate confidence intervals\n", + "ci_lower = np.percentile(bootstrap_means, 2.5)\n", + "ci_upper = np.percentile(bootstrap_means, 97.5)\n", + "\n", + "# Plot the results\n", + "plt.hist(bootstrap_means, bins=50, density=True, alpha=0.7, color='purple')\n", + "plt.axvline(ci_lower, color='red', linestyle='dashed', linewidth=2)\n", + "plt.axvline(ci_upper, color='red', linestyle='dashed', linewidth=2)\n", + "plt.title('Bootstrap Sampling: Histogram of Sample Means')\n", + "plt.xlabel('Sample Mean')\n", + "plt.ylabel('Probability Density')\n", + "plt.show()\n", + "\n", + "print(f'95% confidence interval for the mean: [{ci_lower:.2f}, {ci_upper:.2f}]')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 489 + }, + "id": "eul5JzTsfKEQ", + "outputId": "38e608eb-5d79-4bb8-e65d-7a29d6964106" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "95% confidence interval for the mean: [-0.11, 0.02]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Example 6: Bayesian Inference with MCMC\n", + "Description: This example demonstrates how to use Markov Chain Monte Carlo (MCMC) for Bayesian inference." + ], + "metadata": { + "id": "4UBrZT5_fZj2" + } + }, + { + "cell_type": "code", + "source": [ + "pip install pymc3" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "dKeefPYHfUBD", + "outputId": "e9983ee7-d314-431a-996b-68963ebe670a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pymc3\n", + " Downloading pymc3-3.11.6-py3-none-any.whl (872 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m872.6/872.6 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta 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This could take a while.\n", + "Collecting xarray-einstats>=0.2 (from arviz>=0.11.0->pymc3)\n", + " Downloading xarray_einstats-0.6.0-py3-none-any.whl (31 kB)\n", + "Collecting cftime (from netcdf4->arviz>=0.11.0->pymc3)\n", + " Downloading cftime-1.6.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m25.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from netcdf4->arviz>=0.11.0->pymc3) (2024.6.2)\n", + "Building wheels for collected packages: theano-pymc\n", + " Building wheel for theano-pymc (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for theano-pymc: filename=Theano_PyMC-1.1.2-py3-none-any.whl size=1529960 sha256=bef5802eeefc9e9e98bf10bfa66e81ba073d0f43846a1db67bb6a4d14c5ebc62\n", + " Stored in directory: /root/.cache/pip/wheels/c2/da/87/4e3e2d14772741721d4ebe739c16bcf10ca3c6348f740aa852\n", + "Successfully built theano-pymc\n", + "Installing collected packages: semver, numpy, deprecat, scipy, cftime, theano-pymc, netcdf4, xarray-einstats, arviz, pymc3\n", + " Attempting uninstall: numpy\n", + " Found existing installation: numpy 1.25.2\n", + " Uninstalling numpy-1.25.2:\n", + " Successfully uninstalled numpy-1.25.2\n", + " Attempting uninstall: scipy\n", + " Found existing installation: scipy 1.11.4\n", + " Uninstalling scipy-1.11.4:\n", + " Successfully uninstalled scipy-1.11.4\n", + " Attempting uninstall: xarray-einstats\n", + " Found existing installation: xarray-einstats 0.7.0\n", + " Uninstalling xarray-einstats-0.7.0:\n", + " Successfully uninstalled xarray-einstats-0.7.0\n", + " Attempting uninstall: arviz\n", + " Found existing installation: arviz 0.15.1\n", + " Uninstalling arviz-0.15.1:\n", + " Successfully uninstalled arviz-0.15.1\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.22.1 which is incompatible.\n", + "cudf-cu12 24.4.1 requires numpy<2.0a0,>=1.23, but you have numpy 1.22.1 which is incompatible.\n", + "jax 0.4.26 requires scipy>=1.9, but you have scipy 1.7.3 which is incompatible.\n", + "jaxlib 0.4.26+cuda12.cudnn89 requires scipy>=1.9, but you have scipy 1.7.3 which is incompatible.\n", + "librosa 0.10.2.post1 requires numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3, but you have numpy 1.22.1 which is incompatible.\n", + "numexpr 2.10.1 requires numpy>=1.23.0, but you have numpy 1.22.1 which is incompatible.\n", + "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.22.1 which is incompatible.\n", + "plotnine 0.12.4 requires numpy>=1.23.0, but you have numpy 1.22.1 which is incompatible.\n", + "pymc 5.10.4 requires arviz>=0.13.0, but you have arviz 0.12.1 which is incompatible.\n", + "pywavelets 1.6.0 requires numpy<3,>=1.22.4, but you have numpy 1.22.1 which is incompatible.\n", + "rmm-cu12 24.4.0 requires numpy<2.0a0,>=1.23, but you have numpy 1.22.1 which is incompatible.\n", + "salib 1.5.0 requires scipy>=1.9.3, but you have scipy 1.7.3 which is incompatible.\n", + "statsmodels 0.14.2 requires numpy>=1.22.3, but you have numpy 1.22.1 which is incompatible.\n", + "statsmodels 0.14.2 requires scipy!=1.9.2,>=1.8, but you have scipy 1.7.3 which is incompatible.\n", + "tensorflow 2.15.0 requires numpy<2.0.0,>=1.23.5, but you have numpy 1.22.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed arviz-0.12.1 cftime-1.6.4 deprecat-2.1.1 netcdf4-1.7.1.post1 numpy-1.22.1 pymc3-3.11.6 scipy-1.7.3 semver-3.0.2 theano-pymc-1.1.2 xarray-einstats-0.6.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "numpy", + "scipy" + ] + }, + "id": "0d5c730bdb104f699cfd79804a2853f3" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pymc3 as pm\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Generate synthetic data\n", + "np.random.seed(0)\n", + "true_mean = 5\n", + "true_std = 2\n", + "data = np.random.normal(true_mean, true_std, 100)\n", + "\n", + "# Define the model\n", + "with pm.Model() as model:\n", + " mu = pm.Normal('mu', mu=0, sigma=10)\n", + " sigma = pm.HalfNormal('sigma', sigma=10)\n", + " likelihood = pm.Normal('y', mu=mu, sigma=sigma, observed=data)\n", + "\n", + " # Inference\n", + " trace = pm.sample(2000, return_inferencedata=False)\n", + "\n", + "# Plot the results\n", + "pm.plot_trace(trace)\n", + "plt.show()\n", + "\n", + "# Summary of the posterior distribution\n", + "summary = pm.summary(trace)\n", + "print(summary)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 721 + }, + "id": "0j8H3cq3fXoh", + "outputId": "51fed368-ea95-4e06-afcc-512c171f5228" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "RuntimeError", + "evalue": "\nCould not import 'mkl'. If you are using conda, update the numpy\npackages to the latest build otherwise, set MKL_THREADING_LAYER=GNU in\nyour environment for MKL 2018.\n\nIf you have MKL 2017 install and are not in a conda environment you\ncan set the Theano flag blas__check_openmp to False. Be warned that if\nyou set this flag and don't set the appropriate environment or make\nsure you have the right version you *will* get wrong results.\n", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNoSectionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36mfetch_val_for_key\u001b[0;34m(self, key, delete_key)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_theano_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moption\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mInterpolationError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.10/configparser.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, section, option, raw, vars, fallback)\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 783\u001b[0;31m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unify_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 784\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mNoSectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.10/configparser.py\u001b[0m in \u001b[0;36m_unify_values\u001b[0;34m(self, section, vars)\u001b[0m\n\u001b[1;32m 1153\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msection\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_section\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mNoSectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msection\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1155\u001b[0m \u001b[0;31m# Update with the entry specific variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNoSectionError\u001b[0m: No section: 'blas'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, cls, type_, delete_key)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_val_for_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelete_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdelete_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 355\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_default\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/link/c/cmodule.py\u001b[0m in \u001b[0;36mcheck_mkl_openmp\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2580\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2581\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmkl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'mkl'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/tensor/blas_headers.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1016\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblas__ldflags\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1017\u001b[0m \u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Using NumPy C-API based implementation for BLAS functions.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/configparser.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, cls, type_, delete_key)\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 359\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[0mval_str\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/theano/link/c/cmodule.py\u001b[0m in \u001b[0;36mcheck_mkl_openmp\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2590\u001b[0m )\n\u001b[1;32m 2591\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2592\u001b[0;31m raise RuntimeError(\n\u001b[0m\u001b[1;32m 2593\u001b[0m \"\"\"\n\u001b[1;32m 2594\u001b[0m \u001b[0mCould\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m'mkl'\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mIf\u001b[0m \u001b[0myou\u001b[0m \u001b[0mare\u001b[0m \u001b[0musing\u001b[0m \u001b[0mconda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: \nCould not import 'mkl'. If you are using conda, update the numpy\npackages to the latest build otherwise, set MKL_THREADING_LAYER=GNU in\nyour environment for MKL 2018.\n\nIf you have MKL 2017 install and are not in a conda environment you\ncan set the Theano flag blas__check_openmp to False. Be warned that if\nyou set this flag and don't set the appropriate environment or make\nsure you have the right version you *will* get wrong results.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Reliability Analysis using FORM\n", + "Description: This example demonstrates how to perform a First-Order Reliability Method (FORM) analysis." + ], + "metadata": { + "id": "k5morODpfq11" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from scipy.stats import norm\n", + "\n", + "# Define the limit state function\n", + "def g(x):\n", + " return 3 - x[0] - x[1]\n", + "\n", + "# Mean and standard deviation of the input variables\n", + "mu = np.array([1.5, 1.5])\n", + "sigma = np.array([0.5, 0.5])\n", + "\n", + "# Standardize the inputs\n", + "u = (mu - 0) / sigma\n", + "\n", + "# Perform FORM analysis\n", + "beta = np.linalg.norm(u)\n", + "pf = norm.cdf(-beta)\n", + "\n", + "print(f'Reliability index (beta): {beta:.2f}')\n", + "print(f'Probability of failure (pf): {pf:.5f}')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4_tZOq4UfgH1", + "outputId": "2422982b-1124-49c3-8573-033b35f618b4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Reliability index (beta): 4.24\n", + "Probability of failure (pf): 0.00001\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "pip install smt" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G3jHlC7Efsc_", + "outputId": "74624c2e-1ed1-41dd-be05-39c866e12c6f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting smt\n", + " Downloading smt-2.6.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (819 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m819.2/819.2 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from smt) (1.2.2)\n", + "Collecting pyDOE3 (from smt)\n", + " Downloading pydoe3-1.0.3-py2.py3-none-any.whl (25 kB)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from smt) (1.7.3)\n", + "Collecting jenn (from smt)\n", + " Downloading jenn-1.0.6-py3-none-any.whl (33 kB)\n", + "Collecting jsonpointer>=2.4 (from jenn->smt)\n", + " Downloading jsonpointer-3.0.0-py2.py3-none-any.whl (7.6 kB)\n", + "Collecting jsonschema>=4.22 (from jenn->smt)\n", + " Downloading jsonschema-4.23.0-py3-none-any.whl (88 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.5/88.5 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting orjson>=3.9 (from jenn->smt)\n", + " Downloading orjson-3.10.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (141 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.1/141.1 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from jenn->smt) (1.22.1)\n", + "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->smt) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->smt) (3.5.0)\n", + "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=4.22->jenn->smt) (23.2.0)\n", + "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=4.22->jenn->smt) (2023.12.1)\n", + "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=4.22->jenn->smt) (0.35.1)\n", + "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=4.22->jenn->smt) (0.18.1)\n", + "Installing collected packages: orjson, jsonpointer, pyDOE3, jsonschema, jenn, smt\n", + " Attempting uninstall: jsonschema\n", + " Found existing installation: jsonschema 4.19.2\n", + " Uninstalling jsonschema-4.19.2:\n", + " Successfully uninstalled jsonschema-4.19.2\n", + "Successfully installed jenn-1.0.6 jsonpointer-3.0.0 jsonschema-4.23.0 orjson-3.10.6 pyDOE3-1.0.3 smt-2.6.2\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from smt.surrogate_models import KRG\n", + "\n", + "# Define the function\n", + "def f(x):\n", + " return (6*x - 2)**2 * np.sin(12*x - 4)\n", + "\n", + "# Training data\n", + "x_train = np.linspace(0, 1, 10).reshape(-1, 1)\n", + "y_train = f(x_train)\n", + "\n", + "# Create Kriging surrogate model\n", + "kriging = KRG()\n", + "kriging.set_training_values(x_train, y_train)\n", + "kriging.train()\n", + "\n", + "# Prediction\n", + "x_pred = np.linspace(0, 1, 100).reshape(-1, 1)\n", + "y_pred = kriging.predict_values(x_pred)\n", + "\n", + "# Plot the results\n", + "plt.plot(x_train, y_train, 'o', label='Training data')\n", + "plt.plot(x_pred, y_pred, '-', label='Kriging surrogate')\n", + "plt.plot(x_pred, f(x_pred), '--', label='True function')\n", + "plt.title('Kriging Surrogate Modeling')\n", + "plt.xlabel('x')\n", + "plt.ylabel('f(x)')\n", + "plt.legend()\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 978 + }, + "id": "iuA9eCucfzDi", + "outputId": "e6abc8a4-b03b-438b-bf1c-c5469061e1b0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "___________________________________________________________________________\n", + " \n", + " Kriging\n", + "___________________________________________________________________________\n", + " \n", + " Problem size\n", + " \n", + " # training points. : 10\n", + " \n", + "___________________________________________________________________________\n", + " \n", + " Training\n", + " \n", + " Training ...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/smt/surrogate_models/krg_based.py:968: UserWarning: R is too ill conditioned. Poor combination of regression model and observations.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Training - done. Time (sec): 0.5963030\n", + "___________________________________________________________________________\n", + " \n", + " Evaluation\n", + " \n", + " # eval points. : 100\n", + " \n", + " Predicting ...\n", + " Predicting - done. Time (sec): 0.0006590\n", + " \n", + " Prediction time/pt. (sec) : 0.0000066\n", + " \n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "z3qfXJBzgGW_" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/jarvis-tools-notebooks/ContourExploration.ipynb b/jarvis-tools-notebooks/ContourExploration.ipynb new file mode 100644 index 0000000..7f2c797 --- /dev/null +++ b/jarvis-tools-notebooks/ContourExploration.ipynb @@ -0,0 +1,492 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOygk/uaLGsE+dloLAkK99k", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3YvpDOHA3tfH", + "outputId": "a4fc19f7-85fe-4b25-c0e4-a3dc58ae733c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting ase\n", + " Downloading ase-3.23.0-py3-none-any.whl (2.9 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from ase) (1.25.2)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from ase) (1.11.4)\n", + "Requirement already satisfied: matplotlib>=3.3.4 in /usr/local/lib/python3.10/dist-packages (from ase) (3.7.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (4.53.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (24.1)\n", + "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (9.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.4->ase) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.4->ase) (1.16.0)\n", + "Installing collected packages: ase\n", + "Successfully installed ase-3.23.0\n" + ] + } + ], + "source": [ + "pip install ase" + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from ase.build import bulk\n", + "from ase.calculators.emt import EMT\n", + "from ase.md.contour_exploration import ContourExploration\n", + "from ase.md.velocitydistribution import MaxwellBoltzmannDistribution\n", + "bulk_Al_settings = {\n", + " 'maxstep': 1.0,\n", + " 'parallel_drift': 0.05,\n", + " 'remove_translation': True,\n", + " 'potentiostat_step_scale': None,\n", + " 'use_frenet_serret': True,\n", + " 'angle_limit': 20,\n", + " 'loginterval': 1}\n", + "size = 2\n", + "atoms = bulk('Al', 'fcc', cubic=True).repeat((size, size, size))\n", + "atoms.calc = EMT()\n", + "\n", + "name = 'test_potentiostat'\n", + "seed = 19460926\n", + "\n", + "\n", + "E0 = atoms.get_potential_energy()\n", + "\n", + "atoms.rattle(stdev=0.18, seed=seed)\n", + "initial_energy = atoms.get_potential_energy()\n", + "\n", + "rng = np.random.RandomState(seed)\n", + "MaxwellBoltzmannDistribution(atoms, temperature_K=300, rng=rng)\n", + "with ContourExploration(\n", + " atoms,\n", + " **bulk_Al_settings,\n", + " energy_target=initial_energy,\n", + " rng=rng,\n", + " trajectory=name + '.traj',\n", + " logfile=name + '.log',\n", + ") as dyn:\n", + " print(\"Energy Above Ground State: {: .4f} eV/atom\".format(\n", + " (initial_energy - E0) / len(atoms)))\n", + " for _ in range(5):\n", + " dyn.run(5)\n", + " energy_error = (atoms.get_potential_energy() -\n", + " initial_energy) / len(atoms)\n", + " print(f'Potentiostat Error {energy_error: .4f} eV/atom')\n", + " # assert 0 == pytest.approx(energy_error, abs=0.01)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D9hHpxbI3uzt", + "outputId": "19d773ac-9848-42e2-80a8-c2c6af345715" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Energy Above Ground State: 0.1764 eV/atom\n", + "Potentiostat Error -0.0027 eV/atom\n", + "Potentiostat Error -0.0008 eV/atom\n", + "Potentiostat Error -0.0029 eV/atom\n", + "Potentiostat Error -0.0006 eV/atom\n", + "Potentiostat Error -0.0008 eV/atom\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "from ase.lattice.cubic import FaceCenteredCubic\n", + "#from ase.md.velocitydistribution import MaxwellBoltzmannDistribution\n", + "from ase.optimize import BFGS\n", + "import os\n", + "#from contour_exploration import contour_exploration\n", + "from ase.md.contour_exploration import ContourExploration\n", + "from ase import units, io, Atom\n", + "\n", + "import numpy as np\n", + "\n", + "# Use Asap for a huge performance increase if it is installed\n", + "use_asap = False\n", + "\n", + "if use_asap:\n", + " from asap3 import EMT\n", + "else:\n", + " from ase.calculators.emt import EMT\n", + "\n", + "\n", + "#pds = [0.0, 0.05, 0.1, 0.2]\n", + "pds = [0.2] # only value used in the paper\n", + "#mxs = [0.05, 0.1, 0.2, 0.4]\n", + "mxs = [0.2] # only value used in the paper\n", + "#anglelims = [5, 10 ,20]\n", + "anglelims = [5, 20] # only values used in the paper\n", + "energy_barriers_scales = [0.5, 1.0, 1.5]\n", + "\n", + "\n", + "seed = 453 #'ASE'\n", + "rng = np.random.default_rng(seed)\n", + "\n", + "for parallel_drift in pds:\n", + " for maxstep in mxs:\n", + " for angle_limit in anglelims:\n", + " for ebx in energy_barriers_scales:\n", + "\n", + " traj_name = 'atom_on_frozen_surface_test_%.2f_pd_%.2f_mxstp_%02d_angle_%.2f_ebx.traj' % (parallel_drift, maxstep, angle_limit,ebx)\n", + "\n", + " fmax_relax = 1e-6\n", + " size_xy = 1\n", + " size_z = 1\n", + "\n", + " atoms = FaceCenteredCubic(directions=[[1, 0, 0], [0, 1, 0], [0, 0, 1]],\n", + " symbol='Al',\n", + " size=(size_xy, size_xy, size_z),\n", + " pbc=True)\n", + "\n", + " if False:\n", + " shift= np.dot(np.array([-0.25, -0.25, 0.0])/size_xy,atoms.cell)\n", + " atoms.translate(shift)\n", + " atoms.wrap()\n", + "\n", + "\n", + " from ase.constraints import FixAtoms\n", + " c = FixAtoms(indices=[atom.index for atom in atoms])\n", + " atoms.set_constraint(c)\n", + "\n", + " atoms.append(\n", + " Atom('Cu',\n", + " #position = (0.1, 0.1, atoms.cell[2,2] - .3) ))\n", + " position = (atoms.cell[0,0]/2., atoms.cell[1,1]/2., atoms.cell[2,2] - .3) ))\n", + "\n", + " atoms.cell[2,2] = 4* atoms.cell[2,2]\n", + "\n", + " atoms.set_calculator(EMT())\n", + "\n", + " ############### ground state\n", + "\n", + " relax = BFGS(atoms,trajectory='surface_relax.traj')\n", + " relax.run(fmax=fmax_relax)\n", + " E0 = atoms.get_potential_energy()\n", + " io.write('initial.traj',atoms)\n", + " atoms0 = atoms.copy()\n", + "\n", + "\n", + " ########### barrier with dimer\n", + " from ase.dimer import DimerControl, MinModeAtoms, MinModeTranslate, normalize\n", + "\n", + " #### Set up the dimer\n", + " fdstep = 0.5\n", + " displacement_vector = [[0.0]*3]*(len(atoms))\n", + " displacement_vector[-1] = [0.1, 0.1, 0.1]\n", + " displacement_vector = fdstep * normalize(displacement_vector)\n", + "\n", + "\n", + " #\n", + " d_control = DimerControl(initial_eigenmode_method = 'displacement',\n", + " dimer_separation = 0.00001,\n", + " #extrapolate_forces = False,\n", + " #use_central_forces = False,\n", + " displacement_method = 'vector', logfile = 'dimer.log' )\n", + " #mask = dimer_mask) # we don't need a mask with all other atoms frozen\n", + " d_atoms = MinModeAtoms(atoms, d_control)\n", + "\n", + " # Displace the atoms\n", + " d_atoms.displace(displacement_vector = displacement_vector)\n", + "\n", + " # Converge to a saddle point\n", + " dim_rlx = MinModeTranslate(d_atoms, trajectory = 'dimer_method.traj', )\n", + " #logfile = logfile)\n", + " dim_rlx.run(fmax = fmax_relax)\n", + "\n", + " ### post processing\n", + " #print('DimerControl\\n',dir(d_control))\n", + " #print('MinModeAtoms\\n',dir(d_atoms))\n", + " #print('MinModeTranslate\\n',dir(dim_rlx))\n", + "\n", + " eigenmode = d_atoms.get_eigenmode()\n", + " print('dimer_eigenmode:\\n', d_atoms.get_eigenmode())\n", + " print('dimer_curvature:\\n', d_atoms.get_curvature())\n", + " #atoms.set_positions( atoms.get_positions()+ 0.5*dimer_direction) # gets the middle of the dimer\n", + "\n", + " saddle = atoms.copy()\n", + " saddle.calc = EMT()\n", + "\n", + " Eb = saddle.get_potential_energy()\n", + " forcesb = saddle.get_forces()\n", + " io.write('saddle.traj',saddle)\n", + "\n", + " maxforceb = np.sqrt( (forcesb ** 2).sum(axis=1).max() )\n", + " print(\"saddle forces:\\n\", forcesb)\n", + " print(\"saddle forces max\", maxforceb)\n", + "\n", + "\n", + " barrier_energy = Eb-E0\n", + " print('Saddle barrier',barrier_energy)\n", + "\n", + " ############ CED setup\n", + "\n", + " atoms.set_positions(atoms0.get_positions())\n", + " # Set the momenta corresponding to T=300K\n", + " #MaxwellBoltzmannDistribution(atoms, 300 * units.kB)\n", + "\n", + " #p = np.zeros((len(atoms),3)) #atoms.get_momenta()\n", + " #p[-1] = [-1.0, -1.0, 0.0]\n", + " atoms.set_momenta(eigenmode)\n", + " #atoms.set_momenta( p - np.sum(p,axis =0)/len(atoms))\n", + " atoms.rattle(stdev=0.15 , seed = 60622)\n", + "\n", + " print('initial momenta',atoms.get_momenta())\n", + "\n", + "\n", + " #extra_energy = 0.350 # just above the barrier, Barrier = 0.3196\n", + " extra_energy = ebx* barrier_energy\n", + "\n", + " energy_target = E0 + extra_energy\n", + "\n", + " def printenergy(i,a):\n", + " \"\"\"Function to print the potential, kinetic and total energy\"\"\"\n", + " epot = a.get_potential_energy()\n", + " dev = epot - energy_target\n", + "\n", + " print(i,'Energy Target: {: 6f}, Epot {: 6f} eV, Deviation from target {: 6f} meV'.format( energy_target, epot, dev*1000))\n", + "\n", + "\n", + " #energy_target = 1.28\n", + "\n", + "\n", + " logfile= traj_name.replace('.traj', '.log')\n", + " if os.path.isfile(logfile): os.remove(logfile)\n", + "\n", + " ## pre-release settings/parameters, renamed or\n", + " ## slightly improved for final release\n", + " # dyn = contour_exploration(atoms,\n", + " # maxstep = maxstep,\n", + " # parallel_drift = parallel_drift,\n", + " # remove_translation = False,\n", + " # force_parallel_step_scale = None,\n", + " # energy_target = energy_target,\n", + " # initialize_old = True, use_FS = True,\n", + " # use_target_shift = True,\n", + " # angle_limit = angle_limit,\n", + " # trajectory = traj_name,\n", + " # logfile = logfile )\n", + "\n", + " dyn = ContourExploration(\n", + " atoms,\n", + " maxstep = maxstep,\n", + " parallel_drift = parallel_drift,\n", + " remove_translation = False,\n", + " potentiostat_step_scale = None,\n", + " energy_target = energy_target,\n", + " use_frenet_serret = True,\n", + " use_target_shift = True,\n", + " target_shift_previous_steps=10,\n", + " angle_limit = angle_limit,\n", + " trajectory = traj_name,\n", + " logfile = logfile,\n", + " rng=rng)\n", + "\n", + "\n", + "\n", + " # Now run the dynamics\n", + " printenergy(0,atoms)\n", + " for i in range(10000):\n", + " dyn.run(1)\n", + " printenergy(i,atoms)\n", + "\n", + "\n", + "\n", + "\n", + " if False:\n", + " ###########\n", + " from ase import io\n", + " traj = io.Trajectory(traj_name, 'r')\n", + "\n", + " es = equilibration_steps = 20\n", + "\n", + " energy = np.array( [im.get_potential_energy() for im in traj] )\n", + " mean_deviation = np.mean(energy[es:]-energy_target)#/len(atoms)\n", + " rms_deviation = np.sqrt(np.mean((energy[es:]-energy_target)**2))#/len(atoms)\n", + " standard_deviation = np.std(energy[es:])#/len(atoms)\n", + " print(\"Images\",len(traj))\n", + " print(\"Mean deviation %f\" %mean_deviation)\n", + " print(\"Standard deviation %f\"% standard_deviation)\n", + " print(\"RMS deviation %f\"% rms_deviation)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " X = []\n", + " Y = []\n", + "\n", + " for image in traj:\n", + " image.wrap()\n", + " pos = image.get_positions()\n", + " X.append(pos[-1,0] )\n", + " Y.append(pos[-1,1])\n", + "\n", + " #traj.close()\n", + "\n", + "\n", + "\n", + " import matplotlib.pyplot as plt\n", + " from ase.visualize.plot import plot_atoms\n", + "\n", + " if False:\n", + " fig, ax = plt.subplots(figsize = (3.2,2.5), dpi = 150)\n", + "\n", + " offset = .3085\n", + " plot_atoms(atoms0, ax, offset=(-offset, -offset))\n", + "\n", + " ax.set_xlim(-0.4, 4.4)\n", + " ax.set_ylim(-0.4, 4.4)\n", + " plt.plot(X,Y, marker = 'o', linestyle = '', markersize = 2, color = 'b')\n", + "\n", + " fig.tight_layout(pad= 0.3)\n", + "\n", + " fig.savefig(traj_name.replace('.traj', '.pdf'), transparent = True)\n", + "\n", + "\n", + " if False:\n", + "\n", + " import matplotlib.pyplot as plt\n", + "\n", + " fig, ax = plt.subplots(figsize = (3.2,2.5), dpi = 150)\n", + " ax.axhline(0, color = 'grey', linewidth = 0.5, linestyle = '-')\n", + " ax.axhline(per_atom_energy*1000, color = 'grey', linewidth = 0.5, linestyle = '-')\n", + " ax.plot((energy-E0) *1000, color = 'teal')\n", + "\n", + " ax.set_ylabel('$E - E_{contour}$ (meV)')\n", + " ax.set_xlabel('Iteration #')\n", + " ax.minorticks_on()\n", + "\n", + " fig.tight_layout(pad= 0.3)\n", + "\n", + " fig.savefig(traj_name.replace('.traj', '_energy.pdf'), transparent = True)\n", + "\n", + "\n", + "\n", + "\n", + " if False:\n", + "\n", + " # kwargs can be any ase.io.utils.PlottingVariables.__init__() input\n", + "\n", + " from ase.io.animation import write_gif\n", + " from ase import io\n", + " write_gif(traj_name.replace('.traj', '.gif'), traj,\n", + " interval = 200, show_unit_cell = 2, rotation = '0x, 0y, 0z', )\n", + "\n", + "\n", + "\n", + " if False: plt.show()\n" + ], + "metadata": { + "id": "MFxMhB0p4Nsh", + "outputId": "205b7bd4-6d9c-46bb-b56c-33f26ba9e874", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 541 + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Step Time Energy fmax\n", + "BFGS: 0 18:29:27 2.182145 0.037333\n", + "BFGS: 1 18:29:27 2.182125 0.035584\n", + "BFGS: 2 18:29:27 2.181933 0.000242\n", + "BFGS: 3 18:29:27 2.181933 0.000002\n", + "BFGS: 4 18:29:27 2.181933 0.000000\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":65: DeprecationWarning: Please use atoms.calc = calc\n", + " atoms.set_calculator(EMT())\n" + ] + }, + { + "output_type": "error", + "ename": "ImportError", + "evalue": "cannot import name 'normalize' from 'ase.dimer' (/usr/local/lib/python3.10/dist-packages/ase/dimer.py)", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;31m########### barrier with dimer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDimerControl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMinModeAtoms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMinModeTranslate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;31m#### Set up the dimer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'normalize' from 'ase.dimer' (/usr/local/lib/python3.10/dist-packages/ase/dimer.py)", + "", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" + ], + "errorDetails": { + "actions": [ + { + "action": "open_url", + "actionText": "Open Examples", + "url": "/notebooks/snippets/importing_libraries.ipynb" + } + ] + } + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "Me4r8u3sGzR-" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/jarvis-tools-notebooks/JARVIS_FEM.ipynb b/jarvis-tools-notebooks/JARVIS_FEM.ipynb index c1e3199..61dba5c 100644 --- a/jarvis-tools-notebooks/JARVIS_FEM.ipynb +++ b/jarvis-tools-notebooks/JARVIS_FEM.ipynb @@ -4,7 +4,7 @@ "metadata": { "colab": { "provenance": [], - "authorship_tag": "ABX9TyPUzCJixStpHFSIVjAAIHAe", + "authorship_tag": "ABX9TyNJsg3dxTGLNSva+i9eYZzr", "include_colab_link": true }, "kernelspec": { @@ -26,6 +26,29 @@ "\"Open" ] }, + { + "cell_type": "markdown", + "source": [ + "Table of contents\n", + "\n", + "1. Hello world of FEM: Solving Poisson equation\n", + "\n", + "2. Learning about mesh generation through examples\n", + "\n", + "3. 2D linear elasticity example\n", + "\n", + "4. Orthotropic linear elasticity\n", + "\n", + "5. Axisymmetric formulation for elastic structures of revolution\n", + "\n", + "6. Heat equation\n", + "\n", + "7. Thermo-elastic evolution problem" + ], + "metadata": { + "id": "w27QB30MfuG4" + } + }, { "cell_type": "code", "source": [ @@ -47,7 +70,7 @@ "metadata": { "id": "GjMTkxnsXitT" }, - "execution_count": 1, + "execution_count": null, "outputs": [] }, { @@ -173,7 +196,7 @@ "metadata": { "id": "okxS3hQk6UBq" }, - "execution_count": 2, + "execution_count": null, "outputs": [] }, { @@ -204,7 +227,7 @@ "id": "fEcD8p7k6T-h", "outputId": "8efca51f-5721-4d95-f57f-07004f1a17fb" }, - "execution_count": 3, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -254,7 +277,7 @@ "id": "q3E-ppKC6iqt", "outputId": "60e58c8b-fbf4-43c0-eede-95beb1890a37" }, - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -329,7 +352,7 @@ "metadata": { "id": "b1TjDNUg6ioX" }, - "execution_count": 5, + "execution_count": null, "outputs": [] }, { @@ -358,7 +381,7 @@ "metadata": { "id": "4byWW9X06imH" }, - "execution_count": 6, + "execution_count": null, "outputs": [] }, { @@ -396,7 +419,7 @@ "metadata": { "id": "UKVFFZYE6ijs" }, - "execution_count": 7, + "execution_count": null, "outputs": [] }, { @@ -421,7 +444,7 @@ "metadata": { "id": "4tuz3K3u6ihJ" }, - "execution_count": 8, + "execution_count": null, "outputs": [] }, { @@ -458,7 +481,7 @@ "id": "X0Z4W-Z26ie-", "outputId": "1bbbda54-52eb-4901-9ebd-34f4cecbaced" }, - "execution_count": 9, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -789,7 +812,7 @@ "id": "eE5STSnp6icb", "outputId": "78df840a-723a-421b-8d4e-557723b2fdc3" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -833,7 +856,7 @@ "metadata": { "id": "UAO9Fqa66iZx" }, - "execution_count": 11, + "execution_count": null, "outputs": [] }, { @@ -869,7 +892,7 @@ "id": "ylLvRMsI6T8K", "outputId": "26786d83-b034-43a2-ff21-ce6a1110a20e" }, - "execution_count": 12, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1203,7 +1226,7 @@ "id": "gfOng4PQ65wt", "outputId": "27f0d466-7181-4374-abbf-aa436c9d5eca" }, - "execution_count": 15, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -1229,23 +1252,6 @@ { "cell_type": "code", "source": [ - "# Copyright (C) 2014 Benjamin Kehlet\n", - "#\n", - "# This file is part of mshr.\n", - "#\n", - "# mshr is free software: you can redistribute it and/or modify\n", - "# it under the terms of the GNU Lesser General Public License as published by\n", - "# the Free Software Foundation, either version 3 of the License, or\n", - "# (at your option) any later version.\n", - "#\n", - "# mshr is distributed in the hope that it will be useful,\n", - "# but WITHOUT ANY WARRANTY; without even the implied warranty of\n", - "# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", - "# GNU Lesser General Public License for more details.\n", - "#\n", - "# You should have received a copy of the GNU Lesser General Public License\n", - "# along with mshr. If not, see .\n", - "#\n", "# https://bitbucket.org/fenics-project/mshr/src/master/demo/python/icecream.py\n", "import dolfin\n", "from mshr import *\n", @@ -1286,7 +1292,7 @@ "id": "VX0OCc7G65uA", "outputId": "db089b38-73a5-4d26-e7eb-889a00fdebfc" }, - "execution_count": 18, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1434,7 +1440,7 @@ "id": "nj-RBiBO96s6", "outputId": "0322860b-b3d1-4e97-f0fc-0bcb31596115" }, - "execution_count": 19, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -1481,7 +1487,7 @@ "id": "5dXZHBTU96q8", "outputId": "597fc70f-0bb2-4b22-e949-615755f6aeb6" }, - "execution_count": 20, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1672,7 +1678,7 @@ "id": "QsuJ9lr696no", "outputId": "4754eba6-c4e7-4528-865a-817b3a2eb983" }, - "execution_count": 21, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1698,7 +1704,7 @@ "id": "iTUk7yg065q6", "outputId": "5ae233a3-1a81-440a-ec8d-c39d56c811a2" }, - "execution_count": 22, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1830,7 +1836,7 @@ "id": "1OAmsH1P-bTY", "outputId": "4e13a632-989d-4dc9-e84a-b7c548d14886" }, - "execution_count": 23, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -1987,7 +1993,7 @@ "id": "YOE_V0oh-5XL", "outputId": "7b95641a-ac76-4f52-b4a1-0acd2fdf05b8" }, - "execution_count": 25, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -2129,7 +2135,7 @@ "id": "r7kfFM4h_QBq", "outputId": "f021ee2b-8860-478f-9032-1bec930a118f" }, - "execution_count": 27, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -2281,7 +2287,7 @@ "metadata": { "id": "nnYaf3Eh_P6M" }, - "execution_count": 55, + "execution_count": null, "outputs": [] }, { @@ -2337,7 +2343,7 @@ "id": "Z3KL8QSoXEmW", "outputId": "6d768b9f-4d05-4664-e0c3-9fb4a9b78496" }, - "execution_count": 28, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -2520,7 +2526,7 @@ "id": "ZN2InJAIiAHs", "outputId": "93d25d0c-f82f-463a-ebb6-06396623e5a5" }, - "execution_count": 29, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -3119,7 +3125,7 @@ "id": "kqcMm-lHXEo3", "outputId": "f055c165-92c7-439b-d3a0-b1b1a39ddf5c" }, - "execution_count": 30, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -3243,7 +3249,7 @@ }, "outputId": "b413f7f7-e7ca-4c7e-f7cc-82e7a4ee4a5b" }, - "execution_count": 31, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -3750,7 +3756,7 @@ }, "outputId": "5efffc68-93e9-4547-f23d-45f36ca4bf2e" }, - "execution_count": 32, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -3809,7 +3815,7 @@ "id": "JFA06b2LlsGk", "outputId": "94346f39-5524-474d-d26d-68c64de35e05" }, - "execution_count": 33, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4118,7 +4124,7 @@ "id": "jpllx6d5lwth", "outputId": "e258dd65-c68b-448e-c320-07bafc3c36ae" }, - "execution_count": 34, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4315,7 +4321,7 @@ "id": "TCT4tHdhAnSZ", "outputId": "5dce44fd-5dea-4f13-c575-63869dc8848a" }, - "execution_count": 43, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -4425,7 +4431,7 @@ "metadata": { "id": "PrdBm_2cAnPz" }, - "execution_count": 43, + "execution_count": null, "outputs": [] }, { @@ -4539,7 +4545,7 @@ "id": "uaubDN8yAnIj", "outputId": "8a75f933-5fc0-4a90-9413-8f9ff9642567" }, - "execution_count": 2, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -6215,7 +6221,7 @@ "id": "jC5b8Aw0AnFt", "outputId": "d0f83cb7-04ed-4168-9869-a17177944e08" }, - "execution_count": 3, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -7239,7 +7245,7 @@ "id": "RfpP2pjWMIMI", "outputId": "f0109e61-160c-4d3c-8a45-3bf3d0ee38c7" }, - "execution_count": 6, + "execution_count": null, "outputs": [ { "output_type": "execute_result", diff --git a/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb b/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb index baffc04..5ceb26d 100644 --- a/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb +++ b/jarvis-tools-notebooks/JARVIS_QuantumEspressoColab_Basic_Example.ipynb @@ -5,7 +5,7 @@ "colab": { "name": "JARVIS_QuantumEspressoColab_Basic_Example.ipynb", "provenance": [], - "authorship_tag": "ABX9TyM8aYsEdRVI1tjDG3iAAvM5", + "authorship_tag": "ABX9TyOwGykC/F5K/rh0qaUltmpb", "include_colab_link": true }, "kernelspec": { @@ -30,7 +30,7 @@ { "cell_type": "markdown", "source": [ - "# Run Quantum espresso calculations with JARVIS-Tools" + "# This example shows how to run a Quantum espresso calculations with JARVIS-Tools for silicon and add the contribution to the JARVIS-Leaderboard." ], "metadata": { "id": "JIugyjg85Eep" @@ -44,89 +44,21 @@ "base_uri": "https://localhost:8080/" }, "id": "9CNO-RGBxCqi", - "outputId": "f4814842-c582-4947-c985-deaf5ce614a1" + "outputId": "e1a48224-c691-400a-988e-031353bdff5c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Collecting jarvis-tools\n", - " Downloading jarvis_tools-2023.9.20-py2.py3-none-any.whl (974 kB)\n", - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/974.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.6/974.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m645.1/974.6 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.6/974.6 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - 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"Successfully installed colorama-0.4.6 ghp-import-2.1.0 jarvis-tools-2023.9.20 mergedeep-1.3.4 mkdocs-1.5.3 mkdocs-material-9.4.6 mkdocs-material-extensions-1.2 paginate-0.5.6 pathspec-0.11.2 pymdown-extensions-10.3 pyyaml-env-tag-0.1 spglib-2.1.0 watchdog-3.0.0 xmltodict-0.13.0\n" + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" ] } ], "source": [ - "!pip install jarvis-tools" + "!pip install -q jarvis-tools" ] }, { @@ -138,6 +70,15 @@ "id": "YYF-iR3aUOY3" } }, + { + "cell_type": "markdown", + "source": [ + "It takes about 7 minutes to install QE." + ], + "metadata": { + "id": "HjbUWNz14VTC" + } + }, { "cell_type": "code", "source": [ @@ -161,9 +102,9 @@ "base_uri": "https://localhost:8080/" }, "id": "wnTJPQJyxE0r", - "outputId": "4d87ac65-5997-4a1c-d341-e48b670e4f2e" + "outputId": "f7f4a427-1d16-4cd8-d5fd-cd5e3500ba7d" }, - "execution_count": 6, + "execution_count": 2, "outputs": [ { "output_type": "stream", @@ -172,43 +113,129 @@ "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", - "libfftw3-dev is already the newest version (3.3.8-2ubuntu8).\n", - "libfftw3-doc is already the newest version (3.3.8-2ubuntu8).\n", - "libfftw3-3 is already the newest version (3.3.8-2ubuntu8).\n", - "0 upgraded, 0 newly installed, 0 to remove and 18 not upgraded.\n", + "The following additional packages will be installed:\n", + " libfftw3-bin libfftw3-double3 libfftw3-long3 libfftw3-quad3 libfftw3-single3\n", + "The following NEW packages will be installed:\n", + " libfftw3-3 libfftw3-bin libfftw3-dev libfftw3-doc libfftw3-double3 libfftw3-long3 libfftw3-quad3\n", + " libfftw3-single3\n", + "0 upgraded, 8 newly installed, 0 to remove and 45 not upgraded.\n", + "Need to get 4,918 kB of archives.\n", + "After this operation, 26.2 MB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-double3 amd64 3.3.8-2ubuntu8 [770 kB]\n", + "Get:2 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-long3 amd64 3.3.8-2ubuntu8 [335 kB]\n", + "Get:3 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-single3 amd64 3.3.8-2ubuntu8 [800 kB]\n", + "Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libfftw3-3 amd64 3.3.8-2ubuntu8 [1,756 B]\n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-quad3 amd64 3.3.8-2ubuntu8 [614 kB]\n", + "Get:6 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-bin amd64 3.3.8-2ubuntu8 [35.5 kB]\n", + "Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-dev amd64 3.3.8-2ubuntu8 [2,101 kB]\n", + "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfftw3-doc all 3.3.8-2ubuntu8 [262 kB]\n", + "Fetched 4,918 kB in 1s (4,869 kB/s)\n", + "Selecting previously unselected package libfftw3-double3:amd64.\n", + "(Reading database ... 123594 files and directories currently installed.)\n", + "Preparing to unpack .../0-libfftw3-double3_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-double3:amd64 (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-long3:amd64.\n", + "Preparing to unpack .../1-libfftw3-long3_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-long3:amd64 (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-single3:amd64.\n", + "Preparing to unpack .../2-libfftw3-single3_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-single3:amd64 (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-3:amd64.\n", + "Preparing to unpack .../3-libfftw3-3_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-3:amd64 (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-quad3:amd64.\n", + "Preparing to unpack .../4-libfftw3-quad3_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-quad3:amd64 (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-bin.\n", + "Preparing to unpack .../5-libfftw3-bin_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-bin (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-dev:amd64.\n", + "Preparing to unpack .../6-libfftw3-dev_3.3.8-2ubuntu8_amd64.deb ...\n", + "Unpacking libfftw3-dev:amd64 (3.3.8-2ubuntu8) ...\n", + "Selecting previously unselected package libfftw3-doc.\n", + "Preparing to unpack .../7-libfftw3-doc_3.3.8-2ubuntu8_all.deb ...\n", + "Unpacking libfftw3-doc (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-single3:amd64 (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-long3:amd64 (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-quad3:amd64 (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-double3:amd64 (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-3:amd64 (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-doc (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-bin (3.3.8-2ubuntu8) ...\n", + "Setting up libfftw3-dev:amd64 (3.3.8-2ubuntu8) ...\n", + "Processing triggers for man-db (2.10.2-1) ...\n", + "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", + "\n", "test -d bin || mkdir bin\n", "( cd UtilXlib ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/UtilXlib'\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c parallel_include.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c util_param.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c clib_wrappers.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c nvtx_wrapper.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory 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-cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c device_helper.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c divide.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c data_buffer.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c eval_infix.c\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c error_handler.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_bands_util.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c export_gstart_2_solvers.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c find_free_unit.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c fletcher32_mod.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c fletcher32.c\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c md5.c\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c md5_from_file.c\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c memstat.c\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c memusage.c\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mem_counter.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_base.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c mp_base_gpu.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c print_mem.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c ptrace.c\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c set_mpi_comm_4_solvers.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I. -c thread_util.f90\n", + "\u001b[01m\u001b[Kf951:\u001b[m\u001b[K \u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Nonexistent include directory ‘\u001b[01m\u001b[K/content/q-e//external/devxlib/src\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wmissing-include-dirs\u0007-Wmissing-include-dirs\u001b]8;;\u0007\u001b[m\u001b[K]\n", "ar ruv libutil.a clib_wrappers.o clocks_handler.o cptimer.o copy.o c_mkdir.o device_helper.o divide.o data_buffer.o eval_infix.o error_handler.o export_gstart_2_solvers.o find_free_unit.o fletcher32_mod.o fletcher32.o md5.o md5_from_file.o memstat.o memusage.o mem_counter.o mp.o mp_base.o mp_base_gpu.o mp_bands_util.o parallel_include.o print_mem.o ptrace.o set_mpi_comm_4_solvers.o util_param.o thread_util.o nvtx_wrapper.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libutil.a\n", @@ -247,6 +274,16 @@ "cd install ; make -f extlibs_makefile libcuda\n", "make[1]: Entering directory '/content/q-e/install'\n", "initializing external/devxlib submodule ...\n", + "Cloning into '/content/q-e/external/devxlib'...\n", + "remote: Total 0 (delta 0), reused 0 (delta 0), pack-reused 0 (from 0)\u001b[K\n", + "remote: Enumerating objects: 117, done.\u001b[K\n", + "remote: Counting objects: 100% (117/117), done.\u001b[K\n", + "remote: Compressing objects: 100% (68/68), done.\u001b[K\n", + "remote: Total 77 (delta 41), reused 26 (delta 8), pack-reused 0 (from 0)\u001b[K\n", + "Unpacking objects: 100% (77/77), 94.95 KiB | 1.79 MiB/s, done.\n", + "From https://gitlab.com/max-centre/components/devicexlib\n", + " * branch a6b89ef77b1ceda48e967921f1f5488d2df9226d -> FETCH_HEAD\n", + "Submodule path 'external/devxlib': checked out 'a6b89ef77b1ceda48e967921f1f5488d2df9226d'\n", "/content/q-e/install\n", "cd /content/q-e//external/devxlib/src; \\\n", "ln -fs /content/q-e//make.inc ../; \\\n", @@ -410,92 +447,83 @@ "make[1]: Leaving directory '/content/q-e/FFTXlib'\n", "( cd upflib ; make TLDEPS= all || exit 1 )\n", "make[1]: Entering directory '/content/q-e/upflib'\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_kinds.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_const.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c radial_grids.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c atom.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c pseudo_types.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_io.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_params.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_utils.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gth.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp_param.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c vloc_mod.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_invmat.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_spinorb.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c uspp_data.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gen_us_dj.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c gen_us_dy.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_0.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_b0.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c paw_variables.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_1.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_2_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_atwfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_beta.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_rho.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_rhc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_tab_qrad.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c atomic_number.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dqvan2.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dylmr2.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c interp_atwfc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c interp_rhc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c interp_drhc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c qvan2.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_cpmd.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_fhi.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_ncpp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c splinelib.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c xmltools.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_psml.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_upf_new.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_upf_v1.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_uspp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c read_ps.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c spinor.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c sph_ind.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c sph_bes.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c simpsn.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_auxtools.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_parallel_include.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_error.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_ions.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upf_to_internal.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c write_upf_new.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c ylmr2.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dom.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c wxml.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c dylmr2_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c init_us_2_base_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c ylmr2_gpu.f90\n", - "ar ruv libupf.a vloc_mod.o gen_us_dj.o gen_us_dy.o init_us_0.o init_us_b0.o init_us_1.o init_us_2_base.o init_tab_atwfc.o init_tab_beta.o init_tab_rho.o init_tab_rhc.o init_tab_qrad.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o dylmr2_gpu.o init_us_2_base_gpu.o ylmr2_gpu.o \n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_kinds.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_const.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c radial_grids.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c atom.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c pseudo_types.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c uspp_param.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c atwfc_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_io.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_params.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_utils.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c gth.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c beta_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c qrad_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c rhoat_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c rhoc_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c vloc_mod.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_invmat.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_spinorb.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c uspp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c qvan2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c dqvan2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c gen_us_dj.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c gen_us_dy.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c init_us_0.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c init_us_b0.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c paw_variables.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c init_us_1.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c init_us_2_acc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c atomic_number.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c dylmr2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_cpmd.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_fhi.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_ncpp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c splinelib.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c xmltools.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_psml.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_upf_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_upf_v1.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_uspp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c read_ps.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c spinor.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c sph_ind.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c sph_bes.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c simpsn.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_auxtools.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_parallel_include.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_error.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_ions.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upf_to_internal.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c write_upf_new.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c ylmr2.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c ylmr2_gpu.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c dom.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c wxml.f90\n", + "ar ruv libupf.a atwfc_mod.o beta_mod.o qrad_mod.o rhoat_mod.o rhoc_mod.o vloc_mod.o qvan2.o dqvan2.o gen_us_dj.o gen_us_dy.o init_us_0.o init_us_b0.o init_us_1.o init_us_2_acc.o atom.o atomic_number.o dylmr2.o gth.o paw_variables.o pseudo_types.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o ylmr2_gpu.o dom.o wxml.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libupf.a\n", + "a - atwfc_mod.o\n", + "a - beta_mod.o\n", + "a - qrad_mod.o\n", + "a - rhoat_mod.o\n", + "a - rhoc_mod.o\n", "a - vloc_mod.o\n", + "a - qvan2.o\n", + "a - dqvan2.o\n", "a - gen_us_dj.o\n", "a - gen_us_dy.o\n", "a - init_us_0.o\n", "a - init_us_b0.o\n", "a - init_us_1.o\n", - "a - init_us_2_base.o\n", - "a - init_tab_atwfc.o\n", - "a - init_tab_beta.o\n", - "a - init_tab_rho.o\n", - "a - init_tab_rhc.o\n", - "a - init_tab_qrad.o\n", + "a - init_us_2_acc.o\n", "a - atom.o\n", "a - atomic_number.o\n", - "a - dqvan2.o\n", "a - dylmr2.o\n", "a - gth.o\n", - "a - interp_atwfc.o\n", - "a - interp_rhc.o\n", - "a - interp_drhc.o\n", "a - paw_variables.o\n", "a - pseudo_types.o\n", - "a - qvan2.o\n", "a - radial_grids.o\n", "a - read_cpmd.o\n", "a - read_fhi.o\n", @@ -523,38 +551,27 @@ "a - upf_to_internal.o\n", "a - upf_utils.o\n", "a - uspp.o\n", - "a - uspp_data.o\n", "a - uspp_param.o\n", "a - write_upf_new.o\n", "a - xmltools.o\n", "a - ylmr2.o\n", + "a - ylmr2_gpu.o\n", "a - dom.o\n", "a - wxml.o\n", - "a - dylmr2_gpu.o\n", - "a - init_us_2_base_gpu.o\n", - "a - ylmr2_gpu.o\n", "ranlib libupf.a \n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c virtual_v2.f90\n", - "mpif90 -g -o virtual_v2.x virtual_v2.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c casino_pp.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c upfconv.f90\n", - "mpif90 -g -o upfconv.x upfconv.o casino_pp.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I../external/devxlib/src -c casino2upf.f90\n", - "mpif90 -g -o casino2upf.x casino2upf.o casino_pp.o atom.o atomic_number.o dqvan2.o dylmr2.o gth.o interp_atwfc.o interp_rhc.o interp_drhc.o paw_variables.o pseudo_types.o qvan2.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_data.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c virtual_v2.f90\n", + "mpif90 -g -o virtual_v2.x virtual_v2.o atom.o atomic_number.o dylmr2.o gth.o paw_variables.o pseudo_types.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o ylmr2_gpu.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c casino_pp.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c upfconv.f90\n", + "mpif90 -g -o upfconv.x upfconv.o casino_pp.o atom.o atomic_number.o dylmr2.o gth.o paw_variables.o pseudo_types.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o ylmr2_gpu.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I../UtilXlib -I. -c casino2upf.f90\n", + "mpif90 -g -o casino2upf.x casino2upf.o casino_pp.o atom.o atomic_number.o dylmr2.o gth.o paw_variables.o pseudo_types.o radial_grids.o read_cpmd.o read_fhi.o read_ncpp.o read_ps.o read_psml.o read_upf_new.o read_upf_v1.o read_uspp.o spinor.o sph_ind.o sph_bes.o splinelib.o simpsn.o upf_auxtools.o upf_const.o upf_error.o upf_invmat.o upf_io.o upf_ions.o upf_kinds.o upf_params.o upf_parallel_include.o upf_spinorb.o upf_to_internal.o upf_utils.o uspp.o uspp_param.o write_upf_new.o xmltools.o ylmr2.o ylmr2_gpu.o dom.o wxml.o -lmkl_gf_lp64 -lmkl_sequential -lmkl_core\n", "make[1]: Leaving directory '/content/q-e/upflib'\n", "cd install ; make -f extlibs_makefile libmbd\n", "make[1]: Entering directory '/content/q-e/install'\n", "initializing external/mbd submodule ...\n", "Cloning into '/content/q-e/external/mbd'...\n", - "remote: Total 0 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n", - "remote: Enumerating objects: 112, done.\u001b[K\n", - "remote: Counting objects: 100% (112/112), done.\u001b[K\n", - "remote: Compressing objects: 100% (64/64), done.\u001b[K\n", - "remote: Total 64 (delta 42), reused 10 (delta 0), pack-reused 0\u001b[K\n", - "Unpacking objects: 100% (64/64), 29.22 KiB | 1.17 MiB/s, done.\n", - "From https://github.com/libmbd/libmbd\n", - " * branch 82005cbb65bdf5d32ca021848eec8f19da956a77 -> FETCH_HEAD\n", - "Submodule path 'external/mbd': checked out '82005cbb65bdf5d32ca021848eec8f19da956a77'\n", + "Submodule path 'external/mbd': checked out '1c432853bfe36312671bcd06c537c24a88559a2d'\n", "/content/q-e/install\n", "if test ! -d ../MBD; then \\\n", " mkdir ../MBD; \\\n", @@ -565,6 +582,7 @@ "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_constants.f90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_gradients.f90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_utils.F90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_defaults.f90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_damping.F90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_formulas.f90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_lapack.f90\n", @@ -577,15 +595,16 @@ "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_rpa.F90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_scs.f90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_methods.F90\n", - "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_ts.f90\n", - "echo '#define MBD_VERSION_MAJOR 0' > version.h\n", - "echo '#define MBD_VERSION_MINOR 10'>> version.h\n", - "echo '#define MBD_VERSION_PATCH 0' >> version.h\n", - "echo '#define MBD_VERSION \"0.10.0\"' >> version.h\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_ts.F90\n", + "sed -e 's/@PROJECT_VERSION_MAJOR@/0/' mbd_version.f90.in > mbd_version.f90\n", + "sed -i -e 's/@PROJECT_VERSION_MINOR@/13/' mbd_version.f90\n", + "sed -i -e 's/@PROJECT_VERSION_PATCH@/0/' mbd_version.f90\n", + "sed -i -e 's/@PROJECT_VERSION_SUFFIX@/0/' mbd_version.f90\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_version.f90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd.F90\n", "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_coulomb.f90\n", - "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_c_api.F90\n", - "ar -r libmbd.a mbd.o mbd_c_api.o mbd_constants.o mbd_coulomb.o mbd_damping.o mbd_dipole.o mbd_formulas.o mbd_geom.o mbd_gradients.o mbd_hamiltonian.o mbd_lapack.o mbd_linalg.o mbd_matrix.o mbd_methods.o mbd_rpa.o mbd_scs.o mbd_ts.o mbd_utils.o mbd_vdw_param.o\n", + "gfortran -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -c mbd_density.f90\n", + "ar -r libmbd.a mbd.o mbd_constants.o mbd_coulomb.o mbd_damping.o mbd_defaults.o mbd_density.o mbd_dipole.o mbd_formulas.o mbd_geom.o mbd_gradients.o mbd_hamiltonian.o mbd_lapack.o mbd_linalg.o mbd_matrix.o mbd_methods.o mbd_rpa.o mbd_scs.o mbd_ts.o mbd_utils.o mbd_version.o mbd_vdw_param.o\n", "ar: creating libmbd.a\n", "make[2]: Leaving directory '/content/q-e/external/mbd/src'\n", "make[1]: Leaving directory '/content/q-e/install'\n", @@ -661,13 +680,14 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c input_parameters.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_images.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c additional_kpoints.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c atomic_wfc_mod.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_world.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c parser.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c autopilot.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c basic_algebra_routines.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_bands.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c noncol.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c io_files.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c bfgs_module.f90\n", @@ -777,6 +797,7 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c open_close_input_file.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plugin_arguments.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c plugin_variables.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c printout_base.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c pw_dot.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c qmmm.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wypos.f90\n", @@ -822,9 +843,6 @@ " 154 | call dgemm('T', 'N', nbnd, nbnd, nbnd, 1.d0, sa, nbnd, sa, nbnd, 0.d0, ssa, nbnd)\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c wavefunctions_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c becmod_subs_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c cuda_subroutines.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c random_numbers_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c atom_weight.f90\n", @@ -855,7 +873,6 @@ "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c customize_signals.c\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c qmmm_aux.c\n", "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c sockets.c\n", - "cc -O3 -D__FFTW3 -I. -I/content/q-e//include -c stack.c\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c mp_rism.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c recvec_3drism.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c allocate_fft_3drism.f90\n", @@ -969,10 +986,11 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_laueint.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c suscept_vv.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -c write_rism_type.f90\n", - "ar ruv libqemod.a additional_kpoints.o autopilot.o basic_algebra_routines.o becmod.o bfgs_module.o bspline.o bz_form.o cell_base.o check_stop.o command_line_options.o compute_dipole.o constants.o constraints_module.o control_flags.o coulomb_vcut.o dist.o electrons_base.o environ_base_module.o environment.o extffield.o fd_gradient.o fft_base.o fft_rho.o fft_wave.o fsockets.o funct.o generate_function.o gradutils.o gvecw.o input_parameters.o invmat.o io_files.o io_global.o ions_base.o kind.o lmdif.o makov_payne.o mdiis.o mm_dispersion.o mp_bands.o mp_exx.o mp_global.o mp_images.o mp_pools.o mp_wave.o mp_world.o noncol.o open_close_input_file.o parameters.o parser.o plugin_flags.o plugin_arguments.o plugin_variables.o pw_dot.o qmmm.o random_numbers.o read_cards.o read_input.o read_namelists.o read_pseudo.o recvec.o recvec_subs.o run_info.o space_group.o set_para_diag.o set_signal.o set_vdw_corr.o setqf.o timestep.o tsvdw.o mbdlib.o version.o wannier_gw.o wannier_new.o wavefunctions.o ws_base.o xc_vdW_DF.o xc_rVV10.o io_base.o qes_types_module.o qes_libs_module.o qes_write_module.o qes_read_module.o qes_reset_module.o qes_init_module.o qes_bcast_module.o qexsd.o qexsd_copy.o qexsd_init.o qexsd_input.o hdf5_qe.o qeh5_module.o fox_init_module.o xsf.o wyckoff.o wypos.o zvscal.o wave_gauge.o wavefunctions_gpu.o becmod_gpu.o becmod_subs_gpu.o cuda_subroutines.o random_numbers_gpu.o atom_weight.o capital.o cryst_to_car.o expint.o generate_k_along_lines.o has_xml.o inpfile.o int_to_char.o latgen.o linpack.o matches.o plot_io.o radial_gradients.o rgen.o recips.o remove_tot_torque.o sort.o trimcheck.o test_input_file.o date_and_tim.o volume.o wgauss.o w0gauss.o w1gauss.o deviatoric.o customize_signals.o qmmm_aux.o sockets.o stack.o allocate_fft_3drism.o chempot.o chempot_lauerism.o closure.o corrdipole_laue.o correctat0_vv.o corrgxy0_laue.o cryst_to_car_2d.o data_structure_3drism.o do_1drism.o do_3drism.o do_lauerism.o eqn_1drism.o eqn_3drism.o eqn_lauedipole.o eqn_lauegxy0.o eqn_lauelong.o eqn_lauerism.o eqn_laueshort.o eqn_lauevoid.o err_rism.o guess_3drism.o init_1drism.o init_3drism.o input_1drism.o input_3drism.o io_rism_xml.o lauefft.o lauefft_subs.o lj_forcefield.o lj_solute.o molecorr_vv.o molebridge_vv.o molecule_const.o molecule_types.o mp_rism.o mp_swap_ax_rism.o normalize_lauerism.o plot_rism.o potential_3drism.o potential_esm.o potential_vv.o print_chempot_3drism.o print_chempot_lauerism.o print_chempot_vv.o print_corr_vv.o print_solvavg.o radfft.o read_mol.o read_solv.o recvec_3drism.o rism.o rism1d_facade.o rism3d_facade.o rms_residual.o scale_fft_3drism.o scale_fft_lauerism.o solute.o solvation_3drism.o solvation_esm.o solvation_force.o solvation_lauerism.o solvation_pbc.o solvation_stress.o solvavg.o solvmol.o summary_1drism.o summary_3drism.o suscept_g0.o suscept_laue.o suscept_laueint.o suscept_vv.o write_rism_type.o xml_io_rism.o \n", + "ar ruv libqemod.a additional_kpoints.o atomic_wfc_mod.o autopilot.o basic_algebra_routines.o becmod.o bfgs_module.o bspline.o bz_form.o cell_base.o check_stop.o command_line_options.o compute_dipole.o constants.o constraints_module.o control_flags.o coulomb_vcut.o dist.o electrons_base.o environ_base_module.o environment.o extffield.o fd_gradient.o fft_base.o fft_rho.o fft_wave.o fsockets.o funct.o generate_function.o gradutils.o gvecw.o input_parameters.o invmat.o io_files.o io_global.o ions_base.o kind.o lmdif.o makov_payne.o mdiis.o mm_dispersion.o mp_bands.o mp_exx.o mp_global.o mp_images.o mp_pools.o mp_wave.o mp_world.o noncol.o open_close_input_file.o parameters.o parser.o plugin_flags.o plugin_arguments.o plugin_variables.o printout_base.o pw_dot.o qmmm.o random_numbers.o read_cards.o read_input.o read_namelists.o read_pseudo.o recvec.o recvec_subs.o run_info.o space_group.o set_para_diag.o set_signal.o set_vdw_corr.o setqf.o timestep.o tsvdw.o mbdlib.o version.o wannier_gw.o wannier_new.o wavefunctions.o ws_base.o xc_vdW_DF.o xc_rVV10.o io_base.o qes_types_module.o qes_libs_module.o qes_write_module.o qes_read_module.o qes_reset_module.o qes_init_module.o qes_bcast_module.o qexsd.o qexsd_copy.o qexsd_init.o qexsd_input.o hdf5_qe.o qeh5_module.o fox_init_module.o xsf.o wyckoff.o wypos.o zvscal.o wave_gauge.o cuda_subroutines.o random_numbers_gpu.o atom_weight.o capital.o cryst_to_car.o expint.o generate_k_along_lines.o has_xml.o inpfile.o int_to_char.o latgen.o linpack.o matches.o plot_io.o radial_gradients.o rgen.o recips.o remove_tot_torque.o sort.o trimcheck.o test_input_file.o date_and_tim.o volume.o wgauss.o w0gauss.o w1gauss.o deviatoric.o customize_signals.o qmmm_aux.o sockets.o allocate_fft_3drism.o chempot.o chempot_lauerism.o closure.o corrdipole_laue.o correctat0_vv.o corrgxy0_laue.o cryst_to_car_2d.o data_structure_3drism.o do_1drism.o do_3drism.o do_lauerism.o eqn_1drism.o eqn_3drism.o eqn_lauedipole.o eqn_lauegxy0.o eqn_lauelong.o eqn_lauerism.o eqn_laueshort.o eqn_lauevoid.o err_rism.o guess_3drism.o init_1drism.o init_3drism.o input_1drism.o input_3drism.o io_rism_xml.o lauefft.o lauefft_subs.o lj_forcefield.o lj_solute.o molecorr_vv.o molebridge_vv.o molecule_const.o molecule_types.o mp_rism.o mp_swap_ax_rism.o normalize_lauerism.o plot_rism.o potential_3drism.o potential_esm.o potential_vv.o print_chempot_3drism.o print_chempot_lauerism.o print_chempot_vv.o print_corr_vv.o print_solvavg.o radfft.o read_mol.o read_solv.o recvec_3drism.o rism.o rism1d_facade.o rism3d_facade.o rms_residual.o scale_fft_3drism.o scale_fft_lauerism.o solute.o solvation_3drism.o solvation_esm.o solvation_force.o solvation_lauerism.o solvation_pbc.o solvation_stress.o solvavg.o solvmol.o summary_1drism.o summary_3drism.o suscept_g0.o suscept_laue.o suscept_laueint.o suscept_vv.o write_rism_type.o xml_io_rism.o \n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libqemod.a\n", "a - additional_kpoints.o\n", + "a - atomic_wfc_mod.o\n", "a - autopilot.o\n", "a - basic_algebra_routines.o\n", "a - becmod.o\n", @@ -1025,6 +1043,7 @@ "a - plugin_flags.o\n", "a - plugin_arguments.o\n", "a - plugin_variables.o\n", + "a - printout_base.o\n", "a - pw_dot.o\n", "a - qmmm.o\n", "a - random_numbers.o\n", @@ -1070,9 +1089,6 @@ "a - wypos.o\n", "a - zvscal.o\n", "a - wave_gauge.o\n", - "a - wavefunctions_gpu.o\n", - "a - becmod_gpu.o\n", - "a - becmod_subs_gpu.o\n", "a - cuda_subroutines.o\n", "a - random_numbers_gpu.o\n", "a - atom_weight.o\n", @@ -1103,7 +1119,6 @@ "a - customize_signals.o\n", "a - qmmm_aux.o\n", "a - sockets.o\n", - "a - stack.o\n", "a - allocate_fft_3drism.o\n", "a - chempot.o\n", "a - chempot_lauerism.o\n", @@ -1757,7 +1772,6 @@ "( cd src ; make libpw.a || exit 1 )\n", "make[2]: Entering directory '/content/q-e/PW/src'\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwcom.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c pwcom_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c start_k.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symm_base.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c a2fmod.f90\n", @@ -1766,12 +1780,12 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_efield.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_dmft_occ.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vuspsi.f90\n", - "\u001b[01m\u001b[Kadd_vuspsi.f90:141:57:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Kadd_vuspsi.f90:139:57:\u001b[m\u001b[K\n", "\n", - " 125 | deeq(1,1,na,current_spin), nhm, &\n", + " 123 | deeq(1,1,na,current_spin), nhm, &\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "......\n", - " 141 | CALL DGEMM( 'N', 'N', ( 2 * n ), m, nkb, 1.D0, vkb, &\n", + " 139 | CALL DGEMM( 'N', 'N', ( 2 * n ), m, nkb, 1.D0, vkb, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_gatefield.f90\n", @@ -1785,7 +1799,7 @@ " 230 | CALL simpson( kkbeta, rho_lm(1,lm,is), g(i%t)%rab(1), integral )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "......\n", - " 1637 | g(i%t)%rab, integral_r )\n", + " 1679 | g(i%t)%rab, integral_r )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", "\u001b[01m\u001b[Kpaw_onecenter.f90:236:70:\u001b[m\u001b[K\n", @@ -1793,7 +1807,7 @@ " 236 | CALL simpson( kkbeta, msmall_lm(1,lm,is), g(i%t)%rab(1), integral )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "......\n", - " 1637 | g(i%t)%rab, integral_r )\n", + " 1679 | g(i%t)%rab, integral_r )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Element of assumed-shape or pointer array as actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K cannot correspond to actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scf_mod.f90\n", @@ -1819,18 +1833,18 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusdens.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusforce.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c addusstress.f90\n", - "\u001b[01m\u001b[Kaddusstress.f90:209:31:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Kaddusstress.f90:195:31:\u001b[m\u001b[K\n", "\n", - " 174 | qgm, 2*ngm_l, tbecsum, nij, 0.0_dp, aux2, 2*ngm_l )\n", + " 160 | qgm, 2*ngm_l, tbecsum, nij, 0.0_dp, aux2, 2*ngm_l )\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "......\n", - " 209 | aux2, 2*ngm_l, 0.0_dp, fac, 3 )\n", + " 195 | aux2, 2*ngm_l, 0.0_dp, fac, 3 )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_fft.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_locpot.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_nlpot.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c allocate_wfc.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c starting_scf_mod.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_rho.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c average_pp.f90\n", @@ -1864,11 +1878,8 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_input.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_context.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_base.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c additional_cusolver_subs.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wavefunction_subs.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_occupations.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wavefunction_subs_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wfcO_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_wfcO.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_functions.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c oscdft_functions_gpu.f90\n", @@ -1933,12 +1944,12 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_corr.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_ew.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_hub.f90\n", - "\u001b[01m\u001b[Kforce_hub.f90:2009:20:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Kforce_hub.f90:2522:20:\u001b[m\u001b[K\n", "\n", - " 1880 | CALL MYDGEMM( 'T','N',ldim, nbnd, 2*npw, 2.0_DP, dwfc, 2*npwx, spsi, &\n", + " 2415 | CALL MYDGEMM( 'T','N',ldim, nbnd, 2*npw, 2.0_DP, dwfc, 2*npwx, spsi, &\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "......\n", - " 2009 | wfatdbeta, nwfcU, betapsi(1,nb_s), nh(nt), 1.0_dp, &\n", + " 2522 | wfatdbeta, nwfcU, betapsi(1,nb_s), nh(nt), 1.0_dp, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (REAL(8)/COMPLEX(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c force_lc.f90\n", @@ -2030,14 +2041,23 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c run_pwscf.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_1psi.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_psi.f90\n", - "\u001b[01m\u001b[Ks_psi.f90:256:56:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Ks_psi.f90:253:56:\u001b[m\u001b[K\n", "\n", - " 243 | qq_at(1,1,na), nhm, becp%r(ofsbeta(na)+1,1),&\n", + " 240 | qq_at(1,1,na), nhm, becp%r(ofsbeta(na)+1,1),&\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", "......\n", - " 256 | CALL DGEMM( 'N', 'N', 2 * n, m, nkb, 1.D0, vkb, &\n", + " 253 | CALL DGEMM( 'N', 'N', 2 * n, m, nkb, 1.D0, vkb, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_psi_acc.f90\n", + "\u001b[01m\u001b[Ks_psi_acc.f90:320:58:\u001b[m\u001b[K\n", + "\n", + " 303 | qq_at(1,1,na), nhm, becpr(ofsbeta(na)+1,1),&\n", + " | \u001b[32m\u001b[K2\u001b[m\u001b[K \n", + "......\n", + " 320 | CALL MYDGEMM( 'N', 'N', 2 * n, m, nkb, 1.D0, vkb, &\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c save_in_cbands.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c save_in_electrons.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c scale_h.f90\n", @@ -2061,19 +2081,19 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_nonloc_dft.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stres_mgga.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c stress.f90\n", - "\u001b[01m\u001b[Kstress.f90:110:75:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Kstress.f90:108:75:\u001b[m\u001b[K\n", "\n", - " 110 | nspin, dfftp, g, alat, omega, sigmaxc, rho%kin_r )\n", + " 108 | nspin, dfftp, g, alat, omega, sigmaxc, rho%kin_r )\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K More actual than formal arguments in procedure call at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c struct_fact.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sum_band.f90\n", - "\u001b[01m\u001b[Ksum_band.f90:1032:30:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Ksum_band.f90:1019:30:\u001b[m\u001b[K\n", "\n", - " 999 | 1.0_dp, becp%r(ofsbeta(na)+1,1), nkb, &\n", + " 986 | 1.0_dp, becp%r(ofsbeta(na)+1,1), nkb, &\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "......\n", - " 1032 | 1.0_dp, auxk1, 2*this_bgrp_nbnd, auxk2, 2*this_bgrp_nbnd, &\n", + " 1019 | 1.0_dp, auxk1, 2*this_bgrp_nbnd, auxk2, 2*this_bgrp_nbnd, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sumkg.f90\n", @@ -2082,19 +2102,18 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c symmetrize_at.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_becsum_so.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_becsum_nc.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c transform_qq_so.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c trnvecc.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c usnldiag.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c v_of_rho.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vcsmd.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vcsubs.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vhpsi.f90\n", - "\u001b[01m\u001b[Kvhpsi.f90:381:28:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Kvhpsi.f90:382:28:\u001b[m\u001b[K\n", "\n", - " 378 | rvaux,ldimx, projauxr,ldimx, 0.0_dp, rtemp, ldimx)\n", + " 379 | rvaux,ldimx, projauxr,ldimx, 0.0_dp, rtemp, ldimx)\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "......\n", - " 381 | wfcUaux, 2*np, rtemp, ldimx, &\n", + " 382 | wfcUaux, 2*np, rtemp, ldimx, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vloc_psi.f90\n", @@ -2108,28 +2127,28 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_check.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_clean.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c wannier_occ.f90\n", + "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c d3hess_mod.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c g_psi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c h_psi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vhpsi_gpu.f90\n", - "\u001b[01m\u001b[Kvhpsi_gpu.f90:158:22:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Kvhpsi_gpu.f90:165:22:\u001b[m\u001b[K\n", "\n", - " 154 | vns_d(1,1,na), ldimax, &\n", + " 159 | vns_d(1,1,na), ldimax, &\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "......\n", - " 158 | wfcU_d(1,offsetU(na)+1), 2*ldap, rtemp_d, ldimaxt, &\n", + " 165 | wfcU(1,offsetU(na)+1), 2*ldap, rtemp_d, ldimaxt, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_psi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c vloc_psi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c usnldiag_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_vuspsi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c sum_band_gpu.f90\n", - "\u001b[01m\u001b[Ksum_band_gpu.f90:1151:30:\u001b[m\u001b[K\n", + "\u001b[01m\u001b[Ksum_band_gpu.f90:1133:30:\u001b[m\u001b[K\n", "\n", - " 1117 | 1.0_dp, becp_d%r_d(ofsbeta(na)+1,1), nkb, &\n", + " 1101 | 1.0_dp, auxg1_d, nbnd_loc, &\n", " | \u001b[32m\u001b[K2\u001b[m\u001b[K\n", "......\n", - " 1151 | 1.0_dp, auxk1_d, 2*this_bgrp_nbnd, auxk2_d, 2*this_bgrp_nbnd, &\n", + " 1133 | 1.0_dp, auxk1_d, 2*this_bgrp_nbnd, auxk2_d, 2*this_bgrp_nbnd, &\n", " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Type mismatch between actual argument at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K and actual argument at \u001b[32m\u001b[K(2)\u001b[m\u001b[K (COMPLEX(8)/REAL(8)).\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c add_paw_to_deeq_gpu.f90\n", @@ -2139,8 +2158,7 @@ "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c hs_psi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c s_1psi_gpu.f90\n", "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c utils_gpu.f90\n", - "mpif90 -O3 -g -fallow-argument-mismatch -cpp -D__FFTW3 -I/content/q-e//external/devxlib/src -I. -I/content/q-e//include -I/content/q-e//upflib -I/content/q-e//XClib -I/content/q-e//Modules -I/content/q-e//FFTXlib/src -I/content/q-e//LAXlib -I/content/q-e//UtilXlib -I/content/q-e//MBD -I/content/q-e//KS_Solvers -I../../dft-d3/ -c atomic_wfc_gpu.f90\n", - "ar ruv libpw.a a2fmod.o add_bfield.o add_efield.o add_dmft_occ.o add_vuspsi.o add_gatefield.o add_paw_to_deeq.o add_vhub_to_deeq.o addusdens.o addusforce.o addusstress.o allocate_fft.o allocate_locpot.o allocate_nlpot.o allocate_wfc.o atomic_rho.o atomic_wfc.o atomic_wfc_mod.o average_pp.o acfdt_in_pw.o newd.o beef.o bp_mod.o bp_c_phase.o bp_strings.o buffers.o c_bands.o c_phase_field.o orbm_kubo.o cdiagh.o clean_pw.o close_files.o commutator_Hx_psi.o commutator_Vhubx_psi.o compute_becsum.o compute_deff.o compute_dip.o compute_rho.o compute_qdipol.o compute_qdipol_so.o compute_ux.o coset.o Coul_cut_2D.o d_matrix.o data_structure.o divide_class.o divide_class_so.o divide_et_impera.o rotate_wfc.o run_driver.o dynamics_module.o efermig.o efermit.o electrons.o environ_pw_module.o eqvect.o esm.o esm_common_mod.o esm_ewald_mod.o esm_force_mod.o esm_hartree_mod.o esm_local_mod.o esm_stres_mod.o ewald.o ewald_dipole.o extfield.o exx_base.o exx_band.o exx.o fcp_capacitance.o fcp_dyn_calcavg.o fcp_dyn_printavg.o fcp_dynamics.o fcp_hessian.o fcp_input.o fcp_module.o fcp_relaxation.o find_group.o forces_bp_efield.o force_cc.o force_corr.o force_ew.o force_hub.o force_lc.o force_us.o forces.o g_psi.o g_psi_mod.o gcscf_input.o gcscf_module.o gen_at_dj.o gen_at_dy.o get_locals.o gk_sort.o gradcorr.o gweights.o g2_kin.o hs_psi.o hs_1psi.o h_epsi_her_apply.o h_epsi_her_set.o h_psi.o h_psi_meta.o hinit0.o hinit1.o hubbard.o init_ns.o init_q_aeps.o init_run.o init_us_2.o init_vloc.o input.o io_rho_xml.o irrek.o iweights.o intersite_V.o init_nsg.o nsg_adj.o start_k.o kpoint_grid.o lchk_tauxk.o ldaU.o make_pointlists.o manypw.o martyna_tuckerman.o memory_report.o mix_rho.o move_ions.o multable.o n_plane_waves.o new_ns.o new_nsb.o new_nsg.o new_occ.o ns_adj.o non_scf.o offset_atom_wfc.o openfil.o orthoatwfc.o output_tau.o para.o paw_exx.o paw_init.o paw_onecenter.o paw_symmetry.o plugin_print_energies.o plugin_scf_energy.o plugin_scf_potential.o plugin_init_ions.o plugin_init_cell.o plugin_init_potential.o plugin_initbase.o plugin_clean.o plugin_check.o plugin_clock.o plugin_summary.o plugin_initialization.o plugin_ext_forces.o plugin_int_forces.o plugin_read_input.o plus_u_full.o potinit.o print_clock_pw.o print_ks_energies.o punch.o pw_restart_new.o add_qexsd_step.o pw_init_qexsd_input.o pwcom.o pw2blip.o pw2casino.o pw2casino_write.o rdiagh.o read_conf_from_file.o read_file_new.o realus.o remove_atomic_rho.o report_mag.o restart_in_electrons.o rho2zeta.o rism_module.o ruotaijk.o run_pwscf.o s_1psi.o s_psi.o save_in_cbands.o save_in_electrons.o scale_h.o loc_scdm.o loc_scdm_k.o scf_mod.o set_kplusq.o set_kup_and_kdw.o set_occupations.o set_rhoc.o set_spin_vars.o set_vrs.o setlocal.o setup.o scissor.o sic.o stop_run.o stres_cc.o stres_ewa.o stres_gradcorr.o stres_har.o stres_hub.o stres_knl.o stres_loc.o stres_us.o stres_nonloc_dft.o stres_mgga.o stress.o struct_fact.o sum_band.o sumkg.o sumkt.o summary.o symme.o symm_base.o symmetrize_at.o tetra.o transform_becsum_so.o transform_becsum_nc.o transform_qq_so.o trnvecc.o two_chem.o update_pot.o us_exx.o usnldiag.o v_of_rho.o vcsmd.o vcsubs.o vhpsi.o vloc_psi.o utils.o xdm_dispersion.o wfcinit.o write_ns.o wsweight.o weights.o ortho_wfc.o wannier_init.o wannier_check.o wannier_clean.o wannier_occ.o oscdft_input.o oscdft_context.o oscdft_base.o oscdft_enums.o oscdft_indices.o oscdft_wavefunction.o oscdft_wavefunction_subs.o oscdft_functions.o oscdft_occupations.o oscdft_wfcO.o oscdft_forces.o oscdft_forces_subs.o g_psi_mod_gpu.o g_psi_gpu.o h_psi_gpu.o vhpsi_gpu.o s_psi_gpu.o vloc_psi_gpu.o pwcom_gpu.o scf_mod_gpu.o usnldiag_gpu.o add_vuspsi_gpu.o sum_band_gpu.o newd_gpu.o add_paw_to_deeq_gpu.o add_vhub_to_deeq_gpu.o rotate_wfc_gpu.o hs_1psi_gpu.o hs_psi_gpu.o s_1psi_gpu.o utils_gpu.o atomic_wfc_gpu.o oscdft_wavefunction_subs_gpu.o oscdft_wfcO_gpu.o oscdft_functions_gpu.o additional_cusolver_subs.o\n", + "ar ruv libpw.a a2fmod.o add_bfield.o add_efield.o add_dmft_occ.o add_vuspsi.o add_gatefield.o add_paw_to_deeq.o add_vhub_to_deeq.o addusdens.o addusforce.o addusstress.o allocate_fft.o allocate_locpot.o allocate_wfc.o atomic_rho.o atomic_wfc.o atomic_wfc_mod.o average_pp.o acfdt_in_pw.o newd.o beef.o bp_mod.o bp_c_phase.o bp_strings.o buffers.o c_bands.o c_phase_field.o orbm_kubo.o cdiagh.o clean_pw.o close_files.o commutator_Hx_psi.o commutator_Vhubx_psi.o compute_becsum.o compute_deff.o compute_dip.o compute_rho.o compute_qdipol.o compute_qdipol_so.o compute_ux.o coset.o Coul_cut_2D.o d_matrix.o data_structure.o divide_class.o divide_class_so.o divide_et_impera.o rotate_wfc.o run_driver.o dynamics_module.o efermig.o efermit.o electrons.o environ_pw_module.o eqvect.o esm.o esm_common_mod.o esm_ewald_mod.o esm_force_mod.o esm_hartree_mod.o esm_local_mod.o esm_stres_mod.o ewald.o ewald_dipole.o extfield.o exx_base.o exx_band.o exx.o fcp_capacitance.o fcp_dyn_calcavg.o fcp_dyn_printavg.o fcp_dynamics.o fcp_hessian.o fcp_input.o fcp_module.o fcp_relaxation.o find_group.o forces_bp_efield.o force_cc.o force_corr.o force_ew.o force_hub.o force_lc.o force_us.o forces.o g_psi.o g_psi_mod.o gcscf_input.o gcscf_module.o gen_at_dj.o gen_at_dy.o get_locals.o gk_sort.o gradcorr.o gweights.o g2_kin.o hs_psi.o hs_1psi.o h_epsi_her_apply.o h_epsi_her_set.o h_psi.o h_psi_meta.o hinit0.o hinit1.o hubbard.o init_ns.o init_q_aeps.o init_run.o init_us_2.o init_vloc.o input.o io_rho_xml.o irrek.o iweights.o intersite_V.o init_nsg.o nsg_adj.o start_k.o kpoint_grid.o lchk_tauxk.o ldaU.o make_pointlists.o manypw.o martyna_tuckerman.o memory_report.o mix_rho.o move_ions.o multable.o n_plane_waves.o new_ns.o new_nsb.o new_nsg.o new_occ.o ns_adj.o non_scf.o offset_atom_wfc.o openfil.o orthoatwfc.o output_tau.o para.o paw_exx.o paw_init.o paw_onecenter.o paw_symmetry.o plugin_print_energies.o plugin_scf_energy.o plugin_scf_potential.o plugin_init_ions.o plugin_init_cell.o plugin_init_potential.o plugin_initbase.o plugin_clean.o plugin_check.o plugin_clock.o plugin_summary.o plugin_initialization.o plugin_ext_forces.o plugin_int_forces.o plugin_read_input.o plus_u_full.o potinit.o print_clock_pw.o print_ks_energies.o punch.o pw_restart_new.o add_qexsd_step.o pw_init_qexsd_input.o pwcom.o pw2blip.o pw2casino.o pw2casino_write.o rdiagh.o read_conf_from_file.o read_file_new.o realus.o remove_atomic_rho.o report_mag.o restart_in_electrons.o rho2zeta.o rism_module.o ruotaijk.o run_pwscf.o s_1psi.o s_psi.o s_psi_acc.o save_in_cbands.o save_in_electrons.o scale_h.o loc_scdm.o loc_scdm_k.o scf_mod.o set_kplusq.o set_kup_and_kdw.o set_occupations.o set_rhoc.o set_spin_vars.o set_vrs.o setlocal.o setup.o scissor.o sic.o starting_scf_mod.o stop_run.o stres_cc.o stres_ewa.o stres_gradcorr.o stres_har.o stres_hub.o stres_knl.o stres_loc.o stres_us.o stres_nonloc_dft.o stres_mgga.o stress.o struct_fact.o sum_band.o sumkg.o sumkt.o summary.o symme.o symm_base.o symmetrize_at.o tetra.o transform_becsum_so.o transform_becsum_nc.o trnvecc.o two_chem.o update_pot.o us_exx.o usnldiag.o v_of_rho.o vcsmd.o vcsubs.o vhpsi.o vloc_psi.o utils.o xdm_dispersion.o wfcinit.o write_ns.o wsweight.o weights.o ortho_wfc.o wannier_init.o wannier_check.o wannier_clean.o wannier_occ.o oscdft_input.o oscdft_context.o oscdft_base.o oscdft_enums.o oscdft_indices.o oscdft_wavefunction.o oscdft_wavefunction_subs.o oscdft_functions.o oscdft_occupations.o oscdft_wfcO.o oscdft_forces.o oscdft_forces_subs.o d3hess_mod.o g_psi_mod_gpu.o g_psi_gpu.o h_psi_gpu.o vhpsi_gpu.o vloc_psi_gpu.o scf_mod_gpu.o usnldiag_gpu.o add_vuspsi_gpu.o sum_band_gpu.o newd_gpu.o add_paw_to_deeq_gpu.o add_vhub_to_deeq_gpu.o rotate_wfc_gpu.o hs_1psi_gpu.o hs_psi_gpu.o s_1psi_gpu.o utils_gpu.o oscdft_functions_gpu.o\n", "ar: `u' modifier ignored since `D' is the default (see `U')\n", "ar: creating libpw.a\n", "a - a2fmod.o\n", @@ -2156,7 +2174,6 @@ "a - addusstress.o\n", "a - allocate_fft.o\n", "a - allocate_locpot.o\n", - "a - allocate_nlpot.o\n", "a - allocate_wfc.o\n", "a - atomic_rho.o\n", "a - atomic_wfc.o\n", @@ -2328,6 +2345,7 @@ "a - run_pwscf.o\n", "a - s_1psi.o\n", "a - s_psi.o\n", + "a - s_psi_acc.o\n", "a - save_in_cbands.o\n", "a - save_in_electrons.o\n", "a - scale_h.o\n", @@ -2344,6 +2362,7 @@ "a - setup.o\n", "a - scissor.o\n", "a - sic.o\n", + "a - starting_scf_mod.o\n", "a - stop_run.o\n", "a - stres_cc.o\n", "a - stres_ewa.o\n", @@ -2367,7 +2386,6 @@ "a - tetra.o\n", "a - transform_becsum_so.o\n", "a - transform_becsum_nc.o\n", - "a - transform_qq_so.o\n", "a - trnvecc.o\n", "a - two_chem.o\n", "a - update_pot.o\n", @@ -2401,13 +2419,12 @@ "a - oscdft_wfcO.o\n", "a - oscdft_forces.o\n", "a - oscdft_forces_subs.o\n", + "a - d3hess_mod.o\n", "a - g_psi_mod_gpu.o\n", "a - g_psi_gpu.o\n", "a - h_psi_gpu.o\n", "a - vhpsi_gpu.o\n", - "a - s_psi_gpu.o\n", "a - vloc_psi_gpu.o\n", - "a - pwcom_gpu.o\n", "a - scf_mod_gpu.o\n", "a - usnldiag_gpu.o\n", "a - add_vuspsi_gpu.o\n", @@ -2420,11 +2437,7 @@ "a - hs_psi_gpu.o\n", "a - s_1psi_gpu.o\n", "a - utils_gpu.o\n", - "a - atomic_wfc_gpu.o\n", - "a - oscdft_wavefunction_subs_gpu.o\n", - "a - oscdft_wfcO_gpu.o\n", "a - oscdft_functions_gpu.o\n", - "a - additional_cusolver_subs.o\n", "ranlib libpw.a\n", "make[2]: Leaving directory '/content/q-e/PW/src'\n", "make[1]: Leaving directory '/content/q-e/PW'\n", @@ -2475,8 +2488,8 @@ "( cd ../../bin ; ln -fs ../PW/tools/rism1d.x . )\n", "make[2]: Leaving directory '/content/q-e/PW/tools'\n", "make[1]: Leaving directory '/content/q-e/PW'\n", - "CPU times: user 3.58 s, sys: 446 ms, total: 4.03 s\n", - "Wall time: 7min 16s\n" + "CPU times: user 4.94 s, sys: 574 ms, total: 5.52 s\n", + "Wall time: 9min 34s\n" ] } ] @@ -2491,9 +2504,9 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "ed141deb-14a6-481d-c347-048c0fd0d838" + "outputId": "835b0255-f3ef-487a-9a6d-1cf3e15f36b8" }, - "execution_count": 7, + "execution_count": 3, "outputs": [ { "output_type": "stream", @@ -2518,7 +2531,7 @@ "metadata": { "id": "4QgdG6AMxReT" }, - "execution_count": 8, + "execution_count": null, "outputs": [] }, { @@ -2531,60 +2544,64 @@ "base_uri": "https://localhost:8080/" }, "id": "CtFkbRdT2Frw", - "outputId": "8eb40edc-a0c7-452d-d777-20f1a7c50439" + "outputId": "2edeb6f3-902d-431c-9914-001d36084b3d" }, - "execution_count": 9, + "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Architecture: x86_64\n", - " CPU op-mode(s): 32-bit, 64-bit\n", - " Address sizes: 46 bits physical, 48 bits virtual\n", - " Byte Order: Little Endian\n", - "CPU(s): 2\n", - " On-line CPU(s) list: 0,1\n", - "Vendor ID: GenuineIntel\n", - " Model name: Intel(R) Xeon(R) CPU @ 2.20GHz\n", - " CPU family: 6\n", - " Model: 79\n", - " Thread(s) per core: 2\n", - " Core(s) per socket: 1\n", - " Socket(s): 1\n", - " Stepping: 0\n", - " BogoMIPS: 4399.99\n", - " Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clf\n", - " lush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_\n", - " good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fm\n", - " a cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hyp\n", - " ervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsb\n", - " ase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsa\n", - " veopt arat md_clear arch_capabilities\n", - "Virtualization features: \n", - " Hypervisor vendor: KVM\n", - " Virtualization type: full\n", - "Caches (sum of all): \n", - " L1d: 32 KiB (1 instance)\n", - " L1i: 32 KiB (1 instance)\n", - " L2: 256 KiB (1 instance)\n", - " L3: 55 MiB (1 instance)\n", - "NUMA: \n", - " NUMA node(s): 1\n", - " NUMA node0 CPU(s): 0,1\n", - "Vulnerabilities: \n", - " Itlb multihit: Not affected\n", - " L1tf: Mitigation; PTE Inversion\n", - " Mds: Vulnerable; SMT Host state unknown\n", - " Meltdown: Vulnerable\n", - " Mmio stale data: Vulnerable\n", - " Retbleed: Vulnerable\n", - " Spec store bypass: Vulnerable\n", - " Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swap\n", - " gs barriers\n", - " Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected\n", - " Srbds: Not affected\n", - " Tsx async abort: Vulnerable\n" + "Architecture: x86_64\n", + " CPU op-mode(s): 32-bit, 64-bit\n", + " Address sizes: 46 bits physical, 48 bits virtual\n", + " Byte Order: Little Endian\n", + "CPU(s): 2\n", + " On-line CPU(s) list: 0,1\n", + "Vendor ID: GenuineIntel\n", + " Model name: Intel(R) Xeon(R) CPU @ 2.20GHz\n", + " CPU family: 6\n", + " Model: 79\n", + " Thread(s) per core: 2\n", + " Core(s) per socket: 1\n", + " Socket(s): 1\n", + " Stepping: 0\n", + " BogoMIPS: 4399.99\n", + " Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 cl\n", + " flush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc re\n", + " p_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3\n", + " fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand\n", + " hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp \n", + " fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx sm\n", + " ap xsaveopt arat md_clear arch_capabilities\n", + "Virtualization features: \n", + " Hypervisor vendor: KVM\n", + " Virtualization type: full\n", + "Caches (sum of all): \n", + " L1d: 32 KiB (1 instance)\n", + " L1i: 32 KiB (1 instance)\n", + " L2: 256 KiB (1 instance)\n", + " L3: 55 MiB (1 instance)\n", + "NUMA: \n", + " NUMA node(s): 1\n", + " NUMA node0 CPU(s): 0,1\n", + "Vulnerabilities: \n", + " Gather data sampling: Not affected\n", + " Itlb multihit: Not affected\n", + " L1tf: Mitigation; PTE Inversion\n", + " Mds: Vulnerable; SMT Host state unknown\n", + " Meltdown: Vulnerable\n", + " Mmio stale data: Vulnerable\n", + " Reg file data sampling: Not affected\n", + " Retbleed: Vulnerable\n", + " Spec rstack overflow: Not affected\n", + " Spec store bypass: Vulnerable\n", + " Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swa\n", + " pgs barriers\n", + " Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BH\n", + " I: Vulnerable (Syscall hardening enabled)\n", + " Srbds: Not affected\n", + " Tsx async abort: Vulnerable\n" ] } ] @@ -2592,6 +2609,7 @@ { "cell_type": "code", "source": [ + "# Example of filtering structures\n", "from jarvis.core.atoms import Atoms\n", "from jarvis.db.figshare import data\n", "import numpy as np\n", @@ -2620,9 +2638,9 @@ "height": 335 }, "id": "FuLEmYXq9CKn", - "outputId": "05cda75b-996d-42c9-a7c9-6c5a5ba20516" + "outputId": "c5f9e40c-5562-47c5-8fe7-92e687ab57bb" }, - "execution_count": 10, + "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -2637,7 +2655,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "100%|██████████| 40.8M/40.8M [00:00<00:00, 59.5MiB/s]\n" + "100%|██████████| 40.8M/40.8M [00:01<00:00, 21.4MiB/s]\n" ] }, { @@ -2680,7 +2698,7 @@ ], "text/html": [ "\n", - "
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\n", + "\n", "
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\n" - ] + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df_eform_filter" + } }, "metadata": {}, - "execution_count": 10 + "execution_count": 5 } ] }, @@ -3015,9 +3093,9 @@ "base_uri": "https://localhost:8080/" }, "id": "K6jwRdkX2BmX", - "outputId": "0bdef9aa-6c16-4bfc-effc-ee1401613b17" + "outputId": "880472f6-2496-430a-e026-7d23a9e284a4" }, - "execution_count": 11, + "execution_count": 6, "outputs": [ { "output_type": "stream", @@ -3047,9 +3125,9 @@ "base_uri": "https://localhost:8080/" }, "id": "yUIPERGM2oPC", - "outputId": "a53ae9d2-fc6e-4274-b60b-bdd0d3bb7a05" + "outputId": "5c2883db-61cd-4607-cac2-acda1b6c29d7" }, - "execution_count": 12, + "execution_count": 7, "outputs": [ { "output_type": "stream", @@ -3119,9 +3197,9 @@ "base_uri": "https://localhost:8080/" }, "id": "49lHcBLQ3VcZ", - "outputId": "855fdce4-4eff-40b6-b0f9-6c6c6359ad39" + "outputId": "6f191e14-2d4d-4822-8109-e82f6654ad0f" }, - "execution_count": 13, + "execution_count": 8, "outputs": [ { "output_type": "stream", @@ -3145,9 +3223,9 @@ "base_uri": "https://localhost:8080/" }, "id": "r_0vpTN63m2U", - "outputId": "5dbccbc5-fc5c-4e33-bd4f-ceff63e1e40f" + "outputId": "442d0d89-32f7-4d5e-a632-a736667b0059" }, - "execution_count": 14, + "execution_count": 9, "outputs": [ { "output_type": "stream", @@ -3157,8 +3235,8 @@ "cmd /content/q-e/bin/pw.x\n", + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
idprediction
0JVASP-10020.48839
\n", + "
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\n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df", + "summary": "{\n \"name\": \"df\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"JVASP-1002\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"prediction\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.4883900939631527,\n \"max\": 0.4883900939631527,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.4883900939631527\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now lets make a benchmark json.zip file" + ], + "metadata": { + "id": "g_uuxlV270gP" + } + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.jsonutils import dumpjson\n", + "content = {\"train\": {}, \"test\": {\"JVASP-1002\": 1.17}}\n", + "dumpjson(content, \"dft_3d_bandgap_JVASP_1002_Si.json\")" + ], + "metadata": { + "id": "71Nct1ba794_" + }, + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The `dft_3d_bandgap_JVASP_1002_Si.json.zip` file can go to folders such as this [link](https://github.com/usnistgov/jarvis_leaderboard/blob/main/jarvis_leaderboard/benchmarks/ES/SinglePropertyPrediction/dft_3d_bandgap_JVASP_1002_Si.json.zip)\n", + "\n", + "The `ES-SinglePropertyPrediction-bandgap_JVASP_1002_Si-dft_3d-test-mae.csv` can go to folder such as [this link](https://github.com/usnistgov/jarvis_leaderboard/blob/main/jarvis_leaderboard/contributions/vasp_optb88vdw/ES-SinglePropertyPrediction-bandgap_JVASP_1002_Si-dft_3d-test-mae.csv.zip)" + ], + "metadata": { + "id": "xgFb9qzC8TDE" + } + }, { "cell_type": "markdown", "source": [ @@ -3457,6 +3784,15 @@ "execution_count": null, "outputs": [] }, + { + "cell_type": "markdown", + "source": [ + "Further analysis" + ], + "metadata": { + "id": "O5UPzLXO9AIG" + } + }, { "cell_type": "code", "source": [ @@ -3467,7 +3803,7 @@ "metadata": { "id": "Ep7_3jDgMPNd" }, - "execution_count": 29, + "execution_count": null, "outputs": [] }, { @@ -3494,7 +3830,7 @@ }, "outputId": "cfb90943-e88d-4d64-fb77-b26ebe442e4c" }, - "execution_count": 34, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -3522,7 +3858,7 @@ "id": "Q6Drasbh5rK-", "outputId": "f7345b06-9b26-4ab7-e6b5-ccb58c8c5781" }, - "execution_count": 35, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -8545,7 +8881,7 @@ }, "outputId": "793917e3-5744-4aad-cf29-4f13ba8c2c4b" }, - "execution_count": 36, + "execution_count": null, "outputs": [ { "output_type": "stream", diff --git a/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb b/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb index d2c3b59..86f6800 100644 --- a/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb +++ b/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb @@ -67,6 +67,15 @@ "* After installation, if you want to change the Julia version or activate/deactivate the GPU, you will need to reset the Runtime: _Runtime_ > _Factory reset runtime_ and repeat steps 3 and 4." ] }, + { + "cell_type": "markdown", + "source": [ + "For conda based example See: https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/tb3py.ipynb" + ], + "metadata": { + "id": "oVA84ZhVPM-s" + } + }, { "cell_type": "code", "metadata": { @@ -82,7 +91,7 @@ "set -e\n", "\n", "#---------------------------------------------------#\n", - "JULIA_VERSION=\"1.9.2\" # any version ≥ 0.7.0\n", + "JULIA_VERSION=\"1.8.2\" # any version ≥ 0.7.0\n", "JULIA_PACKAGES=\"IJulia BenchmarkTools\"\n", "JULIA_PACKAGES_IF_GPU=\"CUDA\" # or CuArrays for older Julia versions\n", "JULIA_NUM_THREADS=2\n", @@ -159,26 +168,26 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "de503ebd-e579-4d40-cd42-62693a930343" + "outputId": "23145727-9618-4b66-c74b-8be3cc62caed" }, "source": [ "versioninfo()" ], - "execution_count": 1, + "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Julia Version 1.9.2\n", - "Commit e4ee485e909 (2023-07-05 09:39 UTC)\n", + "Julia Version 1.8.2\n", + "Commit 36034abf260 (2022-09-29 15:21 UTC)\n", "Platform Info:\n", " OS: Linux (x86_64-linux-gnu)\n", " CPU: 2 × Intel(R) Xeon(R) CPU @ 2.20GHz\n", " WORD_SIZE: 64\n", " LIBM: libopenlibm\n", - " LLVM: libLLVM-14.0.6 (ORCJIT, broadwell)\n", - " Threads: 3 on 2 virtual cores\n", + " LLVM: libLLVM-13.0.1 (ORCJIT, broadwell)\n", + " Threads: 2 on 2 virtual cores\n", "Environment:\n", " LD_LIBRARY_PATH = /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", " JULIA_NUM_THREADS = 2\n" @@ -218,9 +227,9 @@ "base_uri": "https://localhost:8080/" }, "id": "YjM_qq54lCcs", - "outputId": "fb7b10d1-7a27-4520-fa8d-fd8ba7d41fd6" + "outputId": "ebaa0d49-99ab-4f58-b1a8-4eba05a10658" }, - "execution_count": 2, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -231,537 +240,545 @@ "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m registry at `~/.julia/registries/General.toml`\n", "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LERC_jll ───────────────────────── v3.0.0+1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m OffsetArrays ───────────────────── v1.12.10\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m x265_jll ───────────────────────── v3.5.0+0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m libfdk_aac_jll ─────────────────── v2.0.2+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m FFTW ───────────────────────────── v1.7.1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m GR_jll ─────────────────────────── v0.72.10+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Libmount_jll ───────────────────── v2.35.0+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m JpegTurbo_jll ──────────────────── v2.1.91+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Libmount_jll ───────────────────── v2.40.1+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m GR_jll ─────────────────────────── v0.73.7+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m x265_jll ───────────────────────── v3.5.0+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m JpegTurbo_jll ──────────────────── v3.0.3+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m libdecor_jll ───────────────────── v0.2.2+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m OffsetArrays ───────────────────── v1.14.1\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Opus_jll ───────────────────────── v1.3.2+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Unitful ────────────────────────── v1.17.0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_xkbcomp_jll ───────────────── v1.4.6+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m FFTW ───────────────────────────── v1.8.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LoggingExtras ──────────────────── v1.0.3\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m RelocatableFolders ─────────────── v1.0.1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Measures ───────────────────────── v0.3.2\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ConcurrentUtilities ────────────── v2.2.1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Grisu ──────────────────────────── v1.0.2\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m XMLDict ────────────────────────── v0.4.1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Contour ────────────────────────── v0.6.2\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LoggingExtras ──────────────────── v1.0.3\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Formatting 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v0.1.9\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Scratch ────────────────────────── v1.2.0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m gperf_jll ──────────────────────── v3.1.1+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m BitTwiddlingConvenienceFunctions ─ v0.1.5\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DiffRules ──────────────────────── v1.15.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m BitTwiddlingConvenienceFunctions ─ v0.1.6\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Scratch ────────────────────────── v1.2.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Dbus_jll ───────────────────────── v1.14.10+0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m eudev_jll ──────────────────────── v3.2.9+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ColorTypes ─────────────────────── v0.11.4\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LayoutPointers ─────────────────── v0.1.14\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_libXext_jll ───────────────── v1.3.4+4\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DiffRules ──────────────────────── v1.15.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m oneTBB_jll ─────────────────────── v2021.12.0+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_libXext_jll ───────────────── v1.3.6+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LayoutPointers ─────────────────── v0.1.17\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m TensorCore ─────────────────────── v0.1.1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Zstd_jll ───────────────────────── v1.5.5+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ColorTypes ─────────────────────── v0.11.5\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Plots ──────────────────────────── v1.40.5\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Zstd_jll 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Setfield ───────────────────────── v1.1.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Format ─────────────────────────── v1.3.7\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m AbstractFFTs ───────────────────── v1.5.0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ConstructionBase ───────────────── v1.5.4\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m LoopVectorization ──────────────── v0.12.171\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Libffi_jll ─────────────────────── v3.2.2+1\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ColorVectorSpace ───────────────── v0.10.0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m OrderedCollections ─────────────── v1.6.2\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_libXrender_jll ────────────── v0.9.10+4\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m ConstructionBase ───────────────── v1.5.6\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m 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"\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_xcb_util_renderutil_jll ───── v0.3.9+1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m UnPack ─────────────────────────── v1.0.2\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DocStringExtensions ────────────── v0.9.3\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Wayland_protocols_jll ──────────── v1.25.0+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_libICE_jll ────────────────── v1.1.1+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Pixman_jll ─────────────────────── v0.43.4+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Adapt ──────────────────────────── v3.7.2\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Graphite2_jll ──────────────────── v1.3.14+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m XML2_jll ───────────────────────── v2.13.1+0\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m libass_jll ─────────────────────── v0.15.1+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Pixman_jll ─────────────────────── v0.42.2+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_xcb_util_renderutil_jll ───── v0.3.9+1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m XML2_jll ───────────────────────── v2.11.5+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m VectorizationBase ──────────────── v0.21.64\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Latexify ───────────────────────── v0.16.1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m VectorizationBase ──────────────── v0.21.70\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Latexify ───────────────────────── v0.16.4\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Wayland_jll ────────────────────── v1.21.0+1\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Suppressor ─────────────────────── v0.2.6\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_xtrans_jll 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Xorg_xtrans_jll ────────────────── v1.5.0+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m OpenSSL_jll ────────────────────── v3.0.14+0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m FFMPEG_jll ─────────────────────── v4.4.4+1\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m BitFlags ───────────────────────── v0.1.9\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m IterTools ──────────────────────── v1.10.0\n", + "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m CPUSummary ─────────────────────── v0.2.6\n", "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_xkeyboard_config_jll ──────── v2.39.0+0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m DataAPI ────────────────────────── v1.15.0\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m Xorg_libXfixes_jll ─────────────── v5.0.3+4\n", - "\u001b[32m\u001b[1m Installed\u001b[22m\u001b[39m RecipesBase ────────────────────── v1.3.4\n", + "\u001b[32m\u001b[1m 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To see why use `status --outdated -m`\n", - "\u001b[32m\u001b[1m Building\u001b[22m\u001b[39m ThreeBodyTB → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/b962e819de62f97743a053c6c1b8ea0c7a3137fa/build.log`\n", + "\u001b[32m\u001b[1m Building\u001b[22m\u001b[39m ThreeBodyTB → `~/.julia/scratchspaces/44cfe95a-1eb2-52ea-b672-e2afdf69b78f/798e1d4d7f476f0fabe5984d6aec82eeb4677bd1/build.log`\n", "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m project...\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mTensorCore\u001b[39m\n", "\u001b[32m ✓ \u001b[39m\u001b[90mLaTeXStrings\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mAbstractFFTs\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mTensorCore\u001b[39m\n", "\u001b[32m ✓ \u001b[39m\u001b[90mStatsAPI\u001b[39m\n", - "\u001b[32m ✓ \u001b[39m\u001b[90mContour\u001b[39m\n", "\u001b[32m ✓ \u001b[39m\u001b[90mSuppressor\u001b[39m\n", + "\u001b[32m ✓ \u001b[39m\u001b[90mContour\u001b[39m\n", "\u001b[32m ✓ 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Downloading\u001b[22m\u001b[39m artifact: MKL\u001b[33m\n", - "└ \u001b[39m\n", + " 191 dependencies successfully precompiled in 313 seconds. 20 already precompiled.\n", "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n", - "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.9/Project.toml`\n", - " \u001b[90m[91a5bcdd] \u001b[39m\u001b[92m+ Plots v1.39.0\u001b[39m\n", - "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.9/Manifest.toml`\n" + "\u001b[32m\u001b[1m Updating\u001b[22m\u001b[39m `~/.julia/environments/v1.8/Project.toml`\n", + " \u001b[90m [91a5bcdd] \u001b[39m\u001b[92m+ Plots v1.40.5\u001b[39m\n", + "\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `~/.julia/environments/v1.8/Manifest.toml`\n" ] } ] @@ -835,6 +841,7 @@ { "cell_type": "code", "source": [ + "using ThreeBodyTB\n", "# https://www.ctcms.nist.gov/~knc6/static/JARVIS-DFT/JVASP-1109.xml\n", "pos=\"\"\"Sn4S4\n", "1.0\n", @@ -860,12 +867,12 @@ ], "metadata": { "id": "iKWCP2C2L1YW", - "outputId": "06842142-cdac-4789-fad2-b18731857d09", + "outputId": "aa80af64-b657-4f5b-a04d-56d297c474bc", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 9, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -888,7 +895,7 @@ ] }, "metadata": {}, - "execution_count": 9 + "execution_count": 3 } ] }, @@ -906,9 +913,9 @@ "base_uri": "https://localhost:8080/" }, "id": "n2fIIVknkwmY", - "outputId": "c7a77b69-7702-4e0d-b7af-f686843d9e62" + "outputId": "f31394f7-3756-4bb9-b4b8-82dadb210d8c" }, - "execution_count": 3, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -925,7 +932,7 @@ ] }, "metadata": {}, - "execution_count": 3 + "execution_count": 4 } ] }, @@ -944,9 +951,9 @@ "id": "XciCcMAJOT3_", "colab": { "base_uri": "https://localhost:8080/", - "height": 456 + "height": 457 }, - "outputId": "02cf4e4a-3e12-4134-deed-5da726886e94" + "outputId": "dade1a0c-ec82-435d-8db0-a60c057d89a9" }, "source": [ "c222 = c * [2,2,2]\n", @@ -965,78 +972,7 @@ { "output_type": "display_data", "data": { - "image/png": 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"HaB30AFVltGM", - "outputId": "b499060d-9669-4c54-ca72-3bd9a10a9e9d" + "outputId": "e2027d50-8cd0-49da-d240-9599206d9e5a" }, - "execution_count": 10, + "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", - "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.2bdy.Sn.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/YQw1U/src/../dats/pbesol/v1.3/els/coef.el.2bdy.P.xml.gz\n", "Tuple{Symbol, Symbol}\n", - "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.3bdy.Sn.xml.gz\n", - "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/binary/coef.el.2bdy.S.Sn.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/YQw1U/src/../dats/pbesol/v1.3/els/coef.el.3bdy.P.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/YQw1U/src/../dats/pbesol/v1.3/binary/coef.el.2bdy.Al.P.xml.gz\n", "Tuple{Symbol, Symbol}\n", "Tuple{Symbol, Symbol}\n", - "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/binary/coef.el.3bdy.S.Sn.xml.gz\n", - "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.2bdy.S.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/YQw1U/src/../dats/pbesol/v1.3/binary/coef.el.3bdy.Al.P.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/YQw1U/src/../dats/pbesol/v1.3/els/coef.el.2bdy.Al.xml.gz\n", "Tuple{Symbol, Symbol}\n", - "found /root/.julia/packages/ThreeBodyTB/4nbDG/src/../dats/pbesol/v1.3/els/coef.el.3bdy.S.xml.gz\n", + "found /root/.julia/packages/ThreeBodyTB/YQw1U/src/../dats/pbesol/v1.3/els/coef.el.3bdy.Al.xml.gz\n", "\n", - "calc tb\n", "\n", "START SCF ----------------\n", - "SCF CALC 0001 energy -37.51018690 \n", - "SCF CALC 0002 energy -37.50406504 en_diff: 6.121863E-03 dq_diff: 5.250650E+00 mix: 1.000000E+00 \n", - "SCF CALC 0003 energy -37.49160900 en_diff: 1.245604E-02 dq_diff: 4.936322E+00 mix: 1.000000E+00 \n", - "SCF CALC 0004 energy -36.52558896 en_diff: 9.660200E-01 dq_diff: 2.664226E-01 mix: 1.000000E+00 \n", - "SCF CALC 0005 energy -36.46117203 en_diff: 6.441693E-02 dq_diff: 9.083951E-02 mix: 1.000000E+00 \n", - "SCF CALC 0006 energy -36.38924636 en_diff: 7.192566E-02 dq_diff: 9.905912E-02 mix: 1.000000E+00 \n", - "SCF CALC 0007 energy -36.42879743 en_diff: 3.955107E-02 dq_diff: 4.597046E-03 mix: 1.000000E+00 \n", - "SCF CALC 0008 energy -36.42512050 en_diff: 3.676935E-03 dq_diff: 5.117216E-03 mix: 1.000000E+00 \n", - "SCF CALC 0009 energy -36.42731532 en_diff: 2.194828E-03 dq_diff: 6.794504E-04 mix: 1.000000E+00 \n", - "SCF CALC 0010 energy -36.42712317 en_diff: 1.921548E-04 dq_diff: 1.717279E-04 mix: 1.000000E+00 \n", + "SCF CALC 0001 energy -10.85101555 \n", + "SCF CALC 0002 energy -10.81197415 en_diff: 3.904139E-02 dq_diff: 8.240624E-01 mix: 4.000000E-01 \n", + "SCF CALC 0003 energy -10.77989818 en_diff: 3.207597E-02 dq_diff: 4.522777E-01 mix: 4.000000E-01 \n", + "SCF CALC 0004 energy -10.75822860 en_diff: 2.166959E-02 dq_diff: 2.481297E-01 mix: 4.000000E-01 \n", + "SCF CALC 0005 energy -10.74511541 en_diff: 1.311318E-02 dq_diff: 1.361000E-01 mix: 4.000000E-01 \n", + "SCF CALC 0006 energy -10.73755410 en_diff: 7.561309E-03 dq_diff: 7.464250E-02 mix: 4.800000E-01 \n", + "SCF CALC 0007 energy -10.73243527 en_diff: 5.118839E-03 dq_diff: 3.419227E-02 mix: 4.800000E-01 \n", + "SCF CALC 0008 energy -10.73005263 en_diff: 2.382634E-03 dq_diff: 1.566190E-02 mix: 4.800000E-01 \n", + "SCF CALC 0009 energy -10.72895333 en_diff: 1.099305E-03 dq_diff: 7.173806E-03 mix: 4.800000E-01 \n", + "SCF CALC 0010 energy -10.72844813 en_diff: 5.051917E-04 dq_diff: 3.285863E-03 mix: 4.800000E-01 \n", "\n", - "YES convergence in 10 iters, energy -36.42712316978109 eV \n", + "YES convergence in 10 iters, energy -10.72844813442714 eV \n", "END SCF ------------------\n", "\n", - "ΔQ = [-0.46, -0.46, -0.46, -0.46, 0.46, 0.46, 0.46, 0.46]\n", + "ΔQ = [-0.84, 0.84]\n", "\n", "scf_energy success, done\n", "\n", - "Formation energy: -0.501 eV/atom\n", + "Formation energy: -0.628 eV/atom\n", "\n" ] } ] }, + { + "cell_type": "code", + "source": [ + "ind2orb, orb2ind, etotal, nval = ThreeBodyTB.CrystalMod.orbital_index(c)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2fI-jf2tVKQh", + "outputId": "bc62e2ac-8e04-445a-a5b0-823a743cc37b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(Dict{Any, Any}(5 => Any[2, :P, :s], 4 => Any[1, :Al, :py], 6 => Any[2, :P, :pz], 7 => Any[2, :P, :px], 2 => Any[1, :Al, :pz], 8 => Any[2, :P, :py], 3 => Any[1, :Al, :px], 1 => Any[1, :Al, :s]), Dict{Any, Any}(2 => 5:8, 1 => 1:4), -21.44734287, 8.0)" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "ind2orb" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FXnT_QvDVL4U", + "outputId": "f05730fd-dd7a-4c3e-80b2-67b05ada500c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dict{Any, Any} with 8 entries:\n", + " 5 => Any[2, :P, :s]\n", + " 4 => Any[1, :Al, :py]\n", + " 6 => Any[2, :P, :pz]\n", + " 7 => Any[2, :P, :px]\n", + " 2 => Any[1, :Al, :pz]\n", + " 8 => Any[2, :P, :py]\n", + " 3 => Any[1, :Al, :px]\n", + " 1 => Any[1, :Al, :s]" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "orb2ind" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "giGjnZW2VVIx", + "outputId": "308c55bf-9f50-45f6-fa21-35ea4d21e10f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dict{Any, Any} with 2 entries:\n", + " 2 => 5:8\n", + " 1 => 1:4" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "etotal" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NHDMP7EkVWNt", + "outputId": "eca40afb-fcfe-499f-e4c6-9c02b3eae3b9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "-21.44734287" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "nval" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oFUXYz2oVXZh", + "outputId": "a177dd5e-14a8-4d4c-fd04-4dfed58740a9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "8.0" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "ind2orb, orb2ind, etotal, nval = ThreeBodyTB.CrystalMod.orbital_index(c)\n", + "\n", + "d = Dict{Tuple{Int, Int, Int, Int, Int, Int, Int, Int, Int}, Tuple{Float64, Float64}}()\n", + "\n", + "for i in 1:tbc.tb.nwan\n", + " for j in 1:tbc.tb.nwan\n", + " for nr in 1:tbc.tb.nr\n", + " a1, t1, s1 = ind2orb[i]\n", + " a2, t2, s2 = ind2orb[j]\n", + " r = tbc.tb.ind_arr[nr, :]\n", + "\n", + " d[(a1-1, t1, s1, a2-1, t2, s2, r[1], r[2], r[3])] = (tbc.tb.H[i, j, nr] * 13.605662285137, tbc.tb.S[i, j, nr])\n", + " end\n", + " end\n", + "end" + ], + "metadata": { + "id": "DtonOxmdV5N-", + "outputId": "5079fcc1-f35d-40d6-ef31-2dfb22b98f86", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 414 + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "LoadError", + "evalue": "MethodError: \u001b[0mCannot `convert` an object of type \u001b[92mSymbol\u001b[39m\u001b[0m to an object of type \u001b[91mInt64\u001b[39m\n\u001b[0mClosest candidates are:\n\u001b[0m convert(::Type{T}, \u001b[91m::Gray24\u001b[39m) where T<:Real at ~/.julia/packages/ColorTypes/vpFgh/src/conversions.jl:114\n\u001b[0m convert(::Type{T}, \u001b[91m::Gray\u001b[39m) where T<:Real at ~/.julia/packages/ColorTypes/vpFgh/src/conversions.jl:113\n\u001b[0m convert(::Type{T}, \u001b[91m::Base.TwicePrecision\u001b[39m) where T<:Number at twiceprecision.jl:273\n\u001b[0m ...", + "traceback": [ + "MethodError: \u001b[0mCannot `convert` an object of type \u001b[92mSymbol\u001b[39m\u001b[0m to an object of type \u001b[91mInt64\u001b[39m\n\u001b[0mClosest candidates are:\n\u001b[0m convert(::Type{T}, \u001b[91m::Gray24\u001b[39m) where T<:Real at ~/.julia/packages/ColorTypes/vpFgh/src/conversions.jl:114\n\u001b[0m convert(::Type{T}, \u001b[91m::Gray\u001b[39m) where T<:Real at ~/.julia/packages/ColorTypes/vpFgh/src/conversions.jl:113\n\u001b[0m convert(::Type{T}, \u001b[91m::Base.TwicePrecision\u001b[39m) where T<:Number at twiceprecision.jl:273\n\u001b[0m ...", + "", + "Stacktrace:", + " [1] cvt1", + " @ ./essentials.jl:338 [inlined]", + " [2] macro expansion", + " @ ./ntuple.jl:74 [inlined]", + " [3] ntuple", + " @ ./ntuple.jl:69 [inlined]", + " [4] convert(#unused#::Type{NTuple{9, Int64}}, x::Tuple{Int64, Symbol, Symbol, Int64, Symbol, Symbol, Int64, Int64, Int64})", + " @ Base ./essentials.jl:339", + " [5] setindex!(h::Dict{NTuple{9, Int64}, Tuple{Float64, Float64}}, v0::Tuple{ComplexF64, ComplexF64}, key0::Tuple{Int64, Symbol, Symbol, Int64, Symbol, Symbol, Int64, Int64, Int64})", + " @ Base ./dict.jl:374", + " [6] top-level scope", + " @ ./In[19]:16" + ] + } + ] + }, { "cell_type": "markdown", "source": [ @@ -1127,54 +1256,102 @@ "base_uri": "https://localhost:8080/" }, "id": "VxnUjEvLn_Iv", - "outputId": "5aaf3a7a-f68f-4997-d63d-7c49e8c51ebd" + "outputId": "b1a0a9a3-b422-438a-d71c-a74836b04e5f" }, - "execution_count": 11, + "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\n", - "tb_crys object \n", + "tb_crys object (DENSE)\n", "\n", "Units: Å\n", "\n", - "A1= 4.03360 0.00000 0.00000\n", - "A2= 0.00000 4.39450 0.00000\n", - "A3= 0.00000 0.00000 11.42432\n", + "A1= 0.00000 2.73000 2.73000\n", + "A2= 2.73000 0.00000 2.73000\n", + "A3= 2.73000 2.73000 0.00000\n", "\n", - "Sn 0.25000 0.37272 0.62101\n", - "Sn 0.75000 0.62728 0.37899\n", - "Sn 0.75000 0.87272 0.87899\n", - "Sn 0.25000 0.12728 0.12101\n", - "S 0.25000 0.02787 0.35089\n", - "S 0.75000 0.97213 0.64911\n", - "S 0.75000 0.52787 0.14911\n", - "S 0.25000 0.47213 0.85089\n", + "Al 0.00000 0.00000 0.00000\n", + "P 0.25000 0.25000 0.25000\n", "\n", "\n", - "nelec: 40.0; nspin (hoppings): 1\n", + "nelec: 8.0; nspin (hoppings): 1\n", "within_fit: true ; scf: true; scfspin: false\n", - "calculated energy: -36.427 eV\n", - "formation energy: -0.501 eV\n", - "charges : [-0.46, -0.46, -0.46, -0.46, 0.46, 0.46, 0.46, 0.46]\n", + "calculated energy: -10.728 eV\n", + "formation energy: -0.628 eV\n", + "efermi : -1.251 eV\n", + "charges : [-0.84, 0.84]\n", "\n", "\n", - "tight binding real space object; nwan = 32, nr = 55, nonorth = true, scf = true, scfmagnetic = false, nspin = 1\n", + "tight binding real space object (DENSE); nwan = 8, nr = 99, nonorth = true, scf = true, scfmagnetic = false, nspin = 1\n", "\n", "\n", "\n" ] }, "metadata": {}, - "execution_count": 11 - }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Write the wannier90_hr.dat format file for tbc object." + ], + "metadata": { + "id": "RqoVRZsqtwlE" + } + }, + { + "cell_type": "code", + "source": [ + "write_hr_dat(tbc,filename=\"my_hr.dat\")" + ], + "metadata": { + "id": "CrWD6HpItiB9", + "outputId": "f1ea2321-88ba-4dd0-c3bf-19768e444648", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "efermi : -3.256 eV\n" + "grid: [10, 10, 10]\n", + " 0.007534 seconds (12.80 k allocations: 4.869 MiB)\n", + " 8.070477 seconds (32.63 M allocations: 2.280 GiB, 12.00% gc time, 23.37% compilation time)\n", + "start write ./my_hr.dat\n", + "wrote ./my_hr.dat\n", + " 32.793008 seconds (73.95 M allocations: 4.129 GiB, 6.94% gc time, 79.16% compilation time)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "println(readdir())" + ], + "metadata": { + "id": "BtSG5wNct3l_", + "outputId": "f33b1204-5fff-4d31-fa87-34c4b6b3ec6c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[\".config\", \"POSCAR\", \"my_hr.dat\", \"sample_data\"]\n" ] } ] @@ -1191,7 +1368,7 @@ "base_uri": "https://localhost:8080/" } }, - "execution_count": 12, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -1260,7 +1437,7 @@ "id": "7MzdWNMbl4uD", "outputId": "3324612a-01df-4ea2-efe2-c0afc7042936" }, - "execution_count": 13, + "execution_count": null, "outputs": [ { "output_type": "stream", diff --git a/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb b/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb index e34032f..57ff866 100644 --- a/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb +++ b/jarvis-tools-notebooks/Train_ALIGNNFF_Mlearn.ipynb @@ -71,18 +71,18 @@ ], "metadata": { "id": "4nIuEyCzsOhT", - "outputId": "7c43cd07-d40c-4daa-c812-4bc641cfde52", + "outputId": "3f8ffda5-1350-4768-9110-7037abdd8e81", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Sun Jun 2 03:16:31 2024 \n", + "Tue Jul 30 12:00:16 2024 \n", "+---------------------------------------------------------------------------------------+\n", "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", "|-----------------------------------------+----------------------+----------------------+\n", @@ -91,7 +91,7 @@ "| | | MIG M. |\n", "|=========================================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", - "| N/A 47C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |\n", + "| N/A 60C P8 11W / 70W | 0MiB / 15360MiB | 0% Default |\n", "| | | N/A |\n", "+-----------------------------------------+----------------------+----------------------+\n", " \n", @@ -115,12 +115,12 @@ ], "metadata": { "id": "53-WlVwEsykA", - "outputId": "1a6ba764-c494-45fc-bc86-7cf291abe9c2", + "outputId": "6c40bfd1-c533-463a-cd32-a69634e4a8e2", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": null, + "execution_count": 2, "outputs": [ { "output_type": "stream", @@ -130,7 +130,7 @@ "📦 Installing...\n", "📌 Adjusting configuration...\n", "🩹 Patching environment...\n", - "⏲ Done in 0:00:15\n", + "⏲ Done in 0:00:10\n", "🔁 Restarting kernel...\n" ] } @@ -143,12 +143,12 @@ ], "metadata": { "id": "5okN2wjitkGn", - "outputId": "57a8fa81-6e29-47b1-e900-10e1cd36c35c", + "outputId": "cdddc02b-d897-4833-a29b-ba6d8f78bd11", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", @@ -167,12 +167,12 @@ ], "metadata": { "id": "2y7BkirptmPn", - "outputId": "d3d5caee-e1dc-4ded-ae18-d4ebfa064d6c", + "outputId": "b6dd39c5-7e4c-4b6e-d4b1-3583c8d4fe20", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": null, + "execution_count": 2, "outputs": [ { "output_type": "stream", @@ -193,9 +193,9 @@ "base_uri": "https://localhost:8080/" }, "id": "SjkgkHKbBUSJ", - "outputId": "339cfe2b-f6d7-4957-b861-a2289f0aa964" + "outputId": "36cac90b-c987-45bf-a4b8-04dd8d165a2b" }, - "execution_count": null, + "execution_count": 3, "outputs": [ { "output_type": "stream", @@ -224,16 +224,16 @@ "source": [ "%%time\n", "\n", - "!conda install alignn dgl=2.1.0 pytorch torchvision torchaudio pytorch-cuda -c pytorch -c nvidia --quiet\n" + "!conda install jarvis-tools dgl=2.1.0 pytorch torchvision torchaudio pytorch-cuda -c pytorch -c nvidia --quiet -y\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "A2yF-sgRv6a0", - "outputId": "9b3e2204-5d64-4d5c-edb4-c40e94be3563" + "outputId": "8cb366e5-fcfd-4ee0-d048-716d23592452" }, - "execution_count": null, + "execution_count": 4, "outputs": [ { "output_type": "stream", @@ -252,7 +252,8 @@ " environment location: /usr/local\n", "\n", " added / updated specs:\n", - " - alignn\n", + " - dgl=2.1.0\n", + " - jarvis-tools\n", " - pytorch\n", " - pytorch-cuda\n", " - torchaudio\n", @@ -265,170 +266,160 @@ " ---------------------------|-----------------\n", " _openmp_mutex-4.5 | 2_kmp_llvm 6 KB conda-forge\n", " absl-py-2.1.0 | pyhd8ed1ab_0 105 KB conda-forge\n", - " alignn-2024.4.10 | pyhd8ed1ab_0 71 KB conda-forge\n", " annotated-types-0.7.0 | pyhd8ed1ab_0 18 KB conda-forge\n", - " ase-3.23.0 | pyhd8ed1ab_0 1.8 MB conda-forge\n", " astunparse-1.6.3 | pyhd8ed1ab_0 15 KB conda-forge\n", " babel-2.14.0 | pyhd8ed1ab_0 7.3 MB conda-forge\n", - " blinker-1.8.2 | pyhd8ed1ab_0 14 KB conda-forge\n", " brotli-1.1.0 | hd590300_1 19 KB conda-forge\n", " brotli-bin-1.1.0 | hd590300_1 19 KB conda-forge\n", - " c-ares-1.28.1 | hd590300_0 165 KB conda-forge\n", - " ca-certificates-2024.2.2 | hbcca054_0 152 KB conda-forge\n", + " c-ares-1.32.3 | h4bc722e_0 176 KB conda-forge\n", + " ca-certificates-2024.7.4 | hbcca054_0 151 KB conda-forge\n", " cached-property-1.5.2 | hd8ed1ab_1 4 KB conda-forge\n", " cached_property-1.5.2 | pyha770c72_1 11 KB conda-forge\n", - " certifi-2024.2.2 | pyhd8ed1ab_0 157 KB conda-forge\n", + " certifi-2024.7.4 | pyhd8ed1ab_0 156 KB conda-forge\n", " click-8.1.7 |unix_pyh707e725_0 82 KB conda-forge\n", " contourpy-1.2.1 | py310hd41b1e2_0 236 KB conda-forge\n", - " cuda-crt-tools-12.5.40 | 0 19 KB nvidia\n", + " cuda-crt-tools-12.5.82 | 0 19 KB nvidia\n", " cuda-cudart-12.4.127 | 0 198 KB nvidia\n", " cuda-cupti-12.4.127 | 0 16.4 MB nvidia\n", " cuda-libraries-12.4.0 | 0 2 KB nvidia\n", - " cuda-nvcc-tools-12.5.40 | 0 26.6 MB nvidia\n", + " cuda-nvcc-tools-12.5.82 | 0 26.6 MB nvidia\n", " cuda-nvrtc-12.4.127 | 0 21.0 MB nvidia\n", " cuda-nvtx-12.4.127 | 0 58 KB nvidia\n", - " cuda-nvvm-tools-12.5.40 | 0 12.9 MB nvidia\n", + " cuda-nvvm-tools-12.5.82 | 0 12.9 MB nvidia\n", " cuda-opencl-12.5.39 | 0 26 KB nvidia\n", " cuda-runtime-12.4.0 | 0 2 KB nvidia\n", " cuda-version-12.5 | 3 16 KB nvidia\n", " cudnn-8.9.7.29 | h092f7fd_3 446.6 MB conda-forge\n", " cycler-0.12.1 | pyhd8ed1ab_0 13 KB conda-forge\n", - " dgl-2.1.0 |cuda120py310h00d5dd0_0 103.8 MB conda-forge\n", - " dm-tree-0.1.8 | py310ha8c1f0e_4 104 KB conda-forge\n", + " dgl-2.1.0 |cuda120py310h00d5dd0_2 103.7 MB conda-forge\n", " ffmpeg-4.3 | hf484d3e_0 9.9 MB pytorch\n", - " filelock-3.14.0 | pyhd8ed1ab_0 16 KB conda-forge\n", - " flake8-7.0.0 | pyhd8ed1ab_0 108 KB conda-forge\n", - " flask-3.0.3 | pyhd8ed1ab_0 79 KB conda-forge\n", + " filelock-3.15.4 | pyhd8ed1ab_0 17 KB conda-forge\n", " flatbuffers-24.3.25 | h59595ed_0 1.4 MB conda-forge\n", - " fonttools-4.53.0 | py310hc51659f_0 2.2 MB conda-forge\n", + " fonttools-4.53.1 | py310h5b4e0ec_0 2.3 MB conda-forge\n", " freetype-2.12.1 | h267a509_2 620 KB conda-forge\n", - " fsspec-2024.5.0 | pyhff2d567_0 211 KB conda-forge\n", - " gast-0.5.4 | pyhd8ed1ab_0 23 KB conda-forge\n", + " fsspec-2024.6.1 | pyhff2d567_0 130 KB conda-forge\n", + " gast-0.5.5 | pyhd8ed1ab_0 23 KB conda-forge\n", " ghp-import-2.1.0 | pyhd8ed1ab_0 15 KB conda-forge\n", " giflib-5.2.2 | hd590300_0 75 KB conda-forge\n", - " gmp-6.3.0 | h59595ed_1 556 KB conda-forge\n", + " gmp-6.3.0 | hac33072_2 449 KB conda-forge\n", " gmpy2-2.1.5 | py310hc7909c9_1 201 KB conda-forge\n", " gnutls-3.6.13 | h85f3911_1 2.0 MB conda-forge\n", " google-pasta-0.2.0 | pyh8c360ce_0 42 KB conda-forge\n", " grpcio-1.62.2 | py310h1b8f574_0 980 KB conda-forge\n", - " h5py-3.11.0 |nompi_py310hf054cd7_101 1.1 MB conda-forge\n", - " hdf5-1.14.3 |nompi_hdf9ad27_102 3.7 MB conda-forge\n", - " importlib-metadata-7.1.0 | pyha770c72_0 26 KB conda-forge\n", + " h5py-3.11.0 |nompi_py310hf054cd7_102 1.1 MB conda-forge\n", + " hdf5-1.14.3 |nompi_hdf9ad27_105 3.7 MB conda-forge\n", + " importlib-metadata-8.2.0 | pyha770c72_0 27 KB conda-forge\n", " importlib-resources-6.4.0 | pyhd8ed1ab_0 9 KB conda-forge\n", " importlib_resources-6.4.0 | pyhd8ed1ab_0 32 KB conda-forge\n", - " itsdangerous-2.2.0 | pyhd8ed1ab_0 19 KB conda-forge\n", - " jarvis-tools-2024.4.20 | pyhd8ed1ab_0 3.8 MB conda-forge\n", + " inflect-7.3.1 | pyhd8ed1ab_0 37 KB conda-forge\n", + " jarvis-tools-2024.5.10 | pyhd8ed1ab_0 3.8 MB conda-forge\n", " jinja2-3.1.4 | pyhd8ed1ab_0 109 KB conda-forge\n", " joblib-1.4.2 | pyhd8ed1ab_0 215 KB conda-forge\n", - " keras-3.1.0 | pyhd8ed1ab_0 768 KB conda-forge\n", + " keras-3.4.1 | pyhd8ed1ab_2 849 KB conda-forge\n", " kiwisolver-1.4.5 | py310hd41b1e2_1 71 KB conda-forge\n", " lame-3.100 | h166bdaf_1003 496 KB conda-forge\n", " lcms2-2.16 | hb7c19ff_0 239 KB conda-forge\n", " lerc-4.0.0 | h27087fc_0 275 KB conda-forge\n", - " libabseil-20240116.2 | cxx17_h59595ed_0 1.2 MB conda-forge\n", + " libabseil-20240116.2 | cxx17_he02047a_1 1.2 MB conda-forge\n", " libaec-1.1.3 | h59595ed_0 35 KB conda-forge\n", - " libblas-3.9.0 |22_linux64_openblas 14 KB conda-forge\n", + " libblas-3.9.0 |23_linux64_openblas 15 KB conda-forge\n", " libbrotlicommon-1.1.0 | hd590300_1 68 KB conda-forge\n", " libbrotlidec-1.1.0 | hd590300_1 32 KB conda-forge\n", " libbrotlienc-1.1.0 | hd590300_1 276 KB conda-forge\n", - " libcblas-3.9.0 |22_linux64_openblas 14 KB conda-forge\n", + " libcblas-3.9.0 |23_linux64_openblas 14 KB conda-forge\n", " libcublas-12.4.2.65 | 0 308.8 MB nvidia\n", " libcufft-11.2.0.44 | 0 190.5 MB nvidia\n", - " libcufile-1.10.0.4 | 0 1.0 MB nvidia\n", - " libcurand-10.3.6.39 | 0 51.8 MB nvidia\n", + " libcufile-1.10.1.7 | 0 1.0 MB nvidia\n", + " libcurand-10.3.6.82 | 0 51.8 MB nvidia\n", " libcurl-8.8.0 | hca28451_0 396 KB conda-forge\n", " libcusolver-11.6.0.99 | 0 114.3 MB nvidia\n", " libcusparse-12.3.0.142 | 0 179.6 MB nvidia\n", " libdeflate-1.20 | hd590300_0 70 KB conda-forge\n", - " libgfortran-ng-13.2.0 | h69a702a_7 24 KB conda-forge\n", - " libgfortran5-13.2.0 | hca663fb_7 1.4 MB conda-forge\n", + " libgfortran-ng-13.2.0 | h69a702a_13 47 KB conda-forge\n", + " libgfortran5-13.2.0 | h3d2ce59_13 1.4 MB conda-forge\n", " libgrpc-1.62.2 | h15f2491_0 7.0 MB conda-forge\n", " libhwloc-2.9.3 |default_h554bfaf_1009 2.5 MB conda-forge\n", " libjpeg-turbo-3.0.0 | hd590300_1 604 KB conda-forge\n", - " liblapack-3.9.0 |22_linux64_openblas 14 KB conda-forge\n", - " liblapacke-3.9.0 |22_linux64_openblas 14 KB conda-forge\n", + " liblapack-3.9.0 |23_linux64_openblas 14 KB conda-forge\n", + " liblapacke-3.9.0 |23_linux64_openblas 14 KB conda-forge\n", " libmagma-2.7.2 | h173bb3b_2 229.9 MB conda-forge\n", " libmagma_sparse-2.7.2 | h173bb3b_3 6.4 MB conda-forge\n", " libnpp-12.2.5.2 | 0 142.8 MB nvidia\n", - " libnvfatbin-12.5.39 | 0 887 KB nvidia\n", + " libnvfatbin-12.5.82 | 0 888 KB nvidia\n", " libnvjitlink-12.4.99 | 0 18.2 MB nvidia\n", " libnvjpeg-12.3.1.89 | 0 3.0 MB nvidia\n", - " libopenblas-0.3.27 |pthreads_h413a1c8_0 5.3 MB conda-forge\n", + " libopenblas-0.3.27 |pthreads_hac2b453_1 5.3 MB conda-forge\n", " libpng-1.6.43 | h2797004_0 281 KB conda-forge\n", " libprotobuf-4.25.3 | h08a7969_0 2.7 MB conda-forge\n", " libre2-11-2023.09.01 | h5a48ba9_2 227 KB conda-forge\n", - " libsqlite-3.45.3 | h2797004_0 840 KB conda-forge\n", + " libsqlite-3.46.0 | hde9e2c9_0 845 KB conda-forge\n", " libtiff-4.6.0 | h1dd3fc0_3 276 KB conda-forge\n", - " libtorch-2.3.0 |cuda120_h2b0da52_301 481.4 MB conda-forge\n", + " libtorch-2.3.1 |cuda120_h2b0da52_300 481.8 MB conda-forge\n", " liburing-2.6 | h297d8ca_0 105 KB conda-forge\n", " libuv-1.48.0 | hd590300_0 879 KB conda-forge\n", " libwebp-base-1.4.0 | hd590300_0 429 KB conda-forge\n", " libxcb-1.15 | h0b41bf4_0 375 KB conda-forge\n", - " llvm-openmp-18.1.6 | ha31de31_0 56.8 MB conda-forge\n", + " llvm-openmp-18.1.7 | ha31de31_0 55.9 MB conda-forge\n", " markdown-3.6 | pyhd8ed1ab_0 76 KB conda-forge\n", " markdown-it-py-3.0.0 | pyhd8ed1ab_0 63 KB conda-forge\n", " markupsafe-2.1.5 | py310h2372a71_0 24 KB conda-forge\n", - " matplotlib-base-3.8.4 | py310hef631a5_2 6.5 MB conda-forge\n", - " mccabe-0.7.0 | pyhd8ed1ab_0 11 KB conda-forge\n", + " matplotlib-base-3.9.1 | py310h0b1de36_0 6.7 MB conda-forge\n", " mdurl-0.1.2 | pyhd8ed1ab_0 14 KB conda-forge\n", " mergedeep-1.3.4 | pyhd8ed1ab_0 9 KB conda-forge\n", " metis-5.1.1 | h59595ed_2 3.7 MB conda-forge\n", " mkdocs-1.6.0 | pyhd8ed1ab_0 3.4 MB conda-forge\n", " mkdocs-get-deps-0.2.0 | pyhd8ed1ab_0 14 KB conda-forge\n", - " mkdocs-material-9.5.24 | pyhd8ed1ab_0 4.8 MB conda-forge\n", + " mkdocs-material-9.5.30 | pyhd8ed1ab_0 4.8 MB conda-forge\n", " mkdocs-material-extensions-1.3.1| pyhd8ed1ab_0 16 KB conda-forge\n", " mkl-2023.2.0 | h84fe81f_50496 156.8 MB conda-forge\n", " ml_dtypes-0.3.2 | py310hcc13569_0 160 KB conda-forge\n", + " more-itertools-10.3.0 | pyhd8ed1ab_0 55 KB conda-forge\n", " mpc-1.3.1 | hfe3b2da_0 114 KB conda-forge\n", - " mpfr-4.2.1 | h9458935_1 628 KB conda-forge\n", + " mpfr-4.2.1 | h38ae2d0_2 626 KB conda-forge\n", " mpmath-1.3.0 | pyhd8ed1ab_0 428 KB conda-forge\n", " munkres-1.1.4 | pyh9f0ad1d_0 12 KB conda-forge\n", " namex-0.0.8 | pyhd8ed1ab_0 11 KB conda-forge\n", - " nccl-2.21.5.1 | h3a97aeb_0 106.6 MB conda-forge\n", + " nccl-2.22.3.1 | hbc370b7_1 107.2 MB conda-forge\n", " nettle-3.6 | he412f7d_0 6.5 MB conda-forge\n", " networkx-3.3 | pyhd8ed1ab_1 1.1 MB conda-forge\n", " numpy-1.26.4 | py310hb13e2d6_0 6.7 MB conda-forge\n", " openh264-2.1.1 | h780b84a_0 1.5 MB conda-forge\n", " openjpeg-2.5.2 | h488ebb8_0 334 KB conda-forge\n", - " openssl-3.3.0 | h4ab18f5_3 2.8 MB conda-forge\n", + " openssl-3.3.1 | h4bc722e_2 2.8 MB conda-forge\n", " opt_einsum-3.3.0 | pyhc1e730c_2 57 KB conda-forge\n", + " optree-0.12.1 | py310haa687a3_0 293 KB conda-forge\n", " paginate-0.5.6 | pyhd8ed1ab_0 18 KB conda-forge\n", " pandas-2.2.2 | py310hf9f9076_1 12.4 MB conda-forge\n", " pathspec-0.12.1 | pyhd8ed1ab_0 40 KB conda-forge\n", " pillow-10.3.0 | py310hf73ecf8_0 39.8 MB conda-forge\n", " protobuf-4.25.3 | py310ha8c1f0e_0 325 KB conda-forge\n", - " psutil-5.9.8 | py310h2372a71_0 360 KB conda-forge\n", + " psutil-6.0.0 | py310hc51659f_0 363 KB conda-forge\n", " pthread-stubs-0.4 | h36c2ea0_1001 5 KB conda-forge\n", - " pycodestyle-2.11.1 | pyhd8ed1ab_0 34 KB conda-forge\n", - " pydantic-2.7.2 | pyhd8ed1ab_0 276 KB conda-forge\n", - " pydantic-core-2.18.3 | py310he421c4c_0 1.5 MB conda-forge\n", - " pydantic-settings-2.2.1 | pyhd8ed1ab_0 18 KB conda-forge\n", - " pydocstyle-6.3.0 | pyhd8ed1ab_0 39 KB conda-forge\n", - " pyflakes-3.2.0 | pyhd8ed1ab_0 57 KB conda-forge\n", + " pydantic-2.8.2 | pyhd8ed1ab_0 286 KB conda-forge\n", + " pydantic-core-2.20.1 | py310h42e942d_0 1.5 MB conda-forge\n", " pygments-2.18.0 | pyhd8ed1ab_0 859 KB conda-forge\n", - " pymdown-extensions-10.8.1 | pyhd8ed1ab_0 155 KB conda-forge\n", - " pyparsing-2.4.7 | pyhd8ed1ab_1 60 KB conda-forge\n", + " pymdown-extensions-10.9 | pyhd8ed1ab_0 154 KB conda-forge\n", + " pyparsing-3.1.2 | pyhd8ed1ab_0 87 KB conda-forge\n", " python-dateutil-2.9.0 | pyhd8ed1ab_0 218 KB conda-forge\n", - " python-dotenv-1.0.1 | pyhd8ed1ab_0 24 KB conda-forge\n", " python-flatbuffers-24.3.25 | pyh59ac667_0 34 KB conda-forge\n", " python-tzdata-2024.1 | pyhd8ed1ab_0 141 KB conda-forge\n", - " pytorch-2.3.0 |cuda120_py310h2c91c31_301 28.9 MB conda-forge\n", + " pytorch-2.3.1 |cuda120_py310h2c91c31_300 29.0 MB conda-forge\n", " pytorch-cuda-12.4 | hc786d27_6 7 KB pytorch\n", " pytorch-mutex-1.0 | cpu 3 KB pytorch\n", " pytz-2024.1 | pyhd8ed1ab_0 184 KB conda-forge\n", " pyyaml-6.0.1 | py310h2372a71_1 167 KB conda-forge\n", " pyyaml-env-tag-0.1 | pyhd8ed1ab_0 7 KB conda-forge\n", + " qhull-2020.2 | h434a139_5 540 KB conda-forge\n", " re2-2023.09.01 | h7f4b329_2 26 KB conda-forge\n", - " regex-2024.5.15 | py310hc51659f_0 340 KB conda-forge\n", + " regex-2024.7.24 | py310h5b4e0ec_0 341 KB conda-forge\n", " rich-13.7.1 | pyhd8ed1ab_0 180 KB conda-forge\n", - " scikit-learn-1.5.0 | py310h981052a_1 8.9 MB conda-forge\n", - " scipy-1.13.1 | py310h93e2701_0 15.7 MB conda-forge\n", + " scikit-learn-1.5.1 | py310h146d792_0 8.8 MB conda-forge\n", + " scipy-1.14.0 | py310h93e2701_1 16.0 MB conda-forge\n", " six-1.16.0 | pyh6c4a22f_0 14 KB conda-forge\n", - " sleef-3.5.1 | h9b69904_2 1.5 MB conda-forge\n", - " snappy-1.2.0 | hdb0a2a9_1 41 KB conda-forge\n", - " snowballstemmer-2.2.0 | pyhd8ed1ab_0 57 KB conda-forge\n", - " spglib-2.4.0 | py310h02519e0_1 631 KB conda-forge\n", - " sympy-1.12 | pypyh9d50eac_103 4.1 MB conda-forge\n", + " sleef-3.6.1 | h3400bea_1 975 KB conda-forge\n", + " snappy-1.2.1 | ha2e4443_0 41 KB conda-forge\n", + " spglib-2.5.0 | py310h355b990_1 635 KB conda-forge\n", + " sympy-1.13.0 | pypyh2585a3b_103 4.3 MB conda-forge\n", " tbb-2021.11.0 | h00ab1b0_1 191 KB conda-forge\n", " tensorboard-2.16.2 | pyhd8ed1ab_0 4.9 MB conda-forge\n", " tensorboard-data-server-0.7.0| py310h75e40e8_1 4.5 MB conda-forge\n", @@ -437,13 +428,13 @@ " tensorflow-estimator-2.16.1|cuda120py310h1fd330c_0 539 KB conda-forge\n", " termcolor-2.4.0 | pyhd8ed1ab_0 12 KB conda-forge\n", " threadpoolctl-3.5.0 | pyhc1e730c_0 23 KB conda-forge\n", - " tomli-2.0.1 | pyhd8ed1ab_0 16 KB conda-forge\n", " toolz-0.12.1 | pyhd8ed1ab_0 51 KB conda-forge\n", - " torchaudio-2.3.0 | py310_cpu 5.0 MB pytorch\n", + " torchaudio-2.3.1 | py310_cpu 5.0 MB pytorch\n", " torchdata-0.7.1 | py310 2.4 MB pytorch\n", - " torchvision-0.18.0 | py310_cpu 6.8 MB pytorch\n", - " typing-extensions-4.12.1 | hd8ed1ab_0 10 KB conda-forge\n", - " typing_extensions-4.12.1 | pyha770c72_0 39 KB conda-forge\n", + " torchvision-0.18.1 | py310_cpu 6.8 MB pytorch\n", + " typeguard-4.3.0 | pyhd8ed1ab_1 34 KB conda-forge\n", + " typing-extensions-4.12.2 | hd8ed1ab_0 10 KB conda-forge\n", + " typing_extensions-4.12.2 | pyha770c72_0 39 KB conda-forge\n", " unicodedata2-15.1.0 | py310h2372a71_0 365 KB conda-forge\n", " watchdog-4.0.1 | py310hff52083_0 108 KB conda-forge\n", " werkzeug-3.0.3 | pyhd8ed1ab_0 237 KB conda-forge\n", @@ -452,9 +443,9 @@ " xorg-libxau-1.0.11 | hd590300_0 14 KB conda-forge\n", " xorg-libxdmcp-1.1.3 | h7f98852_0 19 KB conda-forge\n", " yaml-0.2.5 | h7f98852_2 87 KB conda-forge\n", - " zipp-3.17.0 | pyhd8ed1ab_0 19 KB conda-forge\n", + " zipp-3.19.2 | pyhd8ed1ab_0 20 KB conda-forge\n", " zlib-1.2.13 | hd590300_5 91 KB conda-forge\n", - " zstandard-0.19.0 | py310h5764c6d_0 655 KB conda-forge\n", + " zstandard-0.23.0 | py310h64cae3c_0 398 KB conda-forge\n", " zstd-1.5.6 | ha6fb4c9_0 542 KB conda-forge\n", " ------------------------------------------------------------\n", " Total: 3.19 GB\n", @@ -462,164 +453,154 @@ "The following NEW packages will be INSTALLED:\n", "\n", " absl-py conda-forge/noarch::absl-py-2.1.0-pyhd8ed1ab_0 \n", - " alignn conda-forge/noarch::alignn-2024.4.10-pyhd8ed1ab_0 \n", " annotated-types conda-forge/noarch::annotated-types-0.7.0-pyhd8ed1ab_0 \n", - " ase conda-forge/noarch::ase-3.23.0-pyhd8ed1ab_0 \n", " astunparse conda-forge/noarch::astunparse-1.6.3-pyhd8ed1ab_0 \n", " babel conda-forge/noarch::babel-2.14.0-pyhd8ed1ab_0 \n", - " blinker conda-forge/noarch::blinker-1.8.2-pyhd8ed1ab_0 \n", " brotli conda-forge/linux-64::brotli-1.1.0-hd590300_1 \n", " brotli-bin conda-forge/linux-64::brotli-bin-1.1.0-hd590300_1 \n", " cached-property conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1 \n", " cached_property conda-forge/noarch::cached_property-1.5.2-pyha770c72_1 \n", " click conda-forge/noarch::click-8.1.7-unix_pyh707e725_0 \n", " contourpy conda-forge/linux-64::contourpy-1.2.1-py310hd41b1e2_0 \n", - " cuda-crt-tools nvidia/linux-64::cuda-crt-tools-12.5.40-0 \n", + " cuda-crt-tools nvidia/linux-64::cuda-crt-tools-12.5.82-0 \n", " cuda-cudart nvidia/linux-64::cuda-cudart-12.4.127-0 \n", " cuda-cupti nvidia/linux-64::cuda-cupti-12.4.127-0 \n", " cuda-libraries nvidia/linux-64::cuda-libraries-12.4.0-0 \n", - " cuda-nvcc-tools nvidia/linux-64::cuda-nvcc-tools-12.5.40-0 \n", + " cuda-nvcc-tools nvidia/linux-64::cuda-nvcc-tools-12.5.82-0 \n", " cuda-nvrtc nvidia/linux-64::cuda-nvrtc-12.4.127-0 \n", " cuda-nvtx nvidia/linux-64::cuda-nvtx-12.4.127-0 \n", - " cuda-nvvm-tools nvidia/linux-64::cuda-nvvm-tools-12.5.40-0 \n", + " cuda-nvvm-tools nvidia/linux-64::cuda-nvvm-tools-12.5.82-0 \n", " cuda-opencl nvidia/linux-64::cuda-opencl-12.5.39-0 \n", " cuda-runtime nvidia/linux-64::cuda-runtime-12.4.0-0 \n", " cuda-version nvidia/noarch::cuda-version-12.5-3 \n", " cudnn conda-forge/linux-64::cudnn-8.9.7.29-h092f7fd_3 \n", " cycler conda-forge/noarch::cycler-0.12.1-pyhd8ed1ab_0 \n", - " dgl conda-forge/linux-64::dgl-2.1.0-cuda120py310h00d5dd0_0 \n", - " dm-tree conda-forge/linux-64::dm-tree-0.1.8-py310ha8c1f0e_4 \n", + " dgl conda-forge/linux-64::dgl-2.1.0-cuda120py310h00d5dd0_2 \n", " ffmpeg pytorch/linux-64::ffmpeg-4.3-hf484d3e_0 \n", - " filelock conda-forge/noarch::filelock-3.14.0-pyhd8ed1ab_0 \n", - " flake8 conda-forge/noarch::flake8-7.0.0-pyhd8ed1ab_0 \n", - " flask conda-forge/noarch::flask-3.0.3-pyhd8ed1ab_0 \n", + " filelock conda-forge/noarch::filelock-3.15.4-pyhd8ed1ab_0 \n", " flatbuffers conda-forge/linux-64::flatbuffers-24.3.25-h59595ed_0 \n", - " fonttools conda-forge/linux-64::fonttools-4.53.0-py310hc51659f_0 \n", + " fonttools conda-forge/linux-64::fonttools-4.53.1-py310h5b4e0ec_0 \n", " freetype conda-forge/linux-64::freetype-2.12.1-h267a509_2 \n", - " fsspec conda-forge/noarch::fsspec-2024.5.0-pyhff2d567_0 \n", - " gast conda-forge/noarch::gast-0.5.4-pyhd8ed1ab_0 \n", + " fsspec conda-forge/noarch::fsspec-2024.6.1-pyhff2d567_0 \n", + " gast conda-forge/noarch::gast-0.5.5-pyhd8ed1ab_0 \n", " ghp-import conda-forge/noarch::ghp-import-2.1.0-pyhd8ed1ab_0 \n", " giflib conda-forge/linux-64::giflib-5.2.2-hd590300_0 \n", - " gmp conda-forge/linux-64::gmp-6.3.0-h59595ed_1 \n", + " gmp conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n", " gmpy2 conda-forge/linux-64::gmpy2-2.1.5-py310hc7909c9_1 \n", " gnutls conda-forge/linux-64::gnutls-3.6.13-h85f3911_1 \n", " google-pasta conda-forge/noarch::google-pasta-0.2.0-pyh8c360ce_0 \n", " grpcio conda-forge/linux-64::grpcio-1.62.2-py310h1b8f574_0 \n", - " h5py conda-forge/linux-64::h5py-3.11.0-nompi_py310hf054cd7_101 \n", - " hdf5 conda-forge/linux-64::hdf5-1.14.3-nompi_hdf9ad27_102 \n", - " importlib-metadata conda-forge/noarch::importlib-metadata-7.1.0-pyha770c72_0 \n", + " h5py conda-forge/linux-64::h5py-3.11.0-nompi_py310hf054cd7_102 \n", + " hdf5 conda-forge/linux-64::hdf5-1.14.3-nompi_hdf9ad27_105 \n", + " importlib-metadata conda-forge/noarch::importlib-metadata-8.2.0-pyha770c72_0 \n", " importlib-resourc~ conda-forge/noarch::importlib-resources-6.4.0-pyhd8ed1ab_0 \n", " importlib_resourc~ conda-forge/noarch::importlib_resources-6.4.0-pyhd8ed1ab_0 \n", - " itsdangerous conda-forge/noarch::itsdangerous-2.2.0-pyhd8ed1ab_0 \n", - " jarvis-tools conda-forge/noarch::jarvis-tools-2024.4.20-pyhd8ed1ab_0 \n", + " inflect conda-forge/noarch::inflect-7.3.1-pyhd8ed1ab_0 \n", + " jarvis-tools conda-forge/noarch::jarvis-tools-2024.5.10-pyhd8ed1ab_0 \n", " jinja2 conda-forge/noarch::jinja2-3.1.4-pyhd8ed1ab_0 \n", " joblib conda-forge/noarch::joblib-1.4.2-pyhd8ed1ab_0 \n", - " keras conda-forge/noarch::keras-3.1.0-pyhd8ed1ab_0 \n", + " keras conda-forge/noarch::keras-3.4.1-pyhd8ed1ab_2 \n", " kiwisolver conda-forge/linux-64::kiwisolver-1.4.5-py310hd41b1e2_1 \n", " lame conda-forge/linux-64::lame-3.100-h166bdaf_1003 \n", " lcms2 conda-forge/linux-64::lcms2-2.16-hb7c19ff_0 \n", " lerc conda-forge/linux-64::lerc-4.0.0-h27087fc_0 \n", - " libabseil conda-forge/linux-64::libabseil-20240116.2-cxx17_h59595ed_0 \n", + " libabseil conda-forge/linux-64::libabseil-20240116.2-cxx17_he02047a_1 \n", " libaec conda-forge/linux-64::libaec-1.1.3-h59595ed_0 \n", - " libblas conda-forge/linux-64::libblas-3.9.0-22_linux64_openblas \n", + " libblas conda-forge/linux-64::libblas-3.9.0-23_linux64_openblas \n", " libbrotlicommon conda-forge/linux-64::libbrotlicommon-1.1.0-hd590300_1 \n", " libbrotlidec conda-forge/linux-64::libbrotlidec-1.1.0-hd590300_1 \n", " libbrotlienc conda-forge/linux-64::libbrotlienc-1.1.0-hd590300_1 \n", - " libcblas conda-forge/linux-64::libcblas-3.9.0-22_linux64_openblas \n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-23_linux64_openblas \n", " libcublas nvidia/linux-64::libcublas-12.4.2.65-0 \n", " libcufft nvidia/linux-64::libcufft-11.2.0.44-0 \n", - " libcufile nvidia/linux-64::libcufile-1.10.0.4-0 \n", - " libcurand nvidia/linux-64::libcurand-10.3.6.39-0 \n", + " libcufile nvidia/linux-64::libcufile-1.10.1.7-0 \n", + " libcurand nvidia/linux-64::libcurand-10.3.6.82-0 \n", " libcusolver nvidia/linux-64::libcusolver-11.6.0.99-0 \n", " libcusparse nvidia/linux-64::libcusparse-12.3.0.142-0 \n", " libdeflate conda-forge/linux-64::libdeflate-1.20-hd590300_0 \n", - " libgfortran-ng conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_7 \n", - " libgfortran5 conda-forge/linux-64::libgfortran5-13.2.0-hca663fb_7 \n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_13 \n", + " libgfortran5 conda-forge/linux-64::libgfortran5-13.2.0-h3d2ce59_13 \n", " libgrpc conda-forge/linux-64::libgrpc-1.62.2-h15f2491_0 \n", " libhwloc conda-forge/linux-64::libhwloc-2.9.3-default_h554bfaf_1009 \n", " libjpeg-turbo conda-forge/linux-64::libjpeg-turbo-3.0.0-hd590300_1 \n", - " liblapack conda-forge/linux-64::liblapack-3.9.0-22_linux64_openblas \n", - " liblapacke conda-forge/linux-64::liblapacke-3.9.0-22_linux64_openblas \n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-23_linux64_openblas \n", + " liblapacke conda-forge/linux-64::liblapacke-3.9.0-23_linux64_openblas \n", " libmagma conda-forge/linux-64::libmagma-2.7.2-h173bb3b_2 \n", " libmagma_sparse conda-forge/linux-64::libmagma_sparse-2.7.2-h173bb3b_3 \n", " libnpp nvidia/linux-64::libnpp-12.2.5.2-0 \n", - " libnvfatbin nvidia/linux-64::libnvfatbin-12.5.39-0 \n", + " libnvfatbin nvidia/linux-64::libnvfatbin-12.5.82-0 \n", " libnvjitlink nvidia/linux-64::libnvjitlink-12.4.99-0 \n", " libnvjpeg nvidia/linux-64::libnvjpeg-12.3.1.89-0 \n", - " libopenblas conda-forge/linux-64::libopenblas-0.3.27-pthreads_h413a1c8_0 \n", + " libopenblas conda-forge/linux-64::libopenblas-0.3.27-pthreads_hac2b453_1 \n", " libpng conda-forge/linux-64::libpng-1.6.43-h2797004_0 \n", " libprotobuf conda-forge/linux-64::libprotobuf-4.25.3-h08a7969_0 \n", " libre2-11 conda-forge/linux-64::libre2-11-2023.09.01-h5a48ba9_2 \n", " libtiff conda-forge/linux-64::libtiff-4.6.0-h1dd3fc0_3 \n", - " libtorch conda-forge/linux-64::libtorch-2.3.0-cuda120_h2b0da52_301 \n", + " libtorch conda-forge/linux-64::libtorch-2.3.1-cuda120_h2b0da52_300 \n", " liburing conda-forge/linux-64::liburing-2.6-h297d8ca_0 \n", " libuv conda-forge/linux-64::libuv-1.48.0-hd590300_0 \n", " libwebp-base conda-forge/linux-64::libwebp-base-1.4.0-hd590300_0 \n", " libxcb conda-forge/linux-64::libxcb-1.15-h0b41bf4_0 \n", - " llvm-openmp conda-forge/linux-64::llvm-openmp-18.1.6-ha31de31_0 \n", + " llvm-openmp conda-forge/linux-64::llvm-openmp-18.1.7-ha31de31_0 \n", " markdown conda-forge/noarch::markdown-3.6-pyhd8ed1ab_0 \n", " markdown-it-py conda-forge/noarch::markdown-it-py-3.0.0-pyhd8ed1ab_0 \n", " markupsafe conda-forge/linux-64::markupsafe-2.1.5-py310h2372a71_0 \n", - " matplotlib-base conda-forge/linux-64::matplotlib-base-3.8.4-py310hef631a5_2 \n", - " mccabe conda-forge/noarch::mccabe-0.7.0-pyhd8ed1ab_0 \n", + " matplotlib-base conda-forge/linux-64::matplotlib-base-3.9.1-py310h0b1de36_0 \n", " mdurl conda-forge/noarch::mdurl-0.1.2-pyhd8ed1ab_0 \n", " mergedeep conda-forge/noarch::mergedeep-1.3.4-pyhd8ed1ab_0 \n", " metis conda-forge/linux-64::metis-5.1.1-h59595ed_2 \n", " mkdocs conda-forge/noarch::mkdocs-1.6.0-pyhd8ed1ab_0 \n", " mkdocs-get-deps conda-forge/noarch::mkdocs-get-deps-0.2.0-pyhd8ed1ab_0 \n", - " mkdocs-material conda-forge/noarch::mkdocs-material-9.5.24-pyhd8ed1ab_0 \n", + " mkdocs-material conda-forge/noarch::mkdocs-material-9.5.30-pyhd8ed1ab_0 \n", " mkdocs-material-e~ conda-forge/noarch::mkdocs-material-extensions-1.3.1-pyhd8ed1ab_0 \n", " mkl conda-forge/linux-64::mkl-2023.2.0-h84fe81f_50496 \n", " ml_dtypes conda-forge/linux-64::ml_dtypes-0.3.2-py310hcc13569_0 \n", + " more-itertools conda-forge/noarch::more-itertools-10.3.0-pyhd8ed1ab_0 \n", " mpc conda-forge/linux-64::mpc-1.3.1-hfe3b2da_0 \n", - " mpfr conda-forge/linux-64::mpfr-4.2.1-h9458935_1 \n", + " mpfr conda-forge/linux-64::mpfr-4.2.1-h38ae2d0_2 \n", " mpmath conda-forge/noarch::mpmath-1.3.0-pyhd8ed1ab_0 \n", " munkres conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0 \n", " namex conda-forge/noarch::namex-0.0.8-pyhd8ed1ab_0 \n", - " nccl conda-forge/linux-64::nccl-2.21.5.1-h3a97aeb_0 \n", + " nccl conda-forge/linux-64::nccl-2.22.3.1-hbc370b7_1 \n", " nettle conda-forge/linux-64::nettle-3.6-he412f7d_0 \n", " networkx conda-forge/noarch::networkx-3.3-pyhd8ed1ab_1 \n", " numpy conda-forge/linux-64::numpy-1.26.4-py310hb13e2d6_0 \n", " openh264 conda-forge/linux-64::openh264-2.1.1-h780b84a_0 \n", " openjpeg conda-forge/linux-64::openjpeg-2.5.2-h488ebb8_0 \n", " opt_einsum conda-forge/noarch::opt_einsum-3.3.0-pyhc1e730c_2 \n", + " optree conda-forge/linux-64::optree-0.12.1-py310haa687a3_0 \n", " paginate conda-forge/noarch::paginate-0.5.6-pyhd8ed1ab_0 \n", " pandas conda-forge/linux-64::pandas-2.2.2-py310hf9f9076_1 \n", " pathspec conda-forge/noarch::pathspec-0.12.1-pyhd8ed1ab_0 \n", " pillow conda-forge/linux-64::pillow-10.3.0-py310hf73ecf8_0 \n", " protobuf conda-forge/linux-64::protobuf-4.25.3-py310ha8c1f0e_0 \n", - " psutil conda-forge/linux-64::psutil-5.9.8-py310h2372a71_0 \n", + " psutil conda-forge/linux-64::psutil-6.0.0-py310hc51659f_0 \n", " pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001 \n", - " pycodestyle conda-forge/noarch::pycodestyle-2.11.1-pyhd8ed1ab_0 \n", - " pydantic conda-forge/noarch::pydantic-2.7.2-pyhd8ed1ab_0 \n", - " pydantic-core conda-forge/linux-64::pydantic-core-2.18.3-py310he421c4c_0 \n", - " pydantic-settings conda-forge/noarch::pydantic-settings-2.2.1-pyhd8ed1ab_0 \n", - " pydocstyle conda-forge/noarch::pydocstyle-6.3.0-pyhd8ed1ab_0 \n", - " pyflakes conda-forge/noarch::pyflakes-3.2.0-pyhd8ed1ab_0 \n", + " pydantic conda-forge/noarch::pydantic-2.8.2-pyhd8ed1ab_0 \n", + " pydantic-core conda-forge/linux-64::pydantic-core-2.20.1-py310h42e942d_0 \n", " pygments conda-forge/noarch::pygments-2.18.0-pyhd8ed1ab_0 \n", - " pymdown-extensions conda-forge/noarch::pymdown-extensions-10.8.1-pyhd8ed1ab_0 \n", - " pyparsing conda-forge/noarch::pyparsing-2.4.7-pyhd8ed1ab_1 \n", + " pymdown-extensions conda-forge/noarch::pymdown-extensions-10.9-pyhd8ed1ab_0 \n", + " pyparsing conda-forge/noarch::pyparsing-3.1.2-pyhd8ed1ab_0 \n", " python-dateutil conda-forge/noarch::python-dateutil-2.9.0-pyhd8ed1ab_0 \n", - " python-dotenv conda-forge/noarch::python-dotenv-1.0.1-pyhd8ed1ab_0 \n", " python-flatbuffers conda-forge/noarch::python-flatbuffers-24.3.25-pyh59ac667_0 \n", " python-tzdata conda-forge/noarch::python-tzdata-2024.1-pyhd8ed1ab_0 \n", - " pytorch conda-forge/linux-64::pytorch-2.3.0-cuda120_py310h2c91c31_301 \n", + " pytorch conda-forge/linux-64::pytorch-2.3.1-cuda120_py310h2c91c31_300 \n", " pytorch-cuda pytorch/linux-64::pytorch-cuda-12.4-hc786d27_6 \n", " pytorch-mutex pytorch/noarch::pytorch-mutex-1.0-cpu \n", " pytz conda-forge/noarch::pytz-2024.1-pyhd8ed1ab_0 \n", " pyyaml conda-forge/linux-64::pyyaml-6.0.1-py310h2372a71_1 \n", " pyyaml-env-tag conda-forge/noarch::pyyaml-env-tag-0.1-pyhd8ed1ab_0 \n", + " qhull conda-forge/linux-64::qhull-2020.2-h434a139_5 \n", " re2 conda-forge/linux-64::re2-2023.09.01-h7f4b329_2 \n", - " regex conda-forge/linux-64::regex-2024.5.15-py310hc51659f_0 \n", + " regex conda-forge/linux-64::regex-2024.7.24-py310h5b4e0ec_0 \n", " rich conda-forge/noarch::rich-13.7.1-pyhd8ed1ab_0 \n", - " scikit-learn conda-forge/linux-64::scikit-learn-1.5.0-py310h981052a_1 \n", - " scipy conda-forge/linux-64::scipy-1.13.1-py310h93e2701_0 \n", + " scikit-learn conda-forge/linux-64::scikit-learn-1.5.1-py310h146d792_0 \n", + " scipy conda-forge/linux-64::scipy-1.14.0-py310h93e2701_1 \n", " six conda-forge/noarch::six-1.16.0-pyh6c4a22f_0 \n", - " sleef conda-forge/linux-64::sleef-3.5.1-h9b69904_2 \n", - " snappy conda-forge/linux-64::snappy-1.2.0-hdb0a2a9_1 \n", - " snowballstemmer conda-forge/noarch::snowballstemmer-2.2.0-pyhd8ed1ab_0 \n", - " spglib conda-forge/linux-64::spglib-2.4.0-py310h02519e0_1 \n", - " sympy conda-forge/noarch::sympy-1.12-pypyh9d50eac_103 \n", + " sleef conda-forge/linux-64::sleef-3.6.1-h3400bea_1 \n", + " snappy conda-forge/linux-64::snappy-1.2.1-ha2e4443_0 \n", + " spglib conda-forge/linux-64::spglib-2.5.0-py310h355b990_1 \n", + " sympy conda-forge/noarch::sympy-1.13.0-pypyh2585a3b_103 \n", " tbb conda-forge/linux-64::tbb-2021.11.0-h00ab1b0_1 \n", " tensorboard conda-forge/noarch::tensorboard-2.16.2-pyhd8ed1ab_0 \n", " tensorboard-data-~ conda-forge/linux-64::tensorboard-data-server-0.7.0-py310h75e40e8_1 \n", @@ -628,13 +609,13 @@ " tensorflow-estima~ conda-forge/linux-64::tensorflow-estimator-2.16.1-cuda120py310h1fd330c_0 \n", " termcolor conda-forge/noarch::termcolor-2.4.0-pyhd8ed1ab_0 \n", " threadpoolctl conda-forge/noarch::threadpoolctl-3.5.0-pyhc1e730c_0 \n", - " tomli conda-forge/noarch::tomli-2.0.1-pyhd8ed1ab_0 \n", " toolz conda-forge/noarch::toolz-0.12.1-pyhd8ed1ab_0 \n", - " torchaudio pytorch/linux-64::torchaudio-2.3.0-py310_cpu \n", + " torchaudio pytorch/linux-64::torchaudio-2.3.1-py310_cpu \n", " torchdata pytorch/linux-64::torchdata-0.7.1-py310 \n", - " torchvision pytorch/linux-64::torchvision-0.18.0-py310_cpu \n", - " typing-extensions conda-forge/noarch::typing-extensions-4.12.1-hd8ed1ab_0 \n", - " typing_extensions conda-forge/noarch::typing_extensions-4.12.1-pyha770c72_0 \n", + " torchvision pytorch/linux-64::torchvision-0.18.1-py310_cpu \n", + " typeguard conda-forge/noarch::typeguard-4.3.0-pyhd8ed1ab_1 \n", + " typing-extensions conda-forge/noarch::typing-extensions-4.12.2-hd8ed1ab_0 \n", + " typing_extensions conda-forge/noarch::typing_extensions-4.12.2-pyha770c72_0 \n", " unicodedata2 conda-forge/linux-64::unicodedata2-15.1.0-py310h2372a71_0 \n", " watchdog conda-forge/linux-64::watchdog-4.0.1-py310hff52083_0 \n", " werkzeug conda-forge/noarch::werkzeug-3.0.3-pyhd8ed1ab_0 \n", @@ -643,23 +624,23 @@ " xorg-libxau conda-forge/linux-64::xorg-libxau-1.0.11-hd590300_0 \n", " xorg-libxdmcp conda-forge/linux-64::xorg-libxdmcp-1.1.3-h7f98852_0 \n", " yaml conda-forge/linux-64::yaml-0.2.5-h7f98852_2 \n", - " zipp conda-forge/noarch::zipp-3.17.0-pyhd8ed1ab_0 \n", + " zipp conda-forge/noarch::zipp-3.19.2-pyhd8ed1ab_0 \n", " zlib conda-forge/linux-64::zlib-1.2.13-hd590300_5 \n", "\n", "The following packages will be UPDATED:\n", "\n", - " c-ares 1.24.0-hd590300_0 --> 1.28.1-hd590300_0 \n", - " ca-certificates 2023.11.17-hbcca054_0 --> 2024.2.2-hbcca054_0 \n", - " certifi 2023.11.17-pyhd8ed1ab_0 --> 2024.2.2-pyhd8ed1ab_0 \n", + " c-ares 1.24.0-hd590300_0 --> 1.32.3-h4bc722e_0 \n", + " ca-certificates 2023.11.17-hbcca054_0 --> 2024.7.4-hbcca054_0 \n", + " certifi 2023.11.17-pyhd8ed1ab_0 --> 2024.7.4-pyhd8ed1ab_0 \n", " libcurl 8.5.0-hca28451_0 --> 8.8.0-hca28451_0 \n", - " libsqlite 3.44.2-h2797004_0 --> 3.45.3-h2797004_0 \n", - " openssl 3.2.0-hd590300_1 --> 3.3.0-h4ab18f5_3 \n", + " libsqlite 3.44.2-h2797004_0 --> 3.46.0-hde9e2c9_0 \n", + " openssl 3.2.0-hd590300_1 --> 3.3.1-h4bc722e_2 \n", + " zstandard 0.22.0-py310h1275a96_0 --> 0.23.0-py310h64cae3c_0 \n", " zstd 1.5.5-hfc55251_0 --> 1.5.6-ha6fb4c9_0 \n", "\n", "The following packages will be DOWNGRADED:\n", "\n", " _openmp_mutex 4.5-2_gnu --> 4.5-2_kmp_llvm \n", - " zstandard 0.22.0-py310h1275a96_0 --> 0.19.0-py310h5764c6d_0 \n", "\n", "\n", "Preparing transaction: ...working... done\n", @@ -668,8 +649,8 @@ " https://docs.nvidia.com/deeplearning/cudnn/sla/index.html\n", "\n", "done\n", - "CPU times: user 2.23 s, sys: 317 ms, total: 2.55 s\n", - "Wall time: 6min 44s\n" + "CPU times: user 1.83 s, sys: 272 ms, total: 2.11 s\n", + "Wall time: 6min 18s\n" ] } ] @@ -694,9 +675,9 @@ "base_uri": "https://localhost:8080/" }, "id": "FPx_OI8CEgeM", - "outputId": "b45fb61f-ce70-49e3-dc14-8a146b76672a" + "outputId": "196db742-9041-4413-eac3-784a5505dba0" }, - "execution_count": null, + "execution_count": 5, "outputs": [ { "output_type": "execute_result", @@ -710,6 +691,38 @@ } ] }, + { + "cell_type": "code", + "source": [ + "!pip install -q git+https://github.com/usnistgov/alignn.git@develop" + ], + "metadata": { + "id": "XewJpdmR6Azq", + "outputId": "1d4f8a4b-05cd-4de9-913f-d4315ba5b566", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.6/57.6 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m56.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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"id": "OIfymESSDare" - } + "id": "HTRkvbaB_HGj" + }, + "execution_count": 45, + "outputs": [] }, { "cell_type": "code", "source": [ - "!wget https://figshare.com/ndownloader/files/46761919 -O mlearn_si_id_prop.json.zip\n" + "# !wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HTRkvbaB_HGj", - "outputId": "21639347-bcb5-470c-91ca-5d96ecb67604" + "id": "C7dK40z_xaEc" }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2024-06-02 03:27:23-- https://figshare.com/ndownloader/files/46761919\n", - "Resolving figshare.com (figshare.com)... 54.77.229.197, 54.194.85.175, 2a05:d018:1f4:d000:389f:c65f:6878:a50f, ...\n", - "Connecting to figshare.com (figshare.com)|54.77.229.197|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/46761919/mlearn_si_id_prop.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240602/eu-west-1/s3/aws4_request&X-Amz-Date=20240602T032724Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=627123ec56a76af5babf4d0a41691f630a724be75cdc545659deb5421ef1d1e3 [following]\n", - 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"!wget https://gist.githubusercontent.com/knc6/eb04b911cd5428bb2ac79b7622c0da26/raw/ffdcbbccc9488d536890a3a5ffd69313a2a458bd/config_mlearn_cu.json" + "!pwd" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "FoYHqz8av6YD", - "outputId": "9259ab52-ac5b-427e-d53b-9db5b7da2d5f" + "id": "Hw3dVfYRxorg", + "outputId": "ddfebdcf-d61e-4922-f6cd-fdbefcb8b9b7" }, - "execution_count": null, + "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "--2024-06-02 03:26:58-- https://gist.githubusercontent.com/knc6/eb04b911cd5428bb2ac79b7622c0da26/raw/ffdcbbccc9488d536890a3a5ffd69313a2a458bd/config_mlearn_cu.json\n", - "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.108.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2034 (2.0K) [text/plain]\n", - "Saving to: ‘config_mlearn_cu.json’\n", - "\n", - "\rconfig_mlearn_cu.js 0%[ ] 0 --.-KB/s \rconfig_mlearn_cu.js 100%[===================>] 1.99K --.-KB/s in 0s \n", - "\n", - "2024-06-02 03:26:58 (40.8 MB/s) - ‘config_mlearn_cu.json’ saved [2034/2034]\n", - "\n" + "/content\n" ] } ] @@ -837,111 +814,420 @@ { "cell_type": "code", "source": [ - "!wget https://figshare.com/ndownloader/files/40357663 -O mlearn.json.zip" + "import os\n", + "from jarvis.db.jsonutils import loadjson,dumpjson\n", + "!rm mlearn_si_id_prop.json\n", + "if not os.path.exists('DataDir'):\n", + " os.makedirs('DataDir')\n", + "!unzip mlearn_si_id_prop.json.zip\n", + "!mv mlearn_si_id_prop.json DataDir/id_prop.json\n", + "null=None\n", + "true=True\n", + "false=False\n", + "my_config ={\n", + " \"version\": \"112bbedebdaecf59fb18e11c929080fb2f358246\",\n", + " \"dataset\": \"user_data\",\n", + " \"target\": \"target\",\n", + " \"atom_features\": \"cgcnn\",\n", + " \"neighbor_strategy\": \"radius_graph\",\n", + " \"id_tag\": \"jid\",\n", + " \"random_seed\": 123,\n", + " \"classification_threshold\": null,\n", + " \"n_val\": 25,\n", + " \"n_test\": 25,\n", + " \"n_train\": 214,\n", + " \"train_ratio\": 0.8,\n", + " \"val_ratio\": 0.1,\n", + " \"test_ratio\": 0.1,\n", + " \"target_multiplication_factor\": null,\n", + " \"epochs\": 50,\n", + " \"batch_size\": 6,\n", + " \"weight_decay\": 1e-05,\n", + " \"learning_rate\": 0.001,\n", + " \"filename\": \"sample\",\n", + " \"warmup_steps\": 2000,\n", + " \"criterion\": \"l1\",\n", + " \"optimizer\": \"adamw\",\n", + " \"scheduler\": \"onecycle\",\n", + " \"pin_memory\": false,\n", + " \"save_dataloader\": false,\n", + " \"write_checkpoint\": true,\n", + " \"write_predictions\": true,\n", + " \"store_outputs\": false,\n", + " \"progress\": true,\n", + " \"log_tensorboard\": false,\n", + " \"standard_scalar_and_pca\": false,\n", + " \"use_canonize\": true,\n", + " \"num_workers\": 0,\n", + " \"cutoff\": 5.0,\n", + " \"cutoff_extra\": 3.0,\n", + " \"max_neighbors\": 12,\n", + " \"keep_data_order\": true,\n", + " \"normalize_graph_level_loss\": false,\n", + " \"distributed\": false,\n", + " \"data_parallel\": false,\n", + " \"n_early_stopping\": null,\n", + " \"output_dir\": \"OutputDir\",\n", + " \"use_lmdb\": true,\n", + " \"model\": {\n", + " \"name\": \"alignn_atomwise\",\n", + " \"alignn_layers\": 2,\n", + " \"gcn_layers\": 2,\n", + " \"atom_input_features\": 92,\n", + " \"edge_input_features\": 64,\n", + " \"triplet_input_features\": 40,\n", + " \"embedding_features\": 64,\n", + " \"hidden_features\": 100,\n", + " \"output_features\": 1,\n", + " \"grad_multiplier\": -1,\n", + " \"calculate_gradient\": true,\n", + " \"atomwise_output_features\": 0,\n", + " \"graphwise_weight\": 1.0,\n", + " \"gradwise_weight\": 1.0,\n", + " \"stresswise_weight\": 0.0,\n", + " \"atomwise_weight\": 0.0,\n", + " \"link\": \"identity\",\n", + " \"zero_inflated\": false,\n", + " \"classification\": false,\n", + " \"force_mult_natoms\": true,\n", + " \"energy_mult_natoms\": false,\n", + " \"include_pos_deriv\": false,\n", + " \"use_cutoff_function\": true,\n", + " \"inner_cutoff\": 4.0,\n", + " \"stress_multiplier\": 1.0,\n", + " \"add_reverse_forces\": true,\n", + " \"lg_on_fly\": true,\n", + " \"multiply_cutoff\": true,\n", + " \"batch_stress\": true,\n", + " \"extra_features\": 0\n", + " }\n", + "}\n", + "\n", + "config_name = \"config_Si.json\"\n", + "dumpjson(data=my_config, filename=config_name)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "C7dK40z_xaEc", - "outputId": "a67fe62a-587c-4c7a-ddfe-dec2edae7c1d" + "id": "INmA5IZP_S8P", + "outputId": "42d75a58-82d6-42de-9d14-6522cca161ff" }, - "execution_count": null, + "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "--2024-06-02 03:25:38-- https://figshare.com/ndownloader/files/40357663\n", - "Resolving figshare.com (figshare.com)... 54.194.85.175, 54.77.229.197, 2a05:d018:1f4:d003:c00e:f0da:162f:2cf0, ...\n", - "Connecting to figshare.com (figshare.com)|54.194.85.175|:443... connected.\n", - "HTTP request sent, awaiting response... 302 Found\n", - "Location: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240602/eu-west-1/s3/aws4_request&X-Amz-Date=20240602T032538Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=e1433120407147564cb0382e681f4d8cfc5b7cfce75b26f4eea2afdc8e1249c4 [following]\n", - "--2024-06-02 03:25:38-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240602/eu-west-1/s3/aws4_request&X-Amz-Date=20240602T032538Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=e1433120407147564cb0382e681f4d8cfc5b7cfce75b26f4eea2afdc8e1249c4\n", - "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.116.128, 52.218.112.179, 52.92.36.144, ...\n", - "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.116.128|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2542319 (2.4M) [application/zip]\n", - "Saving to: ‘mlearn.json.zip’\n", - "\n", - "mlearn.json.zip 100%[===================>] 2.42M 2.55MB/s in 1.0s \n", - "\n", - "2024-06-02 03:25:40 (2.55 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", - "\n" + "rm: cannot remove 'mlearn_si_id_prop.json': No such file or directory\n", + "Archive: mlearn_si_id_prop.json.zip\n", + " inflating: mlearn_si_id_prop.json \n" ] } ] }, { - "cell_type": "code", + "cell_type": "markdown", "source": [ - "!pwd" + "Check input data format\n", + "\n", + "The DataDir/id_prop.json contains arrays of dictionaries. Each dictionary has keys such as 'id', 'atoms', 'forces', 'total_energy'.\n", + "\n", + "\n", + "An example to convert a vasprun.xml to id_prop.json is available [here](https://gist.github.com/knc6/5513b21f5fd83a7943509ffdf5c3608b)." ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Hw3dVfYRxorg", - "outputId": "89dea34c-7fcd-4fd5-e61b-250338ef471f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content\n" - ] - } - ] + "id": "t3OfAW7QnHNL" + } }, { "cell_type": "code", "source": [ - "import os\n", - "!rm mlearn_si_id_prop.json\n", - "if not os.path.exists('DataDir'):\n", - " os.makedirs('DataDir')\n", - "!unzip mlearn_si_id_prop.json.zip\n", - "!mv mlearn_si_id_prop.json DataDir/id_prop.json\n", - "example_config = loadjson(\"config_mlearn_cu.json\")\n", - "example_config[\"n_train\"] = 214\n", - "example_config[\"n_val\"] = 25\n", - "example_config[\"n_test\"] = 25\n", - "example_config[\"model\"][\"graphwise_weight\"] = 1\n", - "example_config[\"model\"][\"gradwise_weight\"] = 1\n", - "example_config[\"model\"][\"add_reverse_forces\"] = True\n", - "example_config[\"model\"][\"lg_on_fly\"] = True\n", - "example_config[\"model\"][\"alignn_layers\"] = 2\n", - "example_config[\"model\"][\"gcn_layers\"] = 2\n", - "example_config[\"model\"][\"hidden_features\"] = 256\n", - "example_config[\"model\"][\"force_mult_natoms\"] = True\n", - "example_config[\"model\"][\"use_cutoff_function\"] = False\n", - "example_config[\"model\"][\"inner_cutoff\"] = 6.0\n", - "example_config[\"epochs\"] = 20\n", - "example_config[\"batch_size\"] = 2\n", - "example_config[\"keep_data_order\"] = True\n", - "example_config[\"cutoff\"] = 8\n", - "example_config[\"neighbor_strategy\"] = \"k-nearest\"\n", - "config_name = \"config_Si.json\"\n", - "dumpjson(data=example_config, filename=config_name)" + "from jarvis.db.jsonutils import loadjson\n", + "import pprint\n", + "\n", + "d=loadjson('DataDir/id_prop.json')\n", + "pprint.pprint(d[0])" ], "metadata": { + "id": "0LM6UJ8_nE8H", + "outputId": "f1962535-60fc-4fa0-da9a-08768f751ada", "colab": { "base_uri": "https://localhost:8080/" - }, - "id": "INmA5IZP_S8P", - "outputId": "4bff8137-0d6d-47b7-a71b-99d2e8616ebf" + } }, - "execution_count": null, + "execution_count": 49, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "rm: cannot remove 'mlearn_si_id_prop.json': No such file or directory\n", - "Archive: mlearn_si_id_prop.json.zip\n", - " inflating: mlearn_si_id_prop.json \n" + "{'atoms': {'abc': [21.80853, 9.060029, 9.17819],\n", + " 'angles': [88.2785, 44.4911, 73.8755],\n", + " 'cartesian': False,\n", + " 'coords': [[0.020746, 0.984718, 0.453005],\n", + " [0.908361, 0.247724, 0.794563],\n", + " [0.728738, 0.850089, 0.649934],\n", + " [0.934533, 0.499047, 0.702658],\n", + " [0.092827, 0.23138, 0.778303],\n", + " [0.525491, 0.38274, 0.194068],\n", + " [0.314131, 0.689179, 0.848327],\n", + " [0.444164, 0.631916, 0.257386],\n", + " [0.651362, 0.707748, 0.571055],\n", + " [0.643259, 0.968658, 0.56901],\n", + " [0.762767, 0.282327, 0.144146],\n", + " [0.773039, 0.607004, 0.128177],\n", + " [0.302608, 0.524909, 0.310581],\n", + " [0.986318, 0.267354, 0.370677],\n", + " [0.974725, 0.800436, 0.106763],\n", + " [0.82109, 0.619505, 0.384147],\n", + " [0.746892, 0.620727, 0.883831],\n", + " [0.512415, 0.565065, 0.483669],\n", + " [0.893938, 0.343383, 0.098733],\n", + " [0.245572, 0.817816, 0.654469],\n", + " [0.622644, 0.762612, 0.907581],\n", + " [0.548582, 0.837865, 0.448208],\n", + " [0.474197, 0.253646, 0.642306],\n", + " [0.682863, 0.177057, 0.845322],\n", + " [0.56068, 0.172551, 0.952121],\n", + " [0.274642, 0.466102, 0.872393],\n", + " [0.292187, 0.938846, 0.812262],\n", + " [0.194635, 0.769959, 0.292278],\n", + " [0.974913, 0.531134, 0.856504],\n", + " [0.895447, 0.02675, 0.981625],\n", + " [0.622975, 0.444542, 0.849403],\n", + " [0.167881, 0.558112, 0.248149],\n", + " [0.209872, 0.010947, 0.44777],\n", + " [0.51688, 0.862753, 0.252107],\n", + " [0.383198, 0.2379, 0.271552],\n", + " [0.839476, 0.783127, 0.174681],\n", + " [0.660546, 0.915304, 0.143391],\n", + " [0.417172, 0.532952, 0.918529],\n", + " [0.460447, 0.116548, 0.902824],\n", + " [0.609304, 0.504941, 0.618133],\n", + " [0.355712, 0.139995, 0.821857],\n", + " [0.068793, 0.925154, 0.788588],\n", + " [0.451776, 0.897973, 0.801827],\n", + " [0.350596, 0.940539, 0.324326],\n", + " [0.709195, 0.209614, 0.46081],\n", + " [0.96018, 0.817897, 0.389205],\n", + " [0.655024, 0.109352, 0.333894],\n", + " [0.255303, 0.246734, 0.785123],\n", + " [0.299889, 0.955922, 0.05784],\n", + " [0.110479, 0.299895, 0.429446],\n", + " [0.97658, 0.774621, 0.674218],\n", + " [0.012892, 0.497542, 0.085264],\n", + " [0.479086, 0.685144, 0.939817],\n", + " [0.148681, 0.149197, 0.129429],\n", + " [0.234063, 0.245271, 0.435057],\n", + " [0.45833, 0.164479, 0.377699],\n", + " [0.828868, 0.247709, 0.436974],\n", + " [0.674376, 0.398923, 0.110835],\n", + " [0.185354, 0.642151, 0.818977],\n", + " [0.779212, 0.008419, 0.86676],\n", + " [0.100213, 0.54653, 0.122474],\n", + " [0.123186, 0.613356, 0.67566],\n", + " [0.113886, 0.89902, 0.12885]],\n", + " 'elements': ['Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si',\n", + " 'Si'],\n", + " 'lattice_mat': [[14.22366, 5.591921, 15.557314],\n", + " [0.0, 9.055935, 0.272179],\n", + " [0.0, 0.0, 9.178194]],\n", + " 'props': ['',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '',\n", + " '']},\n", + " 'forces': [[-2.98187262, 0.6069187, 3.48143607],\n", + " [1.02961593, -0.13696487, -0.28212521],\n", + " [1.291237, 2.50367684, 0.18027279],\n", + " [-0.50029697, -0.68283674, -2.67411757],\n", + " [-1.24770628, 0.54526764, 0.45055166],\n", + " [0.2586643, -1.208417, 1.41784669],\n", + " [1.01186331, -0.97490254, 1.38438155],\n", + " [-1.1932141, 1.55031482, -0.29724222],\n", + " [0.97073269, 0.93589369, 0.00586275],\n", + " [-0.19438338, 0.74741519, 0.38356837],\n", + " [0.80306051, -0.49752069, 1.79911729],\n", + " [-0.20877409, 0.21183732, 0.00160493],\n", + " [0.11246361, 0.11640061, 0.02147237],\n", + " [-1.01784245, -0.0391863, -0.19704588],\n", + " [-0.11236379, -0.00469982, -0.5763679],\n", + " [0.98277019, -1.71292947, 0.58267823],\n", + " [-0.02592928, -0.60446721, 0.72929798],\n", + " [0.12309702, 0.12520565, 0.12171308],\n", + " [0.06978948, -0.11812041, -0.04318403],\n", + " [1.15896022, 2.46350168, -0.65856102],\n", + " [-0.21523972, -2.27564512, -0.54804569],\n", + " [0.55480388, -1.38851051, -0.21510671],\n", + " [0.16301783, 0.92802634, 0.41519423],\n", + " [3.40640289, 0.75013542, 1.37223979],\n", + " [-2.46951666, -1.27128002, -1.82875726],\n", + " [0.77515538, -0.42418828, 0.05829842],\n", + " [-0.38288402, 0.80084692, -1.65096026],\n", + " [0.15547306, 1.03537039, 0.40436972],\n", + " [0.64537139, 0.75504105, 2.27144577],\n", + " [0.8122793, -0.75640986, 0.00592284],\n", + " [0.43873918, 1.09364896, 0.07374309],\n", + " [-0.72078846, -1.95767078, 0.18560572],\n", + " [-0.95478533, -1.58579621, -1.21929822],\n", + " [-2.27851165, 0.5208657, 0.65477905],\n", + " [-0.1824737, 0.15047487, -0.79077671],\n", + " [0.5567055, 1.03287679, -1.35382302],\n", + " [0.49422893, -0.6552154, -0.45555184],\n", + " [-1.03156008, -0.96127684, -1.2571219],\n", + " [0.45951877, 1.02520437, 0.45241934],\n", + " [0.40851083, -3.08146549, -1.39598478],\n", + " [-0.06740214, 0.57674294, -0.40837277],\n", + " [0.14704764, -0.28500871, 0.09342045],\n", + " [1.35703739, -0.52225135, 1.41066265],\n", + " [1.27441627, -0.00049754, 0.67858435],\n", + " [0.07520009, 0.66578437, 0.6672163],\n", + " [-0.12813795, 0.1032186, 0.79860542],\n", + " [-0.71131864, 0.09110553, -0.40497972],\n", + " [-0.19249397, -0.29453928, -0.56467361],\n", + " [-2.32345804, -0.4455237, 0.55078323],\n", + " [0.27290009, 0.49336199, 0.0177413],\n", + " [-0.10562167, 0.26118816, -0.02914504],\n", + " [-0.79216322, -0.73486524, -0.72728207],\n", + " [-0.90054849, 1.1319958, 1.50736583],\n", + " [0.40954699, 0.58737487, 0.16213793],\n", + " [-0.41977153, 0.98809896, -0.00961759],\n", + " [0.03683455, -0.12254046, 0.20662823],\n", + " [-0.05428749, 0.22362454, -0.21434472],\n", + " [-1.78240466, 1.40628446, -2.26412968],\n", + " [-0.5476619, -1.30953881, -0.37692125],\n", + " [0.82991247, -0.06968524, -0.1288499],\n", + " [-0.66258876, 0.08911122, 1.3350698],\n", + " [0.12070386, 0.21745881, 0.00928666],\n", + " [3.19994049, -0.61231932, -3.3189373]],\n", + " 'jid': 'Si-1',\n", + " 'stresses': [-6.58094686,\n", + " -9.63365488,\n", + " -2.75897261,\n", + " 9.08682588,\n", + " -1.60618898,\n", + " -5.41948494],\n", + " 'total_energy': -4.690436986666667}\n" ] } ] @@ -954,7 +1240,7 @@ "metadata": { "id": "nAl25z1vC87x" }, - "execution_count": null, + "execution_count": 13, "outputs": [] }, { @@ -969,9 +1255,9 @@ "height": 35 }, "id": "YiqdUiurBgeX", - "outputId": "d94ecb1c-960d-4969-9389-a16bb830c058" + "outputId": "12d2fa1d-5bc8-465f-9467-348dcf2ccdbd" }, - "execution_count": null, + "execution_count": 14, "outputs": [ { "output_type": "execute_result", @@ -984,91 +1270,31 @@ } }, "metadata": {}, - "execution_count": 33 - } - ] - }, - { - "cell_type": "code", - "source": [ - "!rm -rf /usr/local/lib/python3.10/site-packages/alignn*" - ], - "metadata": { - "id": "LOynUmY4Bgb1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!ls /usr/local/lib/python3.10/site-packages/alignn*" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Mjq94l3XBgY2", - "outputId": "5425eba4-0b43-4a2a-8e02-308e44f61e88" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "ls: cannot access '/usr/local/lib/python3.10/site-packages/alignn*': No such file or directory\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install -q git+https://github.com/usnistgov/alignn.git@ddp" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MrdTD_shBwxk", - "outputId": "0f94dbcb-dc02-4c27-b94b-44f339436747" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", - " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", - " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - " Building wheel for alignn (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] + "execution_count": 14 } ] }, { "cell_type": "code", "source": [ - "# %%time\n", - "# !train_alignn.py --root_dir DataDir/ --config config_Si.json --output_dir OutputDirRad" + "%%time\n", + "!train_alignn.py --root_dir DataDir/ --config config_Si.json --output_dir OutputDir" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "klD4U4f_RdcU", - "outputId": "1c50212a-8b2d-44e1-a404-3a7c512871de" + "id": "UUaf0GrRBwvN", + "outputId": "73b86593-8aa4-4143-97fe-2ca8c4d127ed" }, - "execution_count": null, + "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ + "DGL backend not selected or invalid. Assuming PyTorch for now.\n", + "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable. Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n", "fatal: not a git repository (or any of the parent directories): .git\n", "world_size 1\n", "root_dir DataDir/\n", @@ -1079,14 +1305,14 @@ "MAD: 0.2771504487888568\n", "Baseline MAE: 0.2861611033805039\n", "data range -4.56655198359375 -5.4253234771875\n", - "\r 0% 0/214 [00:00" ], - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } @@ -1817,15 +1784,15 @@ "height": 505 }, "id": "YcfFfhtR32U1", - "outputId": "7e29d15d-4d6f-468c-deb3-61831d1c9841" + "outputId": "abe809b4-1a7a-4da9-e4d1-e7f550b9b6e4" }, - "execution_count": null, + "execution_count": 55, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "0.10897047352179134\n" + "0.2471206380571713\n" ] }, { @@ -1836,7 +1803,7 @@ ] }, "metadata": {}, - "execution_count": 189 + "execution_count": 55 }, { "output_type": "display_data", @@ -1844,7 +1811,7 @@ "text/plain": [ "
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\n" + "image/png": 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\n" }, "metadata": {} } @@ -1907,13 +1874,13 @@ ], "metadata": { "id": "eq4qJRvI32P0", - "outputId": "1df31814-865e-42d5-ad04-aea81ebbb1f1", + "outputId": "1d0456db-db9f-4e4a-b6a6-c591a003ac69", "colab": { "base_uri": "https://localhost:8080/", - "height": 500 + "height": 452 } }, - "execution_count": null, + "execution_count": 56, "outputs": [ { "output_type": "display_data", @@ -1921,7 +1888,7 @@ "text/plain": [ "
" ], - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} }, @@ -1929,7 +1896,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "[0.09363929430643718, nan, 0.11911459987524646, nan]\n" + "[0.05175083875656128, nan, 0.13839892769394174, nan]\n" ] } ] @@ -1996,13 +1963,13 @@ ], "metadata": { "id": "5RPgxcrdhfBw", - "outputId": "a32f4f60-37e2-4b84-a0fa-e9d7cee9351c", + "outputId": "87747b0e-d42f-4a97-f6cb-b2afc7f650e0", "colab": { "base_uri": "https://localhost:8080/", - "height": 587 + "height": 543 } }, - "execution_count": null, + "execution_count": 57, "outputs": [ { "output_type": "stream", @@ -2010,10 +1977,10 @@ "text": [ "Val\n", "Baseline MAE: eV 0.29931997259457904\n", - "MAE eV 0.046925107638041176\n", + "MAE eV 0.03530814250310262\n", "Test\n", "Baseline MAE: eV/A 0.574290006651727\n", - "MAE eV/A 0.1272184797275339\n" + "MAE eV/A 0.13043567138171064\n" ] }, { @@ -2022,7 +1989,7 @@ "text/plain": [ "
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\n" + "image/png": 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\n" }, "metadata": {} } @@ -2040,63 +2007,84 @@ { "cell_type": "code", "source": [ - "from alignn.ff.ff import AlignnAtomwiseCalculator,default_path,wt10_path,alignnff_fmult,fd_path,ForceField\n", - "from ase import Atom, Atoms\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "model_path = dir_name #\"alff2_wt_1_determSi\"\n", - "from ase.build import bulk\n", - "\n", - "# Create a silicon crystal structure using the diamond cubic lattice\n", - "\n", - "\n", - "\n", - "# calc = AlignnAtomwiseCalculator(\n", - "# path=model_path,\n", - "# force_mult_natoms=False,\n", - "# force_multiplier=2,\n", - "# stress_wt=-4800,\n", - "# )\n", - "\n", - "#equilibrium constant 5.49 from here:\n", - "# https://www.ctcms.nist.gov/~knc6/static/JARVIS-DFT/JVASP-1002.xml\n", - "lattice_params = np.linspace(4.5, 6.5)\n", - "fcc_energies = []\n", - "ready = True\n", - "for a in lattice_params:\n", - " atoms = bulk('Si', 'diamond', a=a)\n", - "\n", - " atoms.set_tags(np.ones(len(atoms)))\n", - "\n", - " atoms.calc = calc\n", - "\n", - " e = atoms.get_potential_energy()\n", - " fcc_energies.append(e)\n", + "from ase.eos import EquationOfState\n", + "from ase.units import kJ\n", + "from jarvis.db.figshare import get_jid_data\n", + "from jarvis.core.atoms import Atoms\n", + "atoms=Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms']).get_conventional_atoms\n", + "dx=np.arange(-0.1, 0.1, 0.01)\n", + "#dx=np.arange(-0.05, 0.05, 0.01)\n", + "y = []\n", + "vol = []\n", + "for i in dx:\n", + " s1 = atoms.strain_atoms(i)\n", + " ase_atoms = s1.ase_converter()\n", + " ase_atoms.calc = calc\n", + " energy = ase_atoms.get_potential_energy()\n", + " y.append(energy)\n", + " vol.append(s1.volume)\n", + "x = np.array(dx)\n", + "y = np.array(y)\n", + "eos = EquationOfState(vol, y, eos=\"murnaghan\")\n", + "v0, e0, B = eos.fit()\n", + "kv = B / kJ * 1.0e24 # , 'GPa')\n", "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "plt.plot(lattice_params, fcc_energies)\n", - "plt.xlabel('Lattice constant ($\\AA$)')\n", - "plt.ylabel('Total energy (eV)')\n", - "plt.show()" + "plt.plot(vol,y)\n", + "plt.xlabel('Volume ($\\AA^3$)')\n", + "plt.ylabel('Total energy (eV)')\n" ], "metadata": { - "id": "sQRoCA9aBgUl", - "outputId": "78821a1c-028e-428f-cb72-8d859ac0da83", + "id": "fsYDaob64meE", + "outputId": "9ba774db-0218-407c-e650-0e812012213b", "colab": { "base_uri": "https://localhost:8080/", - "height": 475 + "height": 727 } }, - "execution_count": null, + "execution_count": 58, "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 3D dataset 76k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n", + "Loading the zipfile...\n", + "Loading completed.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Total energy (eV)')" + ] + }, + "metadata": {}, + "execution_count": 58 + }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], - "image/png": 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wYQBAr169Kl4wVdjzrf2w50oCtpyLxXt9giGXVW3vNBEREaDngAqZTIbZs2cjKioKCxYsKDXYlcTZ2RmjR4/GX3/9hfDwcPTu3Rupqanlbqcm0Gg0mDBhAnJyctCuXbty91BKkqQLhBs2bMCdO3ce2eaHH35Aeno65HI5hg8fboyyqYK6NfaCi50VHqZl4/SdZFOXQ0REFkqvcHfnzh289957cHR0NMpB27dvjx07duDtt982SnumEBkZiQ8//BBnz55FVlYWAECr1eLo0aPo1asXtm7dCldXV90o3f+aNWuWbiRuceHtnXfegY+PD9RqNZ566imcOXMGQP78eYsXL8bHH38MABg/frxuJQwyLRsrGXqG+AAAdly6b+JqiIjIUukV7owV6qqq3aqgUqnw5ZdfonXr1rC3t4e7uzvs7OzQuXNn/PXXX6hbty72799f4vVyZVEoFNi+fTtq1aqFK1euoE2bNrqBE6+//jpycnLQq1cvLFiwwMivjCqif/PaAIAdEfHQaEsfcERERFQZ9J58bevWraUutWVpAgMDMWPGDHTp0gW+vr7IyMiAi4sLOnXqhPnz5+Pq1ato1apVhY7RunVrXL58GVOmTEHDhg2Rm5sLR0dHdO7cGT///DN27txZKYNEyHCdGnjw1CwREZmUJMqaz+QfMpkM3t7eePnllzFmzBgEBwdXdm1USVQqFRQKBZRKJSc0rgRTN17A5rP3MLJDAD55tuS1gYmIiMpD39/vci2bkJCQgPnz56Np06bo1KkTVqxYUa41U4ksAU/NEhGRKekd7g4dOoSRI0fCwcEBQgiEh4dj3LhxqF27Nl555RUcPXq0MuskqjF4apaIiExJ73DXuXNnrFy5EvHx8Vi2bBk6deoEIQTS09OxcuVKdOnSBU2aNMG8efOQkJBQmTUTVWscNUtERKZU7tXsHR0dMWbMGBw+fBg3btzAe++9h9q1a0MIgevXr+O9996Dv78/nnvuOWzfvh1arbYy6iaq1nhqloiITKXc4a6wBg0aYPbs2bh79y7+/PNPDBw4ENbW1sjLy8O2bdvw7LPPws/PD++//z5u3LhhrJqJqj2emiUiIlOpULjTNSKToW/fvti0aRNiY2OxYMECNG/eHEIIxMfHY+7cuWjSpAm6dOlijMMRVXs8NUtERKZilHBXWK1atfDWW2/h3LlzOH36NF599VVIkgQhBAddkEXhqVkiIjIFo4e7Ajdu3MDGjRuxbds26DmVHpFZ4alZIiIyBStjNpaRkYENGzZg5cqVCA8PBwBdsGvdujXGjh1rzMMRVWsFp2Y3n72HHZfuo329WqYuiYiILIBRwt2hQ4ewcuVKbNq0CWq1Whfo3N3dMXz4cIwdOxbNmzc3xqGIapSnmueHu50R8ZjxdFPIZZKpSyIiIjNncLiLjY3FL7/8glWrVuH27dsA8nvpJEnCk08+ibFjx+K5556DjY2N0Yolqmk6N/CEs50VHvxzapa9d0REVNnKFe5yc3OxdetWrFixAvv27YNWq9X10tWtWxejRo3C6NGjERAQUCnFEtU0NlYy9OKpWSIiqkJ6h7tJkybh119/RUpKCoD8XjobGxsMGDAAY8eOxZNPPglJ4iknov/iqVkiIqpKeoe7RYsW6e6HhoZi7NixeOmll+Du7l4phRGZC56aJSKiqqR3uHN2dsawYcMwduxYtGnTpjJrIjIrPDVLRERVSe9wl5CQADs7u8qshchs8dQsERFVFb0nMS4r2D18+BCnT5/GoUOHKlwUkbn576lZIiKiylLhFSq2bduGVq1awcfHB+3bt0ePHj2KPJ+SkoI+ffqgT58+UCqVFT0cUY1UcGoW4FqzRERUuSoU7r766is899xzOH/+PIQQulthbm5usLe3x969e7Fp06YKFUtUkz3VPD/c7eRas0REVIkMDnfHjx/Hhx9+CCsrKyxYsACJiYnw9vYudtuXXnoJQgjs3bvX4EKJajqemiUioqpgcLj77rvvAADvv/8+3nrrrVKnROnatSsA4Ny5c4YejqjG46lZIiKqCgaHu6NHjwIA3njjjTK39fDwgKOjI+Li4gw9HJFZ4KlZIiKqbAaHuwcPHsDZ2RkeHh56bW9ra4ucnBxDD0dkFjo38ISdtQwP0rIRnZRh6nKIiMgMGRzuHB0doVarodFoytw2PT0dqampXM2CLJ6NlQyBtRwBAHeT1SauhoiIzJHB4a5x48bQaDS4ePFimdtu3boVWq0Wjz32mKGHIzIb/u4OABjuiIiochgc7p555hkIITB79uxSt7t37x6mT58OSZIwaNAgQw9HZDYCCsJdEsMdEREZn8Hh7o033kCdOnWwefNmjBgxAhEREbrncnNzcfPmTXzzzTdo3bo14uLi0KhRI4wcOdIoRRPVZHVr5Ye7aPbcERFRJdB7bdn/cnJywh9//IHevXtj7dq1WLdune65wkuVCSHg6+uLrVu3wtraumLVEpmBuv/03MUw3BERUSWo0AoVjz32GC5cuIDRo0fD1ta2yCoVQghYW1tj1KhROH36NBo3bmysmolqtLqFrrn774ouREREFWVwz10BHx8fLF++HD/++CPOnDmDuLg4aDQa+Pj4oG3btnBwcDBGnURmw8/NATIJUOdo8DA9G17OdmXvREREpKcKh7sCtra26Nixo7GaIzJbNlYy1FbYIzY1EzHJaoY7IiIyqgqdliUiwxScmo3miFkiIjIyvcKdWl05P0CV1S5RdRdQi3PdERFR5dAr3AUFBeGbb75BVlaWUQ56+vRp9O/fH/PnzzdKe0Q1jT/nuiMiokqiV7jLycnBu+++i8DAQEyfPh2XLl0q94HS09OxevVqPPnkk2jfvj127NgBhUJR7naIzEEA57ojIqJKoteAilu3bmHWrFlYunQpvv76a3z99dcICQlBWFgY2rVrhxYtWsDT0xPu7u6wtbVFSkoKkpOTERkZiZMnT+LEiRP4+++/kZmZCSEEQkJCMHfuXPTr16+yXx9RtRTgzvVliYiockiiHBNt3bx5E/Pnz8evv/6K9PR0SJKk134Fh2jXrh0mTpyI4cOHQybjWA5TUalUUCgUUCqVcHFxMXU5FkmpzkWLT/cAAK582hsONkYbuE5ERGZK39/vciWshg0bYsmSJYiLi8NPP/2E5557Dh4eHo9MXlxwk8vlaNu2LaZNm4Zz587h+PHjePnllxnsyOIpHKyhsM9fsSUmOdPE1RARkTkxqLvAyckJr7zyCl555RUAQFRUFOLi4vDw4UNkZWWhVq1a8PT0RMOGDeHo6GjUgonMRV13B1yKVSI6KQONfZxNXQ4REZkJo5wLCgoKQlBQkDGaIrIYdWvlhzted0dERMbE86NEJlJ4jVkiIiJjYbgjMpEArlJBRESVgOGOyETq/jPXXQx77oiIyIgY7ohMpOC0bEyKGhqt3jMSERERlYrhjshEaivsYS2XkKsRiFcZZ2k/IiIihjsiE5HLJPi5FVx3l2HiaoiIyFww3BGZkG7ELAdVEBGRkTDcEZkQp0MhIiJjY7gjMqGAf0bMRjPcERGRkRgc7saMGYMTJ04YsxYii6MbMctwR0RERmJwuFu1ahU6duyIFi1a4Mcff4RKpTJmXUQWoWCuO05kTERExmJwuOvUqROEELh06RLefPNN+Pr6YuzYsezNIyqHgp47ZWYulOpcE1dDRETmwOBwd/jwYVy9ehVTpkxBrVq1oFari/TmLV68mL15RGVwsLGCh5MtAA6qICIi46jQgIrGjRtj/vz5uHfvHn799Vd069YNAHDp0iW88cYb7M0j0sO/gyo41x0REVWcUUbL2tjYYMiQIdi/fz9u3LiBadOmwcvLC2q1GitXrmRvHlEpAjgdChERGZHRp0KpX78+vvrqK8TExGDz5s1o3749hBCIiIjQ9eaNHz8eV69eNfahiWokf05kTERERlRp89wdPnwYGzduxPnz5yFJEoTIXxhdrVZj+fLlaN68OSZPngytVltZJRDVCAEcMUtEREZk1HD38OFDzJ07F40aNcKTTz6J3377DdnZ2WjVqhWWLVuGlJQU/P777wgLC4NGo8HChQsxe/ZsY5ZAVONwlQoiIjImo4S7vXv3YvDgwfD398f777+PW7duwd7eHmPHjsWpU6dw6tQpjBkzBi4uLhg0aBAOHjyIJUuWQAiBVatWGaMEohqrYK67+8pM5OSxJ5uIiCrGytAd4+PjsWLFCixfvhx37tzRnXYNCQnBhAkTMGLECLi4uJS4//jx4zF9+nRER0cbWgKRWfB0soW9tRyZuRrEpmYiyMPR1CUREVENZnC4q1u3LjQaDYQQsLGxwaBBgzBhwgSEhYXp3YaLiwuUSqWhJRCZBUmSUNfdAdcT0hCdlMFwR0REFWLwadm8vDwEBgbiq6++wr1797Bu3bpyBTsA+O233/DXX38ZWkK1cPjwYbz44ovw8/ODra0tvLy80LNnT6xfv75C7c6aNQuSJJV5u3XrlpFeCZlSwalZrjFLREQVZXDP3c6dO9G7d+8KHbx9+/YV2t/Upk+fjjlz5uj+3dXVFampqdi3bx/27duH33//HRs3boSVlcFvM6ytreHu7l7i8xVpm6qPgkEVHDFLREQVZXDPXUWDXU23dOlSXbAbMmQIYmJikJKSgrS0NKxatQqOjo7YsmULpk2bVqHjdOzYEfHx8SXeAgMDjfBqyNT+XaWC4Y6IiCqm0ua5M2d5eXmYOXMmAKBVq1ZYt24d/Pz8AAC2trYYOXIk5s2bBwBYuHAhIiMjTVYr1QwFExnztCwREVWUwef0Pv3003Jtb2dnB1dXVzRt2hRt27aFjY2NoYc2uTNnziAhIQEAMHXqVMhkj2bkcePG4f3330dqairWrl2LGTNmVHWZVIMUXoJMCAFJkkxcERER1VQGh7uCC/4N4e7ujilTpmD69OnFBqPqrvD0LSEhIcVuI5fL0ahRI5w8eRJ79uxhuKNS+bk5QJIAdY4Giek58HS2NXVJRERUQxmcrLp06YIuXbpAoVBACAEhBPz8/NC+fXu0b98efn5+usddXV0RFhaG0NBQWFtbIykpCR9//DGGDh1qzNdiEhqNpsznIiIiDG7/8uXLaNasGRwcHODk5ITGjRtj3LhxOHfunMFtUvVjYyWDr8IeAHA3OcPE1RARUU1mcLg7ePAgOnbsiNTUVIwcORK3bt1CdHQ0jh07hmPHjiE6Ohq3b9/G6NGjkZKSgq5du+L8+fNISkrCrFmzAACbNm3Cli1bjPVaqkzhQQwlBbecnBzcvHkTAKBUKpGRYdgPdmJiIq5evQp7e3tkZ2fjxo0bWLZsGVq3bo2PPvpIrzays7OhUqmK3Kj64YhZIiIyBoPD3ebNm/HVV1/hrbfewsqVK1GvXr1HtgkKCsLy5csxefJkfP7559i2bRscHR0xY8YMvP322xBC4JdffqnQCzCFVq1awdvbGwAwZ84c5OXlPbLNwoULi4So8gaqhg0bYu7cubh+/TqysrKQlJSEjIwM7N69G61bt4YQAl988QXmz59fZluzZ8+GQqHQ3fz9/ctVC1UNrjFLRERGIQzUrVs3IZfLRVJSUpnbJiUlCZlMJp544gndY9HR0UKSJFGnTh1DSyiXlStXCgAG33bu3FmkvR9++EH3XO/evcWZM2dEdna2uH//vpg7d66wtrYW1tbWum3i4+ON9loyMzNF27ZtBQDh5OQkUlNTS90+KytLKJVK3S0mJkYAEEql0mg1UcUt+uumCHhvu5iy4ZypSyEiompIqVTq9fttcM/dxYsX4erqWuoEuwXc3d3h6upa5DqxunXrwtnZGYmJiYaWYFKvv/463nnnHQDQ9abZ2tqidu3amDZtGgIDA4vMcefm5ma0Y9vZ2eHLL78EAKSnp2P//v2lbm9rawsXF5ciN6p+2HNHRETGYPBo2ezsbOTk5ECtVsPBwaHUbTMyMqBSqWBr++gIQHt7e0NLKJehQ4eif//+Bu+vUCgeeezrr7/GgAEDsGzZMpw6dQoqlQq1a9fGM888g8mTJ2Pu3LkAgICAAKNP/dKhQwfdfc6jZx44kTERERmDweGuYcOGuHjxIhYvXoypU6eWuu2SJUug0WjQsGFD3WNKpRJpaWlo0KCBoSWUi62tbbHhsqI6deqETp06Ffvc6dOnAeSvMkFUlgB3RwDAw7RsqHPy4GDDpeWIiKj8DD4t+/LLL0MIgenTp2P27NnIzMx8ZJvMzEx89dVXmD59OiRJwssvv6x77vjx4wCAZs2aGVpCtZaQkIB9+/YBAEaMGGH09gvePyB/4ArVfAoHa7jY5Qe6mORHv09ERET6MLhrYNKkSfjf//6Hw4cP46OPPsKXX36JNm3awNfXF5IkIS4uDqdPn0ZGRgaEEAgLC8OkSZN0+69atQoA0LNnzwq/iOpGo9FgwoQJyMnJQbt27cq9Dq8oY4WC7OxsfPjhhwAAR0dHPPHEExWql6qPgFqOuBSrRHRSBhr7OJu6HCIiqoEM7rmzsrLCzp07MX78eEiShIyMDPz999/YsGED1q9fj4MHDyI9PR2SJGHcuHHYsWMHrKz+zZI///wzUlJSMH78eKO8kKoWGRmJDz/8EGfPnkVWVhYAQKvV4ujRo+jVqxe2bt0KV1dXrFq1qtigVrDChyRJuHPnTpHnDh06hCeffBJr1qzBvXv3dI/n5uZi//79CAsLw4kTJwAAM2bMgKura6W9TqpaHFRBREQVVaGLehwcHLBkyRJ8+OGH2Lx5M86ePYuHDx8CADw9PdGqVSsMHDgQdevWfWRfJyenihza5FQqFb788kvdqFU3Nzekp6cjNzcXQP5o4C1btqBJkyblblsIgf379+tGwdrb28PR0RFKpVLXvkwmw/Tp04uMyKWar24thjsiIqoYg8PdoUOHAADNmzeHv78/Jk+ebKyaaoTAwEDMmDEDBw8exK1bt5CYmAgXFxcEBwdj4MCBmDBhQpmjiEsSGhqKefPmITw8HJcuXUJiYiJSU1Ph4OCAkJAQhIWFYfz48QgNDTXyqyJTC+AqFUREVEGSEEIYsqNMJoNcLseDBw+MOocbVT6VSgWFQgGlUsk576qZY7cSMWzZCdTzcMRf73QzdTlERFSN6Pv7bXDPnUKhgFwuZ7AjMqKC07IxKWpotAJyWckDa4iIiIpj8ICKBg0aIC0tDdnZ2cash8ii1VbYw1ouIVcjEK/KMnU5RERUAxkc7oYMGYLc3Fxs3LjRmPUQWTS5TIKfW8F1dxkmroaIiGoig8PdW2+9hQ4dOuCNN97Ajh07jFkTkUXz/2dQRQxHzBIRkQEMvubuyy+/RJcuXXDp0iU8/fTTaNq0KTp16gQvLy/I5fIS95sxY4ahhySyCBwxS0REFWFwuCuYhLdgsG1ERAQuX75c5n4Md0SlC/hnUEU0e+6IiMgABoe7Ll26lLpEFhEZhqdliYioIgwOdwcPHjRiGURUQNdzx9OyRERkAIMHVBBR5fD/Z7SsMjMXSnWuiashIqKahuGOqJpxtLWCh5MtAK4xS0RE5WfwadnCLl68iN27dyM6OhqZmZlYvny57rnc3Fw8fPgQkiShdu3axjgckdkL8nBAYno2riekIdRPYepyiIioBqlQuFMqlRgzZgy2bt0KABBCQJKkR8JdixYtkJKSggsXLqBp06YVKpjIErQKcMOpOyk4FZWM51v7mbocIiKqQQw+LZubm4u+ffti69atcHBwwFNPPQU7O7tHtnNwcMDo0aOh1WqxadOmChVLZCnaBboDAE7dSTZxJUREVNMYHO6WL1+O48ePo169erh+/Tq2bdsGhaL400eDBg0CABw6dMjQwxFZlDYB7pAkIDIxAw/SuMYsERHpz+Bwt379ekiShAULFsDX17fUbVu2bAmZTIZr164Zejgii6JwsEawjwsA4FRUiomrISKimsTgcHfp0iVIkoRevXqVua2NjQ0UCgWSkpIMPRyRxWkflH9q9mQUvzdERKQ/g8OdWq2Gs7MzbGxs9No+NzcXVlZGGZxLZBHa/nPd3ck77LkjIiL9GRzuPDw8oFKpkJ6eXua2UVFRSE9PL/P0LRH9q22QGwDgWryKkxkTEZHeDA537du3BwD8+eefZW67cOFCAEBYWJihhyOyOF7Odqjn4QghgNPRHDVLRET6MTjcjRkzBkIIfPzxx4iLiytxu6VLl+K7776DJEkYP368oYcjskjtdNfdMdwREZF+DL4I7qmnnsKgQYOwefNmtGnTBsOGDUNmZiYA4KeffkJ0dDS2b9+OiIgICCEwbtw4XW8fEemnbaA7NpyKwUnOd0dERHqq0AiHNWvWwM7ODuvWrcOCBQt0j7/22msA8lesAPJ7+X744YeKHIrIIhX03F26p4Q6Jw8ONhyUREREpTP4tCwA2NnZYc2aNTh06BBefvll1K9fH/b29rCxsUHdunUxbNgwHDx4EMuWLeNIWSID+LnZw1dhhzytwLm7qaYuh4iIagCjJK7OnTujc+fOxmiKiAqRJAltg9zxv/NxOBGVjE4NPExdEhERVXMV6rkjospXcGr2FAdVEBGRHhjuiKq5gpUqzt5NQU6e1sTVEBFRdWeU07Lh4eG4ePEikpOTkZtb+mSrM2bMMMYhiSxGfU8nuDvaIDkjB5diU9E6wN3UJRERUTVWoXC3b98+jB8/HtHR0Xrvw3BHVD6SJKFtoBt2X07AiahkhjsiIiqVweHu5MmT6N+/P3JycgAAQUFB8PX15ahYokrQLqgWdl9OyL/urpupqyEiourM4CT22WefIScnB8HBwdi4cSOaNWtmzLqIqJCC6+5O30mBRisgl0kmroiIiKorgwdUhIeHQ5IkrFmzhsGOqJI1qe0CJ1srpGXn4ep9lanLISKiaszgcKdWq+Hg4IDWrVsbsx4iKoZcJqF1gBsA4BSXIiMiolIYHO4CAgKg1XJaBqKqUjDf3UnOd0dERKUwONwNGjQIWVlZOHTokDHrIaIStC8U7grWbSYiIvovg8Pd9OnTUa9ePUycOBFJSUnGrImIihHqp4CtlQxJGTm4/TDD1OUQEVE1ZfBo2bNnz+Kzzz7DxIkT0bRpU4wfPx7t27eHs7Nzqft16dLF0EMSWTRbKzke83fFiahknLqTjAZeTqYuiYiIqiGDw123bt0gSf9Ox/DFF1+UuY8kScjLyzP0kEQWr32QO05EJeNkVDKGtqtr6nKIiKgaqtDaskKIct04AIOoYtoF1QLAQRVERFQyg3vuGNSIql7Luq6QyyTEpmbiXooafm4Opi6JiIiqmQr13BFR1XK0tUKzOgoAnO+OiIiKx3BHVMO053x3RERUCqOEO61Wi1OnTmHTpk1YvXq1MZokohK0C8wPdycY7oiIqBgVDncLFy5E7dq18fjjj+PFF1/E6NGjizyfkpKCZs2aITg4GAkJCRU9HJHFaxOYvwxZ5MMMPEzLNnE1RERU3VQo3E2cOBGTJ0/Gw4cP4ezsXGRqlAJubm5o1aoVbt68id9//70ihyMiAK4ONgj2yZ9P8jSvuyMiov8wONzt2rULixcvhpOTE7Zs2YLU1FR4enoWu+2wYcMghMC+ffsMLpSI/lWwzixPzRIR0X8ZHO6WLFkCSZLw6aef4tlnny112w4dOgAALl26ZOjhiKiQdhxUQUREJTA43J04cQIAMGbMmDK3VSgUcHFxQXx8vKGHI6JCCgZVXI1XQZmZa+JqiIioOjE43CUnJ0OhUJS5lqzuQDIZJz4mMhIvFzsE1nKAEMDZ6BRTl0NERNWIweHOxcUFKpUKubll9xokJydDqVTCw8PD0MMR0X8UnJo9civRxJUQEVF1YnC4Cw0NhRBCd3q2NOvXr4cQAm3atDH0cET0H90aewEA9l1NgBDCxNUQEVF1YXC4e/755yGEwKxZs0o93XrhwgV89NFHkCQJQ4cONfRwRPQfXRp5wkYuQ3SSGjcfpJu6HCIiqiYMDnfjxo1DSEgIDhw4gJ49e2L79u3QaDQAgJs3b2Lv3r2YNGkSOnbsCKVSiccffxyDBw82WuFEls7J1godG9QCAOy9wgnCiYgon5WhO1pbW+PPP/9Enz59cODAARw8eFD3XHBwsO6+EAKhoaHYvHlzsZMcE5HheoZ44+D1h9hzJQETuzcwdTlERBYvPTsP287HYWg7f5PlngqtUBEQEIAzZ87gk08+Qd26dSGEKHLz9fXFrFmzcOzYMfj4+BirZiL6x5NNvAEAF2JSkaDKMnE1RESWLT07D6NWnMQHWy5h0V+3TFaHwT13BRwcHPDxxx/j448/RlxcHOLi4qDRaODj44OAgABj1EhEJfB2sUMLf1dciEnFvqsJGN6e3zkiIlMoCHano1PgbGeFLo2KX7WrKlQ43BXm6+sLX19fYzZJRGXoFeKNCzGp2HuF4Y6IyBTSsnIxauUpnIlOgYudFda+0h7N/VxNVk+FTssSken1Csk/NXvsVhLSs/NMXA0RkWVJy8rFyBUndcFu3SuPmzTYAQx3RDVeAy8nBNZyQI5Gi0M3Hpq6HCIii6HKysWIFSdx9m4qFPbW+HXc4wj1U5i6LIY7oppOkiT0/Kf3jlOiEBFVDVVWLkYsP4lz/wS7da+0R7M6pg92AMMdkVnoGZI/Gv2vaw+Qq+EazkRElakg2J2PSYWrQ/UKdgDDHZFZaB3gBndHGygzc3HqTrKpyyEiMlvKzFy8XI2DHcBwR2QW5DIJPYLz15rdc5mnZomIKoMyMxcjlp/AhULBrqlv9Qp2gAWHO7VajZ07d+Lzzz/HwIEDERAQAEmSIEkSZs2apXc7CQkJmDp1Kho3bgx7e3u4u7sjLCwMy5YtM8pi7rdv38arr76KoKAg2NnZwdPTE71798bmzZsr3DaZl8LX3Rnjb4+IiP6VlavBqJUnceGeEm4O1vj1lcerZbADjDzPXU1y8uRJ9OvXr0JtnDlzBr1790ZSUhIAwMnJCWlpaThy5AiOHDmCTZs2Ydu2bbCxsTGo/R07dmDw4MFQq9UAABcXFyQnJ2PPnj3Ys2cPRo8ejeXLl3NZNwIAhDX0gK2VDLGpmbh6Pw0hvi6mLomIyCwIITBt00Xd4Ilfxz2OJrWr739jLbbnDgDc3NzwxBNP4N1338X69evLtUSaUqlE//79kZSUhODgYJw6dQppaWnIyMjAokWLYG1tjd27d2Py5MkG1RYVFYUXXngBarUanTp1wvXr16FUKqFUKjFjxgwAwMqVK/H1118b1D6ZHwcbK4Q19ADAUbNERMb0w4Fb2HYhDlYyCYtfalWtgx1gweEuLCwMycnJ2LdvH+bOnYshQ4bA1tZW7/3nzZuH+Ph42NvbY8eOHWjTpg0AwMbGBhMnTsQnn3wCAPjpp59w48aNctc3Y8YMZGRkwMfHB9u3b0ejRo0A5PcOfvLJJxg/fjwA4IsvvkBKSkq52yfzpDs1ezXexJUQEZmHnZfuY96e/N/xT55tio71PUxcUdn0CndyudwoNyur6nMWWC6XV2j/1atXAwCGDBmCoKCgR55/88034eTkBI1Gg3Xr1pWr7YyMDN01da+99hpcXV0f2eb9998HAKhUKmzdurV8xZPZ6hHsDUkCImJViEvNNHU5REQ1WkSsElM2ngcAjO4UWGOWeNQr3AkhjHYzB9evX8fdu3cBAH379i12GycnJ4SFhQEA9uzZU672jxw5gszMzFLbDwwMRJMmTQxqn8yXp7MtWtV1AwDsu8pTs0REhnqgysIrv5xGVq4WXRp54sN+TUxdkt706ko7cOBAZddRo0REROjuN2vWrMTtmjVrhp07d+LKlSuV1v7Vq1dx+fLlcrVP5q1niDfORKdg75UEjOgQaOpyiIhqnKxcDcatPo14VRYaeDlh0bCWsJLXnCvZ9Ap3Xbt2rew6apS4uDjd/Tp16pS4XcFzKpUK6enpcHJyKlf7bm5usLe3L7P9wvUUJzs7G9nZ2bp/V6lUetVBNVPPEG98tfMajkcmQZWVCxc7a1OXRERUYwgh8O6mi7hwTwlXB2ssH9mmxv13tObE0GokLS1Nd9/BwaHE7Qo/V3gffdsvre3Cz5fV9uzZs6FQKHQ3f39/vWuhmqe+pxPqeToiVyNw8PpDU5dDRFSjLPzrFv4oGBk7vDUCajmauqRyqzHhbtWqVbpJhg257dq1y9QvwWTef/993TQqSqUSMTExpi6JKlnhCY2JiEg/Oy7dxzd780fGfj6gGTrUr2XiigxjtOGrQgikpKQgIyOj1IETdevWNdYhTcbZ2Vl3X61Ww8Wl+PluCiYf/u8++rZfeP/S2i+rbVtb23JN80I1X68QHyz9OxIHrz1ATp4WNlY15v/jiIhM4tI9Jd7+Z2TsmE5BGNKu5uaVCoe77du34/vvv0d4eHiZYUSSJOTl5Rl0nKFDh6J///4G7QsACoXxlgjx9fXV3Y+NjS0x3MXGxgLIX1lC3+vtCrefkpKCzMzMEq+7K2i/cD1EANDS3xUeTrZITM/G8cgkdGnkaeqSiIiqrZSMHIxbnT8ytltjT3zQL9jUJVVIhcLdtGnTMH/+fL2nOKnIVCjVqfep8AjWiIgI3ZQk/1Uw6jUkJKRC7bdt27bU9ps2bVqu9sn8yWQSnmzihQ2nYrD3SgLDHRFRCYQQ+GhrBOJVWajn6Yjvh9askbHFMbj6Xbt2Yd68ebCyssK8efN003F4enri1q1bOHLkCGbOnAl3d3d4eHjgjz/+QFRUlNEKN6VGjRrpTi+XdC1fRkYGDh8+DADo1atXudrv3LmzrreupPajo6Nx9epVg9ony1Bw3d2+qwlmM8ckEZGx/e98HP68dB9WMgnfvdiyxo2MLY7B4W7p0qWQJAkff/wx3n77bV3vlVwuR7169dCxY0fMnDkT58+fh0KhwNixY6tNz1tFSZKEESNGAAA2bNiAO3fuPLLNDz/8gPT0dMjlcgwfPrxc7Ts6OmLQoEEAgMWLF0OpVD6yzZw5cwDkX283YMCA8r0AsgidGnjA3lqO+8osRMRy+hsiov+KS83Ex//LPwv21hMNEepnvEu4TMngcHfy5EkAwLhx44o8/t8eAj8/PyxatAgPHjzQBZLqIiUlBYmJibqbVqsFkD9QofDj6enpj+z7zjvvwMfHB2q1Gk899RTOnDkDAMjJycHixYvx8ccfAwDGjx+vWxe2sFGjRulG8hbn008/haOjI+7fv4+nn34aN2/eBJDfI/jpp59iyZIlAICPPvoIbm5uFX8zyOzYWcvRpVH+Goh7r3CtWSKiwrRagXc3XUBaVh5a1nXFa93qm7oko5GEgedr7OzsYG1tXWSONWtrazg4ODzS06TVauHk5AQ/Pz/cuHGjYhUbUWBgIKKjo8vcbuTIkVi1atUjj585cwa9e/dGUlISgPxetKysLOTm5gLIP126bdu2YnssR40ahV9++QVAydci7tixA4MHD9YNVFEoFEhPT4dGowEAjB49GsuXLy8xIJZEpVJBoVBAqVSWOBiEzMOWc/cw5bcL8HCywYF3usHZDE43EBEZw4ojUfh0+xXYW8ux460wBHlU//ns9P39NrjnzsXFBdbWRX8oCsJHRkZG0YPIZLCystKN7jQXrVu3xuXLlzFlyhQ0bNgQubm5cHR0ROfOnfHzzz9j586dFToV3a9fP1y8eBHjxo1DYGAgsrKy4Obmhp49e2LTpk1YsWJFuYMdWZb+zX1Rz8MRiek5+OHAbVOXQ0RULdx6kIY5u64BAD54qkmNCHblYXDPXcuWLXHp0iWkp6fDzs4OANCxY0ecOHECO3fuLHKR/82bN9G4cWM4OzsXe/0YVS323FmWvVcSMG71adjIZdg/tSv83Utf+YSIyJzlarQY+OMxXIpVomsjT6wa3bbGdJRUes9d8+bNIYTAuXPndI/17NkTQgh88MEHiI/Pv8bn4cOHGDduHCRJQps2bQw9HBEZ6MkmXuhYvxZyNFp89c//qRIRWaqF+2/iUmz+urFzn29eY4JdeRgc7vr06QMhBLZu3ap7bOLEiXB1dcW5c+dQt25d1KlTB7Vr19ZNCfLuu+9WuGAiKh9JkvDRUyGQJODPi/dx+k6yqUsiIjKJs3dTsOjALQD5y4t5u9iZuKLKYXC4GzBgAFauXIlOnTrpHvPy8sKff/4Jf39/5OXl4f79+9BqtXBwcMCPP/6IPn36GKVoIiqfEF8XvNjGHwDw2fYr0Go57x0RWRZ1Th7e/u08tAJ49jFf9G9uvqs7GXzNXWk0Gg3Cw8MRExMDhUKBzp0789quaoTX3FmmB2lZ6P71QWTkaLDgxRZ4rqWfqUsiIqoyH229hLXH76K2wg673uoChUPNmz1A39/vCq8tWxy5XI7OnTtXRtNEZCAvZzu83r0Bvt59HXN3XUefprVhbyM3dVlERJXu4PUHWHv8LgBg3uAWNTLYlYfBp2U//fRTfPPNN3pv//333+PTTz819HBEZARjOwehjqs97iuz8PPhSFOXQ0RU6ZIzcjBt00UAwKiOgejUwMPEFVU+g0/LymQy+Pj4IC4uTq/tg4KCcPfuXd0EvGQ6PC1r2bZdiMOk9edgby3HwXe7me0FxUREeRotRqw4iWO3k1Df0xF/TgqDnXXNPWNR6VOhEFHN9HTz2mhV1xWZuRp8vfu6qcshIqo0X+28hmO3k+BgI8ePw1vX6GBXHlUW7pKTk3WTHROR6UiShI/7hwAANp+9h4hYTixORObnf+djsexIFABg/uAWaOzjbOKKqk6VhLvff/8daWlpqFu3blUcjojK0LKuG559zBdC5E+NUgmD5omITCYiVon3NudfZzexe330Da1t4oqqlt6jZb/77jt89913RR57+PAh6tWrV+I+QgikpqZCpVJBkiQ89dRThldKREY1rU8wdkXE40RUMnZfTkCfZj6mLomIqMKSM3Lw6pozyMrVoltjT7zds7GpS6pyeoe71NRU3Llzp8hjGo3mkcdK8sQTT2DGjBnlqY2IKlEdV3uM71IPC/+6hdk7r6J7sCdsrSzjehQiMk95Gi3e+PUsYlMzEVDLAd+92BJymfktL1YWvcPdgAEDEBgYCCC/R27MmDFQKBT49ttvS9xHJpPBxcUFzZo1Q/369StaKxEZ2YSu9bHhVAyik9RYfSwa47qU3BNPRFTdzdn17wCKn15uY/bz2ZWkyqZCoeqDU6FQYRtPxWDa5ouwtZJh7Svt0TbQ3dQlERGV2//Ox+KtDecBAD8Ob4V+ZnidXaVPhaLVahnsiMzAoNZ+eLKJF7LztBiz6hSu3leZuiQionK5HPfvAIrXu9U3y2BXHpznjsjCyWUSFg1rhbaBbkjLysOIFSdxN0lt6rKIiPSSnJGD8avzB1B0beSJqb0sbwDFf1U43Akh8H//938YPHgwgoKC4OjoCEdHRwQFBeGFF17A1q1bOc0CUTVnZy3HspFtEezjjIdp2Xhp+Qk8SMsydVlERKXK02jx5vp/B1B8P8QyB1D8l8HX3AFAQkICnn/+eRw7dgwAHglxkpT/Bnfq1AkbN26Ejw+nWqgOeM0dleSBKgvPLwnH3WQ1mtR2wYbxj0Nhb5kXJBNR9SaEwIdbI/DribtwsJFjy+udzH6iYn1/vw0Odzk5OWjXrh0uXboEIQTatWuHnj17ws/PDwBw79497Nu3DydOnIAkSWjevDlOnjwJa2v+UJgawx2VJjopA4MWhyMxPRvtAt2xemw7i1myh4hqjq93X8MPB25DkoDFw1uhTzPzv86u0sPdd999hylTpsDFxQVr165F//79i91ux44dGDZsGNLS0vDtt9/izTffNORwZEQMd1SWy3FKDFl6HGnZeXiyiReWvNQaVnJeoktE1cOyw5H4/M+rAIAvnwvFsPaWsQJWpY+W3bhxIyRJwg8//FBisAOAfv364YcffoAQAhs2bDD0cERUhZr6KrBsZBvYWsmw7+oDvLf5ErRaXjtLRKa38XSMLti927uxxQS78jC4587d3R1qtRrp6emwsip9LuS8vDw4OTnBwcEBycnJBhVKxsOeO9LXvisJeHXtGWi0AuPCgvBBvya6a2mJiKra7svxeG3tGWgFLPK/SZXec5eZmQkHB4cygx0AWFlZwcHBAZmZmYYejohM4MkQb8wZ1BwA8PPhKCz5O9LEFRGRpTp2OxFv/noOWgEMbu1nccGuPAwOd97e3lAqlbh7926Z2965cwepqanw9vY29HBEZCLPt/bDh/2aAMhf2ufPi/dNXBERWZqL91Ix7pfTyNFo0SvEG7MHhjLYlcLgcNelSxcIITBlypRS57ETQuDtt9+GJEno2rWroYcjIhMa16UexnYOAgC88/sFXInjKhZEVDVuPUjHqJWnkJGjQcf6tfD90JYc4FUGvd+d1atX4/fff9f9e0Fg27p1K3r06IH9+/cjNzdX93xubi727duH7t27Y+vWrZAkCVOmTDFu9URUZd7vG4ywhh7IzNVg3OrTSM7IMXVJRGTmYlMz8fLyE0jOyEFzPwV+GtGGUzPpQe8BFTKZDLVr10ZsbKzusQULFmDq1Km6rlErKyt4eHgAABITE5GXl6fr1fvmm28wefJkI5dPhuCACjJUqjoHz/5wFNFJajxezx1rxraHNf8PmogqQWJ6Nl5YEo7IxAzU93TE7xM6wt3RxtRlmVSlDKj4bw6cMmUKtm3bhsaNG0MIgdzcXNy/fx/3799Hbm4uhBAICQnBH3/8wWBHZAZcHWzw84g2cLSR43hkMj7ffsXUJRGRGXqgysLwn08gMjEDdVztsWZse4sPduVRrp47Hx8fxMXFFfv8pUuXcPr0aTx48AAA4OXlhTZt2iA0NNR41ZJRsOeOKmrvlQSMW30aAPDVwFAMacd5pojIOGKS1Xhp+QlEJ6nh5WyL9eMfR31PJ1OXVS3o+/td9jwmegoNDWWQI7IQPUO8MbVnI8zfewMf/y8CDb2d0DrA3dRlEVENd/thOl5adgL3lVnwd7fHurGPo24tB1OXVePwYhkiMsgbPRqgX6gPcjUCr645i/tKzmNJRIa7HKfEC0vCcV+ZhQZeTvj91Y4MdgZiuCMig0iShK+fb4FgH2ckpmdj/OozyMrVmLosIqqBzkSnYMhPx5GUkYOmvi74bfzj8FHYmbqsGqtc4S4hIQFyudzgmz6rWRBRzeFoa4WfR7SBm4M1LsUqMX3zxVLnvSQi+q+jtxLx8vITSMvKQ5sAN6wf/zhqOdmauqwardw9d0KICt2IyLz4uzvgh+GtIJdJ2Ho+Dj8f5hJlRKSfvVcSMHrlKahzNAhr6IHVY9vBxc7a1GXVeOXqSnN0dMTUqVMrqxYiqqE61vfAzKdDMON/l/HVzmsI8nBCzxAuN0hEJfvf+Vi8vfECNFqB3k298f3QlrC14gTFxmC0qVCo5uBUKFQZhBD4YEsE1p+8C1srGdaMbY92QRxBS0SP+vXEXXy49RKEAAa2rIO5zzfnkmJ6qJRJjImISiJJEj57timebOKF7Dwtxv5yCtfiuQYtERW18mgUPtiSH+xGdAjAvMEtGOyMjO8mERmNlVyGhUNboU2AG9Ky8jBi+UnEJKtNXRYRVRM/HbqNT/7IX9nm1S718MkzTSGTSSauyvww3BGRUdnbyLF8ZFs09nbGg7RsjFxxEknp2aYui4hMbNFfN/HljmsAgEk9GmB632Dd2vRkXAx3RGR0Cgdr/DKmHeq42iMyMQNjVp1CRnaeqcsiIhMQQuCbvTcwb88NAMDbPRvh7V6NGewqEcMdEVUKH4UdfhnTDm4O1rhwT4kJa88gJ09r6rKIqAoJITB393V8v/8mAGB632BMeqKhiasyf3qHO61Wy5GyRFQuDbycsHJ0OzjYyHH4ZiLe+f0CtFrOd0lkCYQQ+OLPq1h88DYA4OP+IZjQtb6Jq7IM7Lkjokr1mL8rFr/UGlYyCdsuxOGzP69wQnMiM6fVCszadhnLjkQBAD57tinGdg4ycVWWg+GOiCpd10aemDe4BQBg5dE7WPz3bRNXRESVRasV+HDrJfwSHg1JAr4aGIqXOwSauiyLwnBHRFViQMs6+Lh/CABg7q7r+L+z90xcEREZW3p2Hqb+fgHrT8ZAJgHznm+BIe3qmrosi1Ou5ceIiCpibOcgPEzLxpK/b2P6/11CPU8nPObvauqyiMgIdl+Ox6xtl3FfmQW5TMKCFx/DMy18TV2WRWLPHRFVqWm9G+PJJt7IydNiwpozeJCWZeqSiKgCYlMz8covp/HqmjO4r8yCv7s9fhndjsHOhBjuiKhKyWQSFrzYAg28nBCvysJra88iO09j6rKIqJzyNFr8fCgSPb/5G/uuJsBKJuH1bvWxZ3JXdG7oYeryLBrDHRFVOWc7a/z0cms421nhTHQKZm27zBG0RDXIubspeHrRUXyx4yrUORq0DXTDjrfCMK1PMOxt5KYuz+Ix3BGRSdTzdMLCoS0hScD6kzFYd+KuqUsiojKosnLx8dYIDFx8DFfvq+DqYI05g0Lx2/gOaOTtbOry6B8Md0RkMt0ae2Fa72AAwKxtl3EyKtnEFRFRSY5HJuHJ+X9jzfFoCAEMbFUH+9/uihfb1oVMxqXEqhOGOyIyqQld6+HpFr7I0wq8vu4M4lIzTV0SERUihMCKI1EYvuwEHqRlo56HI34d1x7fvPAYajnZmro8KgbDHRGZlCRJmDuoOUJquyAxPQfj15xGVi4HWBBVB1m5GkzdeAGfbr8CjVZgwGO++HNSGDrW54CJ6ozhjohMzt5GjqUvt4a7ow0iYlWYvvkiB1gQmdi9FDUGLT6G/zsXC7lMwsf9Q7Dgxcc4YKIGYLgjomrB390BPwxrBblMwtbzcVh2OMrUJRFZrGO3EvH0wiO4HKeCu6MN1oxth7GdgyBJvLauJmC4I6Jqo0P9Wvj4qSYAgNk7r2L/1QQTV0RkWYQQWHY4Ei8tP4EUdS6a1XHBH2925mnYGobhjoiqlZEdAzG4tR+0Ahi3+jS+23cTGi1P0RJVtswcDSb/dh6f/3kV2n9Gw26a0BF1XO1NXRqVE9eWJaJqRZIkfP5cMwDA72fuYcG+GzgRlYRvhzwGL2c7E1dHZJ5uPUjHpPXncOW+Kv/6uqeaYGTHQJ6GraEkwauWLY5KpYJCoYBSqYSLi4upyyEq0eYz9/DR1ghk5mrg4WSL74Y8hk4NeHqIyFjUOXn4fv8tLD8SiVyNQC1HG/wwvBUer1fL1KVRMfT9/Wa4s0AMd1ST3HqQhonrzuF6QhokCXizR0O89URDyDlpKpHBhBDYFRGPz7ZfQZwyCwDwRLAXPhvQDL48DVttMdxRiRjuqKbJzNHgkz8uY8OpGABAh3q18N2Qx+DlwtO0ROUVlZiBmdsu49CNhwAAPzd7zHq6KZ4M8TZxZVQWhjsqEcMd1VRbzt3Dh1sioM7RwMPJBt++2BKdG/I0LZE+MnM0+PHgLSz9OxI5Gi1s5DJM6FoPr3VrwLnragh9f78tdrSsWq3Gzp078fnnn2PgwIEICAiAJEmQJAmzZs3Su52EhARMnToVjRs3hr29Pdzd3REWFoZly5ZVaBLWUaNG6eop7ZaXl2fwMYhqmuda+mHbG50R7OOMxPQcvLziBL7dd4MTHhOVYd+VBPRc8DcW/nULORotujTyxO4pXfB2r8YMdmbIYkfLnjx5Ev369atQG2fOnEHv3r2RlJQEAHByckJaWhqOHDmCI0eOYNOmTdi2bRtsbGwMPoadnR0UCkWJz3MkE1maBl5O2DqxEz754zLWn4zBt/tuIi41E18+FworucX+/yrRIzRagX1XE7D8SBRORiUDAGor7DCjfwj6NPPh74cZs+j/Erq5ueGJJ57Au+++i/Xr18PHx0fvfZVKJfr374+kpCQEBwfj1KlTSEtLQ0ZGBhYtWgRra2vs3r0bkydPrlCNL774IuLj40u8yeX8Py6yPHbWcswe2BxfPhcKmQRsPH0P49ecQWYO16QlysjOw6qjUegx/yBeXXMGJ6OSYS2XMKFrfeyf2hV9Q2sz2Jk5i+25CwsLQ3JycpHHpk+frvf+8+bNQ3x8POzt7bFjxw4EBQUBAGxsbDBx4kSoVCp88MEH+OmnnzB58mQ0atTIqPUTETCsfV14OtvijV/P4q9rDzBs2XEsH9kW7o6G95YT1VRxqZn45dgd/HryLtKy8i/ZUdhbY1j7uhjRIQC1FRwFaykstueuoj1eq1evBgAMGTJEF+wKe/PNN+Hk5ASNRoN169ZV6FhEVLKeId5Y90p7KOytce5uKp5fcgz3UtSmLouoypyPScWb688hbO4BLD0UibSsPAR5OOKzZ5si/P0eeK9PMIOdhbHYcFcR169fx927dwEAffv2LXYbJycnhIWFAQD27NlTZbURWaI2ge7YNKEDfBV2iHyYgYE/HsPV+ypTl0VUqa7FqzB82XEM+OEo/rgQB41WoEO9Wlg+sg32v90VL3cIhIONxZ6gs2gMdwaIiIjQ3W/WrFmJ2xU8d+XKFYOPtX//fjRq1Ah2dnZwcXFBaGgoJk+ejJs3b+rdRnZ2NlQqVZEbkblp6O2Mza93RGNvZzxIy8YLS8IRfjvJ1GURGV2qOgcz/heBft8dxtFbSbCWSxjYqg7+nNQZ68c/jieaeEPGSb4tGsOdAeLi4nT369SpU+J2Bc+pVCqkp6cbdKx79+4hMjISDg4OUKvViIiIwHfffYdmzZph8eLFerUxe/ZsKBQK3c3f39+gWoiqu9oKe2x8tQPaBbojLTsPI1ecxI5L901dFpFRaLQCa49Ho/u8g1gdHg2tAPo288FfU7vhmxceQ1PfkmdWIMvCcGeAtLQ03X0HB4cStyv8XOF99NGqVSssWrQId+7cQXZ2NpKTk6FSqbB582bUr18fOTk5eP3117F58+Yy23r//fehVCp1t5iYmHLVQlSTKByssXpsO/Rp6oMcjRYTfz2LX47dMXVZRBVyMioZ/RcewUdbI5CizkUjbyf8+kp7LH6pNfzdS/4dIstUY8LdqlWr9JrUt6Tbrl27TP0SymXSpEmYOHEiAgICdIM/HBwcMHDgQJw4cUI3iGPq1KllTuBqa2sLFxeXIjcic2ZnLccPw1vhpcfrQghg5rbL+HDLJeTkaU1dGlG5xKVm4s315/DC0nBcva+Ci50VZj0dgh2TwtCxAVdnoeLVmHBXnTg7O+vuq9Ulj8or/FzhfSqqVq1a+OCDDwAA0dHROHfunNHaJjIXcpmEz55thml9GkOSgHUn7mL4suN4mJZt6tKIyhSdlIFv9lzHE/P/xh8X4iBJ+VP/HHinG0Z1CuKE3VSqGjOMZujQoejfv7/B+5e2ykN5+fr66u7HxsaW2BMWGxsLAHBxcYGTk5PRjg8AHTp00N2PjIxEq1atjNo+kTmQJAmvd2uAYB9nvLX+PE7dScEzi45g6cut0dzP1dTlERWRoMrCHxfi8MfF+7gQk6p7vE2AG2Y90xTN6vCaOtJPjQl3tra2sLW1NXUZAIqOkI2IiECTJk2K3a5gVG1ISEiV1EVExesR7I2tb3TCuNWnEfkwA88vCcdXA0MxsJWfqUsjC5eSkYOdEfHYdiEWJ6KSUXCVjUwCOtb3wJB2/niKK0pQOdWYcFedNGrUCHXr1sXdu3exa9cuDB48+JFtMjIycPjwYQBAr169jF7D8ePHdfeLm0SZiIqq75m/Ju3bv53HvqsP8PbGC7gcp8L7fYN5iouqVE6eFjsj7mPruVgcvpmIPO2/1023DnDDMy180S+0Njydq0eHBtU8/C+aASRJwogRIwAAGzZswJ07dx7Z5ocffkB6ejrkcjmGDx9ervbLGiCRnJyML7/8EgDg7++Pli1blqt9IkvlYmeNn15ug0k9GgAAlh+JwogVJ5GSkWPiysgSZOVqsCb8DrrPO4i3NpzHgesPkacVCKntgvf6BOPIe92x+bWOGNkxkMGOKsSiw11KSgoSExN1N602fySdWq0u8nhxc9S988478PHxgVqtxlNPPYUzZ84AAHJycrB48WJ8/PHHAIDx48cXu67sqFGjdCN5/2vt2rUYOHAgNm/ejAcPHugez8zMxNatW9GhQwdERkYCAL7++mvIZBb9MRKVi0wm4e1ejbHkpVZwsJHj2O0kPL3oCK7EcXJvqhwZ2Xn4+VAkwuYewMf/u4zY1Ex4ONli0hMNse/tLtjxVhhe61Yffm6c0oSMQxJldROZscDAQERHR5e53ciRI7Fq1apHHj9z5gx69+6NpKT8WfCdnZ2RlZWF3NxcAPmnY7dt21bstYKjRo3CL7/8AuDRnrpVq1Zh9OjRun93dHSEnZ0dUlNTodFoAORfg/jNN9/g9ddf1+/FFqJSqaBQKKBUKjktClm06/FpGLf6NO4mq2FvLcc3L7RA39Dapi6LzIQyMxdrwu9g+ZEopKjzfxd8FXaY0K0+XmjjDzvriq1xTpZH399vXnNXAa1bt8bly5cxZ84cbN++HTExMXB0dESzZs0wcuRIjBkzxqBete7du+OLL75AeHg4rl69iqSkJN0H2aBBA/To0QOvvvoqr7UjqqDGPs7Y9kYnvLn+HA7fTMRr685iypONMOmJBryAnQyWnJGDFUei8MuxO0jLzgMABNRywMRuDTCgZR3YWPFsC1Uui+65s1TsuSMqKk+jxZc7rmHF0SgAwFPNa2Pe8y1gb8OeFdLP3SQ1/r75EIdvPMThm4nIzM0/y9LQywlv9GiAp0Jrc+AOVRh77oiI9GQll2HG0yFo7OOEj7ZG4M+L9xGdlIGfR7RBbYW9qcujaigtKxfht5Nw6GZ+mItOKjqhfbM6Lnije0P0CvGGTMZeYKpa7LmzQOy5IyrZyahkTFh7BskZOfB0tsVPL7dGy7pupi6LTEwIgav307D/agIO3XyIs3dToSk0hYmVTELrADd0aeSJsIYeCK2j4Kl9Mjp9f78Z7iwQwx1R6WKS1Ri3+jSuxafBxkqGOYNC8VxLTnhsaYQQuBynwo5L97EzIh5RiRlFng/ycERYQw90aeiJx+vXgpMtT4ZR5WK4oxIx3BGVLT07D1N+O4+9VxIAABO61se03o15is3MCSFw8Z4SOyLuY+eleNxN/vd0q42VDF0aeqJ7sCe6NPSEvzunLqGqxXBHJWK4I9KPViswf+91/HDgNgDgySZemDe4BVwdbExcGRmTEAKXYpX440IcdlyKR2xqpu45O2sZujf2Qt/Q2ugR7MXeOTIphjsqEcMdUfn873ws3t10ETl5WjjbWWFC1/oY3SkQDjb8oa/JktKzseVcLH4/fQ/XE9J0j9tby9GjiRf6NauN7sGe/Jyp2mC4oxIx3BGV34WYVLy3+SKuxeeHAE/n/BUGhrT1hzWnuKgx8jRa/H3jITaejsH+qw9067raWMnQK8Qb/ZvXRtdGXpwGh6olhjsqEcMdkWE0WoE/LsRh/t7riEnOP3UXUMsBb/dshKeb+/J6vGrs1oN0/H4mBv93NhYP07J1j7fwU+D5Nv54prkvFA7WJqyQqGwMd1QihjuiisnJ02LDqbv4fv8tJKbnB4UmtV0wrU9jdGvkySkwTCw9Ow9X76twJS7/dilWiSv3/107uJajDZ5rWQeD2/ijsY+zCSslKh+GOyoRwx2RcWRk52Hl0Sgs/TtSt8xUu0B3TOzRAGENPNiTVwUeqLIQEafMD3L/BLo7/5lQGADkMgndG3vi+db+6BHsxSXAqEZiuKMSMdwRGVdKRg4W/30bq47dQU6eFgDg52aPF9v4Y3Abf/go7ExcoXkQQiAyMQOnopJx8k4yTt1J1p0e/6/aCjuE1HZBU18XhPi6oFWAG7yc+TlQzcZwRyViuCOqHPeVmVj6dyT+7+w9qLLye/JkEtAj2BtD2/mjayNPri9aDnkaLa7cV+FkVH6QO30nBUkZOUW2kUlAAy8nhNTOD3EhtRUI8XWBuyOnqyHzw3BHJWK4I6pcWbka7Iy4j/UnYnDyTrLucR8XO7zQ1h8vtPGDnxsnwC1MqxWITlbj4r1UXLqnxMVYJS7HKpGRoymynY2VDI/5u6JdoDvaBrmjVV1XONtxIARZBoY7KhHDHVHVufUgHb+duotNZ+4hRZ0LAJAkILSOAg29nNHQ2wkNvZzQ0MsZfm72FnGdnhAC91IycfGeEhdj88PcpVgl0v7p7SzM2c4KbQLc0DbIHe0C3RHqp4CtFacpIcvEcEclYrgjqnrZeRrsuZyADafu4uitpGK3sbOWob7nP2HP2xkhvi5oH+ReYyfR1WgF7qWocTMhHbcepuf/80Eabj1If6RHDsjvlQup7YLmfgqE1lGguZ8rGng5QW4BgZdIHwx3VCKGOyLTupeiRkSsEjcT0nHzQf7t9sN03WCMwmzkMrQJdENYQ090aeSBJj4u1bJ3T6nOxaXY/J64a/fTcPNBOiIfpiO7mNcEANZyCcE+Lgj1U6B5HQVC/RRo5O3MCaGJSsFwRyViuCOqfjRagbvJatxMyA9GNxPScDo6BfdSio4G9XCyRVhDD3Rp5IHODTzh6Wxb5bWmZeUiIlaFS7GpuPjPKdXoYqYfAQBbKxnq/dMb2cCroFfSCQG1HBnkiMqJ4Y5KxHBHVDMIIRCVmIHDNxNx6MZDhEcmQf2f05kNvJzg5WwLN0cbuDvYwM3BGm6ONnBzsNE95mRnhVR1DhLTc5CYno2HadlITP/nlpb/WFJGDoQQsLGSw9ZKBhsrGWzk//zzn/vWVjLcS1Ej8mFGsfUG1HJAaB0FmvoqdCHOz82Bp1WJjIThjkrEcEdUM+XkaXEmOgWHbj7E4ZsPERGrKnunSlLH1T7/2jg/BZrXcUWzOi5wdeD0I0SVieGOSsRwR2QeEtOzcTlOhZSMHKSoc/75Zy6S1TlIVecgOSMXKRk5SMvKhauDDTycbODhZJt/cy5038kWHk42kCQJOXla5Gi0yNVo8+/naZH9z2M5eVp4ONkgtI4CtZyq/nQwkaXT9/e7Zg7BIiIieDjZomsjT1OXQUTVDK9mJSIiIjIjDHdEREREZoThjoiIiMiMMNwRERERmRGGOyIiIiIzwnBHREREZEYY7oiIiIjMCMMdERERkRlhuCMiIiIyIwx3RERERGaE4Y6IiIjIjDDcEREREZkRhjsiIiIiM8JwR0RERGRGrExdAFU9IQQAQKVSmbgSIiIi0lfB73bB73hJGO4sUFpaGgDA39/fxJUQERFReaWlpUGhUJT4vCTKin9kdrRaLeLi4uDs7AxJkozWrkqlgr+/P2JiYuDi4mK0dqnq8DOs+fgZ1nz8DGu2yvz8hBBIS0uDr68vZLKSr6xjz50Fkslk8PPzq7T2XVxc+B+kGo6fYc3Hz7Dm42dYs1XW51daj10BDqggIiIiMiMMd0RERERmhOGOjMbW1hYzZ86Era2tqUshA/EzrPn4GdZ8/Axrturw+XFABREREZEZYc8dERERkRlhuCMiIiIyIwx3RERERGaE4Y6IiIjIjDDcUZm++uorSJKkuxli1apVRdoo6bZv3z4jV29ZquJ9vn37Nl599VUEBQXBzs4Onp6e6N27NzZv3mzEV2K5KvMzHDVqlF5t5+XlVcIrszwqlQpz5sxBx44d4enpCVtbW/j5+aF79+6YNWsWUlNTDWo3LS0Ns2bNQmhoKJycnKBQKNC2bVvMnz8fOTk5xn0RFs7Yn2FV/RZyhQoq1fXr1/HJJ58YrT2ZTAZPT88Sn+fQf+OorPd5x44dGDx4MNRqNYD8GdiTk5OxZ88e7NmzB6NHj8by5cuNuqydparM74qdnV3p61Ly86uwAwcOYOjQoUhISAAA2NjYwMHBAbGxsYiNjcXBgwcxYMAAPPbYY+VqNzo6Gt26dcOdO3cAAA4ODsjOzsbp06dx+vRprFu3Dvv374ebm5uRX5HlqazPEKiC30JBVAKNRiM6duwoAIgOHToIAMLQP5mVK1cKACIgIMC4RVIRlfk+R0ZGCkdHRwFAdOrUSVy/fl0IIURaWpqYMWOG7u9jzpw5Rj+2JanMz3DkyJECgBg5cqTR26Z/HTlyRNjb2wsAYuDAgeLUqVNCq9UKIYTIyMgQJ0+eFB9++KGIjIwsV7u5ubkiNDRUABC1a9cWe/fuFULk/7d6w4YNwtnZWQAQ/fr1M/prsjSV9RlW1W8he+6oRAsXLsSxY8cwfPhwNGjQAOHh4aYuiUxoxowZyMjIgI+PD7Zv3w5XV1cAgJOTEz755BPEx8fjp59+whdffIFx48ax54AsklqtxogRI5CZmYk333wT33//fZHnHRwc0LZtW7Rt27bcbf/yyy+4dOkSAGDz5s3o0KEDgPxeoBdffBFarRbDhg3Djh07sH//fjzxxBMVf0EWqDI/w6rCa+6oWFFRUfjwww9Rq1YtLFiwwNTlkIllZGTorql77bXXdMGusPfffx9A/jUqW7durcLqiKqPNWvWIDIyEj4+Ppg7d65R2/7ll18AAN27d9cFu8KGDBmCoKAgAMDq1auNemxLUpmfYVVhuKNijRs3DhkZGfjmm29KvS6ALMORI0eQmZkJAOjbt2+x2wQGBqJJkyYAgD179lRZbUTVSUGoGjx4MOzs7IzWrlqtxtGjRwGU/B2UJAl9+vQBwO9gRVTWZ1iVGO7oET///DP279+PJ598EiNGjDBq2w8fPkTr1q3h5OQEe3t71KtXDy+99BIOHjxo1ONYOmO/zxEREbr7zZo1K3G7gucuX75s0HHoX5X5Xdm/fz8aNWoEOzs7uLi4IDQ0FJMnT8bNmzcrXrgFKxjYAACtW7fG3bt3MX78ePj7+8PGxgbe3t54+umn8eeff5a77atXr0Kr1QLQ7zsYHx+P5ORkA16FZavMz7CwSv8trNQr+qjGuXfvnlAoFMLe3l7cvn1b9/jMmTONMqCi4Obm5iZsbGyKPDZ69GiRm5trrJdikSrrfX777bd17ZVm8uTJAoCoVatWRV6GRavM70rBgAoAQi6XCzc3NyGXy3WP2djYiB9//LESXpVluHbtmu69nDFjhm6Ag42NjXB1dS3yGb7yyiu6C/T1sW3bNt2+Fy5cKHG7rVu36ra7dOmSMV6WRanMz1CIqvstZM8dFfHqq69CqVRi1qxZqFevntHa9fX1xcyZM3HhwgVkZWUhOTlZd5rhySefBACsXLkSU6ZMMdoxLVFlvc9paWkA8i8kLk3B8wXbU/lV5nelVatWWLRoEe7cuYPs7GwkJydDpVJh8+bNqF+/PnJycvD6669zzkIDpaSk6O5//vnnsLa2xu+//4709HSkpKQgOjoagwcPBgAsW7asXNczF/5OlfY9LPwcv4flV5mfIVCFv4UVioZkVtasWSMAiMcee+yR/2uoaM9daTQajXj22WcFACGTycSNGzeMfgyq2Ps8btw4AUDUqVOn1O0++OAD3f/lkvFV5nclMTFRBAUF6aZpKG+PBAlx9OjRIj0wW7ZseWQbjUYjWrRooevh1reHZt26dbp2b968WeJ2e/bs0W137NgxQ1+KxarMz7Asxvx+s+eOAAAJCQmYPHky5HI5fv75Z1hZVd0sOTKZDPPmzQMAaLVa/PHHH1V2bEtSkffZ2dkZAHSTF5ek4PmC7cm4KvO7UqtWLXzwwQcA8ifKPXfunNHathSF/+4bNmyIAQMGPLKNTCbDO++8AwBISkrCmTNnyt12ad/Dws/xe1h+lfkZlsWY32+GOwIATJ8+HUlJSRg/fjyCg4ORnp5e5FZ4SZviHquoBg0awMPDAwAQGRlptHapKEPfZ19fXwD5pywKRs0WJzY2tsj2ZHyV+V0pPL0Gv4flV6dOHd394ODgErcLCQnR3Y+Ojtar7cLfqYLvWXEKP8fvYflV5meoD2N9vxnuCED+vHYAsHjxYjg7Oz9ymz17tm7bgsemTZtmqnKpihUenVd45Ox/FTzXtGnTSq+JqLpxd3cvEg5KIoTQ3dd3qbcmTZpAJsv/ydbnO+jj4wN3d3e92qZ/VeZnWJUY7qhauH37NhITEwFANwknGZ+h73Pnzp1hb28PANi1a1ex20RHR+Pq1asAgF69elWwUipJZX5Xjh8/rrvP76FhCv72C74Lxbly5Yruvr7vs4ODAzp16gSg5O+gEAK7d+8uUgeVX2V9hvow2vfbKFcBktmryICKsi7M1mq14rnnntNdRHrt2jVDy7Rolf0+v/TSS7o1LVNTUx95/rXXXhMAhLOzs0hOTi5X25SvMj/DstpOSkoS9erVEwCEv7+/0Gg0erdN/zp06FCZF+M3b95cN0CpPO/zsmXLBAAhSZI4fvz4I8//9ttvumPv27evIi/DolXWZ1iVv4UMd6SXssJd4bl7Dhw4UOS5qKgo0bZtW7FkyRJx+/Zt3R+4RqMR4eHhonfv3rp9X3vttcp+KWarou9z4TnQihMZGSkcHR0FABEWFqYbyZWeni4++eQTIUmSACDmzJlTeS/SzFXmZ7h69Wrx3HPPiU2bNomEhATd42q1WmzZskU0atRIt++GDRsq70VagOeff143knLTpk260ZTR0dHihRde0L3Pq1atKrJfaf8dFUKI3NxcERoaqgsVBQFOo9GIjRs3ChcXFwFA9O3bt9Jfo7mrjM+wKn8LGe5ILxUNdwXPARC2trbCw8ND2NraGn3iRktW0fe5rHAnhBB//vmncHBw0G2nUCiKTII7evRoTqFRAZX5Gf538lRHR0dRq1atIp+fra2t+OGHH6ripZq19PR00aVLlyLvq5ubW5H3f+bMmY/sV1a4EyL/byQwMFC3nYODg7Czs9P9e8uWLdlzbgSV8RlW5W9h1c13QRbL29sbCxcuRHh4OM6fP4+HDx8iJSUFdnZ2CAoKQseOHTFmzBjd9SRkmKp4n/v164eLFy9izpw52Lt3L+7fvw83Nze0bNkSr776KgYNGmTEV2R5KvMz7N69O7744guEh4fj6tWrSEpKglKphIuLCxo0aIAePXrg1Vdf5bV2RuDo6IgDBw5gxYoVWLNmDSIiIpCWloY6deogLCwMb775Jjp27GhQ24GBgbh48SLmzZuH//u//0NUVBSsra3RtGlTDB06FG+++SZsbGyM/IosT2V8hlX5WygJUWjIBxERERHVaBwtS0RERGRGGO6IiIiIzAjDHREREZEZYbgjIiIiMiMMd0RERERmhOGOiIiIyIww3BERERGZEYY7IiIiIjPCcEdERERkRhjuiIiIiMwIwx0RERGRGWG4IyIiIjIjDHdERERV7MSJE+jcuTOcnZ0RGhqKX3/91dQlkRmRhBDC1EUQERFZiitXrqB169bIysqCs7Mz0tLSAADr1q3DsGHDTFwdmQP23BEREVWhhQsXQgiBffv2QaVS4dq1a6hTpw7mzZtn6tLITDDcERERVaE7d+4gJCQETzzxBACgcePG6Nu3L+Li4kxcGZkLhjsiqnFGjRoFSZIwatQoU5dChI8++giSJGHOnDl6bf/YY4/h4sWLWLduHdRqNQ4dOoQtW7agS5cuxW7fp08fSJKEv/76y5hlkxljuCOyQLNmzYIkSZAkqcqOuWrVKsyaNQsHDx40ynZU/dSEz87YNd67dw/ffPMNPD098cYbb+i1z7Rp0xAcHIyXXnoJjo6O6Nq1K+rUqYPvvvuu2O1nzZoFAHjnnXeg1WqNUjeZNytTF0BElmHVqlX4+++/AQDdunWr0Ha1a9dG48aNUbt2bWOXSRWg72dsSsau8cMPP0RmZiY+/fRTODo66rWPm5sb6tevj8uXL+se++yzz0r8e3788cfRu3dv7N69G2vXrsWIESMqXDeZN/bcEVGNM3v2bFy7dg2zZ882dSlkwWJjY7Fu3TrY2NhgzJgxeu+3e/dubNu2DZ07d8Zjjz0GALhw4UKp+0yYMAEAMHfuXIPrJcvBcEdERGSAn3/+GRqNBv369YO7u7te++Tm5mLy5MmQJAnffPMNQkNDAQDnz58vdb+CY1y+fBlHjx6taOlk7gQRWZyZM2cKAKI8/wlITk4Wy5YtE4MHDxbNmjUTbm5uwtbWVtStW1cMHTpUhIeHF7vfypUrdccq6RYVFaX3dkIIMXLkSAFAjBw5ssR67969K959913RokUL4eLiIuzs7ES9evXEM888I3755ReRmZlZ7H5RUVHirbfeEiEhIcLR0VHY29uLxo0bi0mTJono6Gi93y9j1nTgwAHx/PPPC19fX2FjYyNq1aolevToIVasWCHy8vKK3adr164CgJg5c6bQarXip59+Eu3atRPOzs7CyclJPP7442LNmjWl1rthwwbRp08f4eXlJaysrIRCoRANGjQQTz/9tFi0aJGu3vJ8dkIY/rdUkddV3hrLotVqhZ+fnwAgNm7cqPd+8+fPFwDE8OHDhRBCzJkzRwAQ9evXL3PfcePGCQBixIgReh+PLBPDHZEFMiTcFd5HLpfrfpALHpMkSXz33XeP7Ldhwwbh7e0trK2tBQDh6OgovL29i9zu3r2r93ZClB3uVq9eLezs7HS1FQQiKysr3WPnzp17ZL+1a9cWeU22trbC3t5e9+/Ozs5i9+7der9nxqhpypQpRd5jV1dXIZfLdY/16NFDqFSqR/YrCEEfffSRePbZZwUAYWVlJVxcXIoEmhkzZhRb7+jRo4ts5+TkJBwcHIoNQ+X57IQw/G+pIq+rvDWW5eLFi7pj3b9/X699EhIShEKhEPb29rpj7dixQ/eai/scC1uzZo0AILy9vfWukywTwx2RBTIk3C1dulTMnDlTnD59WmRnZwsh8nsvIiMjxVtvvSUkSRJyuVycPXu22P0L97iURp/tSgt327dvF5IkCQCiU6dO4vDhw0Kj0QghhMjOzhaHDx8W48aNE5cvXy6y3549e4RMJhNWVlZi2rRpIioqSmi1WqHVasW1a9fE4MGDBQDh4uJS7h48Q2tauHCh7nMaP368LkSkp6eLBQsW6ILhiy+++MgxC95HNzc3oVAoxKpVq4RarRZCCBETEyOefvppAUDIZDJx48aNIvsePnxY99ycOXNEUlKS7rnExESxe/duMXLkSBEbG1vsMcv6jCvyt1SR11WeGsuyaNEiAUD4+/vrvc+YMWMEAPHhhx/qHrt7967uMz58+HCp+9+4cUO37dWrVw2uncwfwx2RBTIk3JVl4sSJAoAYO3Zssc9XRbjLzc0VQUFBAoDo3LmzLjiURaPRiIYNGwoAYunSpSVu98wzzwgA4q233tKr3YrUpFarhbu7uwAghg4dWuw233//ve5zPH36dJHnCt5HAOKvv/56ZN+srCzh6+srAIjPP/+8yHMFpwp79eql56ssesyKBqfS/pYq8rqMWePLL78sAIj+/fvrtf2pU6eEJEnCx8dHpKWlFXnO1dVVABCLFi0qsx0nJycBQKxYscKguskycEAFERnFU089BQA4cuSIyWo4cOAAoqKiAAALFiyAjY2NXvsdOnQIN2/ehIeHB1555ZUStyuYgmL37t2VXtPevXuRnJwM4N95zv7r9ddf102fUdLC8506dUL37t0fedzW1ha9e/cGAFy8eLHIc66urgCAhw8fQqPR6FWvMenzt2TI6zKmgtUkPD09y9xWCIFJkyZBCIHPPvsMTk5ORZ5v2rQpgLIHVQBArVq1ihyfqDic546I9BYZGYkff/wRBw4cwO3bt5GWlvbIpKr37t0zUXXAsWPHAAA+Pj5o06aN3vsVjD5UKpXw9fUtcbucnBwAQHR0dKXXdPr0aQCAv78/GjVqVOw2crkcPXr0wLp163Tb/1f79u1LPEbBay0IkQWeeOIJ2NnZ4dy5cwgLC8PYsWPRo0cPBAUF6V1/WSr6t2TI6zKmhw8fAoBeo2TXrl2L8PBwNG/evNgpU0JDQ3H06FG9wp27uzuio6N1xycqDsMdEelly5YtGDp0KLKzs3WPubi4wM7ODpIkIScnBykpKcjIyDBZjfHx8QCAgICAcu1X0AuSm5uLhISEMrfPzMys9JoePHgAAKhTp06p2/n5+RXZ/r+cnZ1L3NfKKv8nIDc3t8jj9evXx7JlyzBhwgSEh4cjPDwcQH4vVffu3TFs2DA888wzBq9wYoy/JUNelzFlZWUByO8pLE16ejree+89AMD8+fMhkz16wqxgOpSIiAhoNBrI5fIS27O3ty9yfKLi8LQsEZUpKSkJo0aNQnZ2Nnr06IGDBw9CrVZDqVQiISEB8fHx+P33301dpsFho+DUY/v27SHyr0Uu81bZNZna8OHDER0djSVLluDFF1+Ev78/Hj58iI0bN2LAgAHo2rUrVCpVudutKX9LZSk4PZqSklLqdp9//jnu378PAOjZs6du2b/Ct4kTJwLID2zXr18vtb2C3siC4xMVh+GOiMq0Y8cOqFQquLm54Y8//kDXrl11PQgFCnqoTMnHxwdA+U6bVmS/ymzby8sLQNmnuQueL9jemNzd3fHqq69iw4YNuHv3Lm7duoXp06dDkiQcPny4xGsBS1NT/pbKUnCtXWmnfm/duoUFCxaUq92yVqooOJ4+1/qR5WK4I6IyxcTEAAAaN24MBweHYrfZt29fqW0UnI4qq9dL3+2K07FjRwD54aCka9CK06lTJ4P2q8yaCq7Pu3fvHm7cuFHsNhqNBgcOHAAAtG3btoKVlq1+/fqYPXs2hg0bBiB/0Edh+nx2xvhbqoiK/H0VFhISAiD/2sGSTJkyBTk5ORg7dizS0tJKvfn7+wMofVBFWloaEhMTAQBNmjSpUP1k3hjuiKhMCoUCAHDjxo1ir/U5f/58iaM1C7i4uAAAUlNTjbJdcbp374569eoB+PeHVd/9GjRooPd+5blQ39CaevbsqTv1VlIP2dKlS3XXCw4dOlTvmspS+Fq44hT0tP33+jF9Pjtj/C1VREX+vgrr0qULgPyetuLer127dmH79u2wtbXFzJkz4eTkVOqtcePGAEoPd6dPn4ZWq4WVlZXuf0iIisNwR2ThEhMTS72lpqaiV69ekMlkSE5OxvDhwxEbGwsgf/Toxo0b0atXr1IvcAeAZs2aAcg/LVewf0W2K45cLseiRYsgSRKOHDmCJ554AkeOHNGNwszJycHBgwfx0ksv4cqVK7r9rKyssGTJElhZWeHIkSPo0qUL9u/fX+SC/MjISCxZsgRt27bFjz/+WOk12dvb60Ld+vXrMWHCBN1gD7Vaje+//x6TJ08GALz44oto3bp1ud6r0rzxxht44YUXsHnz5iIDNdLT07FkyRKsXr0awL9TlhTQ57Mzxt9SRVTk76uwTp06wcrKCjk5OY8EsoL1YwFgwoQJul650gQHBwMo/bTsiRMnAACtWrV6ZDoVoiKqfmo9IjK1wpMYl3Vr0aKFEEKI9957r8jjCoVCt5RTUFCQWLduXakTI9+4cUO3/JZMJhPe3t4iICBABAQEiJiYmHJtV9byY7/88ssjy4jps9TXli1bhLOzs24ba2trUatWrSJtoYTJcctiaE3/XX7Mzc2tyD7du3cvdfmx0ibrLfg76Nq1a5HHC97fgpuTk5Nuot2CW+fOnUV6enqR/fT9jCvyt1SR11WeGvVRsPzZBx98UOTxr7/+WrfEWXx8vF5tFV6NpKTlzDp06CAAiG+//bZcdZLlYc8dEenlq6++wurVq9GuXTvY29sjNzcXDRo0wAcffIBz586VOj8cADRs2BAHDhzAM888A09PTyQlJSE6OhrR0dHIy8sr93alGTFiBK5du4bJkycjJCQEVlZWyMzMREBAAAYMGIA1a9YUe83SgAEDcOvWLcycORPt2rWDk5MTUlNTYWtrixYtWuCVV17Bli1b8O6775bvzatATd988w3++usvDBo0CN7e3khPT4ezszO6d++OFStWYO/evUbv6fr444/x/fff47nnnkNwcDCsrKyQnp4OLy8v9OzZEytWrMDBgwfh6OhYZD99P7uK/i1VhDH+vgq8+uqrAPInkBb/XMOXkJCAzz77DAAwadIkeHt769VWQc8dUPyp2cjISISHh8Pe3l43mTZRSSQhKnhVKRERkQXSarVo1KgRbt++jb///lt3HV5l+PTTTzFz5kyMHj0aK1asqLTjkHlguCMiIjLQ+vXrMWzYMPTt2xc7duyolGNkZGQgMDAQaWlpuH79erknxCbLw9OyREREBhoyZAjatWuHnTt34uTJk5VyjEWLFiExMRGTJk1isCO9cPkxIiIiA0mShKVLl2Lr1q2Vtt6ro6MjZs2apRuBS1QWnpYlIiIiMiM8LUtERERkRhjuiIiIiMwIwx0RERGRGWG4IyIiIjIjDHdEREREZoThjoiIiMiMMNwRERERmRGGOyIiIiIzwnBHREREZEb+H/LXyZhilQoAAAAAAElFTkSuQmCC\n" + "image/png": 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\n" }, "metadata": {} } @@ -2105,27 +2093,48 @@ { "cell_type": "markdown", "source": [ - "# Run MD with ASE" + "## Phonon bandstructure" ], "metadata": { - "id": "DfEfRfcslnPl" + "id": "coY0nQnxovsZ" } }, { "cell_type": "code", "source": [ - "from jarvis.db.figshare import get_jid_data\n", + "!pip install -q phonopy" + ], + "metadata": { + "id": "uIyKFi6XovCy" + }, + "execution_count": 59, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from alignn.ff.ff import phonons\n", + "from jarvis.core.atoms import ase_to_atoms\n", "from jarvis.core.atoms import Atoms\n", - "a=Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms'])\n" + "from jarvis.db.figshare import get_jid_data\n", + "\n", + "ph_path=dir_name\n", + "atoms=Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms'])\n", + "ph=phonons(model_path=ph_path,atoms=atoms,force_multiplier=my_config['batch_size'])\n", + "%matplotlib inline\n", + "plt.axis('off')\n", + "plt.imshow(plt.imread(\"phonopy_bands.png\"))\n", + "plt.show()" ], "metadata": { - "id": "hVLl3BzWEqzW", - "outputId": "f708806e-057f-489a-f2f7-18aad527ac2f", + "id": "fhO4ztCBo3QR", + "outputId": "e0ce766e-ab1c-4f7b-d87d-8b37e86d1cd8", "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 806 } }, - "execution_count": null, + "execution_count": 61, "outputs": [ { "output_type": "stream", @@ -2137,57 +2146,284 @@ "Loading the zipfile...\n", "Loading completed.\n" ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['rotations']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['translations']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning:\n", + "\n", + "dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + "\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} } ] }, + { + "cell_type": "markdown", + "source": [ + "You can compare the bandstructure here: https://www.ctcms.nist.gov/~knc6/static/JARVIS-DFT/JVASP-1002.xml#elastic_tensor" + ], + "metadata": { + "id": "Ji36ts2Ypd7b" + } + }, { "cell_type": "code", "source": [ - "#2x2x2 supercell\n", - "sup_a = a.get_conventional_atoms.make_supercell_matrix([2,2,2])\n", - "sup_a.write_poscar('POSCAR-SC')" + "tmp_ph=ph\n", + "print(tprop_dict.keys())\n", + "ph.run_mesh(mesh=[20, 20, 20])\n", + "ph.run_thermal_properties(t_step=10, t_max=1000, t_min=0)\n", + "tprop_dict = ph.get_thermal_properties_dict()\n", + "plt.plot(tprop_dict['temperatures'],tprop_dict['free_energy'],label='Free energy',color='red')\n", + "plt.plot(tprop_dict['temperatures'],tprop_dict['entropy'],label='entropy',color='blue')\n", + "plt.plot(tprop_dict['temperatures'],tprop_dict['heat_capacity'],label='heat_capacity',color='green')\n", + "plt.legend()\n", + "# See https://phonopy.github.io/phonopy/examples.html" ], "metadata": { - "id": "IyFw2SFSFC0k" + "id": "tkmz3K03o3Nl", + "outputId": "c64a0e7f-6d53-4980-b215-60f6f202e047", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + } }, - "execution_count": null, + "execution_count": 62, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "dict_keys(['temperatures', 'free_energy', 'entropy', 'heat_capacity'])\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 62 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Elastic constants" + ], + "metadata": { + "id": "fF1UWEDFpuxu" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q elastic" + ], + "metadata": { + "id": "k1er8TQ2puRM" + }, + "execution_count": 63, "outputs": [] }, { "cell_type": "code", "source": [ - "%%time\n", - "from alignn.ff.ff import AlignnAtomwiseCalculator\n", - "from ase.io.trajectory import Trajectory, TrajectoryReader\n", - "from ase.md.velocitydistribution import MaxwellBoltzmannDistribution\n", - "from ase.md.verlet import VelocityVerlet\n", - "from ase.optimize import QuasiNewton, fire, gpmin, mdmin, LBFGS, BFGS, FIRE\n", - "from ase import units\n", + "from elastic import get_elementary_deformations, get_elastic_tensor\n", + "import elastic\n", + "import ase\n", + "from jarvis.core.atoms import ase_to_atoms\n", "from jarvis.core.atoms import Atoms\n", - "from ase.md.nvtberendsen import NVTBerendsen\n", - "from ase.md.npt import NPT\n", - "from ase.md.langevin import Langevin\n", - "# From https://gist.github.com/leelasd/aaa517ac03d2f03bc1e181833e3a70fd\n", - "# https://mattermodeling.stackexchange.com/questions/11354/basic-md-in-ase\n", - "model_path = dir_name #\"alff2_wt_1_determSi\"\n", - "atoms = Atoms.from_poscar(\"POSCAR-SC\").ase_converter()\n", - "traj_file = \"traj.traj\"\n", - "\n", - "!rm -f {traj_file}\n", - "traj = Trajectory(traj_file, \"w\", atoms)\n", - "calc = AlignnAtomwiseCalculator(\n", - " path=model_path,\n", - " force_mult_natoms=False,\n", - " force_multiplier=2,\n", - " stress_wt=-4800,\n", - ")\n", - "atoms.set_calculator(calc)\n", + "from jarvis.db.figshare import get_jid_data\n", "\n", - "#Please note: for getting a reasonable FF you might have to train for larger number of epochs\n", - "def printenergy(a=atoms): # store a reference to atoms in the definition.\n", - " \"\"\"Function to print the potential, kinetic and total energy.\"\"\"\n", - " epot = a.get_potential_energy() / len(a)\n", - " ekin = a.get_kinetic_energy() / len(a)\n", + "ph_path=dir_name\n", + "atoms=Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms']).get_conventional_atoms\n", + "ase_atoms = atoms.ase_converter()\n", + "ase_atoms.calc = calc\n", + "systems = get_elementary_deformations(ase_atoms)\n", + "cij_order = elastic.elastic.get_cij_order(ase_atoms)\n", + "Cij, Bij = get_elastic_tensor(ase_atoms, systems)\n", + "for i, j in zip(cij_order, Cij):\n", + " print(i, j / ase.units.GPa)" + ], + "metadata": { + "id": "I1Wv2dUFo2qX", + "outputId": "d00b6109-176e-48fc-9cb1-a1140fe27737", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 64, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 3D dataset 76k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n", + "Loading the zipfile...\n", + "Loading completed.\n", + "C_11 2.5300948366522813\n", + "C_12 1.9751228226348776\n", + "C_44 2.1891529961290184\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "4i9893Mzo2nl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Run MD with ASE" + ], + "metadata": { + "id": "DfEfRfcslnPl" + } + }, + { + "cell_type": "code", + "source": [ + "from jarvis.db.figshare import get_jid_data\n", + "from jarvis.core.atoms import Atoms\n", + "a=Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms'])\n" + ], + "metadata": { + "id": "hVLl3BzWEqzW", + "outputId": "7c2c5f39-9be3-4cc5-eb89-3d21f3659b94", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Obtaining 3D dataset 76k ...\n", + "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", + "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 40.8M/40.8M [00:02<00:00, 13.7MiB/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading the zipfile...\n", + "Loading completed.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#2x2x2 supercell\n", + "sup_a = a.get_conventional_atoms.make_supercell_matrix([2,2,2])\n", + "sup_a.write_poscar('POSCAR-SC')" + ], + "metadata": { + "id": "IyFw2SFSFC0k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "from alignn.ff.ff import AlignnAtomwiseCalculator\n", + "from ase.io.trajectory import Trajectory, TrajectoryReader\n", + "from ase.md.velocitydistribution import MaxwellBoltzmannDistribution\n", + "from ase.md.verlet import VelocityVerlet\n", + "from ase.optimize import QuasiNewton, fire, gpmin, mdmin, LBFGS, BFGS, FIRE\n", + "from ase import units\n", + "from jarvis.core.atoms import Atoms\n", + "from ase.md.nvtberendsen import NVTBerendsen\n", + "from ase.md.npt import NPT\n", + "from ase.md.langevin import Langevin\n", + "# From https://gist.github.com/leelasd/aaa517ac03d2f03bc1e181833e3a70fd\n", + "# https://mattermodeling.stackexchange.com/questions/11354/basic-md-in-ase\n", + "model_path = dir_name #\"alff2_wt_1_determSi\"\n", + "atoms = Atoms.from_poscar(\"POSCAR-SC\").ase_converter()\n", + "traj_file = \"traj.traj\"\n", + "\n", + "!rm -f {traj_file}\n", + "traj = Trajectory(traj_file, \"w\", atoms)\n", + "calc = AlignnAtomwiseCalculator(\n", + " path=model_path,\n", + " force_mult_natoms=False,\n", + " force_multiplier=2,\n", + " stress_wt=-4800,\n", + ")\n", + "atoms.set_calculator(calc)\n", + "\n", + "#Please note: for getting a reasonable FF you might have to train for larger number of epochs\n", + "def printenergy(a=atoms): # store a reference to atoms in the definition.\n", + " \"\"\"Function to print the potential, kinetic and total energy.\"\"\"\n", + " epot = a.get_potential_energy() / len(a)\n", + " ekin = a.get_kinetic_energy() / len(a)\n", " print(\n", " \"Energy per atom: Epot = %.3feV Ekin = %.3feV (T=%3.0fK) \"\n", " \"Etot = %.3feV\" % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin)\n", @@ -2254,11 +2490,10 @@ ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 929 + "base_uri": "https://localhost:8080/" }, "id": "qGl9WZL7he-6", - "outputId": "88b7be39-8dac-40a8-da2f-fc08eb5e2bac" + "outputId": "9b735513-ec31-4dbb-839d-f8ba33a00822" }, "execution_count": null, "outputs": [ @@ -2275,56 +2510,1142 @@ "text": [ "Running Equilibration\n", " Step Time Energy fmax\n", - "FIRE: 0 06:58:11 -342.009125 0.026749\n", - "Energy per atom: Epot = -5.344eV Ekin = 0.000eV (T= 0K) Etot = -5.344eV\n", + "FIRE: 0 06:32:06 -297.719727 0.000000\n", + "Energy per atom: Epot = -4.652eV Ekin = 0.000eV (T= 0K) Etot = -4.652eV\n", "Equilibration finished\n", "Running NVE\n", - "Energy per atom: Epot = -5.344eV Ekin = 0.040eV (T=307K) Etot = -5.304eV\n", - "Energy per atom: Epot = -5.336eV Ekin = 0.015eV (T=113K) Etot = -5.321eV\n", - "Energy per atom: Epot = -5.338eV Ekin = 0.019eV (T=147K) Etot = -5.319eV\n", - "Energy per atom: Epot = -5.341eV Ekin = 0.025eV (T=194K) Etot = -5.316eV\n", - "Energy per atom: Epot = -5.339eV Ekin = 0.022eV (T=169K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.337eV Ekin = 0.017eV (T=135K) Etot = -5.320eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.022eV (T=173K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.022eV (T=171K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.338eV Ekin = 0.019eV (T=151K) Etot = -5.319eV\n", - "Energy per atom: Epot = -5.338eV Ekin = 0.019eV (T=150K) Etot = -5.318eV\n", - "Energy per atom: Epot = -5.339eV Ekin = 0.022eV (T=168K) Etot = -5.317eV\n", + "Energy per atom: Epot = -4.652eV Ekin = 0.031eV (T=242K) Etot = -4.621eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=119K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=110K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=103K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.011eV (T= 83K) Etot = -4.633eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T= 99K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=108K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=119K) Etot = -4.631eV\n", "NVE finished\n", "Running NVT\n", - "Energy per atom: Epot = -5.339eV Ekin = 0.022eV (T=168K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n", - "Energy per atom: Epot = -5.340eV Ekin = 0.023eV (T=176K) Etot = -5.317eV\n" + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=119K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=124K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=123K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=117K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=120K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=116K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n" ] }, { - "output_type": "error", - "ename": "KeyboardInterrupt", - "evalue": "", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/md/md.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmaximum\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0msteps\u001b[0m \u001b[0mare\u001b[0m \u001b[0mreached\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \"\"\"\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mDynamics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/optimize/optimize.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 273\u001b[0m \"\"\"\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mconverged\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDynamics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mirun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconverged\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/optimize/optimize.py\u001b[0m in \u001b[0;36mirun\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;31m# compute the initial step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_forces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;31m# log the initial step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/optimize/optimize.py\u001b[0m in \u001b[0;36mget_forces\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_forces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_forces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mlazyproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/atoms.py\u001b[0m in \u001b[0;36mget_forces\u001b[0;34m(self, apply_constraint, md)\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_calc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 811\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Atoms object has no calculator.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 812\u001b[0;31m \u001b[0mforces\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_calc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_forces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 814\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mapply_constraint\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/calculators/abc.py\u001b[0m in \u001b[0;36mget_forces\u001b[0;34m(self, atoms)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_forces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'forces'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_stress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/calculators/calculator.py\u001b[0m in \u001b[0;36mget_property\u001b[0;34m(self, name, atoms, allow_calculation)\u001b[0m\n\u001b[1;32m 536\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msystem_changes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/alignn/ff/ff.py\u001b[0m in \u001b[0;36mcalculate\u001b[0;34m(self, atoms, properties, system_changes)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0mj_atoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mase_to_atoms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0mnum_atoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj_atoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_atoms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m g, lg = Graph.atom_dgl_multigraph(\n\u001b[0m\u001b[1;32m 309\u001b[0m \u001b[0mj_atoms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0mneighbor_strategy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"neighbor_strategy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/alignn/graphs.py\u001b[0m in \u001b[0;36matom_dgl_multigraph\u001b[0;34m(atoms, neighbor_strategy, cutoff, max_neighbors, atom_features, max_attempts, id, compute_line_graph, use_canonize, use_lattice_prop, cutoff_extra)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0muse_canonize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_canonize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m )\n\u001b[0m\u001b[1;32m 383\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_undirected_edgedata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mneighbor_strategy\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"radius_graph\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/alignn/graphs.py\u001b[0m in \u001b[0;36mbuild_undirected_edgedata\u001b[0;34m(atoms, edges)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;31m# fractional coordinate for periodic image of dst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0mdst_coord\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrac_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdst_id\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdst_image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m \u001b[0;31m# cartesian displacement vector pointing from src -> dst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 147\u001b[0m d = atoms.lattice.cart_coords(\n\u001b[1;32m 148\u001b[0m \u001b[0mdst_coord\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrac_coords\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msrc_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/jarvis/core/lattice.py\u001b[0m in \u001b[0;36mcart_coords\u001b[0;34m(self, frac_coords)\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inv_lat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mcart_coords\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrac_coords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 227\u001b[0m \u001b[0;34m\"\"\"Return cartesian coords from fractional coords using Lattice.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrac_coords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING: NPT: Setting the center-of-mass momentum to zero (was -2.85928 -3.37466 3.46694)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Energy per atom: Epot = -4.645eV Ekin = 0.015eV (T=112K) Etot = -4.631eV\n", + "NVT finished\n", + "Running NPT\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.017eV (T=128K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 92K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.016eV (T=127K) Etot = -4.630eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=116K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.644eV Ekin = 0.012eV (T= 97K) Etot = -4.632eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.014eV (T=111K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.646eV Ekin = 0.015eV (T=118K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "NPT finished\n", + "CPU times: user 2min 35s, sys: 1min 53s, total: 4min 29s\n", + "Wall time: 2min 42s\n" ] } ] @@ -2340,230 +3661,105 @@ }, { "cell_type": "code", - "source": [ - "!pip install -q phonopy" - ], + "source": [], + "metadata": { + "id": "U5e5RePpQqGF" + }, + "execution_count": 62, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "JiEA_OU3QYq6" + }, + "execution_count": 62, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], "metadata": { - "id": "U5e5RePpQqGF", - "outputId": "84e4d8d5-5326-49d9-acec-170d4275a254", + "id": "XzlInPp-bW7-", + "outputId": "6c0961ae-2692-42d2-dab0-b1a89d60fdbf", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": null, + "execution_count": 27, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/4.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/627.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m627.0/627.0 kB\u001b[0m \u001b[31m24.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" ] } ] }, { "cell_type": "code", - "source": [ - "from alignn.ff.ff import phonons\n", - "from jarvis.core.atoms import ase_to_atoms\n", - "from jarvis.core.atoms import Atoms\n", - "from jarvis.db.figshare import get_jid_data\n", - "\n", - "ph_path=dir_name\n", - "atoms=Atoms.from_dict(get_jid_data(jid='JVASP-1002',dataset='dft_3d')['atoms'])\n", - "ph=phonons(model_path=ph_path,atoms=atoms)\n", - "%matplotlib inline\n", - "plt.axis('off')\n", - "plt.imshow(plt.imread(\"phonopy_bands.png\"))\n", - "plt.show()" - ], + "source": [], "metadata": { - "id": "JiEA_OU3QYq6", - "outputId": "1a97d2f9-9dc9-4462-cf78-a1621df6fdb4", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 372 - } + "id": "91iSCxc7bgyN" }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Obtaining 3D dataset 76k ...\n", - "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", - "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n", - "Loading the zipfile...\n", - "Loading completed.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] + "execution_count": 63, + "outputs": [] }, { "cell_type": "code", - "source": [ - "tmp_ph=ph" - ], + "source": [], "metadata": { - "id": "pBk5OLbMb8sj" + "id": "y6zECqdObgtn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "source": [ - "ph.run_mesh(mesh=[20, 20, 20])\n", - "ph.run_thermal_properties(t_step=10, t_max=1000, t_min=0)\n", - "tprop_dict = ph.get_thermal_properties_dict()" - ], + "source": [], "metadata": { - "id": "ujVhL2Occ0wK" + "id": "U9BIqgcObgq9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "source": [ - "tprop_dict.keys()" - ], + "source": [], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "g1srWmE6c78H", - "outputId": "5d9654c3-1e25-4730-9af0-95c0e44a697b" + "id": "pBk5OLbMb8sj" }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "dict_keys(['temperatures', 'free_energy', 'entropy', 'heat_capacity'])" - ] - }, - "metadata": {}, - "execution_count": 199 - } - ] + "execution_count": 30, + "outputs": [] }, { "cell_type": "code", - "source": [ - "plt.plot(tprop_dict['temperatures'],tprop_dict['free_energy'],label='Free energy',color='red')\n", - "plt.plot(tprop_dict['temperatures'],tprop_dict['entropy'],label='entropy',color='blue')\n", - "plt.plot(tprop_dict['temperatures'],tprop_dict['heat_capacity'],label='heat_capacity',color='green')\n", - "plt.legend()\n", - "# See https://phonopy.github.io/phonopy/examples.html" - ], + "source": [], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 457 - }, - "id": "0AAFuakmc_hc", - "outputId": "3262b2cf-1072-44fb-c835-0cfad5b96aab" + "id": "ujVhL2Occ0wK" }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 200 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] + "execution_count": 31, + "outputs": [] }, { "cell_type": "code", - "source": [ - "# \"\"\"Example of QHA calculation by Al.\"\"\"\n", - "\n", - "# import numpy as np\n", - "# import yaml\n", - "# from yaml import CLoader as Loader\n", - "\n", - "# from phonopy import PhonopyQHA\n", - "\n", - "# volumes = []\n", - "# energies = []\n", - "# for line in open(\"e-v.dat\"):\n", - "# v, e = line.split()\n", - "# volumes.append(float(v))\n", - "# energies.append(float(e))\n", - "\n", - "# entropy = []\n", - "# cv = []\n", - "# fe = []\n", - "# for index in range(-5, 6):\n", - "# filename = \"thermal_properties.yaml-%d\" % index\n", - "# print(\"Reading %s\" % filename)\n", - "# thermal_properties = yaml.load(open(filename), Loader=Loader)[\"thermal_properties\"]\n", - "# temperatures = [v[\"temperature\"] for v in thermal_properties]\n", - "# cv.append([v[\"heat_capacity\"] for v in thermal_properties])\n", - "# entropy.append([v[\"entropy\"] for v in thermal_properties])\n", - "# fe.append([v[\"free_energy\"] for v in thermal_properties])\n", - "\n", - "# qha = PhonopyQHA(\n", - "# volumes,\n", - "# energies,\n", - "# temperatures=temperatures,\n", - "# free_energy=np.transpose(fe),\n", - "# cv=np.transpose(cv),\n", - "# entropy=np.transpose(entropy),\n", - "# t_max=400,\n", - "# verbose=True,\n", - "# )\n", - "\n", - "# # qha.plot_helmholtz_volume().show()\n", - "# # qha.plot_volume_temperature().show()\n", - "# qha.plot_thermal_expansion().show()\n", - "# # plot = qha.plot_volume_expansion()\n", - "# # if plot:\n", - "# # plot.show()\n", - "# # qha.plot_gibbs_temperature().show()\n", - "# # qha.plot_bulk_modulus_temperature().show()\n", - "# # qha.plot_heat_capacity_P_numerical().show()\n", - "# # qha.plot_heat_capacity_P_polyfit().show()\n", - "# # qha.plot_gruneisen_temperature().show()" - ], + "source": [], "metadata": { - "id": "YbLhUl5Kfpqa" + "id": "g1srWmE6c78H" }, - "execution_count": null, + "execution_count": 64, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "0AAFuakmc_hc" + }, + "execution_count": 64, "outputs": [] }, { @@ -2587,7 +3783,7 @@ { "cell_type": "code", "source": [ - "\n", + "#Check translationally equivariant\n", "from ase import Atom, Atoms\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2618,7 +3814,7 @@ " dist_111_more.append(np.sign(a)*np.sqrt(np.sum(np.dot(A, [a,a,a])**2)))\n", "plt.plot(dist,en)\n", "plt.plot(dist_111_more,en_111_more)\n", - "plt.ylim([-10.7,-10])\n", + "#plt.ylim([-10.7,-10])\n", "# plt.savefig('kevin.png')\n", "# plt.close()\n", "#\"\"\"\n", @@ -2629,21 +3825,21 @@ "id": "UlxYtRR-MIP8", "colab": { "base_uri": "https://localhost:8080/", - "height": 466 + "height": 458 }, - "outputId": "15175c39-9b66-456f-c2bf-3f15003979b5" + "outputId": "b7f06d53-479e-4fea-e8ee-b38ff329fecb" }, - "execution_count": null, + "execution_count": 34, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "(-10.7, -10.0)" + "[]" ] }, "metadata": {}, - "execution_count": 202 + "execution_count": 34 }, { "output_type": "display_data", @@ -2651,7 +3847,7 @@ "text/plain": [ "
" ], - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {} } @@ -2660,6 +3856,7 @@ { "cell_type": "code", "source": [ + "#Check rotationally equivariant\n", "atoms_si = Atoms([Atom('Si', (0, 0, 0)), Atom('Si', (0.25, 0.25, 0.25))], cell = A, pbc=True)\n", "atoms_si.calc = calc\n", "#translations\n", @@ -2674,21 +3871,21 @@ "id": "BBzZ3QtsQYoY", "colab": { "base_uri": "https://localhost:8080/", - "height": 480 + "height": 458 }, - "outputId": "aa6dabd3-aef6-42ce-a981-5f4b004533de" + "outputId": "62c82068-51bb-4c68-f233-b65a4cf8fa1a" }, - "execution_count": null, + "execution_count": 35, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "[]" + "[]" ] }, "metadata": {}, - "execution_count": 203 + "execution_count": 35 }, { "output_type": "display_data", @@ -2696,12 +3893,21 @@ "text/plain": [ "
" ], - "image/png": 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\n" 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\n" }, "metadata": {} } ] }, + { + "cell_type": "markdown", + "source": [ + "Interface design, gamma surface plot" + ], + "metadata": { + "id": "QfyrAxKTluWq" + } + }, { "cell_type": "code", "source": [ @@ -2712,18 +3918,16 @@ "base_uri": "https://localhost:8080/" }, "id": "zBX73fQ7ZVHr", - "outputId": "ae544994-db9a-4ecc-894e-9d2f814fbf80" + "outputId": "147e1fe8-1395-46f9-8120-3f3a96e1da9a" }, - "execution_count": null, + "execution_count": 36, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Building wheel for intermat (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", - "\u001b[0m" + " Building wheel for intermat (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ] @@ -2755,5028 +3959,41 @@ "metadata": { "id": "JvLvES1DZeto" }, - "execution_count": null, + "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ - "!run_intermat.py --config_file config_example3.json" + "%%time\n", + "!run_intermat.py --config_file config_example3.json > out" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "koZygxC-i1xg", - "outputId": "3fa93046-e1aa-4e63-95a6-5c7df65121fa" + "outputId": "7c8f3b91-8edd-4d1a-a498-6bdbfbf0e848" }, - "execution_count": null, + "execution_count": 38, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - "-0.12499999999999994 0.2916662856085588 0.15207754170210352 top\n", - 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alignn_ff\n", - "len generated 400\n", - " 0% 0/400 [00:00" ], - "image/png": 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\n" + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "from jarvis.db.jsonutils import loadjson\n", + "res=loadjson('intermat_results.json')\n", + "fig = go.Figure(data=[go.Surface(z=res['wads'])])\n", + "\n", + "# fig = go.Figure(data=[go.Contour(z=res['wads'])])\n", + "#fig.write_html(\"file2.html\")\n", + "fig.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 542 + }, + "id": "r_aAWtLDzlYA", + "outputId": "3932fe45-d9d8-4949-99ee-e446ef6af989" + }, + "execution_count": 40, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "" + ] }, "metadata": {} } ] }, + { + "cell_type": "markdown", + "source": [ + "General optimization from POSCAR" + ], + "metadata": { + "id": "A_-Q32cjmdAd" + } + }, { "cell_type": "code", "source": [ @@ -7842,7 +4131,7 @@ " jarvis_atoms=s,\n", " model_path=dir_name,\n", " stress_wt=0.3,\n", - " force_multiplier=2,\n", + " force_multiplier=my_config['batch_size'],\n", " force_mult_natoms=False,\n", ")\n", "opt, en, fs = ff.optimize_atoms(fmax=fmax, steps=100)\n" @@ -7852,27 +4141,26 @@ "base_uri": "https://localhost:8080/" }, "id": "u-ebGsB3Zerg", - "outputId": "b62b8e31-4c88-410d-b0bb-e7407bc62801" + "outputId": "7a1e38bd-2ac0-4783-cac4-849f8dbbd644" }, - "execution_count": null, + "execution_count": 66, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "/usr/local/lib/python3.10/site-packages/alignn/ff/ff.py:472: FutureWarning: Import ExpCellFilter from ase.filters\n", - " self.atoms = ExpCellFilter(self.atoms)\n" - ] + "/usr/local/lib/python3.10/site-packages/alignn/ff/ff.py:459: FutureWarning:\n", + "\n", + "Import ExpCellFilter from ase.filters\n", + "\n" + ] }, { "output_type": "stream", "name": "stdout", "text": [ "OPTIMIZATION\n", - "a= 5.494 Ang b= 5.494 Ang c= 5.494 Ang Volume= 165.798 amu/a3 PE=-42.74306 eV KE= 0.00000 eV T= 0.000 K \n", - "a= 5.497 Ang b= 5.497 Ang c= 5.497 Ang Volume= 166.087 amu/a3 PE=-42.91042 eV KE= 0.00000 eV T= 0.000 K \n", - "a= 5.504 Ang b= 5.504 Ang c= 5.504 Ang Volume= 166.761 amu/a3 PE=-42.91231 eV KE= 0.00000 eV T= 0.000 K \n", - "a= 5.512 Ang b= 5.513 Ang c= 5.513 Ang Volume= 167.559 amu/a3 PE=-42.91712 eV KE= 0.00000 eV T= 0.000 K \n" + "a= 5.494 Ang b= 5.494 Ang c= 5.494 Ang Volume= 165.798 amu/a3 PE=-37.71804 eV KE= 0.00000 eV T= 0.000 K \n" ] } ] @@ -7887,9 +4175,9 @@ "base_uri": "https://localhost:8080/" }, "id": "A6al4VZlZepF", - "outputId": "62c202d7-9f1b-4946-d87d-1d4aa3e77775" + "outputId": "94fa3b1a-b529-48fe-925c-0d7db2ebe19c" }, - "execution_count": null, + "execution_count": 67, "outputs": [ { "output_type": "execute_result", @@ -7897,24 +4185,24 @@ "text/plain": [ "System\n", "1.0\n", - "5.512372932288064 7.12675450755528e-05 -2.1343758934169594e-05\n", - "7.12675450755528e-05 5.513263467772916 9.509683324333408e-05\n", - "-2.1343758934169594e-05 9.509683324333408e-05 5.513406258801486\n", + "5.49363 0.0 0.0\n", + "-0.0 5.49363 0.0\n", + "0.0 0.0 5.49363\n", "Si \n", "8 \n", "direct\n", - "0.2500528928510943 0.749985015848329 0.24997249377533926 Si\n", - "-5.132219115432231e-05 -1.9604506848612828e-05 0.4999895076659299 Si\n", - "0.24994113474143367 0.25001075233654807 0.7500058018062661 Si\n", - "-4.2429218122353014e-05 0.5000335513649001 4.4480001659413175e-05 Si\n", - "0.7499697572598353 0.7500457369459477 0.7500040032174887 Si\n", - "0.500069371782183 -4.3460126356238116e-05 3.595209229726583e-05 Si\n", - "0.7499937997874895 0.2500141075745357 0.2499441309515288 Si\n", - "0.5000683493419571 0.4999738093088746 0.49999819568519865 Si" + "0.25 0.75 0.25 Si\n", + "0.0 0.0 0.5 Si\n", + "0.25 0.25 0.75 Si\n", + "0.0 0.5 0.0 Si\n", + "0.75 0.75 0.75 Si\n", + "0.5 0.0 0.0 Si\n", + "0.75 0.25 0.25 Si\n", + "0.5 0.5 0.5 Si" ] }, "metadata": {}, - "execution_count": 157 + "execution_count": 67 } ] }, @@ -7968,10 +4256,10 @@ ], "metadata": { "id": "zPlpQ1NimvpU", - "outputId": "6aed5108-743c-4ac0-f5e8-38f5e119dded", + "outputId": "4a6d8fec-f9f1-4b33-8833-d7f1724a7fa5", "colab": { "base_uri": "https://localhost:8080/", - "height": 425 + "height": 1000 } }, "execution_count": null, @@ -7981,7 +4269,2069 @@ "name": "stdout", "text": [ "Running NVT\n", - "Energy per atom: Epot = -4.384eV Ekin = 11.803eV (T=91309K) Etot = 7.419eV\n" + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n", + "Energy per atom: Epot = -4.645eV Ekin = 0.013eV (T=104K) Etot = -4.631eV\n" ] }, { @@ -7994,14 +6344,20 @@ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/md/md.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmaximum\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0msteps\u001b[0m \u001b[0mare\u001b[0m \u001b[0mreached\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \"\"\"\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mDynamics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/optimize/optimize.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 273\u001b[0m \"\"\"\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mconverged\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDynamics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mirun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconverged\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/optimize/optimize.py\u001b[0m in 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convergence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/site-packages/ase/optimize/optimize.py\u001b[0m in \u001b[0;36mcall_observers\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcall\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mirun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/site-packages/alignn/ff/ff.py\u001b[0m in \u001b[0;36mcalculate\u001b[0;34m(self, atoms, properties, system_changes)\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0mj_atoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mase_to_atoms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[0mnum_atoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj_atoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_atoms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m g, lg = Graph.atom_dgl_multigraph(\n\u001b[0m\u001b[1;32m 309\u001b[0m \u001b[0mj_atoms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m 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\u001b[0;34m\"k-nearest\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 376\u001b[0m edges = nearest_neighbor_edges(\n\u001b[1;32m 377\u001b[0m \u001b[0matoms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/site-packages/alignn/graphs.py\u001b[0m in \u001b[0;36mnearest_neighbor_edges\u001b[0;34m(atoms, cutoff, max_neighbors, id, use_canonize)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0muse_canonize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0medges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc_id\u001b[0m\u001b[0;34m,\u001b[0m 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atoms, properties, system_changes)\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0mj_atoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mase_to_atoms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0mnum_atoms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mj_atoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_atoms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m g, lg = Graph.atom_dgl_multigraph(\n\u001b[0m\u001b[1;32m 291\u001b[0m \u001b[0mj_atoms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0mneighbor_strategy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"neighbor_strategy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/site-packages/alignn/graphs.py\u001b[0m in \u001b[0;36matom_dgl_multigraph\u001b[0;34m(atoms, neighbor_strategy, cutoff, max_neighbors, atom_features, max_attempts, id, compute_line_graph, use_canonize, use_lattice_prop, cutoff_extra)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0;31m# (nodes are bonds, edges are bond pairs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 435\u001b[0m \u001b[0;31m# and add bond angle cosines as edge features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m \u001b[0mlg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 437\u001b[0m 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\u001b[0mlg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mdgl/_ffi/_cython/./function.pxi\u001b[0m in \u001b[0;36mdgl._ffi._cy3.core.FunctionBase.__call__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mdgl/_ffi/_cython/./function.pxi\u001b[0m in \u001b[0;36mdgl._ffi._cy3.core.make_ret\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mdgl/_ffi/_cython/./object.pxi\u001b[0m in \u001b[0;36mdgl._ffi._cy3.core.make_ret_object\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/site-packages/dgl/heterograph_index.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls)\u001b[0m\n\u001b[1;32m 25\u001b[0m \"\"\"\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 28\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mObjectBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } @@ -8054,7 +6410,7 @@ ], "metadata": { "id": "t7TN2gWRsXCz", - "outputId": "3739e760-dad7-4f59-95bd-350220d91a99", + "outputId": "27fa117a-9be9-4cba-df90-73c126fd7125", "colab": { "base_uri": "https://localhost:8080/", "height": 317 @@ -8080,10 +6436,10 @@ "\n", "\n", "\n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8092,17 +6448,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8112,23 +6468,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8136,7 +6492,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8144,7 +6500,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8152,7 +6508,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8160,7 +6516,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8168,7 +6524,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8176,7 +6532,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8184,7 +6540,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8192,7 +6548,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8200,7 +6556,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8208,7 +6564,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8216,7 +6572,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8224,7 +6580,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8232,7 +6588,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8240,7 +6596,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8248,7 +6604,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8256,7 +6612,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8264,7 +6620,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8272,7 +6628,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8280,7 +6636,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8288,7 +6644,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8296,7 +6652,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8304,7 +6660,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8312,7 +6668,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8320,7 +6676,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8328,7 +6684,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8336,7 +6692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8344,7 +6700,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8352,7 +6708,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8360,7 +6716,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8368,7 +6724,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8376,7 +6732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8384,7 +6740,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8392,7 +6748,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8400,7 +6756,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8408,7 +6764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8416,7 +6772,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8424,7 +6780,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8432,7 +6788,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8440,7 +6796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8448,7 +6804,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8456,7 +6812,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8464,7 +6820,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8472,7 +6828,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8480,7 +6836,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8488,7 +6844,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8496,7 +6852,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8504,7 +6860,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8512,7 +6868,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8520,7 +6876,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8528,7 +6884,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8536,7 +6892,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8544,7 +6900,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8552,7 +6908,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8560,131 +6916,39 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - "\n", - "\n" - ] - }, - "metadata": {}, - "execution_count": 220 - } - ] - }, - { - "cell_type": "code", - "source": [ - "traj.atoms" - ], - "metadata": { - "id": "SPdL0zXQtWg3", - "outputId": "dd16613f-562f-44ea-e81f-45d5163fca7e", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Atoms(symbols='Si64', pbc=True, cell=[10.98726, 10.98726, 10.98726], momenta=..., calculator=AlignnAtomwiseCalculator(...))" - ] - }, - "metadata": {}, - "execution_count": 231 - } - ] - }, - { - "cell_type": "code", - "source": [ - "view(f_atoms[-1],viewer='x3d')" - ], - "metadata": { - "id": "PO0MRkUVsmgH", - "outputId": "ba116fa9-8134-41d8-b0cc-256a589bba00", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 317 - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " ASE atomic visualization\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8692,7 +6956,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8700,7 +6964,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8708,7 +6972,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8716,7 +6980,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8724,7 +6988,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8732,7 +6996,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8740,7 +7004,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8748,7 +7012,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8756,7 +7020,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8764,7 +7028,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8772,7 +7036,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8780,7 +7044,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8788,7 +7052,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8796,7 +7060,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8804,7 +7068,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8812,7 +7076,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8820,7 +7084,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8828,7 +7092,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8836,7 +7100,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8844,7 +7108,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8852,7 +7116,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8860,7 +7124,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8868,7 +7132,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8876,7 +7140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8884,7 +7148,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8892,7 +7156,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8900,7 +7164,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8908,7 +7172,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8916,7 +7180,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8924,7 +7188,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8932,7 +7196,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8940,7 +7204,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8948,7 +7212,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8956,7 +7220,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8964,7 +7228,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8972,7 +7236,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8980,7 +7244,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8988,7 +7252,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8996,7 +7260,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9004,7 +7268,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9012,7 +7276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9020,7 +7284,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9028,7 +7292,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9036,7 +7300,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9044,7 +7308,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9052,7 +7316,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9060,7 +7324,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9068,7 +7332,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9076,7 +7340,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9084,7 +7348,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9092,7 +7356,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9100,7 +7364,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9108,7 +7372,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9116,7 +7380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9124,7 +7388,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9132,7 +7396,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9140,7 +7404,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9148,7 +7412,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9156,7 +7420,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9164,7 +7428,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9172,7 +7436,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9180,7 +7444,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9188,7 +7452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9196,570 +7460,1776 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - "\n", - "\n" - ] - }, - "metadata": {}, - "execution_count": 230 - } - ] - }, - { - "cell_type": "code", - "source": [ - "dists=[]\n", - "nbrs = f_supercell.get_all_neighbors()\n", - "for i in nbrs:\n", - " for j in i:\n", - " dists.append(j[2])\n", - "plt.hist(dists)" - ], - "metadata": { - "id": "hJHN_QJZmvkW", - "outputId": "56c6434e-5976-4c5a-e458-149163928c3c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 527 - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(array([256., 0., 0., 0., 0., 22., 726., 20., 212., 556.]),\n", - " array([2.24936867, 2.50121118, 2.75305369, 3.00489621, 3.25673872,\n", - " 3.50858123, 3.76042374, 4.01226625, 4.26410876, 4.51595128,\n", - " 4.76779379]),\n", - " )" - ] - }, - "metadata": {}, - "execution_count": 232 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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BgwYpLy9Pgwd3vrnb7bbnfT5fm/UCywLb3N0+Pj4+qPZ3i46OVnR0dPudBgAAYSOoIz3r1q1TRUWFli9frvHjx6u6urrZFPgIibuXjRgxwi67du1am9sILAtsE2z7+Pj4Ns/nAQAA5gkq9JSUlEiSduzYIbfb3WLauHGjXbdp2dq1ayVJEyZMUERE4+YCr8S6W1NZcnKyfRKz1PyKrc60nzhxYjC7BgAAwlyvPWXd6XRq+vTpkqTDhw+3WseyLBUWFkpSi7s7jx07VqNHj263fU1NjT788MNW2wMAALMFFXqOHz8uy7LanNavX2/XbVr26quv2suWLFkiSTp27JhOnTrVYv0FBQW6fPmyJLW494/D4bCX7d27135gaaDXXntN1dXVGjRokBYuXBjMrgEAgDDXa0d6pMbQk5aWJsuy9MQTT9jP1/L7/SooKNCyZcskNd5xecaMGS3aZ2dnKzk5WT6fT3PmzNHp06clSfX19dqxY4eef/55SdLy5cs1duzYXtorAAAwEIR0c8KQNzZ4sPbv36+MjAx5PB5lZmbK6XTK7/ertrZWkjRp0qQ2n5uVkJCgAwcOKCsrSxcuXNCUKVPkdrtVW1ur27dvS2r8WaszzwEDAABm6dUjPZKUkpKis2fP6gc/+IFSU1PlcDgUGRmpyZMnKzc3Vx9//LESExPbbD958mSdP39ezzzzjO6//37dvn1bcXFxeuSRR5SXl6f333+fS9EBAEALDsuyrL7uRH9QVVWlhIQEVVZWtnkPIADobSnrDvZ1F4LmeXlOX3fBCLw2GgXz/d3rR3oAAAD6AqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABiB0AMAAIxA6AEAAEYg9AAAACMQegAAgBEIPQAAwAiEHgAAYARCDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAIxB6AACAEQg9AADACIQeAABgBEIPAAAwAqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABiB0AMAAIxA6AEAAEYg9AAAACMQegAAgBEIPQAAwAiEHgAAYARCDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAIxB6AACAEQg9AADACIQeAABgBEIPAAAwAqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABiB0AMAAIwwuK87YIqUdQf7ugtB87w8p6+7AABAt+FIDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAIxB6AACAEQg9AADACIQeAABgBEIPAAAwAqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABghpNBTUVGht956S4sWLdLEiRMVFxen6Oho3XvvvXr88cf14x//uMN1eL1e5eTkKC0tTS6XSwkJCZo6daq2bNmi+vr6DtuXl5dr9erVGjdunGJjY5WUlKT09HTt2rVLlmWFslsAACCMDQ6lUXJysu7cuWP/HRMTo8jISF27dk3Xrl3Tf/7nf2r27Nl699135XQ6W7S/cuWKHn30UXk8HkmS0+lUXV2diouLVVxcrD179qioqEiJiYmtbv/06dPKyspSRUWFJMnlcsnr9eqjjz7SRx99pHfffVf79+9XVFRUKLsHAADCUEih586dO3rooYf0d3/3d8rKytJ9990nSfJ4PHrhhRf05ptv6v3339eKFSv09ttvt2g7d+5ceTweDR8+XLt371ZmZqb8fr8KCgq0bNkynTlzRosWLdLBgwdbbLuyslKPPfaYKioqNH78eL399tuaMmWK6uvrlZeXp2eeeUaFhYVatWqVXn/99VB2DwBgmJR1Lb9vEH5C+nnrpz/9qU6dOqXvfe97duCRpJSUFO3atUsrVqyQJL3zzju6evVqs7b5+fk6d+6cJGnfvn3KzMxs7EhEhObNm6c33nhDknTo0CEVFRW12HZubq6uX7+u2NhYHTp0SFOmTJEkRUVF6emnn9aGDRskSTt37tRnn30Wyu4BAIAwFFLoycjIaLf8qaeesueLi4ubleXn59vrmDZtWou28+fP15gxYyRJu3fvblHetCywXqCVK1fK5XKpoaFBe/bs6WBPAACAKXrk6q2YmBh7vqGhwZ73+Xw6ceKEJGn27NmttnU4HJo1a5Yk6ciRI83KLl26pNLS0nbbu1wupaent9oeAACYq0dCz/Hjx+35tLQ0e/7ixYvy+/2SpNTU1DbbN5Vdv35dN27csJd/+umnLeq01/7ChQvBdRwAAIStkE5kbs+tW7e0ceNGSVJ6errGjRtnl5WVldnzI0eObHMdgWVlZWVKSkoKqX1VVZWqq6vlcrla1Kmrq1NdXZ39d1VVVZvrAwAAA1+3Hunx+/168skn9cUXXygmJkbbt29vVu71eu351i5lb60ssE1X2wfauHGjEhIS7GnUqFFtrg8AAAx83Rp6/uEf/kEHDhyQJL322mv60z/90+5cfbd69tlnVVlZaU93X2UGAADCS7f9vJWdnW0f2dm2bZuWLl3aoo7b7bbnfT5fm+sKLAtsc3f7+Pj4oNoHio6OVnR0dJt9AAAA4aVbjvSsXbtWW7ZskdR4H51Vq1a1Wm/EiBH2/LVr19pcX2BZYJtg28fHx7d6Pg8AADBPl0PPmjVr9Morr0iSNm/erNWrV7dZd8KECYqIaNxk4JVYd2sqS05Otk9ilppfsdWZ9hMnTuzEHgAAABN0KfRkZ2crNzdXUmPgWbNmTbv1nU6npk+fLkk6fPhwq3Usy1JhYaEkaebMmc3Kxo4dq9GjR7fbvqamRh9++GGr7QEAgLlCDj3Z2dnNftLqKPA0WbJkiSTp2LFjOnXqVIvygoICXb58WZK0ePHiZmUOh8NetnfvXvuBpYFee+01VVdXa9CgQVq4cGGn9wcAAIS3kEJP4Dk8W7dubfcnrbstWbJEaWlpsixLTzzxhP18rcAHjkqNd1yeMWNGi/bZ2dlKTk6Wz+fTnDlzdPr0aUlSfX29duzYoeeff16StHz5co0dOzaU3QMAAGEo6Ku3SktL7XN4IiIitGnTJm3atKnN+tnZ2crOzv7/DQ4erP379ysjI0Mej0eZmZlyOp3y+/2qra2VJE2aNKnN52YlJCTowIEDysrK0oULFzRlyhS53W7V1tbq9u3bkhp/1tq2bVuwuwYAAMJY0KGn6TESTfPl5eXt1q+urm6xLCUlRWfPnlVubq7ee+89lZSUKDIyUl/72te0YMECrVy5UlFRUW2uc/LkyTp//rw2bdqkAwcO6OrVq4qLi1NqaqqWLFmipUuX2idMAwAASCGEnpSUFFmW1eUNu91ubdiwQRs2bAip/bBhw7R161Zt3bq1y30BAADhj8MhAADACIQeAABgBEIPAAAwAqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABiB0AMAAIxA6AEAAEYg9AAAACMQegAAgBEIPQAAwAiEHgAAYARCDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAIxB6AACAEQg9AADACIQeAABgBEIPAAAwAqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABiB0AMAAIxA6AEAAEYg9AAAACMQegAAgBEIPQAAwAiEHgAAYARCDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAIxB6AACAEQg9AADACIQeAABgBEIPAAAwAqEHAAAYgdADAACMQOgBAABGIPQAAAAjEHoAAIARCD0AAMAIhB4AAGAEQg8AADACoQcAABiB0AMAAIxA6AEAAEYg9AAAACMQegAAgBEIPQAAwAiEHgAAYARCDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAIxB6AACAEQg9AADACIQeAABgBEIPAAAwAqEHAAAYYXBfdwAAekPKuoN93QUAfYzQAwDoVgRM9FcD+uctr9ernJwcpaWlyeVyKSEhQVOnTtWWLVtUX1/f190DAAD9yIA90nPlyhU9+uij8ng8kiSn06m6ujoVFxeruLhYe/bsUVFRkRITE/u2owAAoF8YkEd67ty5o7lz58rj8Wj48OH64IMPVFNTI5/Pp71798rtduvMmTNatGhRX3cVAAD0EwMy9OTn5+vcuXOSpH379ikzM1OSFBERoXnz5umNN96QJB06dEhFRUV91k8AANB/DNjQI0kZGRmaNm1ai/L58+drzJgxkqTdu3f3at8AAED/NOBCj8/n04kTJyRJs2fPbrWOw+HQrFmzJElHjhzptb4BAID+a8CFnosXL8rv90uSUlNT26zXVHb9+nXduHGjV/oGAAD6rwF39VZZWZk9P3LkyDbrBZaVlZUpKSmpWXldXZ3q6ursvysrKyVJVVVV3dXVZvx1vh5Zb0/qqbEA+sJAfA8C4aYnvlea1mlZVod1B1zo8Xq99rzT6WyzXmBZYJsmGzdu1IYNG1osHzVqVBd7GD4SXu3rHgAAwklPfq94vV4lJCS0W2fAhZ7u8uyzz+r73/++/bff79eNGzc0ZMgQORyOPuxZ/1BVVaVRo0bp6tWrio+P7+vuhCXGuHcwzj2PMe4djHPrLMuS1+vViBEjOqw74EKP2+22532+tg9XB5YFtmkSHR2t6OjoZsv+6I/+qOsdDDPx8fG8uXoYY9w7GOeexxj3Dsa5pY6O8DQZcCcyBya5a9eutVkvsKwz6Q8AAIS3ARd6JkyYoIiIxm5/+umnbdZrKktOTm5xEjMAADDPgAs9TqdT06dPlyQdPny41TqWZamwsFCSNHPmzF7rWziJjo7W+vXrW/wEiO7DGPcOxrnnMca9g3HuOofVmWu8+pk333xT3/3ud+VwOHTy5El9/etfb1b+ox/9SPPmzZMkHT16VDNmzOiLbgIAgH5kwB3pkaQlS5YoLS1NlmXpiSeesJ+v5ff7VVBQoGXLlklqvGMzgQcAAEgD9EiPJHk8HmVkZMjj8Uhq/NnL7/ertrZWkjRp0iQVFRUpMTGxD3sJAAD6iwEbeqTGGxHl5ubqvffeU0lJiSIiIjR27FgtWLBAK1euVFRUVF93EQAA9BMDOvQAAAB01oA8pwcdq6io0FtvvaVFixZp4sSJiouLU3R0tO699149/vjj+vGPfxzyuv/t3/5NDoejw+no0aPduEf9zy9+8Qtt2LBBf/VXf6Xx48dryJAhioyM1JAhQzR9+nS9+OKLXX7YbXl5uVavXq1x48YpNjZWSUlJSk9P165duzr1nJlw0JPjnJOT06nX8q9//etu3quB4eWXX242Dl3h9XqVk5OjtLQ0uVwuJSQkaOrUqdqyZYvq6+u7qccDT3eMMZ/JQbAQlgYPHmxJsqeYmBgrLi6u2bLZs2dbNTU1Qa/7rbfesiRZERER1rBhw9qcfvazn/XAnvUfTz/9dIsxdrvdzZYNHTrU+vnPfx7S+ouLi60hQ4bY63K5XM3+XbOysqy6urpu3qv+pyfHef369ZYkKzIyst3XcklJSffvWD/3q1/9yoqJiWk2zqHyeDxWSkqKvR6n02lFR0fbf0+aNMm6ceNGN/Z+YOiuMeYzufMIPWFKkvXQQw9Zr7/+uvX555/by0tKSqynnnrKfoMtWrQo6HU3vcH++I//uBt7PPDk5+dbr7zyinXy5Enr5s2b9nKv12vl5+db99xzjyXJ+spXvmLdunUrqHXfunXLSk5OtiRZ48ePtz755BPLsiyrrq7O2r59uxUZGWlJsr73ve915y71Sz05zk2h5y//8i+7t9MDXENDg/Xwww9bkqxp06Z16Qv59u3bVlpamiXJGj58uPXBBx/Y29i7d68dYL/xjW905y70e905xnwmdx6hJ0z99Kc/bbd8xYoV9pustLQ0qHXzBuucwsJCe4zfeeedoNr+0z/9kyXJio2NtS5fvtyi/KWXXrIkWYMGDbIuXbrUXV0ekLoyzoSe1r366quWJGvhwoX2GIX6hbxr1y67fWtH4/793//dLj969GhXuz5gdOcY85nceZzTE6YyMjLaLX/qqafs+eLi4p7ujpH+/M//3J7/zW9+E1Tb3bt3S5Lmz5+vMWPGtChfuXKlXC6XGhoatGfPnq51dIDryjijpZKSEj333HMaMmSItm3b1uX15efnS2r8TJo2bVqL8sDXeNPrPtx19xij8wg9hoqJibHnGxoa+rAn4evDDz+05//kT/6k0+0uXbqk0tJSSY032GyNy+VSenq6JOnIkSNd6OXAF+o4o3XLli1TTU2Ntm7dqnvuuadL6/L5fDpx4oSktl/LDodDs2bNkmTOa7k7xxjBIfQY6vjx4/Z8WlpaSOv43e9+p8mTJ8vlcik2Nlb33XefFi1a1Gzdpqmrq5PH49H27dv15JNPSpK++tWvau7cuZ1eR+CDdFNTU9us11R24cKFEHs7cHXHOAc6f/68UlNT5XQ65XK5NG7cOC1btkxnzpzpzm73e3l5eSoqKlJmZqYWL17c5fVdvHhRfr9fUudey9evX+/yFY/9XXePcSA+kzuhr39fQ++7efOmNXz4cEuSlZ6eHnT7pt+Pm6bExEQrKiqq2bLvfOc71u3bt3ug9/1T4JUogdP06dOtK1euBLWuf/mXf7HbV1ZWtlmv6ZwASZbX6+3qLgwI3TnOlmU1O5ciIiLCSkpKanaFnMPhsJ577rke2JP+5ze/+Y2VkJBgxcbGNrv4oSvnm+zfv99u+8tf/rLNej/5yU/seufOnQup/wNBT4yxZfGZHAyO9BjG7/frySef1BdffKGYmBht37496HWMGDFC69ev1y9/+UvV1tbqxo0b9mHszMxMSdJbb72lZ555pru7328lJydr2LBhiouLs5dlZGTo1Vdf1ejRo4Nal9frteedTmeb9QLLAtuEs+4cZ0m6//77tXnzZl26dEm1tbWqqKhQTU2NCgsLNXnyZFmWpRdffFFbtmzpzt3ol1asWKHKykrl5OTovvvu65Z18lpurifGWOIzOSh9nbrQu/7+7//eTv5vvvlmt6+/oaHB+uY3v2n/z/mzzz7r9m30d+Xl5VZubq6VmJhoORwO6/nnnw+q/Ysvvmj/G7X3P7OdO3fa9crKyrra7QGnq+Pckd///vfW1KlT7XskBXs5/EDy9ttvW5KsBx98sMVrritHIfbs2WO3/d///d826x05cqTdK7zCQU+NcUf4TG6OIz0Gyc7Oto/sbNu2TUuXLu32bURERCg3N1dS41Gl//qv/+r2bfR3X/nKV7R69WodPnxYDodD//zP/6wDBw50ur3b7bbnfT5fm/UCywLbmKKr49yRmJgYvfTSS5Kk6upqFRUVddu6+5Py8nKtWrVKgwYNUl5engYPHtxt6+a13Kgnx7gjfCY3R+gxxNq1a+1D9Lm5uVq1alWPbeurX/2qhg4dKkm6fPlyj22nv3vooYf0yCOPSJJ27tzZ6XYjRoyw569du9Zmvaay+Ph4uVyuEHs58IU6zp0ReIl1uL6W161bp4qKCi1fvlzjx49XdXV1synwERGtLWtPsK/lu9uEi54c487gM/n/EXoMsGbNGr3yyiuSpM2bN2v16tV93CNzjBw5UpKCenZT4FUugVdy3a2pbOLEiSH2LnyEMs5oVFJSIknasWOH3G53i2njxo123aZla9eu7dS6J0yYoIiIxq+ZzryWk5OTlZSUFOqu9Fs9OcYIDqEnzGVnZ9uHNjdv3qw1a9b0+DY///xzffnll5LU6o31TNL0v6pgDtmPHTvWPin38OHDrdapqamx708zc+bMLvZy4AtlnDvj448/tudNfy2Hwul0avr06ZLafi1blqXCwkJJvJZ7Cp/JAfr6pCL0nNWrV9snx+Xm5nbLOv1+f4flf/3Xf22fNPerX/2qW7bb39y5c6fDsTh69KjlcDgsSdbatWuDWn/TYyicTmerD7vctGmTEY+h6Mlx7mi9tbW11te//nVLkhUXF9fsuV8m6a7HUDgcDuvjjz9uUf4f//EfRj6GIlBXxpjP5OAQesLUmjVr7DfR1q1bg2obeM+HY8eONSsrKSmxpk6dav3rv/6r9fnnn9tvuIaGBuvkyZNWVlaW3TacH4ZZUlJiPfDAAy3GwbIsq7S01Nq4caP9VPukpCTriy++aNY+8EOutVAT+MDRiRMnWsXFxZZlNT5w9PXXX7fvwRHOY2xZPTvOx48ft2bMmGHt3r3bunr1qr28vr7eOnr0qH3lliRr06ZNPbqf/VlHX8jtfV5YVvMHjo4cOdIONg0NDdaPfvQjKz4+3pJkzZ49uyd3o1/ryhjzmRwcQk8YunLlSrMbrg0bNqzd6ZVXXmnWvqM3WFOZJCs6OtoaOnRoi5vGhfuNsO4eh6ioKGvo0KH2F3DTNGbMGOsXv/hFi/YdhR7Lsqzi4mJryJAhdj23220/XV2SNXPmTKu2traH97Rv9eQ4Hzt2rNk6YmNjraFDhzYb44iICOsf//Efe2lv+6euhh7Lavx3TElJses5nU4rJibG/nvSpEnWjRs3enAv+reuhh4+kzuv966bQ69puu1703x5eXm79aurqzu97mHDhumHP/yhTp48qf/5n//R7373O928eVMxMTEaM2aMHn74YS1dutT+HT9cjRgxQgUFBTp+/LhOnTqlsrIyffnllxo0aJBGjx6tBx54QN/85jf1t3/7t4qNjQ1pG5MnT9b58+e1adMmHThwQFevXlVcXJxSU1O1ZMkSLV261D5JNFz15DinpaUpNzdXJ0+e1Llz5/Tll1/q1q1bcjqdmjhxotLT07V8+fKQH9OC/5eSkqKzZ88qNzdX7733nkpKShQZGamvfe1rWrBggVauXKmoqKi+7uaAxGdycByWZVl93QkAAICeFt7/TQQAAPgDQg8AADACoQcAABiB0AMAAIxA6AEAAEYg9AAAACMQegAAgBEIPQAAwAiEHgAAYARCDwAAMAKhBwAAGIHQAwAAjEDoAQAARiD0AAAAI/wfGMkEqQImoQkAAAAASUVORK5CYII=\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "dists=[]\n", - "nbrs = supercell.get_all_neighbors()\n", - "for i in nbrs:\n", - " for j in i:\n", - " dists.append(j[2])\n", - "plt.hist(dists)" - ], - "metadata": { - "id": "DjyniEHGmvhc", - "outputId": "9ae29b64-cb53-4dbe-b9ee-0609255e89e2", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 527 - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(array([216., 0., 0., 0., 0., 0., 648., 0., 0., 648.]),\n", - " array([2.37881195, 2.5964394 , 2.81406686, 3.03169432, 3.24932178,\n", - " 3.46694924, 3.6845767 , 3.90220416, 4.11983162, 4.33745908,\n", - " 4.55508654]),\n", - " )" - ] - }, - "metadata": {}, - "execution_count": 233 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" + }, + "metadata": {} + } + ] }, { "cell_type": "code", "source": [ - "import os\n", - "os.chdir('/content/jarvis_leaderboard/jarvis_leaderboard/contributions/')\n", - "os.makedirs('alignnff_su')\n", - "os.chdir('alignnff_su')" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "\n", + "\n", + "# Creating a customized histogram with a density plot\n", + "sns.histplot(dists, bins=30, kde=True, color='lightgreen', edgecolor='red')\n", + "\n", + "# Adding labels and title\n", + "plt.xlabel('Values')\n", + "plt.ylabel('Density')\n", + "plt.title('Customized Histogram with Density Plot')\n", + "\n", + "# Display the plot\n", + "plt.show()" ], "metadata": { - "id": "s1rn9Unhmydg" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 501 + }, + "id": "gjfHdj3j0T0z", + "outputId": "4be26945-81ee-4ea2-c4aa-6e8c8b9ad75e" }, "execution_count": null, - "outputs": [] + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240312/eu-west-1/s3/aws4_request&X-Amz-Date=20240312T190521Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=75d5083d6b8b8453a4c22d60663451699261e82a1152c0d19ef0690340da849e [following]\n", - "--2024-03-12 19:05:21-- https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/40357663/mlearn.json.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIYCQYOYV5JSSROOA/20240312/eu-west-1/s3/aws4_request&X-Amz-Date=20240312T190521Z&X-Amz-Expires=10&X-Amz-SignedHeaders=host&X-Amz-Signature=75d5083d6b8b8453a4c22d60663451699261e82a1152c0d19ef0690340da849e\n", - "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.92.34.136, 52.218.88.91, 52.218.28.195, ...\n", - "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.92.34.136|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2542319 (2.4M) [application/zip]\n", - "Saving to: ‘mlearn.json.zip’\n", - "\n", - "mlearn.json.zip 100%[===================>] 2.42M 2.39MB/s in 1.0s \n", - "\n", - "2024-03-12 19:05:23 (2.39 MB/s) - ‘mlearn.json.zip’ saved [2542319/2542319]\n", - "\n" - ] + "output_type": "execute_result", + "data": { + "text/plain": [ + "System\n", + "1.0\n", + "10.98726 0.0 0.0\n", + "0.0 10.98726 0.0\n", + "0.0 0.0 10.98726\n", + "Si \n", + "64 \n", + "direct\n", + "0.11032192973508376 0.368194310181432 0.1154429679796411 Si\n", + "0.12497644446872942 0.37712966148465393 0.6355779022732296 Si\n", + "0.06090151017432405 0.8289459499433295 0.17697730304500792 Si\n", + "0.11969314835209018 0.8795943740808218 0.6254563907373039 Si\n", + "0.6260991618838236 0.34200254403476427 0.11599849989552259 Si\n", + "0.6101003457236029 0.3856291660162119 0.6220843417428248 Si\n", + "0.6014998832892736 0.8924533865255972 0.12079143935197796 Si\n", + "0.6278304762498705 0.8609786106986407 0.6717202314510263 Si\n", + "-0.0064458573515348006 -0.03686998042671442 0.29489662452464493 Si\n", + "0.0029203664527438504 -0.030313140381857617 0.7861345039864348 Si\n", + "-0.009702302869560865 0.48554609064747556 0.2555446028710098 Si\n", + "0.004891329114559029 0.4986654814735981 0.7541434780845202 Si\n", + "0.4830329489102913 -0.009888214705916387 0.2743490708741593 Si\n", + "0.4805750197239558 -0.002175955930496465 0.7484343671768487 Si\n", + "0.5018610432418152 0.4619353246742855 0.24639955426489282 Si\n", + "0.4856162613096166 0.500458970331564 0.7745545781935378 Si\n", + "0.11963420584960266 0.12433059429496351 0.33612871105847464 Si\n", + "0.1260078594714673 0.10470096595901024 0.9105898879961819 Si\n", + "0.09064869828435557 0.6254873841917705 0.38215810684934204 Si\n", + "0.11950403671366389 0.6294788674926368 0.8676273107434619 Si\n", + "0.6069691473123028 0.07892402963944839 0.4090649844497954 Si\n", + "0.6071908236850149 0.11440101288292243 0.8636113946415295 Si\n", + "0.680397190052382 0.6938717691426561 0.42119827671152027 Si\n", + "0.6390239847415158 0.618277991882652 0.8780127239278849 Si\n", + "-0.0062366356770695575 0.21624910389368568 0.021221451293324534 Si\n", + "-0.03071196141933175 0.2754105274887269 0.5410026110178311 Si\n", + "-0.017742399901157113 0.6984310328345887 0.014830109815840044 Si\n", + "-0.03427729807930724 0.7342616437584254 0.5099946316179603 Si\n", + "0.4866393531739179 0.23749670829015906 -0.005312338086073121 Si\n", + "0.4983880425717616 0.24942229489506668 0.5008004898293188 Si\n", + "0.5022569738965517 0.7456772057225564 -1.566182960988094e-06 Si\n", + "0.5132420929936523 0.7607625586272342 0.5200957604246116 Si\n", + "0.3477545925991698 0.3593401273474569 0.36416983786004176 Si\n", + "0.36357100831358863 0.372650001493225 0.8733729335315985 Si\n", + "0.381348758555563 0.8414081862336005 0.36770722515766013 Si\n", + "0.35878402382079055 0.8719888416422427 0.9054074639786717 Si\n", + "0.8627095554175954 0.3672505008237682 0.38220781235327383 Si\n", + "0.88393398956049 0.35932143929261595 0.8588507477590421 Si\n", + "0.8347010963303967 0.8605597649229649 0.3956910127640054 Si\n", + "0.885995315379451 0.8576586348182316 0.9237841772952977 Si\n", + "0.2541913678493165 0.021787539577945825 0.033022400871607904 Si\n", + "0.23383330239652644 -0.02647932490759053 0.46245497299472516 Si\n", + "0.243003317607282 0.509697769010256 0.004069199645621302 Si\n", + "0.22640004322435636 0.49245296750504886 0.4787554091528216 Si\n", + "0.7542223701957562 0.020228506027523574 0.011835434245031584 Si\n", + "0.7608862197109986 -0.030136139627399916 0.5441503437532238 Si\n", + "0.7406938341224522 0.4801312939506089 -0.014110614819371592 Si\n", + "0.7460261912411359 0.48984822075420575 0.5063317763086835 Si\n", + "0.39748075821349466 0.12423592524674709 0.14236436105013517 Si\n", + "0.35420382940407014 0.11815512987028615 0.6238884631507633 Si\n", + "0.3961748869600508 0.5957216464761641 0.13866960437595444 Si\n", + "0.40981632851361716 0.5801142861267878 0.5908752965852608 Si\n", + "0.8689795328433573 0.1168464436442957 0.1630598013880889 Si\n", + "0.8679529492370079 0.11138002839575611 0.6351802985331163 Si\n", + "0.8490316177762065 0.5843737686723601 0.13214369158023748 Si\n", + "0.8660060616056269 0.6070513825265201 0.6326080181456397 Si\n", + "0.24632985756770653 0.2501670613594237 0.2219221409569186 Si\n", + "0.22155153250370824 0.22994916854054348 0.751526950610889 Si\n", + "0.2510821007771813 0.7251533387809761 0.266283968958989 Si\n", + "0.17600943268746516 0.8457286098345355 0.8212836468221556 Si\n", + "0.7316959237069474 0.23298001419572908 0.27391592826594835 Si\n", + "0.7330227114497655 0.2317577063634662 0.7438381954714406 Si\n", + "0.7281981804458376 0.749198172630697 0.21015027489356694 Si\n", + "0.7868321870517155 0.7351648430518812 0.7864447386063383 Si" + ] + }, + "metadata": {}, + "execution_count": 74 } ] }, + { + "cell_type": "markdown", + "source": [ + "RDF of initial structure" + ], + "metadata": { + "id": "07WY9JJB1kJo" + } + }, { "cell_type": "code", "source": [ - "import zipfile\n", - "import json\n", - "import glob\n", - "import pandas as pd\n", - "import numpy as np\n", - "from jarvis.core.atoms import Atoms\n", - "import os\n", - "from alignn.ff.ff import AlignnAtomwiseCalculator, default_path, ForceField\n", - "import torch\n", - "from ase.stress import full_3x3_to_voigt_6_stress, voigt_6_to_full_3x3_stress\n", - "from jarvis.db.figshare import data\n", - "# mdata = data('mlearn')\n", - "\n", - "# torch.cuda.is_available = lambda : False\n", - "model_path = '/content/'+dir_name\n", - "\n", - "# calc = AlignnAtomwiseCalculator(path=model_path)\n", - "calc = AlignnAtomwiseCalculator(\n", - " path=model_path,\n", - " force_mult_natoms=False,\n", - " force_multiplier=2,\n", - " stress_wt=-4800,\n", - ")\n", - "\n", - "\n", - "\n", - "def get_alignn_forces(atoms):\n", - " energy = 0.0\n", - " forces = np.zeros((atoms.num_atoms, 3))\n", - " stress = np.zeros((3, 3))\n", - " # try:\n", - " ase_atoms = atoms.ase_converter()\n", - " ase_atoms.calc = calc # M3GNetCalculator(potential=potential)\n", - " forces = np.array(ase_atoms.get_forces())\n", - " energy = ase_atoms.get_potential_energy()\n", - " stress = voigt_6_to_full_3x3_stress(ase_atoms.get_stress())\n", - " # except:\n", - " # print ('Failed for',atoms)\n", - " # pass\n", - " return energy, forces, stress\n", - "\n", - "# df = pd.DataFrame(mdata)\n", - "df = pd.DataFrame(\n", - " json.loads(\n", - " zipfile.ZipFile(\"mlearn.json.zip\").read(\n", - " \"mlearn.json\"\n", - " )\n", - " )\n", - ")\n", - "print(df)\n", - "for i in glob.glob(\"../../benchmarks/AI/MLFF/*energy*.zip\"):\n", - " if \"mlearn\" in i and \"Si\" in i:\n", - " fname_e = (\n", - " \"AI-MLFF-energy-\"\n", - " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", - " + \"-test-mae.csv\"\n", - " )\n", - " fname_f = (\n", - " \"AI-MLFF-forces-\"\n", - " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", - " + \"-test-multimae.csv\"\n", - " )\n", - " fname_s = (\n", - " \"AI-MLFF-stresses-\"\n", - " + i.split(\"/\")[-1].split(\"_energy.json.zip\")[0]\n", - " + \"-test-multimae.csv\"\n", - " )\n", - " f_e = open(fname_e, \"w\")\n", - " f_f = open(fname_f, \"w\")\n", - " f_s = open(fname_s, \"w\")\n", - "\n", - " f_e.write(\"id,prediction\\n\")\n", - " f_f.write(\"id,prediction\\n\")\n", - " f_s.write(\"id,prediction\\n\")\n", - "\n", - " print(i)\n", - " dat = json.loads(\n", - " zipfile.ZipFile(i).read(i.split(\"/\")[-1].split(\".zip\")[0])\n", - " )\n", - " print(dat[\"test\"])\n", - " for key, val in dat[\"test\"].items():\n", - " entry = df[df[\"jid\"] == key]\n", - " atoms = Atoms.from_dict(entry.atoms.values[0])\n", - " # print(key,val,df[df['jid']==key],atoms)\n", - " # energy,forces=get_alignn_forces(atoms)\n", - " energy, forces, stress = get_alignn_forces(atoms)\n", - " print(key, val, energy, atoms.num_atoms)\n", - " line = key + \",\" + str(energy) + \"\\n\"\n", - " f_e.write(line)\n", - " line = (\n", - " key\n", - " + \",\"\n", - " + str(\";\".join(map(str, np.array(forces).flatten())))\n", - " + \"\\n\"\n", - " )\n", - " f_f.write(line)\n", - " line = (\n", - " key\n", - " + \",\"\n", - " + str(\";\".join(map(str, np.array(stress).flatten())))\n", - " + \"\\n\"\n", - " )\n", - " f_s.write(line)\n", - " f_e.close()\n", - " f_f.close()\n", - " f_s.close()\n", - " zname = fname_e + \".zip\"\n", - " with zipfile.ZipFile(zname, \"w\") as myzip:\n", - " myzip.write(fname_e)\n", - "\n", - " zname = fname_f + \".zip\"\n", - " with zipfile.ZipFile(zname, \"w\") as myzip:\n", - " myzip.write(fname_f)\n", - "\n", - " zname = fname_s + \".zip\"\n", - " with zipfile.ZipFile(zname, \"w\") as myzip:\n", - " myzip.write(fname_s)\n", - " # cmd = \"zip \" + fname_e + \".zip \" + fname_e\n", - " # os.system(cmd)\n", - " # cmd = \"zip \" + fname_f + \".zip \" + fname_f\n", - " # os.system(cmd)\n", - " # cmd = \"zip \" + fname_s + \".zip \" + fname_s\n", - " # os.system(cmd)\n", - " # cmd = \"rm \" + fname_e\n", - " # os.system(cmd)\n", - " # cmd = \"rm \" + fname_f\n", - " # os.system(cmd)\n", - " # cmd='rm '+fname_s\n", - " # os.system(cmd)\n", - " # break" + "dists=[]\n", + "nbrs = supercell.get_all_neighbors()\n", + "for i in nbrs:\n", + " for j in i:\n", + " dists.append(j[2])\n", + "plt.hist(dists,width=0.1)" ], "metadata": { + "id": "DjyniEHGmvhc", + "outputId": "7e64b19d-c772-4a02-a8b7-9b962a630865", "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_cH9ME5HmybY", - "outputId": "a37ae838-52e2-48a8-ca48-ec82f2f88f75" + "base_uri": "https://localhost:8080/", + "height": 544 + } }, "execution_count": null, "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - " jid atoms energy \\\n", - "0 Ni-1 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.262250 \n", - "1 Ni-2 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.933146 \n", - "2 Ni-3 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -603.735818 \n", - "3 Ni-4 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -604.967429 \n", - "4 Ni-5 {'lattice_mat': [[10.524109, 0.0, 0.0], [0.0, ... -602.935586 \n", - "... ... ... ... \n", - "1561 Li-266 {'lattice_mat': [[4.8442, 0.0, 0.0], [2.4221, ... -21.410502 \n", - "1562 Li-267 {'lattice_mat': [[3.429887, 0.0, 0.0], [-1.714... -17.848979 \n", - "1563 Li-268 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797287 \n", - "1564 Li-269 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797589 \n", - "1565 Li-270 {'lattice_mat': [[3.426817, 0.0, 0.0], [0.0, 3... -3.797713 \n", - "\n", - " forces \\\n", - "0 [[-0.1928178, -1.87931786, -0.66374007], [-0.0... \n", - "1 [[-0.71128299, -1.10528691, -2.20081632], [0.9... \n", - "2 [[-1.04325671, 0.32574515, 0.30692968], [-0.75... \n", - "3 [[-1.136544, 1.56868294, -1.09210495], [-1.274... \n", - "4 [[3.11483946, -0.55002862, 0.11103391], [1.460... \n", - "... ... \n", - "1561 [[-0.0, -0.0, -0.00808912], [-0.0, -0.0, -0.01... \n", - "1562 [[0.0, 0.00387929, 0.00568886], [0.0, 0.002831... \n", - "1563 [[-0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", - "1564 [[0.0, -0.0, -0.0], [-0.0, 0.0, 0.0]] \n", - "1565 [[0.0, 0.0, 0.0], [0.0, -0.0, -0.0]] \n", - "\n", - " stresses \n", - "0 [41.40636025, 41.1876322, 51.06529127, 1.04252... \n", - "1 [44.88288149, 44.70823804, 44.73856806, 1.6211... \n", - "2 [52.07802628, 45.27925996, 47.31874643, -4.461... \n", - "3 [38.03742565, 44.72767184, 39.73179484, -5.093... \n", - "4 [47.1482229, 51.19066271, 46.54196562, 4.22313... \n", - "... ... \n", - "1561 [3.17420482, 3.62499364, 2.54337296, -0.926102... \n", - "1562 [2.38724801, 3.61190439, 2.1257323, 0.83494626... \n", - "1563 [3.65117155, 4.28869211, 3.70795776, -0.817898... \n", - "1564 [2.79230607, 2.46389956, 3.81491553, 1.9486931... \n", - "1565 [2.74849324, 3.62957339, 3.05891903, -2.404687... \n", - "\n", - "[1566 rows x 5 columns]\n", - "../../benchmarks/AI/MLFF/mlearn_Si_energy.json.zip\n", - "{'Si-215': -297.62773938, 'Si-216': -295.77170067, 'Si-217': -291.28958206, 'Si-218': -296.24088456, 'Si-219': -294.41361742, 'Si-220': -334.75283939, 'Si-221': -334.69215136, 'Si-222': -184.71808052, 'Si-223': -121.41180043, 'Si-224': -338.93899696, 'Si-225': -338.83557056, 'Si-226': -335.68901422, 'Si-227': -333.7064957, 'Si-228': -344.85564046, 'Si-229': -344.81108268, 'Si-230': -298.83222646, 'Si-231': -298.96501782, 'Si-232': -295.20943762, 'Si-233': -291.86293882, 'Si-234': -344.74080048, 'Si-235': -344.74080047, 'Si-236': -344.74080046, 'Si-237': -341.22165747, 'Si-238': -341.22165734, 'Si-239': -341.22165747}\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/dgl/backend/pytorch/tensor.py:445: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n", - " assert input.numel() == input.storage().size(), (\n" - ] + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 864., 0., 0., 0., 0., 0., 2592., 0., 0.,\n", + " 2592.]),\n", + " array([2.37881157, 2.59643815, 2.81406473, 3.03169131, 3.24931789,\n", + " 3.46694447, 3.68457104, 3.90219762, 4.1198242 , 4.33745078,\n", + " 4.55507736]),\n", + " )" + ] + }, + "metadata": {}, + "execution_count": 68 }, { - "output_type": "stream", - "name": "stdout", - "text": [ - "Si-215 -297.62773938 -296.4256896972656 63\n", - "Si-216 -295.77170067 -295.13730239868164 63\n", - "Si-217 -291.28958206 -295.6260051727295 63\n", - "Si-218 -296.24088456 -294.80436086654663 63\n", - "Si-219 -294.41361742 -294.920738697052 63\n", - "Si-220 -334.75283939 -334.01050186157227 63\n", - "Si-221 -334.69215136 -334.056884765625 63\n", - "Si-222 -184.71808052 -189.66070747375488 36\n", - "Si-223 -121.41180043 -125.12671279907227 24\n", - "Si-224 -338.93899696 -336.7873229980469 64\n", - "Si-225 -338.83557056 -336.64794921875 64\n", - "Si-226 -335.68901422 -334.8691101074219 64\n", - "Si-227 -333.7064957 -333.5328369140625 64\n", - "Si-228 -344.85564046 -340.09295654296875 64\n", - "Si-229 -344.81108268 -340.095947265625 64\n", - "Si-230 -298.83222646 -300.92999267578125 64\n", - "Si-231 -298.96501782 -301.02496337890625 64\n", - "Si-232 -295.20943762 -299.6366882324219 64\n", - "Si-233 -291.86293882 -298.3892517089844 64\n", - "Si-234 -344.74080048 -339.5257873535156 64\n", - "Si-235 -344.74080047 -339.5257873535156 64\n", - "Si-236 -344.74080046 -339.5257873535156 64\n", - "Si-237 -341.22165747 -338.6831970214844 64\n", - "Si-238 -341.22165734 -338.6831970214844 64\n", - "Si-239 -341.22165747 -338.6831970214844 64\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} } ] }, { "cell_type": "code", "source": [ - "!wget https://raw.githubusercontent.com/usnistgov/jarvis_leaderboard/main/jarvis_leaderboard/contributions/alignnff_mlearn_all_wt1/metadata.json" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "\n", + "\n", + "# Creating a customized histogram with a density plot\n", + "sns.histplot(dists, bins=30, kde=True, color='lightgreen', edgecolor='red')\n", + "\n", + "# Adding labels and title\n", + "plt.xlabel('Values')\n", + "plt.ylabel('Density')\n", + "plt.title('Customized Histogram with Density Plot')\n", + "\n", + "# Display the plot\n", + "plt.show()" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 501 }, - "id": "Q4WhUkLxsNDZ", - "outputId": "029a14e2-1dc5-4dc8-fbc0-9189c38784a0" + "id": "YMPzf5ag090j", + "outputId": "90287754-372f-4f36-e78b-5ec497d8de27" }, "execution_count": null, "outputs": [ { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2024-03-12 19:07:03-- https://raw.githubusercontent.com/usnistgov/jarvis_leaderboard/main/jarvis_leaderboard/contributions/alignnff_mlearn_all_wt1/metadata.json\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 1672 (1.6K) [text/plain]\n", - "Saving to: ‘metadata.json’\n", - "\n", - "metadata.json 100%[===================>] 1.63K --.-KB/s in 0s \n", - "\n", - "2024-03-12 19:07:03 (21.7 MB/s) - ‘metadata.json’ saved [1672/1672]\n", - "\n" - ] + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} } ] }, { - "cell_type": "code", + "cell_type": "markdown", "source": [ - "!pwd" + "Scaling" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NR6FotY5sNAf", - "outputId": "43753872-61ac-4829-ab0a-49bffbf44104" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/jarvis_leaderboard/jarvis_leaderboard/contributions/alignnff_su\n" - ] - } - ] + "id": "qHd-YV2rmonb" + } }, { "cell_type": "code", "source": [ - "!ls -altr" + "from ase.build.supercells import make_supercell\n", + "import numpy as np\n", + "import time\n", + "from alignn.ff.ff import AlignnAtomwiseCalculator, default_path, ev_curve\n", + "import pandas as pd\n", + "import numpy as np\n", + "import zipfile\n", + "import json\n", + "import os\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "from ase.stress import full_3x3_to_voigt_6_stress, voigt_6_to_full_3x3_stress\n", + "from ase import Atoms,Atom\n", + "A = 2.7223*np.array([[1,1,0],[1,0,1],[0,1,1]])\n", + "model_path = \"OutputDir\"\n", + "dir_name = model_path\n", + "calc = AlignnAtomwiseCalculator(\n", + " path=model_path,\n", + " force_mult_natoms=False,\n", + " force_multiplier=my_config['batch_size'],\n", + " stress_wt=0.3,\n", + ")\n", + "atoms_si = Atoms([Atom('Si', (0, 0, 0)), Atom('Si', (0.25, 0.25, 0.25))], cell = A, pbc=True)\n", + "atoms_si.calc = calc\n", + "en = atoms_si.get_potential_energy()\n", + "numbers = np.arange(1,8,1)\n", + "times = []\n", + "natoms=[]\n", + "for i in numbers:\n", + "\n", + " sc=make_supercell(atoms_si, [[i,0,0],[0,i,0],[0,0,i]])\n", + " t1=time.time()\n", + " sc.calc = calc\n", + "\n", + " en = sc.get_potential_energy()/len(sc)\n", + " t2=time.time()\n", + "\n", + " times.append(t2-t1)\n", + " natoms.append(len(sc))\n", + " print(i,en,t2-t1)" ], "metadata": { - "id": "hKGDcw4cld6L", - "outputId": "c5dc26b0-e68a-49de-a3e7-11c0d265db61", + "id": "OAPqhG4j1_Zz", + "outputId": "49dd7b34-e2b6-4912-d58a-1a621e5dc82d", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": null, + "execution_count": 43, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "total 2652\n", - "-rw-r--r-- 1 root root 2542319 Apr 29 2023 mlearn.json.zip\n", - "drwxr-xr-x 388 root root 28672 Mar 12 19:05 ..\n", - "-rw-r--r-- 1 root root 667 Mar 12 19:06 AI-MLFF-energy-mlearn_Si-test-mae.csv\n", - "-rw-r--r-- 1 root root 4509 Mar 12 19:06 AI-MLFF-stresses-mlearn_Si-test-multimae.csv\n", - "-rw-r--r-- 1 root root 53167 Mar 12 19:06 AI-MLFF-forces-mlearn_Si-test-multimae.csv.zip\n", - "-rw-r--r-- 1 root root 52985 Mar 12 19:06 AI-MLFF-forces-mlearn_Si-test-multimae.csv\n", - "-rw-r--r-- 1 root root 839 Mar 12 19:06 AI-MLFF-energy-mlearn_Si-test-mae.csv.zip\n", - "-rw-r--r-- 1 root root 4695 Mar 12 19:06 AI-MLFF-stresses-mlearn_Si-test-multimae.csv.zip\n", - "-rw-r--r-- 1 root root 1672 Mar 12 19:07 metadata.json\n", - "drwxr-xr-x 2 root root 4096 Mar 12 19:07 .\n" + "1 -4.535089015960693 0.03203010559082031\n", + "2 -4.536269664764404 0.03604269027709961\n", + "3 -4.536270618438721 0.04846024513244629\n", + "4 -4.536270618438721 0.10721230506896973\n", + "5 -4.536269664764404 0.2597355842590332\n", + "6 -4.536270618438721 0.627591609954834\n", + "7 -4.536269187927246 1.3484349250793457\n" ] } ] @@ -9767,28 +9237,40 @@ { "cell_type": "code", "source": [ - "os.chdir('/content')" + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "plt.plot(natoms,times,'o-')\n", + "plt.xlabel('Number of atoms')\n", + "plt.ylabel('Time (s)')\n", + "plt.show()" ], "metadata": { - "id": "4GQo10LHl436" + "id": "kXF260Oo1_W5", + "outputId": "815c73da-2126-4741-db65-f6a2269656e8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + } }, - "execution_count": null, - "outputs": [] + "execution_count": 44, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] }, { "cell_type": "code", - "source": [ - "# from jarvis_leaderboard.rebuild import get_metric_value,get_results\n", - "# %matplotlib inline\n", - "# import numpy as np\n", - "# import matplotlib.pyplot as plt\n", - "# names,vals=get_results(bench_name='AI-MLFF-forces-mlearn_Cu-test-multimae.csv.zip')\n", - "# plt.bar(np.arange(len(vals)),vals,color=(0.2, 0.4, 0.6, 0.6),edgecolor='blue')\n", - "# plt.xticks(np.arange(len(vals)),names,rotation=90)\n", - "# plt.ylabel('MAE (eV/A)')" - ], + "source": [], "metadata": { - "id": "KJLhrks2l9gm" + "id": "F4wnSs4t1_S5" }, "execution_count": null, "outputs": [] @@ -9796,83 +9278,67 @@ { "cell_type": "code", "source": [ - "# res_forces = get_metric_value(csv_path='/content/jarvis_leaderboard/jarvis_leaderboard/contributions/alignnff_su/AI-MLFF-forces-mlearn_Cu-test-multimae.csv.zip')\n", - "# res_energy = get_metric_value(csv_path='/content/jarvis_leaderboard/jarvis_leaderboard/contributions/alignnff_su/AI-MLFF-energy-mlearn_Cu-test-mae.csv.zip')\n", - "# res_stress = get_metric_value(csv_path='/content/jarvis_leaderboard/jarvis_leaderboard/contributions/alignnff_su/AI-MLFF-stresses-mlearn_Cu-test-multimae.csv.zip')" + "!nvidia-smi" ], "metadata": { - "id": "a3w4aYKelaum" + "id": "9BaHGXkzyyo8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8ba2ad0f-225d-4a9e-f6fa-202bb190422e" }, "execution_count": null, - "outputs": [] + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Sat May 18 19:30:12 2024 \n", + "+---------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", + "|-----------------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 72C P0 28W / 70W | 11039MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+----------------------+----------------------+\n", + " \n", + "+---------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=======================================================================================|\n", + "+---------------------------------------------------------------------------------------+\n" + ] + } + ] }, { "cell_type": "code", - "source": [ - "# pred = np.concatenate(\n", - "# [\n", - "# np.array(i.split(\";\"), dtype=\"float\")\n", - "# for i in res_forces[\"df\"][\"prediction\"].values\n", - "# ]\n", - "# )\n", - "# actual = np.concatenate(\n", - "# [\n", - "# np.array(i.split(\";\"), dtype=\"float\")\n", - "# for i in res_forces[\"df\"][\"actual\"].values\n", - "# ]\n", - "# )\n", - "# print(\"MAE F\", mean_absolute_error(actual, pred))\n", - "# plt.plot(actual, pred, \".\")\n", - "# plt.xlabel('DFT forces (eV/A)')\n", - "# plt.ylabel('FF forces (eV/A)')\n" - ], + "source": [], "metadata": { - "id": "yX23DFfAlbWb" + "id": "QuQ20q5-y36y" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "source": [ - "# #Adjust stress_wt\n", - "# pred = np.concatenate(\n", - "# [\n", - "# np.array(i.split(\";\"), dtype=\"float\")\n", - "# for i in res_stress[\"df\"][\"prediction\"].values\n", - "# ]\n", - "# )\n", - "# actual = np.concatenate(\n", - "# [\n", - "# np.array(i.split(\";\"), dtype=\"float\")\n", - "# for i in res_stress[\"df\"][\"actual\"].values\n", - "# ]\n", - "# )\n", - "# print(\"MAE F\", mean_absolute_error(actual, pred))\n", - "# plt.plot(actual, pred, \".\")\n", - "# plt.xlabel('DFT stress (kBar)') #TODO: check units of stress in mlearn\n", - "# plt.ylabel('FF stress (kBar)')\n" - ], + "source": [], "metadata": { - "id": "6sm_V0EilbUE" + "id": "NOWF55ley34t" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "source": [ - "# actual = res_energy[\"df\"][\"actual\"].values\n", - "# pred = res_energy[\"df\"][\"prediction\"].values\n", - "# print(actual, actual.shape)\n", - "# print(pred, pred.shape)\n", - "# print(\"MAE E\", mean_absolute_error(actual, pred))\n", - "# plt.plot(actual, pred, \".\")\n", - "# plt.xlabel('DFT energies (eV/atom)')\n", - "# plt.ylabel('FF energies (eV/atom)')\n" - ], + "source": [], "metadata": { - "id": "pUzktFsvmVGO" + "id": "MiwMvAWcy32L" }, "execution_count": null, "outputs": [] @@ -9881,7 +9347,7 @@ "cell_type": "code", "source": [], "metadata": { - "id": "FOprnsvCmVDp" + "id": "FynEcSoey3zm" }, "execution_count": null, "outputs": [] @@ -9890,7 +9356,7 @@ "cell_type": "code", "source": [], "metadata": { - "id": "Cx5MVzHOmVBQ" + "id": "A1daSd81y3w7" }, "execution_count": null, "outputs": [] @@ -9898,111 +9364,28 @@ { "cell_type": "code", "source": [ - "username = \"xyz\"\n", - "email = \"abc@gmail.com\"\n", - "passwd = #\"ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\"" + "os.chdir('/content')" ], "metadata": { - "id": "inHoFBEKmU-v" + "id": "4GQo10LHl436" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "source": [ - "#Osmium phonon\n", - "from alignn.ff.ff import phonons\n", - "from jarvis.core.atoms import ase_to_atoms\n", - "from jarvis.db.figshare import get_jid_data\n", - "from jarvis.core.atoms import Atoms\n", - "from alignn.ff.ff import AlignnAtomwiseCalculator,default_path,wt10_path,alignnff_fmult,fd_path,ForceField\n", - "atoms=Atoms.from_dict(get_jid_data(jid='JVASP-952',dataset='dft_3d')['atoms'])\n", - "ph_path=fd_path()\n", - "ph=phonons(model_path=ph_path,atoms=(atoms))\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.axis('off')\n", - "plt.imshow(plt.imread(\"phonopy_bands.png\"))\n", - "plt.show()" - ], - "metadata": { - "id": "W2zxpWQftXvP", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "outputId": "1d55a5e5-c9a1-401a-a7f4-25d5ab488ea6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Obtaining 3D dataset 76k ...\n", - "Reference:https://www.nature.com/articles/s41524-020-00440-1\n", - "Other versions:https://doi.org/10.6084/m9.figshare.6815699\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 40.8M/40.8M [00:02<00:00, 16.6MiB/s]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Loading the zipfile...\n", - "Loading completed.\n", - "dir_path /usr/local/lib/python3.10/dist-packages/alignn/ff/alignnff_fd\n", - "model_path /usr/local/lib/python3.10/dist-packages/alignn/ff/alignnff_fd\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "Ce=\"\"\"Ce\n", - "1.0\n", - "2.883577080372866 -0.0 1.6648337892833467\n", - "0.9611923601242888 2.7186624460117796 1.6648337892833467\n", - "0.0 -0.0 3.3296675785666934\n", - "Ce\n", - "1\n", - "Cartesian\n", - "0.0 0.0 0.0\n", - "\"\"\"" - ], + "source": [], "metadata": { - "id": "YL1_EAc7tXtB" + "id": "FOprnsvCmVDp" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", - "source": [ - "from jarvis.io.vasp.inputs import Poscar\n", - "pos = Poscar.from_string(Ce)" - ], + "source": [], "metadata": { - "id": "1QDwNl-5tXrE" + "id": "Cx5MVzHOmVBQ" }, "execution_count": null, "outputs": [] @@ -10010,47 +9393,178 @@ { "cell_type": "code", "source": [ - "ph=phonons(model_path=ph_path,atoms=(pos.atoms))\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.axis('off')\n", - "plt.imshow(plt.imread(\"phonopy_bands.png\"))\n", - "plt.show()" + "!pip freeze" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 285 + "base_uri": "https://localhost:8080/" }, - "id": "-6tTFmU3WDzb", - "outputId": "cb2612d6-bd89-4e41-a750-c968d68dd7e5" + "id": "7aPp-8e4pQ5E", + "outputId": "5bfad7da-f57f-4e69-e777-b0ddd9b495d7" }, "execution_count": null, "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} + "output_type": "stream", + "name": "stdout", + "text": [ + "absl-py==1.4.0\n", + "alignn @ file:///home/conda/feedstock_root/build_artifacts/alignn_1719545708215/work\n", + "annotated-types @ file:///home/conda/feedstock_root/build_artifacts/annotated-types_1716290248287/work\n", + "archspec @ file:///home/conda/feedstock_root/build_artifacts/archspec_1699370045702/work\n", + "ase @ file:///home/conda/feedstock_root/build_artifacts/ase_1717201223653/work\n", + "astunparse @ file:///home/conda/feedstock_root/build_artifacts/astunparse_1610696312422/work\n", + "Babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1702422572539/work\n", + "blinker @ file:///home/conda/feedstock_root/build_artifacts/blinker_1715091184126/work\n", + "boltons @ file:///home/conda/feedstock_root/build_artifacts/boltons_1703154663129/work\n", + "Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work\n", + "cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work\n", + "certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1720457958366/work/certifi\n", + "cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1696001684923/work\n", + "charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1698833585322/work\n", + "click @ file:///home/conda/feedstock_root/build_artifacts/click_1692311806742/work\n", + "colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work\n", + "conda @ file:///home/conda/feedstock_root/build_artifacts/conda_1701731572133/work\n", + "conda-libmamba-solver @ file:///home/conda/feedstock_root/build_artifacts/conda-libmamba-solver_1702406360642/work/src\n", + "conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1691048088238/work\n", + "conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1691009212940/work\n", + "contourpy @ file:///home/conda/feedstock_root/build_artifacts/contourpy_1712429905637/work\n", + "cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1696677705766/work\n", + "dgl @ file:///home/conda/feedstock_root/build_artifacts/dgl_1720590306534/work/python\n", + "distro @ file:///home/conda/feedstock_root/build_artifacts/distro_1675116244235/work\n", + "elastic==5.2.3.6\n", + "filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1719088281970/work\n", + "flake8 @ file:///home/conda/feedstock_root/build_artifacts/flake8_1718643520047/work\n", + "Flask @ file:///home/conda/feedstock_root/build_artifacts/flask_1712667726126/work\n", + "flatbuffers @ file:///home/conda/feedstock_root/build_artifacts/python-flatbuffers_1711466727397/work\n", + "fonttools @ file:///home/conda/feedstock_root/build_artifacts/fonttools_1720359045723/work\n", + "fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1719514913127/work\n", + "gast @ file:///home/conda/feedstock_root/build_artifacts/gast_1719403123000/work\n", + "ghp-import @ file:///home/conda/feedstock_root/build_artifacts/ghp-import_1651585738538/work\n", + "gmpy2 @ file:///home/conda/feedstock_root/build_artifacts/gmpy2_1715527283764/work\n", + "google-pasta==0.2.0\n", + "grpcio @ file:///home/conda/feedstock_root/build_artifacts/grpc-split_1713388282847/work\n", + "h5py @ file:///home/conda/feedstock_root/build_artifacts/h5py_1717664841189/work\n", + "idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1701026962277/work\n", + "importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1721856510709/work\n", + "importlib_resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1711040877059/work\n", + "inflect @ file:///home/conda/feedstock_root/build_artifacts/inflect_1719896001275/work\n", + "intermat @ git+https://github.com/usnistgov/intermat.git@df52d4521ddb560f16222d6ae29e926ba8897668\n", + "itsdangerous @ file:///home/conda/feedstock_root/build_artifacts/itsdangerous_1713372668944/work\n", + "-e git+https://github.com/usnistgov/jarvis_leaderboard.git@c26e98ac67eb40577e77840fb92bf94e61790957#egg=jarvis_leaderboard\n", + "jarvis-tools @ file:///home/conda/feedstock_root/build_artifacts/jarvis-tools_1719546412351/work\n", + "Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1715127149914/work\n", + "joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1714665484399/work\n", + "jsonpatch @ file:///home/conda/feedstock_root/build_artifacts/jsonpatch_1695536281965/work\n", + "jsonpointer @ file:///home/conda/feedstock_root/build_artifacts/jsonpointer_1695397238043/work\n", + "keras @ file:///home/conda/feedstock_root/build_artifacts/keras_1721206236539/work\n", + "kiwisolver @ file:///home/conda/feedstock_root/build_artifacts/kiwisolver_1695379902431/work\n", + "libmambapy @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1702310393080/work/libmambapy\n", + "lmdb @ file:///home/conda/feedstock_root/build_artifacts/python-lmdb_1719843605447/work\n", + "mamba @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1702310393080/work/mamba\n", + "Markdown @ file:///home/conda/feedstock_root/build_artifacts/markdown_1710435156458/work\n", + "markdown-it-py @ file:///home/conda/feedstock_root/build_artifacts/markdown-it-py_1686175045316/work\n", + "MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1706899921127/work\n", + "matplotlib==3.9.1\n", + "mccabe @ file:///home/conda/feedstock_root/build_artifacts/mccabe_1643049622439/work\n", + "mdurl @ file:///home/conda/feedstock_root/build_artifacts/mdurl_1704317613764/work\n", + "menuinst @ file:///home/conda/feedstock_root/build_artifacts/menuinst_1702317041727/work\n", + "mergedeep @ file:///home/conda/feedstock_root/build_artifacts/mergedeep_1612711302171/work\n", + "mkdocs @ file:///home/conda/feedstock_root/build_artifacts/mkdocs_1714253083629/work\n", + "mkdocs-get-deps @ file:///home/conda/feedstock_root/build_artifacts/mkdocs-get-deps_1713710820061/work\n", + "mkdocs-material @ file:///home/conda/feedstock_root/build_artifacts/mkdocs-material_1721745605433/work\n", + "mkdocs-material-extensions @ file:///home/conda/feedstock_root/build_artifacts/mkdocs-material-extensions_1700695061696/work\n", + "ml-dtypes @ file:///home/conda/feedstock_root/build_artifacts/ml_dtypes_1704727647076/work\n", + "more-itertools @ file:///home/conda/feedstock_root/build_artifacts/more-itertools_1718048476694/work\n", + "mpmath @ file:///home/conda/feedstock_root/build_artifacts/mpmath_1678228039184/work\n", + "munkres==1.1.4\n", + "namex @ file:///home/conda/feedstock_root/build_artifacts/namex_1713169736451/work\n", + "networkx @ file:///home/conda/feedstock_root/build_artifacts/networkx_1712540363324/work\n", + "nltk==3.8.1\n", + "numpy @ file:///home/conda/feedstock_root/build_artifacts/numpy_1707225380409/work/dist/numpy-1.26.4-cp310-cp310-linux_x86_64.whl#sha256=51131fd8fc130cd168aecaf1bc0ea85f92e8ffebf211772ceb16ac2e7f10d7ca\n", + "opt-einsum @ file:///home/conda/feedstock_root/build_artifacts/opt_einsum_1696448916724/work\n", + "optree @ file:///home/conda/feedstock_root/build_artifacts/optree_1720352118001/work\n", + "packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1696202382185/work\n", + "paginate @ file:///home/conda/feedstock_root/build_artifacts/paginate_1693246827447/work\n", + "pandas @ file:///home/conda/feedstock_root/build_artifacts/pandas_1715897614105/work\n", + "pathspec @ file:///home/conda/feedstock_root/build_artifacts/pathspec_1702249949303/work\n", + "phonopy==2.26.6\n", + "pillow @ file:///home/conda/feedstock_root/build_artifacts/pillow_1712154467551/work\n", + "platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1701708255999/work\n", + "plotly==5.23.0\n", + "pluggy @ file:///home/conda/feedstock_root/build_artifacts/pluggy_1693086607691/work\n", + "protobuf==4.25.3\n", + "psutil @ file:///home/conda/feedstock_root/build_artifacts/psutil_1719274566094/work\n", + "pycodestyle @ file:///home/conda/feedstock_root/build_artifacts/pycodestyle_1718525032775/work\n", + "pycosat @ file:///home/conda/feedstock_root/build_artifacts/pycosat_1696355758174/work\n", + "pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work\n", + "pydantic @ file:///home/conda/feedstock_root/build_artifacts/pydantic_1720293063581/work\n", + "pydantic-settings @ file:///home/conda/feedstock_root/build_artifacts/pydantic-settings_1719264359828/work\n", + "pydantic_core @ file:///home/conda/feedstock_root/build_artifacts/pydantic-core_1720041207964/work\n", + "pydocstyle @ file:///home/conda/feedstock_root/build_artifacts/pydocstyle_1673997487070/work\n", + "pyflakes @ file:///home/conda/feedstock_root/build_artifacts/pyflakes_1704424584912/work\n", + "Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1714846767233/work\n", + "pymdown-extensions @ file:///home/conda/feedstock_root/build_artifacts/pymdown-extensions_1714261890444/work\n", + "pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1635267989520/work\n", + "PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work\n", + "python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work\n", + "python-dotenv @ file:///home/conda/feedstock_root/build_artifacts/python-dotenv-split_1706018097647/work\n", + "pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1706886791323/work\n", + "PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1695373428874/work\n", + "pyyaml_env_tag @ file:///home/conda/feedstock_root/build_artifacts/pyyaml-env-tag_1624388951658/work\n", + "regex @ file:///home/conda/feedstock_root/build_artifacts/regex_1721873020962/work\n", + "requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work\n", + "rich @ file:///home/conda/feedstock_root/build_artifacts/rich-split_1709150387247/work/dist\n", + "rouge==1.0.1\n", + "ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1699007337104/work\n", + "ruamel.yaml.clib @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml.clib_1695996839082/work\n", + "scikit-learn @ file:///home/conda/feedstock_root/build_artifacts/scikit-learn_1719998080760/work/dist/scikit_learn-1.5.1-cp310-cp310-linux_x86_64.whl#sha256=aa11b5abf01e3a97e913c177e81b5734e58752358cf3e6509740fee1e3dbd537\n", + "scipy @ file:///home/conda/feedstock_root/build_artifacts/scipy-split_1720323032635/work/dist/scipy-1.14.0-cp310-cp310-linux_x86_64.whl#sha256=637202cdf828ce0ac658d3377770005d77a276adeac622f0dd2d5d970b1b865e\n", + "six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work\n", + "snowballstemmer @ file:///home/conda/feedstock_root/build_artifacts/snowballstemmer_1637143057757/work\n", + "spglib @ file:///home/conda/feedstock_root/build_artifacts/spglib_1720711874866/work\n", + "sympy @ file:///home/conda/feedstock_root/build_artifacts/sympy_1721004824012/work\n", + "tenacity==8.5.0\n", + "tensorboard @ file:///home/conda/feedstock_root/build_artifacts/tensorboard_1708285739699/work/tensorboard-2.16.2-py3-none-any.whl#sha256=9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45\n", + "tensorboard-data-server @ file:///home/conda/feedstock_root/build_artifacts/tensorboard-data-server_1695425366946/work/tensorboard_data_server-0.7.0-py3-none-manylinux2014_x86_64.whl#sha256=aa1f69b2111bb4309cc6277ac277c89a9f67d074aa666b96eebe7401a359e1d5\n", + "tensorflow @ file:///home/conda/feedstock_root/build_artifacts/tensorflow-split_1716512443563/work/tensorflow_pkg/tensorflow-2.16.1-cp310-cp310-linux_x86_64.whl#sha256=c2176c7ac5ef8369a9d5f214ce8c348753e7f9fd21d76a922c4ce41f446d82b8\n", + "tensorflow_estimator @ file:///home/conda/feedstock_root/build_artifacts/tensorflow-split_1716512443563/work/tensorflow-estimator/wheel_dir/tensorflow_estimator-2.15.0-py2.py3-none-any.whl#sha256=1daa661aa86989691b23c17a16cf0213f715575a0b57e4c93558050539853874\n", + "termcolor @ file:///home/conda/feedstock_root/build_artifacts/termcolor_1704357939450/work\n", + "threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1714400101435/work\n", + "tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1644342247877/work\n", + "toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1706112571092/work\n", + "torch @ file:///home/conda/feedstock_root/build_artifacts/libtorch_1718580740865/work\n", + "torchaudio==2.3.1\n", + "torchdata @ file:///__w/_temp/conda_build_env/conda-bld/torchdata_1698872112169/work\n", + "torchvision==0.18.1\n", + "tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work\n", + "truststore @ file:///home/conda/feedstock_root/build_artifacts/truststore_1694154605758/work\n", + "typeguard @ file:///home/conda/feedstock_root/build_artifacts/typeguard_1721540425920/work\n", + "typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1717802530399/work\n", + "tzdata @ file:///home/conda/feedstock_root/build_artifacts/python-tzdata_1707747584337/work\n", + "unicodedata2 @ file:///home/conda/feedstock_root/build_artifacts/unicodedata2_1695847980273/work\n", + "urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1699933488691/work\n", + "watchdog @ file:///home/conda/feedstock_root/build_artifacts/watchdog_1716561795369/work\n", + "Werkzeug @ file:///home/conda/feedstock_root/build_artifacts/werkzeug_1715000201436/work\n", + "wrapt @ file:///home/conda/feedstock_root/build_artifacts/wrapt_1699532811524/work\n", + "xmltodict @ file:///home/conda/feedstock_root/build_artifacts/xmltodict_1652020822199/work\n", + "zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1718013267051/work\n", + "zstandard==0.23.0\n" + ] } ] }, { "cell_type": "code", "source": [ - "!pip freeze" + "!conda env export" ], "metadata": { + "id": "bnsmfvOepjVl", "colab": { "base_uri": "https://localhost:8080/" }, - "id": "7aPp-8e4pQ5E", - "outputId": "666c37eb-10d3-44a8-f59c-d3e905a20c8d" + "outputId": "18595469-0b44-465d-f990-eb56ee649c82" }, "execution_count": null, "outputs": [ @@ -10058,533 +9572,291 @@ "output_type": "stream", "name": "stdout", "text": [ - "absl-py==1.4.0\n", - "accelerate==0.28.0\n", - "aiohttp==3.9.3\n", - "aiosignal==1.3.1\n", - "alabaster==0.7.16\n", - "albumentations==1.3.1\n", - "alignn==2024.2.4\n", - "altair==4.2.2\n", - "annotated-types==0.6.0\n", - "anyio==3.7.1\n", - "appdirs==1.4.4\n", - "argon2-cffi==23.1.0\n", - "argon2-cffi-bindings==21.2.0\n", - "array-record==0.5.0\n", - "arviz==0.15.1\n", - "ase==3.22.1\n", - "astropy==5.3.4\n", - "astunparse==1.6.3\n", - "async-timeout==4.0.3\n", - "atpublic==4.0\n", - "attrs==23.2.0\n", - "audioread==3.0.1\n", - "autograd==1.6.2\n", - "Babel==2.14.0\n", - "backcall==0.2.0\n", - "beautifulsoup4==4.12.3\n", - "bidict==0.23.1\n", - "bigframes==0.23.0\n", - "bleach==6.1.0\n", - "blinker==1.4\n", - "blis==0.7.11\n", - "blosc2==2.0.0\n", - "bokeh==3.3.4\n", - "bqplot==0.12.43\n", - "branca==0.7.1\n", - "build==1.1.1\n", - "CacheControl==0.14.0\n", - "cachetools==5.3.3\n", - "catalogue==2.0.10\n", - "certifi==2024.2.2\n", - "cffi==1.16.0\n", - "chardet==5.2.0\n", - "charset-normalizer==3.3.2\n", - "chex==0.1.85\n", - "click==8.1.7\n", - "click-plugins==1.1.1\n", - "cligj==0.7.2\n", - "cloudpathlib==0.16.0\n", - "cloudpickle==2.2.1\n", - "cmake==3.27.9\n", - "cmdstanpy==1.2.1\n", - "colorama==0.4.6\n", - "colorcet==3.1.0\n", - "colorlover==0.3.0\n", - "colour==0.1.5\n", - "community==1.0.0b1\n", - "confection==0.1.4\n", - "cons==0.4.6\n", - "contextlib2==21.6.0\n", - "contourpy==1.2.0\n", - "cryptography==42.0.5\n", - "cufflinks==0.17.3\n", - "cupy-cuda12x==12.2.0\n", - "cvxopt==1.3.2\n", - "cvxpy==1.3.3\n", - "cycler==0.12.1\n", - "cymem==2.0.8\n", - "Cython==3.0.9\n", - "dask==2023.8.1\n", - "datascience==0.17.6\n", - "db-dtypes==1.2.0\n", - "dbus-python==1.2.18\n", - "debugpy==1.6.6\n", - "decorator==4.4.2\n", - "defusedxml==0.7.1\n", - "dgl==1.0.1+cu117\n", - "distributed==2023.8.1\n", - "distro==1.7.0\n", - "dlib==19.24.2\n", - "dm-tree==0.1.8\n", - "docutils==0.18.1\n", - "dopamine-rl==4.0.6\n", - "duckdb==0.9.2\n", - "earthengine-api==0.1.393\n", - "easydict==1.13\n", - "ecos==2.0.13\n", - "editdistance==0.6.2\n", - "eerepr==0.0.4\n", - "en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889\n", - "entrypoints==0.4\n", - "et-xmlfile==1.1.0\n", - "etils==1.7.0\n", - "etuples==0.3.9\n", - "exceptiongroup==1.2.0\n", - "fastai==2.7.14\n", - "fastcore==1.5.29\n", - "fastdownload==0.0.7\n", - "fastjsonschema==2.19.1\n", - "fastprogress==1.0.3\n", - "fastrlock==0.8.2\n", - "filelock==3.13.1\n", - "fiona==1.9.5\n", - "firebase-admin==5.3.0\n", - "flake8==7.0.0\n", - "Flask==2.2.5\n", - "flatbuffers==24.3.6\n", - "flax==0.8.1\n", - "folium==0.14.0\n", - "fonttools==4.49.0\n", - "frozendict==2.4.0\n", - "frozenlist==1.4.1\n", - "fsspec==2023.6.0\n", - "future==0.18.3\n", - "gast==0.5.4\n", - "gcsfs==2023.6.0\n", - "GDAL==3.6.4\n", - "gdown==4.7.3\n", - "geemap==0.32.0\n", - "gensim==4.3.2\n", - "geocoder==1.38.1\n", - "geographiclib==2.0\n", - "geopandas==0.13.2\n", - "geopy==2.3.0\n", - "ghp-import==2.1.0\n", - "gin-config==0.5.0\n", - "glob2==0.7\n", - "google==2.0.3\n", - "google-ai-generativelanguage==0.4.0\n", - "google-api-core==2.11.1\n", - "google-api-python-client==2.84.0\n", - "google-auth==2.27.0\n", - "google-auth-httplib2==0.1.1\n", - "google-auth-oauthlib==1.2.0\n", - "google-cloud-aiplatform==1.43.0\n", - "google-cloud-bigquery==3.12.0\n", - "google-cloud-bigquery-connection==1.12.1\n", - "google-cloud-bigquery-storage==2.24.0\n", - "google-cloud-core==2.3.3\n", - "google-cloud-datastore==2.15.2\n", - "google-cloud-firestore==2.11.1\n", - "google-cloud-functions==1.13.3\n", - "google-cloud-iam==2.14.3\n", - "google-cloud-language==2.13.3\n", - "google-cloud-resource-manager==1.12.3\n", - "google-cloud-storage==2.8.0\n", - "google-cloud-translate==3.11.3\n", - "google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz#sha256=e8d2f54a76901d53c6434c11f4301cc1fb1c4ed74fedffef65cb1848587400cf\n", - "google-crc32c==1.5.0\n", - "google-generativeai==0.3.2\n", - "google-pasta==0.2.0\n", - "google-resumable-media==2.7.0\n", - "googleapis-common-protos==1.62.0\n", - "googledrivedownloader==0.4\n", - "graphviz==0.20.1\n", - "greenlet==3.0.3\n", - "grpc-google-iam-v1==0.13.0\n", - "grpcio==1.62.0\n", - "grpcio-status==1.48.2\n", - "gspread==3.4.2\n", - "gspread-dataframe==3.3.1\n", - "gym==0.25.2\n", - "gym-notices==0.0.8\n", - "h5netcdf==1.3.0\n", - "h5py==3.9.0\n", - "holidays==0.44\n", - "holoviews==1.17.1\n", - "html5lib==1.1\n", - "httpimport==1.3.1\n", - "httplib2==0.22.0\n", - "huggingface-hub==0.20.3\n", - "humanize==4.7.0\n", - "hyperopt==0.2.7\n", - "ibis-framework==8.0.0\n", - "idna==3.6\n", - "imageio==2.31.6\n", - "imageio-ffmpeg==0.4.9\n", - "imagesize==1.4.1\n", - "imbalanced-learn==0.10.1\n", - "imgaug==0.4.0\n", - "importlib_metadata==7.0.2\n", - "importlib_resources==6.1.3\n", - "imutils==0.5.4\n", - "inflect==7.0.0\n", - "iniconfig==2.0.0\n", - "intel-openmp==2023.2.3\n", - "ipyevents==2.0.2\n", - "ipyfilechooser==0.6.0\n", - "ipykernel==5.5.6\n", - "ipyleaflet==0.18.2\n", - "ipython==7.34.0\n", - "ipython-genutils==0.2.0\n", - "ipython-sql==0.5.0\n", - "ipytree==0.2.2\n", - "ipywidgets==7.7.1\n", - "itsdangerous==2.1.2\n", - "-e git+https://github.com/usnistgov/jarvis_leaderboard.git@2a13340c9365aa39c377f1ff21b3cd559cb10b58#egg=jarvis_leaderboard\n", - "jarvis-tools==2023.12.12\n", - "jax==0.4.23\n", - "jaxlib @ https://storage.googleapis.com/jax-releases/cuda12/jaxlib-0.4.23+cuda12.cudnn89-cp310-cp310-manylinux2014_x86_64.whl#sha256=8e42000672599e7ec0ea7f551acfcc95dcdd0e22b05a1d1f12f97b56a9fce4a8\n", - "jeepney==0.7.1\n", - "jieba==0.42.1\n", - "Jinja2==3.1.3\n", - "joblib==1.3.2\n", - "jsonpickle==3.0.3\n", - "jsonschema==4.19.2\n", - "jsonschema-specifications==2023.12.1\n", - "jupyter-client==6.1.12\n", - "jupyter-console==6.1.0\n", - "jupyter-server==1.24.0\n", - "jupyter_core==5.7.1\n", - "jupyterlab_pygments==0.3.0\n", - "jupyterlab_widgets==3.0.10\n", - "kaggle==1.5.16\n", - "kagglehub==0.2.0\n", - "keras==2.15.0\n", - "keyring==23.5.0\n", - "kiwisolver==1.4.5\n", - "langcodes==3.3.0\n", - "launchpadlib==1.10.16\n", - "lazr.restfulclient==0.14.4\n", - "lazr.uri==1.0.6\n", - "lazy_loader==0.3\n", - "libclang==16.0.6\n", - "librosa==0.10.1\n", - "lightgbm==4.1.0\n", - "linkify-it-py==2.0.3\n", - "lit==18.1.1\n", - "llvmlite==0.41.1\n", - "locket==1.0.0\n", - "logical-unification==0.4.6\n", - "lxml==4.9.4\n", - "malloy==2023.1067\n", - "Markdown==3.5.2\n", - "markdown-it-py==3.0.0\n", - "MarkupSafe==2.1.5\n", - "matplotlib==3.7.1\n", - "matplotlib-inline==0.1.6\n", - "matplotlib-venn==0.11.10\n", - "mccabe==0.7.0\n", - "mdit-py-plugins==0.4.0\n", - "mdurl==0.1.2\n", - "mergedeep==1.3.4\n", - "miniKanren==1.0.3\n", - "missingno==0.5.2\n", - "mistune==0.8.4\n", - "mizani==0.9.3\n", - "mkdocs==1.5.3\n", - "mkdocs-material==9.5.13\n", - "mkdocs-material-extensions==1.3.1\n", - "mkl==2023.2.0\n", - "ml-dtypes==0.2.0\n", - "mlxtend==0.22.0\n", - "more-itertools==10.1.0\n", - "moviepy==1.0.3\n", - "mpmath==1.3.0\n", - "msgpack==1.0.8\n", - "multidict==6.0.5\n", - "multipledispatch==1.0.0\n", - "multitasking==0.0.11\n", - "murmurhash==1.0.10\n", - "music21==9.1.0\n", - "natsort==8.4.0\n", - "nbclassic==1.0.0\n", - "nbclient==0.9.0\n", - "nbconvert==6.5.4\n", - "nbformat==5.9.2\n", - "nest-asyncio==1.6.0\n", - "networkx==3.2.1\n", - "nibabel==4.0.2\n", - "nltk==3.8.1\n", - "notebook==6.5.5\n", - "notebook_shim==0.2.4\n", - "numba==0.58.1\n", - "numexpr==2.9.0\n", - "numpy==1.25.2\n", - "nvidia-cublas-cu11==11.10.3.66\n", - "nvidia-cuda-cupti-cu11==11.7.101\n", - "nvidia-cuda-nvrtc-cu11==11.7.99\n", - "nvidia-cuda-runtime-cu11==11.7.99\n", - "nvidia-cudnn-cu11==8.5.0.96\n", - "nvidia-cufft-cu11==10.9.0.58\n", - "nvidia-curand-cu11==10.2.10.91\n", - "nvidia-cusolver-cu11==11.4.0.1\n", - "nvidia-cusparse-cu11==11.7.4.91\n", - "nvidia-nccl-cu11==2.14.3\n", - "nvidia-nvtx-cu11==11.7.91\n", - "oauth2client==4.1.3\n", - "oauthlib==3.2.2\n", - "opencv-contrib-python==4.8.0.76\n", - "opencv-python==4.8.0.76\n", - "opencv-python-headless==4.9.0.80\n", - "openpyxl==3.1.2\n", - "opt-einsum==3.3.0\n", - "optax==0.2.1\n", - "orbax-checkpoint==0.4.4\n", - "osqp==0.6.2.post8\n", - "packaging==23.2\n", - "paginate==0.5.6\n", - "pandas==1.5.3\n", - "pandas-datareader==0.10.0\n", - "pandas-gbq==0.19.2\n", - "pandas-stubs==1.5.3.230304\n", - "pandocfilters==1.5.1\n", - "panel==1.3.8\n", - "param==2.0.2\n", - "parso==0.8.3\n", - "parsy==2.1\n", - "partd==1.4.1\n", - "pathlib==1.0.1\n", - "pathspec==0.12.1\n", - "patsy==0.5.6\n", - "peewee==3.17.1\n", - "pexpect==4.9.0\n", - "phonopy==2.21.2\n", - "pickleshare==0.7.5\n", - "Pillow==9.4.0\n", - "pip-tools==6.13.0\n", - "platformdirs==4.2.0\n", - "plotly==5.15.0\n", - "plotnine==0.12.4\n", - "pluggy==1.4.0\n", - "polars==0.20.2\n", - "pooch==1.8.1\n", - "portpicker==1.5.2\n", - "prefetch-generator==1.0.3\n", - "preshed==3.0.9\n", - "prettytable==3.10.0\n", - "proglog==0.1.10\n", - "progressbar2==4.2.0\n", - "prometheus_client==0.20.0\n", - "promise==2.3\n", - "prompt-toolkit==3.0.43\n", - "prophet==1.1.5\n", - "proto-plus==1.23.0\n", - "protobuf==3.20.3\n", - "psutil==5.9.5\n", - "psycopg2==2.9.9\n", - "ptyprocess==0.7.0\n", - "py-cpuinfo==9.0.0\n", - "py4j==0.10.9.7\n", - "pyarrow==14.0.2\n", - "pyarrow-hotfix==0.6\n", - "pyasn1==0.5.1\n", - "pyasn1-modules==0.3.0\n", - "pycocotools==2.0.7\n", - "pycodestyle==2.11.1\n", - "pycparser==2.21\n", - "pydantic==1.8.1\n", - "pydantic_core==2.16.3\n", - "pydata-google-auth==1.8.2\n", - "pydocstyle==6.3.0\n", - "pydot==1.4.2\n", - "pydot-ng==2.0.0\n", - "pydotplus==2.0.2\n", - "PyDrive==1.3.1\n", - "PyDrive2==1.6.3\n", - "pyerfa==2.0.1.1\n", - "pyflakes==3.2.0\n", - "pygame==2.5.2\n", - "Pygments==2.16.1\n", - "PyGObject==3.42.1\n", - "PyJWT==2.3.0\n", - "pymc==5.10.4\n", - "pymdown-extensions==10.7.1\n", - "pymystem3==0.2.0\n", - "PyOpenGL==3.1.7\n", - "pyOpenSSL==24.0.0\n", - "pyparsing==2.4.7\n", - "pyperclip==1.8.2\n", - "pyproj==3.6.1\n", - "pyproject_hooks==1.0.0\n", - "pyshp==2.3.1\n", - "PySocks==1.7.1\n", - "pytensor==2.18.6\n", - "pytest==7.4.4\n", - "python-apt @ file:///backend-container/containers/python_apt-0.0.0-cp310-cp310-linux_x86_64.whl#sha256=b209c7165d6061963abe611492f8c91c3bcef4b7a6600f966bab58900c63fefa\n", - "python-box==7.1.1\n", - "python-dateutil==2.8.2\n", - "python-louvain==0.16\n", - "python-slugify==8.0.4\n", - "python-utils==3.8.2\n", - "pytorch-ignite==0.5.0.dev20240311\n", - "pytz==2023.4\n", - "pyviz_comms==3.0.1\n", - "PyWavelets==1.5.0\n", - "PyYAML==6.0.1\n", - "pyyaml_env_tag==0.1\n", - "pyzmq==23.2.1\n", - "qdldl==0.1.7.post0\n", - "qudida==0.0.4\n", - "ratelim==0.1.6\n", - "referencing==0.33.0\n", - "regex==2023.12.25\n", - "requests==2.31.0\n", - "requests-oauthlib==1.3.1\n", - "requirements-parser==0.5.0\n", - "rich==13.7.1\n", - "rouge==1.0.1\n", - "rpds-py==0.18.0\n", - "rpy2==3.4.2\n", - "rsa==4.9\n", - "safetensors==0.4.2\n", - "scikit-image==0.19.3\n", - "scikit-learn==1.2.2\n", - "scipy==1.11.4\n", - "scooby==0.9.2\n", - "scs==3.2.4.post1\n", - "seaborn==0.13.1\n", - "SecretStorage==3.3.1\n", - "Send2Trash==1.8.2\n", - "sentencepiece==0.1.99\n", - "shapely==2.0.3\n", - "six==1.16.0\n", - "sklearn-pandas==2.2.0\n", - "smart-open==6.4.0\n", - "sniffio==1.3.1\n", - "snowballstemmer==2.2.0\n", - "sortedcontainers==2.4.0\n", - "soundfile==0.12.1\n", - "soupsieve==2.5\n", - "soxr==0.3.7\n", - "spacy==3.7.4\n", - "spacy-legacy==3.0.12\n", - "spacy-loggers==1.0.5\n", - "spglib==2.0.2\n", - "Sphinx==5.0.2\n", - "sphinxcontrib-applehelp==1.0.8\n", - "sphinxcontrib-devhelp==1.0.6\n", - "sphinxcontrib-htmlhelp==2.0.5\n", - "sphinxcontrib-jsmath==1.0.1\n", - "sphinxcontrib-qthelp==1.0.7\n", - "sphinxcontrib-serializinghtml==1.1.10\n", - "SQLAlchemy==2.0.28\n", - "sqlglot==20.11.0\n", - "sqlparse==0.4.4\n", - "srsly==2.4.8\n", - "stanio==0.3.0\n", - "statsmodels==0.14.1\n", - "sympy==1.12\n", - "tables==3.8.0\n", - "tabulate==0.9.0\n", - "tbb==2021.11.0\n", - "tblib==3.0.0\n", - "tenacity==8.2.3\n", - "tensorboard==2.15.2\n", - "tensorboard-data-server==0.7.2\n", - "tensorflow==2.15.0\n", - "tensorflow-datasets==4.9.4\n", - "tensorflow-estimator==2.15.0\n", - "tensorflow-gcs-config==2.15.0\n", - "tensorflow-hub==0.16.1\n", - "tensorflow-io-gcs-filesystem==0.36.0\n", - "tensorflow-metadata==1.14.0\n", - "tensorflow-probability==0.23.0\n", - "tensorstore==0.1.45\n", - "termcolor==2.4.0\n", - "terminado==0.18.0\n", - "text-unidecode==1.3\n", - "textblob==0.17.1\n", - "tf-keras==2.15.0\n", - "tf-slim==1.1.0\n", - "thinc==8.2.3\n", - "threadpoolctl==3.3.0\n", - "tifffile==2024.2.12\n", - "tinycss2==1.2.1\n", - "tokenizers==0.15.2\n", - "toml==0.10.2\n", - "tomli==2.0.1\n", - "toolz==0.12.1\n", - "torch==2.0.0\n", - "torchaudio @ https://download.pytorch.org/whl/cu121/torchaudio-2.1.0%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=676bda4042734eda99bc59b2d7f761f345d3cde0cad492ad34e3aefde688c6d8\n", - "torchdata==0.7.0\n", - "torchsummary==1.5.1\n", - "torchtext==0.16.0\n", - "torchvision @ https://download.pytorch.org/whl/cu121/torchvision-0.16.0%2Bcu121-cp310-cp310-linux_x86_64.whl#sha256=e76e78d0ad43636c9884b3084ffaea8a8b61f21129fbfa456a5fe734f0affea9\n", - "tornado==6.3.3\n", - "tqdm==4.66.2\n", - "traitlets==5.7.1\n", - "traittypes==0.2.1\n", - "transformers==4.38.2\n", - "triton==2.0.0\n", - "tweepy==4.14.0\n", - "typer==0.9.0\n", - "types-pytz==2024.1.0.20240203\n", - "types-setuptools==69.1.0.20240310\n", - "typing_extensions==4.10.0\n", - "tzlocal==5.2\n", - "uc-micro-py==1.0.3\n", - "uritemplate==4.1.1\n", - "urllib3==2.0.7\n", - "vega-datasets==0.9.0\n", - "wadllib==1.3.6\n", - "wasabi==1.1.2\n", - "watchdog==4.0.0\n", - "wcwidth==0.2.13\n", - "weasel==0.3.4\n", - "webcolors==1.13\n", - "webencodings==0.5.1\n", - "websocket-client==1.7.0\n", - "Werkzeug==3.0.1\n", - "widgetsnbextension==3.6.6\n", - "wordcloud==1.9.3\n", - "wrapt==1.14.1\n", - "xarray==2023.7.0\n", - "xarray-einstats==0.7.0\n", - "xgboost==2.0.3\n", - "xlrd==2.0.1\n", - "xmltodict==0.13.0\n", - "xyzservices==2023.10.1\n", - "yarl==1.9.4\n", - "yellowbrick==1.5\n", - "yfinance==0.2.37\n", - "zict==3.0.0\n", - "zipp==3.17.0\n" + "name: base\n", + "channels:\n", + " - pytorch\n", + " - nvidia\n", + " - conda-forge\n", + "dependencies:\n", + " - _libgcc_mutex=0.1=conda_forge\n", + " - _openmp_mutex=4.5=2_kmp_llvm\n", + " - alignn=2024.5.27=pyhd8ed1ab_0\n", + " - annotated-types=0.7.0=pyhd8ed1ab_0\n", + " - archspec=0.2.2=pyhd8ed1ab_0\n", + " - ase=3.23.0=pyhd8ed1ab_0\n", + " - astunparse=1.6.3=pyhd8ed1ab_0\n", + " - babel=2.14.0=pyhd8ed1ab_0\n", + " - blinker=1.8.2=pyhd8ed1ab_0\n", + " - boltons=23.1.1=pyhd8ed1ab_0\n", + " - brotli=1.1.0=hd590300_1\n", + " - brotli-bin=1.1.0=hd590300_1\n", + " - brotli-python=1.1.0=py310hc6cd4ac_1\n", + " - bzip2=1.0.8=hd590300_5\n", + " - c-ares=1.32.3=h4bc722e_0\n", + " - ca-certificates=2024.7.4=hbcca054_0\n", + " - cached-property=1.5.2=hd8ed1ab_1\n", + " - cached_property=1.5.2=pyha770c72_1\n", + " - certifi=2024.7.4=pyhd8ed1ab_0\n", + " - cffi=1.16.0=py310h2fee648_0\n", + " - charset-normalizer=3.3.2=pyhd8ed1ab_0\n", + " - click=8.1.7=unix_pyh707e725_0\n", + " - colorama=0.4.6=pyhd8ed1ab_0\n", + " - conda=23.11.0=py310hff52083_1\n", + " - conda-libmamba-solver=23.12.0=pyhd8ed1ab_0\n", + " - conda-package-handling=2.2.0=pyh38be061_0\n", + " - conda-package-streaming=0.9.0=pyhd8ed1ab_0\n", + " - contourpy=1.2.1=py310hd41b1e2_0\n", + " - cuda-crt-tools=12.5.82=0\n", + " - cuda-cudart=12.4.127=0\n", + " - cuda-cupti=12.4.127=0\n", + " - cuda-libraries=12.4.0=0\n", + " - cuda-nvcc-tools=12.5.82=0\n", + " - cuda-nvrtc=12.4.127=0\n", + " - cuda-nvtx=12.4.127=0\n", + " - cuda-nvvm-tools=12.5.82=0\n", + " - cuda-opencl=12.5.39=0\n", + " - cuda-runtime=12.4.0=0\n", + " - cuda-version=12.5=3\n", + " - cudnn=8.9.7.29=h092f7fd_3\n", + " - cycler=0.12.1=pyhd8ed1ab_0\n", + " - dgl=2.1.0=cuda120py310h00d5dd0_2\n", + " - distro=1.8.0=pyhd8ed1ab_0\n", + " - ffmpeg=4.3=hf484d3e_0\n", + " - filelock=3.15.4=pyhd8ed1ab_0\n", + " - flake8=7.1.0=pyhd8ed1ab_0\n", + " - flask=3.0.3=pyhd8ed1ab_0\n", + " - flatbuffers=24.3.25=h59595ed_0\n", + " - fmt=10.1.1=h00ab1b0_1\n", + " - fonttools=4.53.1=py310h5b4e0ec_0\n", + " - freetype=2.12.1=h267a509_2\n", + " - fsspec=2024.6.1=pyhff2d567_0\n", + " - gast=0.5.5=pyhd8ed1ab_0\n", + " - ghp-import=2.1.0=pyhd8ed1ab_0\n", + " - giflib=5.2.2=hd590300_0\n", + " - gmp=6.3.0=hac33072_2\n", + " - gmpy2=2.1.5=py310hc7909c9_1\n", + " - gnutls=3.6.13=h85f3911_1\n", + " - google-pasta=0.2.0=pyh8c360ce_0\n", + " - grpcio=1.62.2=py310h1b8f574_0\n", + " - h5py=3.11.0=nompi_py310hf054cd7_102\n", + " - hdf5=1.14.3=nompi_hdf9ad27_105\n", + " - icu=73.2=h59595ed_0\n", + " - idna=3.6=pyhd8ed1ab_0\n", + " - importlib-metadata=8.2.0=pyha770c72_0\n", + " - importlib-resources=6.4.0=pyhd8ed1ab_0\n", + " - importlib_resources=6.4.0=pyhd8ed1ab_0\n", + " - inflect=7.3.1=pyhd8ed1ab_0\n", + " - itsdangerous=2.2.0=pyhd8ed1ab_0\n", + " - jarvis-tools=2024.5.10=pyhd8ed1ab_0\n", + " - jinja2=3.1.4=pyhd8ed1ab_0\n", + " - joblib=1.4.2=pyhd8ed1ab_0\n", + " - jsonpatch=1.33=pyhd8ed1ab_0\n", + " - jsonpointer=2.4=py310hff52083_3\n", + " - keras=3.4.1=pyhd8ed1ab_2\n", + " - keyutils=1.6.1=h166bdaf_0\n", + " - kiwisolver=1.4.5=py310hd41b1e2_1\n", + " - krb5=1.21.2=h659d440_0\n", + " - lame=3.100=h166bdaf_1003\n", + " - lcms2=2.16=hb7c19ff_0\n", + " - ld_impl_linux-64=2.40=h41732ed_0\n", + " - lerc=4.0.0=h27087fc_0\n", + " - libabseil=20240116.2=cxx17_he02047a_1\n", + " - libaec=1.1.3=h59595ed_0\n", + " - libarchive=3.7.2=h2aa1ff5_1\n", + " - libblas=3.9.0=23_linux64_openblas\n", + " - 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ncurses=6.4=h59595ed_2\n", + " - nettle=3.6=he412f7d_0\n", + " - networkx=3.3=pyhd8ed1ab_1\n", + " - numpy=1.26.4=py310hb13e2d6_0\n", + " - openh264=2.1.1=h780b84a_0\n", + " - openjpeg=2.5.2=h488ebb8_0\n", + " - openssl=3.3.1=h4bc722e_2\n", + " - opt_einsum=3.3.0=pyhc1e730c_2\n", + " - optree=0.12.1=py310haa687a3_0\n", + " - packaging=23.2=pyhd8ed1ab_0\n", + " - paginate=0.5.6=pyhd8ed1ab_0\n", + " - pandas=2.2.2=py310hf9f9076_1\n", + " - pathspec=0.12.1=pyhd8ed1ab_0\n", + " - pillow=10.3.0=py310hf73ecf8_0\n", + " - pip=23.3.2=pyhd8ed1ab_0\n", + " - platformdirs=4.1.0=pyhd8ed1ab_0\n", + " - pluggy=1.3.0=pyhd8ed1ab_0\n", + " - protobuf=4.25.3=py310ha8c1f0e_0\n", + " - psutil=6.0.0=py310hc51659f_0\n", + " - pthread-stubs=0.4=h36c2ea0_1001\n", + " - pybind11-abi=4=hd8ed1ab_3\n", + " - pycodestyle=2.12.0=pyhd8ed1ab_0\n", + " - pycosat=0.6.6=py310h2372a71_0\n", + " - pycparser=2.21=pyhd8ed1ab_0\n", + " - pydantic=2.8.2=pyhd8ed1ab_0\n", + " - pydantic-core=2.20.1=py310h42e942d_0\n", + " - pydantic-settings=2.3.4=pyhd8ed1ab_0\n", + " - pydocstyle=6.3.0=pyhd8ed1ab_0\n", + " - pyflakes=3.2.0=pyhd8ed1ab_0\n", + " - pygments=2.18.0=pyhd8ed1ab_0\n", + " - pymdown-extensions=10.8.1=pyhd8ed1ab_0\n", + " - pyparsing=2.4.7=pyhd8ed1ab_1\n", + " - pysocks=1.7.1=pyha2e5f31_6\n", + " - python=3.10.13=hd12c33a_0_cpython\n", + " - python-dateutil=2.9.0=pyhd8ed1ab_0\n", + " - python-dotenv=1.0.1=pyhd8ed1ab_0\n", + " - python-flatbuffers=24.3.25=pyh59ac667_0\n", + " - python-lmdb=1.5.1=py310h6a71df1_0\n", + " - python-tzdata=2024.1=pyhd8ed1ab_0\n", + " - python_abi=3.10=4_cp310\n", + " - pytorch=2.3.1=cuda120_py310h2c91c31_300\n", + " - pytorch-cuda=12.4=hc786d27_6\n", + " - pytorch-mutex=1.0=cpu\n", + " - pytz=2024.1=pyhd8ed1ab_0\n", + " - pyyaml=6.0.1=py310h2372a71_1\n", + " - pyyaml-env-tag=0.1=pyhd8ed1ab_0\n", + " - qhull=2020.2=h434a139_5\n", + " - re2=2023.09.01=h7f4b329_2\n", + " - readline=8.2=h8228510_1\n", + " - regex=2024.7.24=py310h5b4e0ec_0\n", + " - reproc=14.2.4.post0=hd590300_1\n", + " - reproc-cpp=14.2.4.post0=h59595ed_1\n", + " - requests=2.31.0=pyhd8ed1ab_0\n", + " - rich=13.7.1=pyhd8ed1ab_0\n", + " - ruamel.yaml=0.18.5=py310h2372a71_0\n", + " - ruamel.yaml.clib=0.2.7=py310h2372a71_2\n", + " - scikit-learn=1.5.1=py310h146d792_0\n", + " - scipy=1.14.0=py310h93e2701_1\n", + " - setuptools=68.2.2=pyhd8ed1ab_0\n", + " - six=1.16.0=pyh6c4a22f_0\n", + " - sleef=3.6.1=h3400bea_1\n", + " - snappy=1.2.1=ha2e4443_0\n", + " - snowballstemmer=2.2.0=pyhd8ed1ab_0\n", + " - spglib=2.5.0=py310h355b990_1\n", + " - sympy=1.13.0=pypyh2585a3b_103\n", + " - tbb=2021.11.0=h00ab1b0_1\n", + " - tensorboard=2.16.2=pyhd8ed1ab_0\n", + " - tensorboard-data-server=0.7.0=py310h75e40e8_1\n", + " - tensorflow=2.16.1=cuda120py310hfaee7bf_0\n", + " - tensorflow-base=2.16.1=cuda120py310h68a2f4f_0\n", + " - tensorflow-estimator=2.16.1=cuda120py310h1fd330c_0\n", + " - termcolor=2.4.0=pyhd8ed1ab_0\n", + " - threadpoolctl=3.5.0=pyhc1e730c_0\n", + " - tk=8.6.13=noxft_h4845f30_101\n", + " - tomli=2.0.1=pyhd8ed1ab_0\n", + " - toolz=0.12.1=pyhd8ed1ab_0\n", + " - torchaudio=2.3.1=py310_cpu\n", + " - torchdata=0.7.1=py310\n", + " - torchvision=0.18.1=py310_cpu\n", + " - tqdm=4.66.1=pyhd8ed1ab_0\n", + " - truststore=0.8.0=pyhd8ed1ab_0\n", + " - typeguard=4.3.0=pyhd8ed1ab_1\n", + " - typing-extensions=4.12.2=hd8ed1ab_0\n", + " - typing_extensions=4.12.2=pyha770c72_0\n", + " - tzdata=2023c=h71feb2d_0\n", + " - unicodedata2=15.1.0=py310h2372a71_0\n", + " - urllib3=2.1.0=pyhd8ed1ab_0\n", + " - watchdog=4.0.1=py310hff52083_0\n", + " - werkzeug=3.0.3=pyhd8ed1ab_0\n", + " - wheel=0.42.0=pyhd8ed1ab_0\n", + " - wrapt=1.16.0=py310h2372a71_0\n", + " - xmltodict=0.13.0=pyhd8ed1ab_0\n", + " - xorg-libxau=1.0.11=hd590300_0\n", + " - xorg-libxdmcp=1.1.3=h7f98852_0\n", + " - xz=5.2.6=h166bdaf_0\n", + " - yaml=0.2.5=h7f98852_2\n", + " - yaml-cpp=0.8.0=h59595ed_0\n", + " - zipp=3.19.2=pyhd8ed1ab_0\n", + " - zlib=1.2.13=hd590300_5\n", + " - zstandard=0.23.0=py310h64cae3c_0\n", + " - zstd=1.5.6=ha6fb4c9_0\n", + " - pip:\n", + " - absl-py==1.4.0\n", + " - elastic==5.2.3.6\n", + " - intermat==2024.5.24\n", + " - nltk==3.8.1\n", + " - phonopy==2.26.6\n", + " - plotly==5.23.0\n", + " - rouge==1.0.1\n", + " - tenacity==8.5.0\n", + "prefix: /usr/local\n" ] } ] }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "bnsmfvOepjVl" - }, - "execution_count": null, - "outputs": [] - }, { "cell_type": "code", "source": [], diff --git a/jarvis-tools-notebooks/atomgpt_example.ipynb b/jarvis-tools-notebooks/atomgpt_example.ipynb index de8d0c8..e3c8c40 100644 --- a/jarvis-tools-notebooks/atomgpt_example.ipynb +++ b/jarvis-tools-notebooks/atomgpt_example.ipynb @@ -5,7 +5,7 @@ "colab": { "provenance": [], "gpuType": "T4", - "authorship_tag": "ABX9TyMIgnUws+BOl4h8c9cJPmtJ", + "authorship_tag": "ABX9TyPeu5PcKtLunpt18N+J5nuG", "include_colab_link": true }, "kernelspec": { @@ -38,6 +38,24 @@ "id": "TkkuV4Hyib2K" } }, + { + "cell_type": "markdown", + "source": [ + "# Table of contents\n", + "\n", + "1. Installing [AtomGPT](https://github.com/usnistgov/atomgpt)\n", + "2. Example inverse model training for 5 materials\n", + "3. Using the trained model for inference\n", + "4. Relaxing structures with ALIGNN-FF\n", + "5. Generating a database of atomic structures\n", + "6. Train a forward prediction model using AtomGPT\n", + "\n", + "Author: Kamal Choudhary (kamal.choudhary@nist.gov)" + ], + "metadata": { + "id": "bhdCyKO2tSeu" + } + }, { "cell_type": "code", "execution_count": 1, @@ -46,7 +64,7 @@ "base_uri": "https://localhost:8080/" }, "id": "0HURIAsbZgMF", - "outputId": "8442ec8c-ced9-4153-ec93-14204cba7403" + "outputId": "c300751d-5917-40fc-a75d-40a9ff13070d" }, "outputs": [ { @@ -57,7 +75,7 @@ "📦 Installing...\n", "📌 Adjusting configuration...\n", "🩹 Patching environment...\n", - "⏲ Done in 0:00:13\n", + "⏲ Done in 0:00:16\n", "🔁 Restarting kernel...\n" ] } @@ -71,7 +89,7 @@ { "cell_type": "markdown", "source": [ - "# Installation" + "# 1. Installation" ], "metadata": { "id": "ZfBX7ilpiouF" @@ -91,6 +109,7 @@ " !git clone https://github.com/usnistgov/atomgpt.git\n", " os.chdir('atomgpt')\n", " !pip install -qqq -r dev-requirements.txt\n", + " !pip install -q protobuf\n", " !pip install -q -e .\n" ], "metadata": { @@ -98,7 +117,7 @@ "base_uri": "https://localhost:8080/" }, "id": "-0SQ7cmLgC8l", - "outputId": "d1c4aeb7-defb-4ecc-8e8d-94b9f7ab9083" + "outputId": "c6678f73-0c51-46d0-8d72-ded77d2dc76d" }, "execution_count": 1, "outputs": [ @@ -107,115 +126,98 @@ "name": "stdout", "text": [ "Cloning into 'atomgpt'...\n", - "remote: Enumerating objects: 546, done.\u001b[K\n", - "remote: Counting objects: 100% (47/47), done.\u001b[K\n", - "remote: Compressing objects: 100% (43/43), done.\u001b[K\n", - "remote: Total 546 (delta 7), reused 11 (delta 1), pack-reused 499\u001b[K\n", - "Receiving objects: 100% (546/546), 66.29 MiB | 48.11 MiB/s, done.\n", - "Resolving deltas: 100% (242/242), done.\n", - "\u001b[2K 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Example inverse model training for 5 materials" + ], + "metadata": { + "id": "HhAC-Tfetscz" + } + }, { "cell_type": "markdown", "source": [ @@ -225,9 +227,65 @@ "id": "DVt6XyJjiUVv" } }, + { + "cell_type": "markdown", + "source": [ + "We are going to use default config:\n", + "\n", + "TrainingPropConfig(id_prop_path='id_prop.csv', prefix='atomgpt_run', model_name='unsloth/mistral-7b-bnb-4bit', batch_size=2, num_epochs=2, seed_val=42, num_train=2, num_val=2, num_test=2, model_save_path='lora_model_m')\n" + ], + "metadata": { + "id": "t7t4PWmRA6zK" + } + }, + { + "cell_type": "markdown", + "source": [ + "We are going to use a small id_prop.csv dataset with 5 materials only for training as given [here](https://github.com/usnistgov/atomgpt/blob/main/atomgpt/examples/inverse_model/id_prop.csv) . For production results, use larger dataset.\n", + "\n", + "\n", + "\n", + "An example for creating a sample id_prop.csv for `\"optb88vdw_bandgap\"` bandgap is kept [here](https://github.com/usnistgov/alignn/blob/main/alignn/examples/sample_data/scripts/generate_sample_data_reg.py). For superconductor database use `\"Tc_supercon\"` key instad." + ], + "metadata": { + "id": "b8hLHzxCBJ_z" + } + }, + { + "cell_type": "code", + "source": [ + "# Lets' look at an example config file before running the training\n", + "import os\n", + "os.chdir('/content')\n", + "from jarvis.db.jsonutils import loadjson,dumpjson\n", + "import pprint\n", + "config = loadjson('Software/atomgpt/atomgpt/examples/inverse_model/config.json')\n", + "config['model_name'] = \"knc6/atomgpt_mistral_tc_supercon\"\n", + "dumpjson(data=config,filename='Software/atomgpt/atomgpt/examples/inverse_model/config.json')\n", + "pprint.pprint(config)" + ], + "metadata": { + "id": "ZrHsPD2VoxtO", + "outputId": "e9b20453-7a00-45a1-8bea-cc037d03c595", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'model_name': 'knc6/atomgpt_mistral_tc_supercon'}\n" + ] + } + ] + }, { "cell_type": "code", "source": [ + "%%time\n", "import os\n", "os.chdir('/content')\n", "!python Software/atomgpt/atomgpt/inverse_models/inverse_models.py --config_name Software/atomgpt/atomgpt/examples/inverse_model/config.json" @@ -237,9 +295,9 @@ "base_uri": "https://localhost:8080/" }, "id": "AB9iNlo9gC6J", - "outputId": "97966071-f59e-46dc-982d-c047e500edba" + "outputId": "a68ada3a-e25b-4fb6-d2c0-099186f69c36" }, - "execution_count": 2, + "execution_count": 3, "outputs": [ { "output_type": "stream", @@ -253,41 +311,40 @@ "\n", "You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ('settings_',)`.\n", " warnings.warn(\n", - "TrainingPropConfig(id_prop_path='id_prop.csv', prefix='atomgpt_run', model_name='unsloth/mistral-7b-bnb-4bit', batch_size=2, num_epochs=2, seed_val=42, num_train=2, num_val=2, num_test=2, model_save_path='lora_model_m')\n", - "100% 6/6 [00:00<00:00, 652.45it/s]\n", - "config.json: 100% 1.05k/1.05k [00:00<00:00, 7.62MB/s]\n", + "TrainingPropConfig(id_prop_path='id_prop.csv', prefix='atomgpt_run', model_name='knc6/atomgpt_mistral_tc_supercon', batch_size=2, num_epochs=2, seed_val=42, num_train=2, num_val=2, num_test=2, model_save_path='lora_model_m')\n", + "100% 6/6 [00:00<00:00, 524.03it/s]\n", + "config.json: 100% 1.19k/1.19k [00:00<00:00, 6.02MB/s]\n", " GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", " Pytorch: 2.2.2+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", " Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n", "\n", - "model.safetensors: 100% 4.13G/4.13G [00:39<00:00, 103MB/s]\n", - "generation_config.json: 100% 116/116 [00:00<00:00, 934kB/s]\n", - "tokenizer_config.json: 100% 971/971 [00:00<00:00, 7.64MB/s]\n", - "tokenizer.model: 100% 493k/493k [00:00<00:00, 199MB/s]\n", - "special_tokens_map.json: 100% 438/438 [00:00<00:00, 3.81MB/s]\n", - "tokenizer.json: 100% 1.80M/1.80M [00:00<00:00, 22.7MB/s]\n", + "model.safetensors: 100% 4.13G/4.13G [00:23<00:00, 173MB/s]\n", + "generation_config.json: 100% 155/155 [00:00<00:00, 793kB/s]\n", + "tokenizer_config.json: 100% 1.02k/1.02k [00:00<00:00, 5.67MB/s]\n", + "tokenizer.model: 100% 493k/493k [00:00<00:00, 178MB/s]\n", + "special_tokens_map.json: 100% 438/438 [00:00<00:00, 2.56MB/s]\n", + "tokenizer.json: 100% 1.80M/1.80M [00:00<00:00, 2.11MB/s]\n", "Unsloth 2024.5 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n", - "Generating train split: 2 examples [00:00, 24.16 examples/s]\n", - "Map: 100% 2/2 [00:00<00:00, 520.00 examples/s]\n", - "Map (num_proc=2): 100% 2/2 [00:00<00:00, 5.48 examples/s]\n", + "Generating train split: 2 examples [00:00, 15.62 examples/s]\n", + "Map: 100% 2/2 [00:00<00:00, 510.85 examples/s]\n", + "Map (num_proc=2): 100% 2/2 [00:00<00:00, 8.00 examples/s]\n", "Num GPUs = 1\n", "Num examples = 2 | Num Epochs = 5\n", "Batch size per device = 2 | Gradient Accumulation steps = 4\n", "Total batch size = 8 | Total steps = 5\n", "Number of trainable parameters = 41,943,040\n", - "{'loss': 0.4395, 'grad_norm': 0.7865490317344666, 'learning_rate': 4e-05, 'epoch': 1.0}\n", - "{'loss': 0.4395, 'grad_norm': 0.786447286605835, 'learning_rate': 8e-05, 'epoch': 2.0}\n", - "{'loss': 0.3938, 'grad_norm': 0.5286679267883301, 'learning_rate': 0.00012, 'epoch': 3.0}\n", - "{'loss': 0.3236, 'grad_norm': 0.46380284428596497, 'learning_rate': 0.00016, 'epoch': 4.0}\n", - "{'loss': 0.2361, 'grad_norm': 0.46915146708488464, 'learning_rate': 0.0, 'epoch': 5.0}\n", - "{'train_runtime': 13.2136, 'train_samples_per_second': 0.757, 'train_steps_per_second': 0.378, 'train_loss': 0.36650351285934446, 'epoch': 5.0}\n", - "100% 5/5 [00:13<00:00, 2.64s/it]\n", + "{'loss': 0.4395, 'grad_norm': 0.7866447567939758, 'learning_rate': 4e-05, 'epoch': 1.0}\n", + "{'loss': 0.4395, 'grad_norm': 0.7865124940872192, 'learning_rate': 8e-05, 'epoch': 2.0}\n", + "{'loss': 0.3939, 'grad_norm': 0.5283757448196411, 'learning_rate': 0.00012, 'epoch': 3.0}\n", + "{'loss': 0.3236, 'grad_norm': 0.4637981057167053, 'learning_rate': 0.00016, 'epoch': 4.0}\n", + "{'loss': 0.236, 'grad_norm': 0.469246506690979, 'learning_rate': 0.0, 'epoch': 5.0}\n", + "{'train_runtime': 13.4283, 'train_samples_per_second': 0.745, 'train_steps_per_second': 0.372, 'train_loss': 0.36651417016983034, 'epoch': 5.0}\n", + "100% 5/5 [00:13<00:00, 2.68s/it]\n", " GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", " Pytorch: 2.2.2+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", " Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n", "\n", " 0% 0/4 [00:00 2 and ii < len(tmp_atoms_array):\n", + " tmp = i.split()\n", + " elements.append(tmp[0])\n", + " coords.append([float(tmp[1]), float(tmp[2]), float(tmp[3])])\n", + " atoms = Atoms(\n", + " coords=coords,\n", + " elements=elements,\n", + " lattice_mat=lat.lattice(),\n", + " cartesian=False,\n", + " )\n", + " return atoms\n", + "\n", + "def gen_atoms(prompt=\"\", max_new_tokens=512, model=\"\", tokenizer=\"\"):\n", + " inputs = tokenizer(\n", + " [\n", + " alpaca_prompt.format(\n", + " \"Below is a description of a superconductor material.\", # instruction\n", + " prompt, # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + " ],\n", + " return_tensors=\"pt\",\n", + " ).to(\"cuda\")\n", + " outputs = model.generate(\n", + " **inputs, max_new_tokens=max_new_tokens, use_cache=True\n", + " )\n", + " response = tokenizer.batch_decode(outputs)[0].split(\"# Output:\")[1].strip('')\n", + " # print('response',response)\n", + " atoms = text2atoms(response)\n", + " return atoms\n", + "\n", + "if __name__==\"__main__\":\n", + " prompt_example = \"The chemical formula is MgB2 The Tc_supercon is 6.483. The spacegroup is 12. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + " prompt_example = \"The chemical formula is FeBN The Tc_supercon is 36.483. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + "\n", + " gen_mat = gen_atoms(prompt=prompt_example,model=model,tokenizer=tokenizer)\n", + " print(gen_mat)" + ], + "metadata": { + "id": "Z0Gf0CKSr8oB", + "outputId": "0eaed0b1-03fd-4807-db30-6b2e5dba1f17", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", + " Pytorch: 2.2.2+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", + " Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n", + "\n", + "System\n", + "1.0\n", + "3.5 0.0 0.0\n", + "0.0 3.5 0.0\n", + "0.0 0.0 3.5\n", + "Fe B N \n", + "1 1 1 \n", + "direct\n", + "0.0 0.0 0.0 Fe\n", + "0.5 0.5 0.5 B\n", + "0.5 0.5 0.5 N\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#4. Relaxing structures with ALIGNN-FF" + ], + "metadata": { + "id": "SOp5oZSpt68_" + } + }, + { + "cell_type": "markdown", + "source": [ + "The generated atomic structures can be relaxed with ALIGNN-FF, see example [here](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb)." + ], + "metadata": { + "id": "wLEaeWSkstav" + } + }, + { + "cell_type": "markdown", + "source": [ + "The above example used 5 materials during train only. We used about 1000 materials database in JARVIS-DFT, and the fine-tuned model is kept on [huggingface](https://huggingface.co/knc6/atomgpt_mistral_tc_supercon)." + ], + "metadata": { + "id": "BkzSijjds4o5" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 5. Generating a database" + ], + "metadata": { + "id": "IDpOtWsLuABd" + } + }, + { + "cell_type": "code", + "source": [ + "from jarvis.core.specie import atomic_numbers_to_symbols\n", + "import numpy as np\n", + "from jarvis.db.jsonutils import loadjson, dumpjson\n", + "from jarvis.core.composition import Composition\n", + "from tqdm import tqdm\n", + "from inf import gen_atoms\n", + "\n", + "Z = np.arange(100) + 1\n", + "els = atomic_numbers_to_symbols(Z)\n", + "\n", + "m = 1\n", + "n = 2\n", + "\n", + "\n", + "def gen_binary_samples(element=\"B\"):\n", + " mem = []\n", + " for m in np.arange(1, 4):\n", + " for n in np.arange(1, 4):\n", + " for i in tqdm(els):\n", + " try:\n", + " comp = Composition.from_dict({i: m, element: n})\n", + " prompt_example = (\n", + " \"The chemical formula is \"\n", + " + comp.reduced_formula\n", + " + \" The Tc_supercon is 100. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + " )\n", + " gen_mat = gen_atoms(prompt_example)\n", + " print(i)\n", + " print(gen_mat, len(mem))\n", + " mem.append([int(m), int(n), i, gen_mat.to_dict()])\n", + " # dumpjson(data=mem,filename='superB.json')\n", + " except:\n", + " pass\n", + " fname=\"binary_super\"+element+\".json\"\n", + " dumpjson(data=mem, filename=fname)\n", + "gen_binary_samples(\"S\")\n", + "gen_binary_samples(\"Se\")\n", + "gen_binary_samples(\"Te\")\n", + "def gen_ternary_samples(element=\"B\"):\n", + " mem = []\n", + " for m in np.arange(1, 4):\n", + " for n in np.arange(1, 4):\n", + " for j in tqdm(els):\n", + " for i in tqdm(els):\n", + " try:\n", + " comp = Composition.from_dict({i: m, j:n, element: n})\n", + " prompt_example = (\n", + " \"The chemical formula is \"\n", + " + comp.reduced_formula\n", + " + \" The Tc_supercon is 100. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + " )\n", + " gen_mat = gen_atoms(prompt_example)\n", + " print(i)\n", + " print(gen_mat, len(mem))\n", + " mem.append([int(m), int(n), i, gen_mat.to_dict()])\n", + " # dumpjson(data=mem,filename='superB.json')\n", + " except:\n", + " pass\n", + " fname=\"binary_super\"+element+\".json\"\n", + " dumpjson(data=mem, filename=fname)\n", + "gen_ternary_samples(\"B\")\n", + "\n", + "\"\"\"\n", + "\n", + "m=1\n", + "n=2\n", + "mem=[]\n", + "for m in np.arange(1,4):\n", + " for n in np.arange(1,4):\n", + " for i in tqdm(els):\n", + " try:\n", + " comp=Composition.from_dict({i:m,\"C\":n})\n", + " prompt_example = \"The chemical formula is \"+comp.reduced_formula+\" The Tc_supercon is 100. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + " gen_mat = gen_atoms(prompt_example)\n", + " print(i)\n", + " print(gen_mat,len(mem))\n", + " mem.append([int(m),int(n),i,gen_mat.to_dict()])\n", + " #mem.append([m,n,i,gen_mat.to_dict()])\n", + " except:\n", + " pass\n", + "dumpjson(data=mem,filename='superC.json')\n", + "\n", + "\n", + "\n", + "m=1\n", + "n=2\n", + "mem=[]\n", + "for m in np.arange(1,4):\n", + " for n in np.arange(1,4):\n", + " for i in tqdm(els):\n", + " try:\n", + " comp=Composition.from_dict({i:m,\"N\":n})\n", + " prompt_example = \"The chemical formula is \"+comp.reduced_formula+\" The Tc_supercon is 100. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + " gen_mat = gen_atoms(prompt_example)\n", + " print(i)\n", + " print(gen_mat,len(mem))\n", + " mem.append([int(m),int(n),i,gen_mat.to_dict()])\n", + " #mem.append([m,n,i,gen_mat.to_dict()])\n", + " except:\n", + " pass\n", + "dumpjson(data=mem,filename='superN.json')\n", + "\"\"\"\n" + ], + "metadata": { + "id": "k5qoILFysR-A" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "K_6ZFPJMsR7e" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# 6. Forward model example" ], "metadata": { "id": "jwp0_WPSiYBg" } }, + { + "cell_type": "markdown", + "source": [ + "The default config for this model is:\n", + "\n", + "TrainingPropConfig(id_prop_path='atomgpt/examples/forward_model/id_prop.csv', prefix='atomgpt_run', model_name='google/flan-t5-small', batch_size=2, max_length=512, num_epochs=1, latent_dim=1024, learning_rate=0.001, test_each_run=True, include_struct=False, pretrained_path='', seed_val=42, n_train=2, n_val=2, n_test=2, output_dir='atomgpt/examples/forward_model/out', train_ratio=None, val_ratio=0.1, test_ratio=0.1, keep_data_order=True)\n", + "\n", + "\n", + "The sample dataset is at: https://github.com/usnistgov/atomgpt/blob/main/atomgpt/examples/forward_model/id_prop.csv" + ], + "metadata": { + "id": "M1tDVC2bBdfv" + } + }, { "cell_type": "code", "source": [ @@ -398,7 +893,7 @@ "id": "2RXFduQBgC3J", "outputId": "35764dfd-eb9c-47ce-a8c5-bf154b0dd9c1" }, - "execution_count": 3, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -541,12 +1036,30 @@ { "cell_type": "markdown", "source": [ - "More deatiled examples would be coming soon!" + "For forward prediction model, also refer to [ALIGNN](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/alignn_jarvis_leaderboard.ipynb)" ], "metadata": { - "id": "Jxkm4vK_uBg-" + "id": "4295ylfJ0FD5" } }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "g4Px2rjJz_AL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "q4ulYeQizxii" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "source": [], @@ -563,12 +1076,12 @@ ], "metadata": { "id": "-IUbsYWmgCx_", - "outputId": "0c885b50-7e5c-4011-8b43-be57d70c3c9b", + "outputId": "66ba204b-dc5a-47f4-8221-6fe4949328cd", "colab": { "base_uri": "https://localhost:8080/" } }, - "execution_count": 4, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -674,7 +1187,7 @@ " - fonttools==4.53.0\n", " - frozenlist==1.4.1\n", " - fsspec==2024.5.0\n", - " - gmpy2==2.2.0\n", + " - gmpy2==2.2.1\n", " - huggingface-hub==0.23.4\n", " - idna==3.7\n", " - importlib-resources==6.4.0\n", @@ -704,7 +1217,7 @@ " - nvidia-cusolver-cu12==11.4.5.107\n", " - nvidia-cusparse-cu12==12.1.0.106\n", " - nvidia-nccl-cu12==2.19.3\n", - " - nvidia-nvjitlink-cu12==12.5.40\n", + " - nvidia-nvjitlink-cu12==12.6.20\n", " - nvidia-nvtx-cu12==12.1.105\n", " - packaging==24.1\n", " - pandas==2.2.2\n", @@ -712,6 +1225,7 @@ " - peft==0.11.1\n", " - pillow==10.3.0\n", " - platformdirs==4.2.2\n", + " - protobuf==5.27.3\n", " - psutil==6.0.0\n", " - pyarrow==16.1.0\n", " - pyarrow-hotfix==0.6\n", @@ -726,7 +1240,7 @@ " - python-dateutil==2.9.0.post0\n", " - python-dotenv==1.0.1\n", " - pytz==2024.1\n", - " - pyyaml==6.0.1\n", + " - pyyaml==6.0.2\n", " - regex==2024.5.15\n", " - requests==2.32.3\n", " - rich==13.7.1\n", @@ -738,7 +1252,7 @@ " - six==1.16.0\n", " - snowballstemmer==2.2.0\n", " - spglib==2.4.0\n", - " - sympy==1.12.1\n", + " - sympy==1.13.1\n", " - threadpoolctl==3.5.0\n", " - tokenizers==0.19.1\n", " - tomli==2.0.1\n", @@ -773,7 +1287,7 @@ "base_uri": "https://localhost:8080/" }, "id": "vnGe03dra32v", - "outputId": "7cf8f699-6b63-42db-d9ae-cbe8559968d3" + "outputId": "2c98316b-d257-47de-e99b-a9aa1f771645" }, "execution_count": null, "outputs": [ @@ -789,7 +1303,7 @@ "archspec @ file:///home/conda/feedstock_root/build_artifacts/archspec_1699370045702/work\n", "ase==3.23.0\n", "async-timeout==4.0.3\n", - "-e git+https://github.com/usnistgov/atomgpt.git@90303df9b53bb77de8fad2e41ecfccf2d35fabf8#egg=atomgpt\n", + "-e git+https://github.com/usnistgov/atomgpt.git@a516955aa3348e628175d024c6b16896ba34e31a#egg=atomgpt\n", "attrs==23.2.0\n", "autopep8==2.3.1\n", "bitsandbytes==0.43.1\n", @@ -819,7 +1333,7 @@ "fonttools==4.53.0\n", "frozenlist==1.4.1\n", "fsspec==2024.5.0\n", - "gmpy2==2.1.5\n", + "gmpy2==2.2.1\n", "huggingface-hub==0.23.4\n", "idna==3.7\n", "importlib_resources==6.4.0\n", @@ -854,7 +1368,7 @@ "nvidia-cusolver-cu12==11.4.5.107\n", "nvidia-cusparse-cu12==12.1.0.106\n", "nvidia-nccl-cu12==2.19.3\n", - "nvidia-nvjitlink-cu12==12.5.40\n", + "nvidia-nvjitlink-cu12==12.6.20\n", "nvidia-nvtx-cu12==12.1.105\n", "packaging==24.1\n", "pandas==2.2.2\n", @@ -863,6 +1377,7 @@ "pillow==10.3.0\n", "platformdirs==4.2.2\n", "pluggy @ file:///home/conda/feedstock_root/build_artifacts/pluggy_1693086607691/work\n", + "protobuf==5.27.3\n", "psutil==6.0.0\n", "pyarrow==16.1.0\n", "pyarrow-hotfix==0.6\n", @@ -880,7 +1395,7 @@ "python-dateutil==2.9.0.post0\n", "python-dotenv==1.0.1\n", "pytz==2024.1\n", - "PyYAML==6.0.1\n", + "PyYAML==6.0.2\n", "regex==2024.5.15\n", "requests==2.32.3\n", "rich==13.7.1\n", @@ -894,7 +1409,7 @@ "six==1.16.0\n", "snowballstemmer==2.2.0\n", "spglib==2.4.0\n", - "sympy==1.12.1\n", + "sympy==1.13.1\n", "threadpoolctl==3.5.0\n", "tokenizers==0.19.1\n", "tomli==2.0.1\n", diff 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"TkkuV4Hyib2K" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0HURIAsbZgMF", + "outputId": "abb7dc5a-aca5-49ee-88cc-41db5f70c4fc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.11.0-0/Mambaforge-23.11.0-0-Linux-x86_64.sh...\n", + "📦 Installing...\n", + "📌 Adjusting configuration...\n", + "🩹 Patching environment...\n", + "⏲ Done in 0:00:19\n", + "🔁 Restarting kernel...\n" + ] + } + ], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Installation" + ], + "metadata": { + "id": "ZfBX7ilpiouF" + } + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "!pip install -q atomgpt bitsandbytes" + ], + "metadata": { + "id": "39aSQCLpbiN5", + "outputId": 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\u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for multidict (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "CPU times: user 1.54 s, sys: 208 ms, total: 1.75 s\n", + "Wall time: 3min 36s\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "To learn how to train the model on a custom dataset, see https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/atomgpt_example.ipynb" + ], + "metadata": { + "id": "qx6dI7qzZB5c" + } + }, + { + "cell_type": "markdown", + "source": [ + "An example prompt" + ], + "metadata": { + "id": "SqSqAovNYkQa" + } + }, + { + "cell_type": "code", + "source": [ + "prompt_example = \"The chemical formula is MgB2 The Tc_supercon is 36.483. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"" + ], + "metadata": { + "id": "uoDrF5V3YdoP" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Load model" + ], + "metadata": { + "id": "FLvXBfcMYmWA" + } + }, + { + "cell_type": "code", + "source": [ + "from peft import PeftModel, PeftConfig\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "import numpy as np\n", + "from jarvis.core.atoms import Atoms\n", + "from jarvis.core.lattice import Lattice\n", + "from tqdm import tqdm\n", + "from jarvis.io.vasp.inputs import Poscar\n", + "model_name = \"knc6/atomgpt_mistral_tc_supercon\"\n", + "config = PeftConfig.from_pretrained(model_name)\n", + "base_model = AutoModelForCausalLM.from_pretrained(model_name)\n", + "model = PeftModel.from_pretrained(base_model, model_name)\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "alpaca_prompt = \"\"\"Below is a description of a superconductor material..\n", + "\n", + "### Instruction:\n", + "{}\n", + "\n", + "### Input:\n", + "{}\n", + "\n", + "### Output:\n", + "{}\"\"\"\n" + ], + "metadata": { + "id": "EhVkn0ukYdl9", + "outputId": "e4686316-1f17-4f62-86da-f3eff7696aba", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412, + "referenced_widgets": [ + "dcd5fabb53994340ba7b371bafddd2a7", + "46bd6cf888dd47a8bf33b02723a87b4d", + "ee392c2ea16440ee89955b81590a07cb", + "d7a07eed81754b92b5dd872a6bfd708a", + "1df07e61ea3e4f3ba0fe81b28759c404", + "33c4a1e1e24f4c78a39cde4a7b9c4c22", + "55eb008aff6f4c71b04ff06de46cca42", + "8743806df43a4d14a58494063a86f5b3", + "a5f4a113e13e45b592297c091c1c4ef4", + "774d0c5bab664bafb6ddc2e0f4f0084a", + "2273f57936c14547b022b6c4949e36a6", + "a52fdf82e9bd400a96af7b1fb8bae714", + "9dfd5cc65b5846fa89f914c6e637358d", + "14fc727449264da7a5344c1877284d2e", + "d40641f89c6245d9ba14de0e4c37103b", + "c1423f0ce1bb465fbd4ba7d800c3a735", + "6242604f563349f4b58bad1dcaf32b0e", + "3c37f9ab2d3e415c8085f229248b32d8", + "82888611e5964c27ac15fe2bc8d77630", + "8d4f86ce720340a0bcf2a2bd979ff39d", + "a1e1ee4ab3a74f07844acb75918d2c0b", + "98dbf336cefc48bcbfb4b12f8bdd4a52", + "041b0fcac2ca4eb0a07968696bbb31c6", + "ee11742e86a049808ef953feca4757cf", + "a7f068db8d274f638cc229ac94cad4c4", + "559e86c35d8f4aaca1f3537b640d0163", + "e2c0d9553d8d4dda9cbd89c7adccdcec", + "f5a4f43e9d0249b2b8b7723bda1f62cc", + "8396dfb204e44758a567e81f0c1c1d43", + "2b7cea736de84b07a6c079895fa7b119", + "58b02468c2b743458bf66a294e8c1a52", + "73ed16b76ce247bdbe61481c96844968", + "68a132577d504461b00ed0231ab54088", + "29e3d45a486c4be09078721fdc1e0ce9", + "760faca99b984863a181926646cb8493", + "d3bf4595d9e74e1da9ff6065665f0b01", + "c403e9f5416f4b1d95fe7f1392ed0fc1", + "b54bbf27b1fc432db9e847a88b682251", + "e19771cd6033429596542268ad3c3820", + "1f31b104dc0a4b4eb592d78a3b13ec2e", + "73640ae1e1da42e3bc698039c45b9b01", + "c60d0cee600646f1857e98265bad1db4", + "8396331105734ae588ffbee3045bd3d8", + "07232a6dc7454a2fb7079ee6b55fa8fc", + "609fceb2979742ffa6887446595d392f", + "adf14ed890684b23814ad03bcb2df988", + "07d05e15c85b49dfb0a8e4460489d9cb", + "1d573efb5f404321863ea2f32bf23d0d", + "b38a9583dea545ffbd726a90d9966421", + "e66a3479335c40edaa5eceb4ce145c9a", + "3731fcba611e4b858f14affb10e0292b", + "b01137e0cec341599ea5b23582d38697", + "14139d4ffe3944c69f376b01193369c2", + "78b6cf93a75343d09245953942c45c05", + "60e4849d0c35481888affb9f3c1a9ae6", + "1b7b88f768d74d59880cd9fc959f0fa9", + "9c83df08f14441c5aca2cf5452a0894f", + "f625602449624bcd88e7f1a6e76e6974", + "34f9beaee75745939d496c1835b9b04c", + "121b1719ca584d5a903d493a66f9467a", + "93d89ef8999d49e7885b4f3a428174f0", + "fa2cabc9e95e4bf388a089c5c9c77d0d", + "5c9e83c0ca4445a68ad2acca0b79ddd7", + "6c0cdcfced334415adbaaebd44fd2e4d", + "b304f88d90024fa69014879e518bffcb", + "7db8e5f78c4c47dcb0d8866e52817832", + "9dd2f048c98c4bde84865f914c9387f9", + "384e5fd422364b85879a98b862a2a042", + "6deb76f37f34423a849d2ef104f4551d", + "bcb3af16101c4f86ae91723bb0107a66", + "a293128ac9134c2eae05d94e18353eb4", + "ac1528cf9fad46cf8946c9ce409d421d", + "4c5da4ae34584f9bb4d5b94e21ddac1d", + "31fbca900c1443a2a3d87a42853d77e6", + "bd8d7824efbf492d9c607f9bed307832", + "9bed8d66e6814ff09c54fc583e04ceea", + "62bb94b19d474383952d5d8baef6e086", + "dd635766e3684bf497e665346930c817", + "91913b9a16344a228def20c2d694c16f", + "f638210b7373450fac2a9bede6ecb34b", + "294e4e144cd94e69ac379d13556446c7", + "26f8344f95364757bccea8445f76b682", + "7671feefdc7c4d67be0006cf1b76bbb4", + "754c44ced7754026ad3e8f02500753b5", + "e5a5a2adefae49b49c8d38ce062bc749", + "d05329459d4a486b9f6b36d0efe2ee64", + "37da014c238c4d6a825a91259ab60be5", + "a3783b2c18124dc99200757f8a3dc357" + ] + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/site-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "adapter_config.json: 0%| | 0.00/732 [00:00.\n", + "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/4.13G [00:00 2 and ii < len(tmp_atoms_array):\n", + " tmp = i.split()\n", + " elements.append(tmp[0])\n", + " coords.append([float(tmp[1]), float(tmp[2]), float(tmp[3])])\n", + " atoms = Atoms(\n", + " coords=coords,\n", + " elements=elements,\n", + " lattice_mat=lat.lattice(),\n", + " cartesian=False,\n", + " )\n", + " return atoms\n", + "\n", + "def gen_atoms(prompt=\"\", max_new_tokens=512, model=\"\", tokenizer=\"\"):\n", + " inputs = tokenizer(\n", + " [\n", + " alpaca_prompt.format(\n", + " \"Below is a description of a superconductor material.\", # instruction\n", + " prompt, # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + " ],\n", + " return_tensors=\"pt\",\n", + " ).to(\"cuda\")\n", + " outputs = model.generate(\n", + " **inputs, max_new_tokens=max_new_tokens, use_cache=True\n", + " )\n", + " response = tokenizer.batch_decode(outputs)[0].split(\"# Output:\")[1].strip('')\n", + " # print('response',response)\n", + " atoms = text2atoms(response)\n", + " return atoms\n", + "\n", + "def general_relaxer(atoms=\"\", calculator=\"\", fmax=0.05, steps=150):\n", + " ase_atoms = atoms.ase_converter()\n", + " ase_atoms.calc = calculator\n", + " ase_atoms = ExpCellFilter(ase_atoms)\n", + "\n", + " dyn = FIRE(ase_atoms)\n", + " dyn.run(fmax=fmax, steps=steps)\n", + " return ase_to_atoms(ase_atoms.atoms)" + ], + "metadata": { + "id": "O_hFyB1YYdjA" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "atoms = gen_atoms(prompt=prompt_example, model=model, tokenizer=tokenizer)\n", + "print(atoms)\n" + ], + "metadata": { + "id": "HGp3xsd1Yxmr", + "outputId": "9679e982-125b-4c38-876c-7c1b2e86cdb8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "System\n", + "1.0\n", + "3.07 0.0 0.0\n", + "-0.0 3.07 0.0\n", + "0.0 0.0 3.51\n", + "Mg B \n", + "1 2 \n", + "direct\n", + "0.0 0.0 0.0 Mg\n", + "0.667 0.333 0.5 B\n", + "0.333 0.667 0.5 B\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "prompt_example = \"The chemical formula is MgCB2 The Tc_supercon is 36.483. Generate atomic structure description with lattice lengths, angles, coordinates and atom types.\"\n", + "atoms = gen_atoms(prompt=prompt_example, model=model, tokenizer=tokenizer)\n", + "print(atoms)\n" + ], + "metadata": { + "id": "FNAfj9Y9zddU", + "outputId": "c19b66ac-ba07-43ac-f93f-463c5172f7a6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "System\n", + "1.0\n", + "3.1 0.0 0.0\n", + "-0.0 3.1 0.0\n", + "0.0 0.0 3.51\n", + "Mg C B \n", + "1 1 2 \n", + "direct\n", + "0.0 0.0 0.0 Mg\n", + "0.5 0.5 0.5 C\n", + "0.5 0.0 0.25 B\n", + "0.0 0.5 0.75 B\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from alignn.ff.ff import AlignnAtomwiseCalculator, default_path\n", + "from tqdm import tqdm\n", + "from ase.constraints import ExpCellFilter\n", + "from sklearn.metrics import mean_absolute_error\n", + "import time\n", + "from jarvis.core.atoms import ase_to_atoms\n", + "from ase.optimize.fire import FIRE\n", + "\n", + "model_path = default_path()\n", + "calc = AlignnAtomwiseCalculator(path=model_path, stress_wt=0.3)" + ], + "metadata": { + "id": "FQYC6V0BYxkU", + "outputId": "bd39e987-543f-4b27-a2f5-7032ff1bf24d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "dir_path /usr/local/lib/python3.10/site-packages/alignn/ff/alignnff_wt10\n", + "model_path /usr/local/lib/python3.10/site-packages/alignn/ff/alignnff_wt10\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "AtomGPT based generated structures might be high energy. To opimize the structure, ALIGNN-FF can be useful. See an example in the notebook [here](https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ALIGNN_Structure_Relaxation_Phonons_Interface.ipynb)." + ], + "metadata": { + "id": "7jcJC790nlSV" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MxZsiRLuapAL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "oVo65QHPao-B" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "kS1YCGFXao7J" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "c-z8n7mbao4h" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!conda env export" + ], + "metadata": { + "id": "-IUbsYWmgCx_", + "outputId": "cdd58d12-c7d7-4b09-93bd-e6b1d2c25557", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "name: base\n", + "channels:\n", + " - conda-forge\n", + "dependencies:\n", + " - _libgcc_mutex=0.1=conda_forge\n", + " - _openmp_mutex=4.5=2_gnu\n", + " - archspec=0.2.2=pyhd8ed1ab_0\n", + " - boltons=23.1.1=pyhd8ed1ab_0\n", + " - brotli-python=1.1.0=py310hc6cd4ac_1\n", + " - bzip2=1.0.8=hd590300_5\n", + " - c-ares=1.24.0=hd590300_0\n", + " - ca-certificates=2023.11.17=hbcca054_0\n", + " - certifi=2023.11.17=pyhd8ed1ab_0\n", + " - cffi=1.16.0=py310h2fee648_0\n", + " - charset-normalizer=3.3.2=pyhd8ed1ab_0\n", + " - colorama=0.4.6=pyhd8ed1ab_0\n", + " - conda=23.11.0=py310hff52083_1\n", + " - conda-libmamba-solver=23.12.0=pyhd8ed1ab_0\n", + " - conda-package-handling=2.2.0=pyh38be061_0\n", + " - conda-package-streaming=0.9.0=pyhd8ed1ab_0\n", + " - distro=1.8.0=pyhd8ed1ab_0\n", + " - fmt=10.1.1=h00ab1b0_1\n", + " - icu=73.2=h59595ed_0\n", + " - idna=3.6=pyhd8ed1ab_0\n", + " - jsonpatch=1.33=pyhd8ed1ab_0\n", + " - jsonpointer=2.4=py310hff52083_3\n", + " - keyutils=1.6.1=h166bdaf_0\n", + " - krb5=1.21.2=h659d440_0\n", + " - ld_impl_linux-64=2.40=h41732ed_0\n", + " - libarchive=3.7.2=h2aa1ff5_1\n", + " - libcurl=8.5.0=hca28451_0\n", + " - libedit=3.1.20191231=he28a2e2_2\n", + " - libev=4.33=hd590300_2\n", + " - libffi=3.4.2=h7f98852_5\n", + " - libgcc-ng=13.2.0=h807b86a_3\n", + " - libgomp=13.2.0=h807b86a_3\n", + " - libiconv=1.17=hd590300_2\n", + " - libmamba=1.5.5=had39da4_0\n", + " - libmambapy=1.5.5=py310h39ff949_0\n", + " - libnghttp2=1.58.0=h47da74e_1\n", + " - libnsl=2.0.1=hd590300_0\n", + " - libsolv=0.7.27=hfc55251_0\n", + " - libsqlite=3.44.2=h2797004_0\n", + " - libssh2=1.11.0=h0841786_0\n", + " - libstdcxx-ng=13.2.0=h7e041cc_3\n", + " - libuuid=2.38.1=h0b41bf4_0\n", + " - libxml2=2.12.3=h232c23b_0\n", + " - libzlib=1.2.13=hd590300_5\n", + " - lz4-c=1.9.4=hcb278e6_0\n", + " - lzo=2.10=h516909a_1000\n", + " - mamba=1.5.5=py310h51d5547_0\n", + " - menuinst=2.0.1=py310hff52083_0\n", + " - ncurses=6.4=h59595ed_2\n", + " - openssl=3.2.0=hd590300_1\n", + " - packaging=23.2=pyhd8ed1ab_0\n", + " - pip=23.3.2=pyhd8ed1ab_0\n", + " - platformdirs=4.1.0=pyhd8ed1ab_0\n", + " - pluggy=1.3.0=pyhd8ed1ab_0\n", + " - pybind11-abi=4=hd8ed1ab_3\n", + " - pycosat=0.6.6=py310h2372a71_0\n", + " - pycparser=2.21=pyhd8ed1ab_0\n", + " - pysocks=1.7.1=pyha2e5f31_6\n", + " - python=3.10.13=hd12c33a_0_cpython\n", + " - python_abi=3.10=4_cp310\n", + " - readline=8.2=h8228510_1\n", + " - reproc=14.2.4.post0=hd590300_1\n", + " - reproc-cpp=14.2.4.post0=h59595ed_1\n", + " - ruamel.yaml=0.18.5=py310h2372a71_0\n", + " - ruamel.yaml.clib=0.2.7=py310h2372a71_2\n", + " - setuptools=68.2.2=pyhd8ed1ab_0\n", + " - tk=8.6.13=noxft_h4845f30_101\n", + " - truststore=0.8.0=pyhd8ed1ab_0\n", + " - urllib3=2.1.0=pyhd8ed1ab_0\n", + " - wheel=0.42.0=pyhd8ed1ab_0\n", + " - xz=5.2.6=h166bdaf_0\n", + " - yaml-cpp=0.8.0=h59595ed_0\n", + " - zstandard=0.22.0=py310h1275a96_0\n", + " - zstd=1.5.5=hfc55251_0\n", + " - pip:\n", + " - accelerate==0.33.0\n", + " - aiohappyeyeballs==2.3.5\n", + " - aiohttp==3.10.3\n", + " - aiosignal==1.3.1\n", + " - annotated-types==0.7.0\n", + " - async-timeout==4.0.3\n", + " - atomgpt==2024.6.8\n", + " - attrs==24.2.0\n", + " - bitsandbytes==0.43.3\n", + " - contourpy==1.2.1\n", + " - cycler==0.12.1\n", + " - datasets==2.20.0\n", + " - dill==0.3.8\n", + " - docstring-parser==0.16\n", + " - filelock==3.15.4\n", + " - fonttools==4.53.1\n", + " - frozenlist==1.4.1\n", + " - fsspec==2024.5.0\n", + " - huggingface-hub==0.24.5\n", + " - inflect==7.3.1\n", + " - jarvis-tools==2024.5.10\n", + " - jinja2==3.1.4\n", + " - joblib==1.4.2\n", + " - kiwisolver==1.4.5\n", + " - markdown-it-py==3.0.0\n", + " - markupsafe==2.1.5\n", + " - matplotlib==3.9.1.post1\n", + " - mdurl==0.1.2\n", + " - more-itertools==10.4.0\n", + " - mpmath==1.3.0\n", + " - multidict==6.0.5\n", + " - multiprocess==0.70.16\n", + " - networkx==3.3\n", + " - numpy==1.26.4\n", + " - nvidia-cublas-cu12==12.1.3.1\n", + " - nvidia-cuda-cupti-cu12==12.1.105\n", + " - nvidia-cuda-nvrtc-cu12==12.1.105\n", + " - nvidia-cuda-runtime-cu12==12.1.105\n", + " - nvidia-cudnn-cu12==9.1.0.70\n", + " - nvidia-cufft-cu12==11.0.2.54\n", + " - nvidia-curand-cu12==10.3.2.106\n", + " - nvidia-cusolver-cu12==11.4.5.107\n", + " - nvidia-cusparse-cu12==12.1.0.106\n", + " - nvidia-nccl-cu12==2.20.5\n", + " - nvidia-nvjitlink-cu12==12.6.20\n", + " - nvidia-nvtx-cu12==12.1.105\n", + " - pandas==2.2.2\n", + " - peft==0.12.0\n", + " - pillow==10.4.0\n", + " - psutil==6.0.0\n", + " - pyarrow==17.0.0\n", + " - pyarrow-hotfix==0.6\n", + " - pydantic==2.8.2\n", + " - pydantic-core==2.20.1\n", + " - pydantic-settings==2.4.0\n", + " - pygments==2.18.0\n", + " - pyparsing==3.1.2\n", + " - python-dateutil==2.9.0.post0\n", + " - python-dotenv==1.0.1\n", + " - pytz==2024.1\n", + " - pyyaml==6.0.2\n", + " - regex==2024.7.24\n", + " - requests==2.32.3\n", + " - rich==13.7.1\n", + " - safetensors==0.4.4\n", + " - scikit-learn==1.5.1\n", + " - scipy==1.14.0\n", + " - sentencepiece==0.2.0\n", + " - shtab==1.7.1\n", + " - six==1.16.0\n", + " - spglib==2.5.0\n", + " - sympy==1.13.1\n", + " - threadpoolctl==3.5.0\n", + " - tokenizers==0.19.1\n", + " - toolz==0.12.1\n", + " - torch==2.4.0\n", + " - tqdm==4.66.5\n", + " - transformers==4.44.0\n", + " - triton==3.0.0\n", + " - trl==0.9.6\n", + " - typeguard==4.3.0\n", + " - typing-extensions==4.12.2\n", + " - tyro==0.8.6\n", + " - tzdata==2024.1\n", + " - xmltodict==0.13.0\n", + " - xxhash==3.4.1\n", + " - yarl==1.9.4\n", + "prefix: /usr/local\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip freeze" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vnGe03dra32v", + "outputId": "5c4cbb6f-3f03-4fcb-ffbe-c4a99acb4574" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "accelerate==0.33.0\n", + "aiohappyeyeballs==2.3.5\n", + "aiohttp==3.10.3\n", + "aiosignal==1.3.1\n", + "annotated-types==0.7.0\n", + "archspec @ file:///home/conda/feedstock_root/build_artifacts/archspec_1699370045702/work\n", + "async-timeout==4.0.3\n", + "atomgpt==2024.6.8\n", + "attrs==24.2.0\n", + "bitsandbytes==0.43.3\n", + "boltons @ file:///home/conda/feedstock_root/build_artifacts/boltons_1703154663129/work\n", + "Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work\n", + "certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1700303426725/work/certifi\n", + "cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1696001684923/work\n", + "charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1698833585322/work\n", + "colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work\n", + "conda @ file:///home/conda/feedstock_root/build_artifacts/conda_1701731572133/work\n", + "conda-libmamba-solver @ file:///home/conda/feedstock_root/build_artifacts/conda-libmamba-solver_1702406360642/work/src\n", + "conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1691048088238/work\n", + "conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1691009212940/work\n", + "contourpy==1.2.1\n", + "cycler==0.12.1\n", + "datasets==2.20.0\n", + "dill==0.3.8\n", + "distro @ file:///home/conda/feedstock_root/build_artifacts/distro_1675116244235/work\n", + "docstring_parser==0.16\n", + "filelock==3.15.4\n", + "fonttools==4.53.1\n", + "frozenlist==1.4.1\n", + "fsspec==2024.5.0\n", + "huggingface-hub==0.24.5\n", + "idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1701026962277/work\n", + "inflect==7.3.1\n", + "jarvis-tools==2024.5.10\n", + "Jinja2==3.1.4\n", + "joblib==1.4.2\n", + "jsonpatch @ file:///home/conda/feedstock_root/build_artifacts/jsonpatch_1695536281965/work\n", + "jsonpointer @ file:///home/conda/feedstock_root/build_artifacts/jsonpointer_1695397238043/work\n", + "kiwisolver==1.4.5\n", + "libmambapy @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1702310393080/work/libmambapy\n", + "mamba @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1702310393080/work/mamba\n", + "markdown-it-py==3.0.0\n", + "MarkupSafe==2.1.5\n", + "matplotlib==3.9.1.post1\n", + "mdurl==0.1.2\n", + "menuinst @ file:///home/conda/feedstock_root/build_artifacts/menuinst_1702317041727/work\n", + "more-itertools==10.4.0\n", + "mpmath==1.3.0\n", + "multidict==6.0.5\n", + "multiprocess==0.70.16\n", + "networkx==3.3\n", + "numpy==1.26.4\n", + "nvidia-cublas-cu12==12.1.3.1\n", + "nvidia-cuda-cupti-cu12==12.1.105\n", + "nvidia-cuda-nvrtc-cu12==12.1.105\n", + "nvidia-cuda-runtime-cu12==12.1.105\n", + "nvidia-cudnn-cu12==9.1.0.70\n", + "nvidia-cufft-cu12==11.0.2.54\n", + "nvidia-curand-cu12==10.3.2.106\n", + "nvidia-cusolver-cu12==11.4.5.107\n", + "nvidia-cusparse-cu12==12.1.0.106\n", + "nvidia-nccl-cu12==2.20.5\n", + "nvidia-nvjitlink-cu12==12.6.20\n", + "nvidia-nvtx-cu12==12.1.105\n", + "packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1696202382185/work\n", + "pandas==2.2.2\n", + "peft==0.12.0\n", + "pillow==10.4.0\n", + "platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1701708255999/work\n", + "pluggy @ file:///home/conda/feedstock_root/build_artifacts/pluggy_1693086607691/work\n", + "psutil==6.0.0\n", + "pyarrow==17.0.0\n", + "pyarrow-hotfix==0.6\n", + "pycosat @ file:///home/conda/feedstock_root/build_artifacts/pycosat_1696355758174/work\n", + "pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work\n", + "pydantic==2.8.2\n", + "pydantic-settings==2.4.0\n", + "pydantic_core==2.20.1\n", + "Pygments==2.18.0\n", + "pyparsing==3.1.2\n", + "PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work\n", + "python-dateutil==2.9.0.post0\n", + "python-dotenv==1.0.1\n", + "pytz==2024.1\n", + "PyYAML==6.0.2\n", + "regex==2024.7.24\n", + "requests==2.32.3\n", + "rich==13.7.1\n", + "ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1699007337104/work\n", + "ruamel.yaml.clib @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml.clib_1695996839082/work\n", + "safetensors==0.4.4\n", + "scikit-learn==1.5.1\n", + "scipy==1.14.0\n", + "sentencepiece==0.2.0\n", + "shtab==1.7.1\n", + "six==1.16.0\n", + "spglib==2.5.0\n", + "sympy==1.13.1\n", + "threadpoolctl==3.5.0\n", + "tokenizers==0.19.1\n", + "toolz==0.12.1\n", + "torch==2.4.0\n", + "tqdm==4.66.5\n", + "transformers==4.44.0\n", + "triton==3.0.0\n", + "trl==0.9.6\n", + "truststore @ file:///home/conda/feedstock_root/build_artifacts/truststore_1694154605758/work\n", + "typeguard==4.3.0\n", + "typing_extensions==4.12.2\n", + "tyro==0.8.6\n", + "tzdata==2024.1\n", + "urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1699933488691/work\n", + "xmltodict==0.13.0\n", + "xxhash==3.4.1\n", + "yarl==1.9.4\n", + "zstandard==0.22.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!conda env export" + ], + "metadata": { + "id": "zkNYlup4mNHL", + "outputId": "3e6dd98c-2915-49e2-e674-6946fa172eff", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "name: base\n", + "channels:\n", + " - conda-forge\n", + "dependencies:\n", + " - _libgcc_mutex=0.1=conda_forge\n", + " - _openmp_mutex=4.5=2_gnu\n", + " - archspec=0.2.2=pyhd8ed1ab_0\n", + " - boltons=23.1.1=pyhd8ed1ab_0\n", + " - brotli-python=1.1.0=py310hc6cd4ac_1\n", + " - bzip2=1.0.8=hd590300_5\n", + " - c-ares=1.24.0=hd590300_0\n", + " - ca-certificates=2023.11.17=hbcca054_0\n", + " - cffi=1.16.0=py310h2fee648_0\n", + " - charset-normalizer=3.3.2=pyhd8ed1ab_0\n", + " - colorama=0.4.6=pyhd8ed1ab_0\n", + " - conda=23.11.0=py310hff52083_1\n", + " - conda-libmamba-solver=23.12.0=pyhd8ed1ab_0\n", + " - conda-package-handling=2.2.0=pyh38be061_0\n", + " - conda-package-streaming=0.9.0=pyhd8ed1ab_0\n", + " - distro=1.8.0=pyhd8ed1ab_0\n", + " - fmt=10.1.1=h00ab1b0_1\n", + " - icu=73.2=h59595ed_0\n", + " - jsonpatch=1.33=pyhd8ed1ab_0\n", + " - jsonpointer=2.4=py310hff52083_3\n", + " - keyutils=1.6.1=h166bdaf_0\n", + " - krb5=1.21.2=h659d440_0\n", + " - ld_impl_linux-64=2.40=h41732ed_0\n", + " - libarchive=3.7.2=h2aa1ff5_1\n", + " - libcurl=8.5.0=hca28451_0\n", + " - libedit=3.1.20191231=he28a2e2_2\n", + " - libev=4.33=hd590300_2\n", + " - libffi=3.4.2=h7f98852_5\n", + " - libgcc-ng=13.2.0=h807b86a_3\n", + " - libgomp=13.2.0=h807b86a_3\n", + " - libiconv=1.17=hd590300_2\n", + " - libmamba=1.5.5=had39da4_0\n", + " - libmambapy=1.5.5=py310h39ff949_0\n", + " - libnghttp2=1.58.0=h47da74e_1\n", + " - libnsl=2.0.1=hd590300_0\n", + " - libsolv=0.7.27=hfc55251_0\n", + " - libsqlite=3.44.2=h2797004_0\n", + " - libssh2=1.11.0=h0841786_0\n", + " - libstdcxx-ng=13.2.0=h7e041cc_3\n", + " - libuuid=2.38.1=h0b41bf4_0\n", + " - libxml2=2.12.3=h232c23b_0\n", + " - libzlib=1.2.13=hd590300_5\n", + " - lz4-c=1.9.4=hcb278e6_0\n", + " - lzo=2.10=h516909a_1000\n", + " - mamba=1.5.5=py310h51d5547_0\n", + " - menuinst=2.0.1=py310hff52083_0\n", + " - ncurses=6.4=h59595ed_2\n", + " - openssl=3.2.0=hd590300_1\n", + " - pip=23.3.2=pyhd8ed1ab_0\n", + " - pluggy=1.3.0=pyhd8ed1ab_0\n", + " - pybind11-abi=4=hd8ed1ab_3\n", + " - pycosat=0.6.6=py310h2372a71_0\n", + " - pycparser=2.21=pyhd8ed1ab_0\n", + " - pysocks=1.7.1=pyha2e5f31_6\n", + " - python=3.10.13=hd12c33a_0_cpython\n", + " - python_abi=3.10=4_cp310\n", + " - readline=8.2=h8228510_1\n", + " - reproc=14.2.4.post0=hd590300_1\n", + " - reproc-cpp=14.2.4.post0=h59595ed_1\n", + " - ruamel.yaml=0.18.5=py310h2372a71_0\n", + " - ruamel.yaml.clib=0.2.7=py310h2372a71_2\n", + " - setuptools=68.2.2=pyhd8ed1ab_0\n", + " - tk=8.6.13=noxft_h4845f30_101\n", + " - truststore=0.8.0=pyhd8ed1ab_0\n", + " - wheel=0.42.0=pyhd8ed1ab_0\n", + " - xz=5.2.6=h166bdaf_0\n", + " - yaml-cpp=0.8.0=h59595ed_0\n", + " - zstandard=0.22.0=py310h1275a96_0\n", + " - zstd=1.5.5=hfc55251_0\n", + " - pip:\n", + " - accelerate==0.31.0\n", + " - aiohttp==3.9.5\n", + " - aiosignal==1.3.1\n", + " - alignn==2024.4.20\n", + " - annotated-types==0.7.0\n", + " - ase==3.23.0\n", + " - async-timeout==4.0.3\n", + " - attrs==23.2.0\n", + " - autopep8==2.3.1\n", + " - bitsandbytes==0.43.1\n", + " - black==24.4.2\n", + " - certifi==2024.6.2\n", + " - chardet==3.0.4\n", + " - click==8.1.7\n", + " - contourpy==1.2.1\n", + " - cycler==0.12.1\n", + " - datasets==2.20.0\n", + " - dgl==1.1.1\n", + " - dill==0.3.8\n", + " - docstring-parser==0.16\n", + " - eval-type-backport==0.2.0\n", + " - filelock==3.15.4\n", + " - flake8==7.1.0\n", + " - fonttools==4.53.0\n", + " - frozenlist==1.4.1\n", + " - fsspec==2024.5.0\n", + " - gmpy2==2.1.5\n", + " - huggingface-hub==0.23.4\n", + " - idna==3.7\n", + " - importlib-resources==6.4.0\n", + " - jarvis-tools==2024.4.30\n", + " - jinja2==3.1.4\n", + " - joblib==1.4.2\n", + " - kiwisolver==1.4.5\n", + " - lmdb==1.4.1\n", + " - markdown-it-py==3.0.0\n", + " - markupsafe==2.1.5\n", + " - matplotlib==3.9.0\n", + " - mccabe==0.7.0\n", + " - mdurl==0.1.2\n", + " - mpmath==1.3.0\n", + " - multidict==4.7.6\n", + " - multiprocess==0.70.16\n", + " - mypy-extensions==1.0.0\n", + " - networkx==3.3\n", + " - numpy==1.26.4\n", + " - nvidia-cublas-cu12==12.1.3.1\n", + " - nvidia-cuda-cupti-cu12==12.1.105\n", + " - nvidia-cuda-nvrtc-cu12==12.1.105\n", + " - nvidia-cuda-runtime-cu12==12.1.105\n", + " - nvidia-cudnn-cu12==8.9.2.26\n", + " - nvidia-cufft-cu12==11.0.2.54\n", + " - nvidia-curand-cu12==10.3.2.106\n", + " - nvidia-cusolver-cu12==11.4.5.107\n", + " - nvidia-cusparse-cu12==12.1.0.106\n", + " - nvidia-nccl-cu12==2.19.3\n", + " - nvidia-nvjitlink-cu12==12.5.40\n", + " - nvidia-nvtx-cu12==12.1.105\n", + " - packaging==24.1\n", + " - pandas==2.2.2\n", + " - pathspec==0.12.1\n", + " - peft==0.11.1\n", + " - pillow==10.3.0\n", + " - platformdirs==4.2.2\n", + " - psutil==6.0.0\n", + " - pyarrow==16.1.0\n", + " - pyarrow-hotfix==0.6\n", + " - pycodestyle==2.12.0\n", + " - pydantic==2.7.4\n", + " - pydantic-core==2.18.4\n", + " - pydantic-settings==2.3.3\n", + " - pydocstyle==6.3.0\n", + " - pyflakes==3.2.0\n", + " - pygments==2.18.0\n", + " - pyparsing==2.4.7\n", + " - python-dateutil==2.9.0.post0\n", + " - python-dotenv==1.0.1\n", + " - pytz==2024.1\n", + " - pyyaml==6.0.1\n", + " - regex==2024.5.15\n", + " - requests==2.32.3\n", + " - rich==13.7.1\n", + " - safetensors==0.4.3\n", + " - scikit-learn==1.5.0\n", + " - scipy==1.13.1\n", + " - sentencepiece==0.2.0\n", + " - shtab==1.7.1\n", + " - six==1.16.0\n", + " - snowballstemmer==2.2.0\n", + " - spglib==2.4.0\n", + " - sympy==1.12.1\n", + " - threadpoolctl==3.5.0\n", + " - tokenizers==0.19.1\n", + " - tomli==2.0.1\n", + " - toolz==0.12.1\n", + " - torch==2.2.2\n", + " - torchdata==0.7.1\n", + " - tqdm==4.66.4\n", + " - transformers==4.41.2\n", + " - triton==2.2.0\n", + " - trl==0.8.6\n", + " - typing-extensions==4.12.2\n", + " - tyro==0.8.4\n", + " - tzdata==2024.1\n", + " - urllib3==2.2.2\n", + " - xformers==0.0.25.post1\n", + " - xmltodict==0.13.0\n", + " - xxhash==3.4.1\n", + " - yarl==1.9.4\n", + " - zipp==3.19.2\n", + "prefix: /usr/local\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "opxC-PlXmQtx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "2cW8QpTombNV" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# env=\"\"\"name:base\n", + "# channels:\n", + "# - xformers\n", + "# - pytorch\n", + "# - nvidia\n", + "# - conda-forge\n", + "# - defaults\n", + "# dependencies:\n", + "# - _libgcc_mutex=0.1=conda_forge\n", + "# - _openmp_mutex=4.5=2_gnu\n", + "# - blas=1.0=mkl\n", + "# - bzip2=1.0.8=h7f98852_4\n", + "# - ca-certificates=2024.2.2=hbcca054_0\n", + "# - cairo=1.18.0=h3faef2a_0\n", + "# - cffi=1.16.0=py39h7a31438_0\n", + "# - cuda-cudart=12.1.105=0\n", + "# - cuda-cupti=12.1.105=0\n", + "# - cuda-libraries=12.1.0=0\n", + "# - cuda-nvrtc=12.1.105=0\n", + "# - cuda-nvtx=12.1.105=0\n", + "# - cuda-opencl=12.4.99=0\n", + "# - cuda-runtime=12.1.0=0\n", + "# - cudatoolkit=11.7.0=hd8887f6_10\n", + "# - expat=2.5.0=hcb278e6_1\n", + "# - filelock=3.15.4=pyhd8ed1ab_0\n", + "# - font-ttf-dejavu-sans-mono=2.37=hab24e00_0\n", + "# - font-ttf-inconsolata=3.000=h77eed37_0\n", + "# - font-ttf-source-code-pro=2.038=h77eed37_0\n", + "# - font-ttf-ubuntu=0.83=hab24e00_0\n", + "# - fontconfig=2.14.2=h14ed4e7_0\n", + "# - fonts-conda-ecosystem=1=0\n", + "# - fonts-conda-forge=1=0\n", + "# - freetype=2.12.1=h267a509_2\n", + "# - gettext=0.21.1=h27087fc_0\n", + "# - gmp=6.3.0=h59595ed_1\n", + "# - gmpy2=2.1.2=py39h376b7d2_1\n", + "# - icu=73.2=h59595ed_0\n", + "# - intel-openmp=2022.1.0=h9e868ea_3769\n", + "# - jinja2=3.1.4=pyhd8ed1ab_0\n", + "# - ld_impl_linux-64=2.40=h41732ed_0\n", + "# - libblas=3.9.0=16_linux64_mkl\n", + "# - libcblas=3.9.0=16_linux64_mkl\n", + "# - libcublas=12.1.0.26=0\n", + "# - libcufft=11.0.2.4=0\n", + "# - libcufile=1.9.0.20=0\n", + "# - libcurand=10.3.5.119=0\n", + "# - libcusolver=11.4.4.55=0\n", + "# - libcusparse=12.0.2.55=0\n", + "# - libexpat=2.5.0=hcb278e6_1\n", + "# - libffi=3.4.2=h7f98852_5\n", + "# - libgcc-ng=13.2.0=h807b86a_2\n", + "# - libgfortran-ng=13.2.0=h69a702a_5\n", + "# - libgfortran5=13.2.0=ha4646dd_5\n", + "# - libglib=2.78.0=hebfc3b9_0\n", + "# - libgomp=13.2.0=h807b86a_2\n", + "# - libiconv=1.17=h166bdaf_0\n", + "# - liblapack=3.9.0=16_linux64_mkl\n", + "# - libnpp=12.0.2.50=0\n", + "# - libnsl=2.0.0=h7f98852_0\n", + "# - libnvjitlink=12.1.105=0\n", + "# - libnvjpeg=12.1.1.14=0\n", + "# - libopenblas=0.3.26=pthreads_h413a1c8_0\n", + "# - libpng=1.6.39=h753d276_0\n", + "# - libprotobuf=3.21.12=hfc55251_2\n", + "# - libsqlite=3.43.0=h2797004_0\n", + "# - libstdcxx-ng=13.2.0=h7e041cc_2\n", + "# - libuuid=2.38.1=h0b41bf4_0\n", + "# - libxcb=1.15=h0b41bf4_0\n", + "# - libxml2=2.11.5=h232c23b_1\n", + "# - libzlib=1.2.13=hd590300_5\n", + "# - llvm-openmp=15.0.7=h0cdce71_0\n", + "# - markupsafe=2.1.5=py39hd1e30aa_0\n", + "# - mkl=2022.1.0=hc2b9512_224\n", + "# - mpc=1.3.1=hfe3b2da_0\n", + "# - mpfr=4.2.1=h9458935_0\n", + "# - mpmath=1.3.0=pyhd8ed1ab_0\n", + "# - ncurses=6.4=hcb278e6_0\n", + "# - networkx=3.2.1=pyhd8ed1ab_0\n", + "# - ninja=1.11.1=h924138e_0\n", + "# - openbabel=3.1.1=py39h421517d_8\n", + "# - openssl=3.2.1=hd590300_1\n", + "# - pcre2=10.40=hc3806b6_0\n", + "# - pip=23.2.1=pyhd8ed1ab_0\n", + "# - pixman=0.42.2=h59595ed_0\n", + "# - pthread-stubs=0.4=h36c2ea0_1001\n", + "# - pycparser=2.22=pyhd8ed1ab_0\n", + "# - python=3.9.18=h0755675_0_cpython\n", + "# - python_abi=3.9=4_cp39\n", + "# - pytorch=2.2.2=py3.9_cuda12.1_cudnn8.9.2_0\n", + "# - pytorch-cuda=12.1=ha16c6d3_5\n", + "# - pytorch-mutex=1.0=cuda\n", + "# - pyyaml=6.0.1=py39hd1e30aa_1\n", + "# - readline=8.2=h8228510_1\n", + "# - setuptools=68.2.2=pyhd8ed1ab_0\n", + "# - sleef=3.5.1=h9b69904_2\n", + "# - sympy=1.12=pypyh9d50eac_103\n", + "# - tk=8.6.13=h2797004_0\n", + "# - torchtriton=2.2.0=py39\n", + "# - typing_extensions=4.10.0=pyha770c72_0\n", + "# - wheel=0.43.0=pyhd8ed1ab_1\n", + "# - xformers=0.0.25.post1=py39_cu12.1.0_pyt2.2.2\n", + "# - xorg-kbproto=1.0.7=h7f98852_1002\n", + "# - xorg-libice=1.1.1=hd590300_0\n", + "# - xorg-libsm=1.2.4=h7391055_0\n", + "# - xorg-libx11=1.8.7=h8ee46fc_0\n", + "# - xorg-libxau=1.0.11=hd590300_0\n", + "# - xorg-libxdmcp=1.1.3=h7f98852_0\n", + "# - xorg-libxext=1.3.4=h0b41bf4_2\n", + "# - xorg-libxrender=0.9.11=hd590300_0\n", + "# - xorg-renderproto=0.11.1=h7f98852_1002\n", + "# - xorg-xextproto=7.3.0=h0b41bf4_1003\n", + "# - xorg-xproto=7.0.31=h7f98852_1007\n", + "# - xz=5.2.6=h166bdaf_0\n", + "# - yaml=0.2.5=h7f98852_2\n", + "# - zlib=1.2.13=hd590300_5\n", + "# - pip:\n", + "# - accelerate==0.31.0\n", + "# - aiohttp==3.9.5\n", + "# - aiosignal==1.3.1\n", + "# - alignn==2024.4.20\n", + "# - annotated-types==0.7.0\n", + "# - ase==3.23.0\n", + "# - async-timeout==4.0.3\n", + "# - attrs==23.2.0\n", + "# - autopep8==2.3.1\n", + "# - bitsandbytes==0.43.1\n", + "# - black==24.4.2\n", + "# - certifi==2024.6.2\n", + "# - chardet==3.0.4\n", + "# - charset-normalizer==3.3.2\n", + "# - click==8.1.7\n", + "# - contourpy==1.2.1\n", + "# - cycler==0.12.1\n", + "# - datasets==2.20.0\n", + "# - dgl==1.1.1\n", + "# - dill==0.3.8\n", + "# - docstring-parser==0.16\n", + "# - eval-type-backport==0.2.0\n", + "# - flake8==7.1.0\n", + "# - fonttools==4.53.0\n", + "# - frozenlist==1.4.1\n", + "# - fsspec==2024.5.0\n", + "# - huggingface-hub==0.23.4\n", + "# - idna==3.7\n", + "# - importlib-resources==6.4.0\n", + "# - jarvis-tools==2024.4.30\n", + "# - joblib==1.4.2\n", + "# - kiwisolver==1.4.5\n", + "# - lmdb==1.4.1\n", + "# - markdown-it-py==3.0.0\n", + "# - matplotlib==3.9.0\n", + "# - mccabe==0.7.0\n", + "# - mdurl==0.1.2\n", + "# - multidict==4.7.6\n", + "# - multiprocess==0.70.16\n", + "# - mypy-extensions==1.0.0\n", + "# - numpy==1.26.4\n", + "# - packaging==24.1\n", + "# - pandas==2.2.2\n", + "# - pathspec==0.12.1\n", + "# - peft==0.11.1\n", + "# - pillow==10.3.0\n", + "# - platformdirs==4.2.2\n", + "# - psutil==6.0.0\n", + "# - pyarrow==16.1.0\n", + "# - pyarrow-hotfix==0.6\n", + "# - pycodestyle==2.12.0\n", + "# - pydantic==2.7.4\n", + "# - pydantic-core==2.18.4\n", + "# - pydantic-settings==2.3.3\n", + "# - pydocstyle==6.3.0\n", + "# - pyflakes==3.2.0\n", + "# - pygments==2.18.0\n", + "# - pyparsing==2.4.7\n", + "# - python-dateutil==2.9.0.post0\n", + "# - python-dotenv==1.0.1\n", + "# - pytz==2024.1\n", + "# - regex==2024.5.15\n", + "# - requests==2.32.3\n", + "# - rich==13.7.1\n", + "# - safetensors==0.4.3\n", + "# - scikit-learn==1.5.0\n", + "# - scipy==1.13.1\n", + "# - sentencepiece==0.2.0\n", + "# - shtab==1.7.1\n", + "# - six==1.16.0\n", + "# - snowballstemmer==2.2.0\n", + "# - spglib==2.4.0\n", + "# - threadpoolctl==3.5.0\n", + "# - tokenizers==0.19.1\n", + "# - tomli==2.0.1\n", + "# - toolz==0.12.1\n", + "# - torchdata==0.7.1\n", + "# - tqdm==4.66.4\n", + "# - transformers==4.41.2\n", + "# - trl==0.8.6\n", + "# - tyro==0.8.4\n", + "# - tzdata==2024.1\n", + "# - urllib3==2.2.2\n", + "# - xmltodict==0.13.0\n", + "# - xxhash==3.4.1\n", + "# - yarl==1.9.4\n", + "# - zipp==3.19.2\n", + "# \"\"\"\n", + "# with open(f'/content/conda.yaml', 'w') as f:\n", + "# f.write(env)\n", + "# # !conda env update --name base -f conda.yaml" + ], + "metadata": { + "id": "jvEq4Wu0mbPp" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/jarvis-tools-notebooks/tb3py.ipynb b/jarvis-tools-notebooks/tb3py.ipynb new file mode 100644 index 0000000..c1f3bcd --- /dev/null +++ b/jarvis-tools-notebooks/tb3py.ipynb @@ -0,0 +1,622 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Julia", + "language": "julia", + "name": "julia" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia" + }, + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Example of using TB3Py https://github.com/usnistgov/tb3py/ and get wannier90_hr.dat file." + ], + "metadata": { + "id": "XNx0xoLoeZyG" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ], + "metadata": { + "id": "Sm83y9I9jdpE", + "outputId": "aec5d234-a893-4cbf-b33d-54ff87503fa9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.11.0-0/Mambaforge-23.11.0-0-Linux-x86_64.sh...\n", + "📦 Installing...\n", + "📌 Adjusting configuration...\n", + "🩹 Patching environment...\n", + "⏲ Done in 0:00:15\n", + "🔁 Restarting kernel...\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!conda install conda-forge::julia -y --quiet" + ], + "metadata": { + "id": "OQ9I2SREj6mw", + "outputId": "0c24cfc7-48f9-4bf6-a550-419957d9b7ea", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Channels:\n", + " - conda-forge\n", + "Platform: linux-64\n", + "Collecting package metadata (repodata.json): ...working... done\n", + "Solving environment: ...working... done\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - conda-forge::julia\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " arpack-3.9.1 |nompi_h77f6705_101 127 KB conda-forge\n", + " ca-certificates-2024.7.4 | hbcca054_0 151 KB conda-forge\n", + " certifi-2024.7.4 | pyhd8ed1ab_0 156 KB conda-forge\n", + " curl-8.5.0 | hca28451_0 93 KB conda-forge\n", + " gettext-0.22.5 | h59595ed_2 464 KB conda-forge\n", + " gettext-tools-0.22.5 | h59595ed_2 2.6 MB conda-forge\n", + " git-2.45.0 | pl5321hd39f443_0 9.8 MB conda-forge\n", + " gmp-6.3.0 | hac33072_2 449 KB conda-forge\n", + " julia-1.10.3 | h78b6aeb_1 101.6 MB conda-forge\n", + " libasprintf-0.22.5 | h661eb56_2 42 KB conda-forge\n", + " libasprintf-devel-0.22.5 | h661eb56_2 33 KB conda-forge\n", + " libblas-3.9.0 |23_linux64_openblas 15 KB conda-forge\n", + " libcblas-3.9.0 |23_linux64_openblas 14 KB conda-forge\n", + " libexpat-2.6.2 | h59595ed_0 72 KB conda-forge\n", + " libgettextpo-0.22.5 | h59595ed_2 167 KB conda-forge\n", + " libgettextpo-devel-0.22.5 | h59595ed_2 36 KB conda-forge\n", + " libgfortran-ng-13.2.0 | h69a702a_13 47 KB conda-forge\n", + " libgfortran5-13.2.0 | h3d2ce59_13 1.4 MB conda-forge\n", + " libgit2-1.8.1 | ha5f426f_0 859 KB conda-forge\n", + " libhwloc-2.11.1 |default_hecaa2ac_1000 2.3 MB conda-forge\n", + " liblapack-3.9.0 |23_linux64_openblas 14 KB conda-forge\n", + " libopenblas-0.3.27 |pthreads_hac2b453_1 5.3 MB conda-forge\n", + " libopenblas-ilp64-0.3.27 |pthreads_h0afdb33_1 5.2 MB conda-forge\n", + " libopenlibm4-0.8.1 | hd590300_1 102 KB conda-forge\n", + " libunwind-1.6.2 | h9c3ff4c_0 74 KB conda-forge\n", + " libutf8proc-2.8.0 | h166bdaf_0 99 KB conda-forge\n", + " libxcrypt-4.4.36 | hd590300_1 98 KB conda-forge\n", + " libxml2-2.12.7 | hc051c1a_1 688 KB conda-forge\n", + " mbedtls-3.5.1 | h59595ed_0 845 KB conda-forge\n", + " metis-5.1.0 | h59595ed_1007 3.7 MB conda-forge\n", + " mpfr-4.2.1 | h9458935_1 628 KB conda-forge\n", + " openblas-ilp64-0.3.27 |pthreads_h3d04fff_1 5.4 MB conda-forge\n", + " openlibm-0.8.1 | hd590300_1 29 KB conda-forge\n", + " openssl-3.3.1 | h4bc722e_2 2.8 MB conda-forge\n", + " p7zip-16.02 | h9c3ff4c_1001 2.2 MB conda-forge\n", + " pcre2-10.43 | hcad00b1_0 929 KB conda-forge\n", + " perl-5.32.1 | 7_hd590300_perl5 12.7 MB conda-forge\n", + " suitesparse-7.7.0 | hf4753ba_1 1.7 MB conda-forge\n", + " tbb-2021.12.0 | h434a139_3 189 KB conda-forge\n", + " zlib-1.2.13 | hd590300_5 91 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 163.0 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " arpack conda-forge/linux-64::arpack-3.9.1-nompi_h77f6705_101 \n", + " curl conda-forge/linux-64::curl-8.5.0-hca28451_0 \n", + " gettext conda-forge/linux-64::gettext-0.22.5-h59595ed_2 \n", + " gettext-tools conda-forge/linux-64::gettext-tools-0.22.5-h59595ed_2 \n", + " git conda-forge/linux-64::git-2.45.0-pl5321hd39f443_0 \n", + " gmp conda-forge/linux-64::gmp-6.3.0-hac33072_2 \n", + " julia conda-forge/linux-64::julia-1.10.3-h78b6aeb_1 \n", + " libasprintf conda-forge/linux-64::libasprintf-0.22.5-h661eb56_2 \n", + " libasprintf-devel conda-forge/linux-64::libasprintf-devel-0.22.5-h661eb56_2 \n", + " libblas conda-forge/linux-64::libblas-3.9.0-23_linux64_openblas \n", + " libcblas conda-forge/linux-64::libcblas-3.9.0-23_linux64_openblas \n", + " libexpat conda-forge/linux-64::libexpat-2.6.2-h59595ed_0 \n", + " libgettextpo conda-forge/linux-64::libgettextpo-0.22.5-h59595ed_2 \n", + " libgettextpo-devel conda-forge/linux-64::libgettextpo-devel-0.22.5-h59595ed_2 \n", + " libgfortran-ng conda-forge/linux-64::libgfortran-ng-13.2.0-h69a702a_13 \n", + " libgfortran5 conda-forge/linux-64::libgfortran5-13.2.0-h3d2ce59_13 \n", + " libgit2 conda-forge/linux-64::libgit2-1.8.1-ha5f426f_0 \n", + " libhwloc conda-forge/linux-64::libhwloc-2.11.1-default_hecaa2ac_1000 \n", + " liblapack conda-forge/linux-64::liblapack-3.9.0-23_linux64_openblas \n", + " libopenblas conda-forge/linux-64::libopenblas-0.3.27-pthreads_hac2b453_1 \n", + " libopenblas-ilp64 conda-forge/linux-64::libopenblas-ilp64-0.3.27-pthreads_h0afdb33_1 \n", + " libopenlibm4 conda-forge/linux-64::libopenlibm4-0.8.1-hd590300_1 \n", + " libunwind conda-forge/linux-64::libunwind-1.6.2-h9c3ff4c_0 \n", + " libutf8proc conda-forge/linux-64::libutf8proc-2.8.0-h166bdaf_0 \n", + " libxcrypt conda-forge/linux-64::libxcrypt-4.4.36-hd590300_1 \n", + " mbedtls conda-forge/linux-64::mbedtls-3.5.1-h59595ed_0 \n", + " metis conda-forge/linux-64::metis-5.1.0-h59595ed_1007 \n", + " mpfr conda-forge/linux-64::mpfr-4.2.1-h9458935_1 \n", + " openblas-ilp64 conda-forge/linux-64::openblas-ilp64-0.3.27-pthreads_h3d04fff_1 \n", + " openlibm conda-forge/linux-64::openlibm-0.8.1-hd590300_1 \n", + " p7zip conda-forge/linux-64::p7zip-16.02-h9c3ff4c_1001 \n", + " pcre2 conda-forge/linux-64::pcre2-10.43-hcad00b1_0 \n", + " perl conda-forge/linux-64::perl-5.32.1-7_hd590300_perl5 \n", + " suitesparse conda-forge/linux-64::suitesparse-7.7.0-hf4753ba_1 \n", + " tbb conda-forge/linux-64::tbb-2021.12.0-h434a139_3 \n", + " zlib conda-forge/linux-64::zlib-1.2.13-hd590300_5 \n", + "\n", + "The following packages will be UPDATED:\n", + "\n", + " ca-certificates 2023.11.17-hbcca054_0 --> 2024.7.4-hbcca054_0 \n", + " certifi 2023.11.17-pyhd8ed1ab_0 --> 2024.7.4-pyhd8ed1ab_0 \n", + " libxml2 2.12.3-h232c23b_0 --> 2.12.7-hc051c1a_1 \n", + " openssl 3.2.0-hd590300_1 --> 3.3.1-h4bc722e_2 \n", + "\n", + "\n", + "Preparing transaction: ...working... done\n", + "Verifying transaction: ...working... done\n", + "Executing transaction: ...working... done\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy" + ], + "metadata": { + "id": "SPFIMctkkkBT" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!julia --version" + ], + "metadata": { + "id": "8xeBrFJAk0ad", + "outputId": "453877d0-1d55-4434-9dcc-cda1801640f5", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "julia version 1.10.3\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!julia -e 'using Pkg; pkg\"add '$PKG'; precompile;\"' &> /dev/null" + ], + "metadata": { + "id": "hhufo6y7lF5k" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "\n", + "!julia -e 'using Pkg; Pkg.add(url = \"https://github.com/usnistgov/ThreeBodyTB.jl\")' &> /dev/null" + ], + "metadata": { + "id": "s6_LoaPnlF2h", + "outputId": "9d0d81f8-e11c-409d-e196-12100ab6b9bb", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 4.12 s, sys: 538 ms, total: 4.66 s\n", + "Wall time: 9min 18s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%%time\n", + "!julia -e 'using Pkg; Pkg.add(\"Plot\")' &> /dev/null" + ], + "metadata": { + "id": "cQ_lX459lmud", + "outputId": "c95267d5-0056-48b4-ed1c-1f94d7dfaf17", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 70.7 ms, sys: 7.9 ms, total: 78.6 ms\n", + "Wall time: 7.85 s\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install -q jarvis-tools" + ], + "metadata": { + "id": "5Z3yH_k4s_zQ", + "outputId": "6fa43c32-b23e-4128-80c0-4a125ba4f0a6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 7, + 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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m229.9/229.9 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "pos=\"\"\"Si2\n", + "1.0\n", + "3.3641499856336465 -2.5027128e-09 1.94229273881412\n", + "1.121382991333525 3.1717517190189715 1.9422927388141193\n", + "-2.5909987e-09 -1.8321133e-09 3.884586486670313\n", + "Si\n", + "2\n", + "Cartesian\n", + "3.92483875 2.77528125 6.7980237500000005\n", + "0.56069125 0.39646875 0.9711462500000001\n", + "\"\"\"\n", + "pos=\"\"\"C2\n", + "1.0\n", + "1.2320971008984494 -2.1340542301747005 0.0\n", + "1.2320971008984494 2.1340542301747005 0.0\n", + "0.0 0.0 30.803073\n", + "C\n", + "2\n", + "Cartesian\n", + "0.0 0.0 1.9507584231000183\n", + "1.2321 0.7113514226999862 1.9507584231000183\n", + "\"\"\"\n", + "with open(\"POSCAR\",\"w\") as f:\n", + " f.write(pos)\n", + "\n", + "lines=\"\"\"using ThreeBodyTB\n", + "c = makecrys(\"POSCAR\")\n", + "energy, tbc, flag = scf_energy(c);\n", + "println(\"directgap, indirectgap, gaptype, bandwidth\",string(ThreeBodyTB.BandStruct.band_summary(tbc)),tbc.efermi)\n", + "write_hr_dat(tbc,filename=\"my_hr.dat\")\n", + "\"\"\"\n", + "with open(\"input.jl\",\"w\") as f:\n", + " f.write(lines)\n", + "#cmd = \"!julia input.jl\"\n", + "#os.system(cmd)" + ], + "metadata": { + "id": "OI2aDCTemCSQ" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!julia input.jl" + ], + "metadata": { + "id": "I7AzuW3nmCIT", + "outputId": "507c20a7-b1f4-4a24-90dc-bbea49ed499b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mCHOLMOD version incompatibility\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mJulia was compiled with CHOLMOD version 4.0.4. It is\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mcurrently linked with version 5.2.1.\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mThis might cause Julia to terminate when working with\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39msparse matrix factorizations, e.g. solving systems of\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mequations with \\.\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39m\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mIt is recommended that you use Julia with the same major\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mversion of CHOLMOD as the one used during the build, or\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mdownload the generic binaries from www.julialang.org,\n", + "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mwhich ship with the correct versions of all dependencies.\n", + "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ SparseArrays.CHOLMOD /usr/local/share/julia/stdlib/v1.10/SparseArrays/src/solvers/cholmod.jl:206\u001b[39m\n", + "\n", + "found /root/.julia/packages/ThreeBodyTB/0oXfM/src/../dats/pbesol/v1.3/els/coef.el.2bdy.C.xml.gz\n", + "Tuple{Symbol, Symbol}\n", + "found /root/.julia/packages/ThreeBodyTB/0oXfM/src/../dats/pbesol/v1.3/els/coef.el.3bdy.C.xml.gz\n", + "\n", + "\n", + "START SCF ----------------\n", + "SCF CALC 0001 energy -18.94188245 \n", + "SCF CALC 0002 energy -18.94188245 en_diff: 0.000000E+00 dq_diff: 1.243450E-14 mix: 5.000000E-02 \n", + "\n", + "YES convergence in 2 iters, energy -18.941882449600506 eV \n", + "END SCF ------------------\n", + "\n", + "ΔQ = [0.0, 0.0]\n", + "\n", + "scf_energy success, done\n", + "\n", + "Formation energy: -0.029 eV/atom\n", + "\n", + "using kgrid [14, 14, 2]\n", + "directgap, indirectgap, gaptype, bandwidth(1.4015695994427786, 1.4015695994427777, :direct, 19.15938547509438, [-3.0105339343503896, -1.6089643349076117])-0.16976079591050647\n", + "grid: [14, 14, 2]\n", + " 0.002478 seconds (6.69 k allocations: 1.970 MiB)\n", + " 5.238754 seconds (27.16 M allocations: 1.918 GiB, 16.01% gc time, 37.12% compilation time)\n", + "start write ./my_hr.dat\n", + "wrote ./my_hr.dat\n", + " 41.866431 seconds (54.69 M allocations: 3.548 GiB, 7.11% gc time, 91.34% compilation time)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "You can download my_hr.dat file by clicking on older icon on lef and download." + ], + "metadata": { + "id": "YjnjltTQgVFD" + } + }, + { + "cell_type": "code", + "source": [ + "from jarvis.io.wannier.outputs import WannierHam\n", + "w = WannierHam(filename='my_hr.dat')" + ], + "metadata": { + "id": "mwe1k4pZmCC9", + "outputId": "abfb2796-6b18-4ab1-a638-27b7c5d25dba", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "H size 19 21 3 8 8\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from jarvis.core.kpoints import generate_kgrid\n", + "kpoints = generate_kgrid([20, 20, 20])\n", + "energies, dos, pdos = w.dos(kpoints)\n", + "plt.plot(energies, dos)\n", + "plt.xlabel('E')\n", + "plt.ylabel('DOS')" + ], + "metadata": { + "id": "phswjWi3mCB0", + "outputId": "54c47a7e-70a0-4747-8264-b4030d3800f4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 501 + } + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "DOS BAND GAP 0.08522355620102896 -0.029350392461461993 0.05587316373956697\n", + "np.sum(dos) 7.600461843923312\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'DOS')" + ] + }, + "metadata": {}, + "execution_count": 12 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from jarvis.io.vasp.inputs import Poscar\n", + "atoms=Poscar.from_string(pos).atoms\n", + "filename='bands.png'\n", + "w.get_bandstructure_plot(atoms=atoms, filename=filename,yrange=[-8, 4])" + ], + "metadata": { + "id": "n3E1gBGpgfTM", + "outputId": "7b4d5e7d-3581-4ebc-9e5b-2e631ad3d2de", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import Image\n", + "Image('bands.png')" + ], + "metadata": { + "id": "o2MnFpVhgfQ9", + "outputId": "41273b48-d241-4d08-f8f9-e94e09d2f8ff", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + } + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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k3Os7gCjsAnYHJoCkFbuBwyc310y0/VKJoPzatpX1H48cAbZv1x2N96k2d8EFQPXqWkMJG3YBuwMTQNKK45HCZ/Nm6WqvUAFo3Vp3NKRLdDTQrp2cs93Zz49Vd3YBuwMTQNKKS8GEj/o/7tABKOfr0b/Edhc+fhv/B7AL2C2YAJJW6q5482ZZLJXs48dKBBWMlffw8WO7YxewOzABJK1q1gTU+tucCGIvP1YiqGAqGUlN5UQQOx06BOzYIed+mQACsALoFkwASTt2R9kveAKInyoRVLA2bWQs4LFj5hZlZD31N61ZM6BaNb2xhBPHALoDE0DSjt1R9tu0CTh+HIiJAVq10h0N6Va+vCwGDrDd2cmvVXdWAN2BCSBpx6Vg7Kf+bzkBhBS2O/v5cfwfwDGAbsEEkLRTfxy3bZNFU8l6fq1EUOHU78LSpXrj8DK/tjt2AbsDE0DSrnp1oHlzOV+yRGsonuXXSgQVrls3Oaam8kJth4MHgZ075bxjR72xhBu7gN2BCSA5QnKyHBcv1huHF+XkACtWyDkTQFJatQIqVwZOnADWrtUdjfeo6l/z5v7b55xdwO7ABJAcQVUjWAG03saNcpGvWJETQMgUGWl2TbLdWU9V3bt21RuHDuwCdgcmgOQIqgK4ZAnXJbOauhB16mTemRMBrLzbSbU7v43/A9gF7BZMAMkRkpJkaYrDh4EtW3RH4y1+vhBR0ZgA2sfP7Y5dwO7ABJAcoXx5c6A0u6Os5ecLERVNDb1YswY4eVJvLF6ybx+wZ490hXbooDua8GMF0B2YAJJjsBphvbNnzQkgTADpfPXqyUduLnfisZL6v1QTbfyGYwDdgQkgOQYTQOutWwecPg1UqSLbURGdj+3Oen6vurMC6A5MAMkxVHfUihVAZqbeWLwieP2/CLZ2KoBqd0wArcMEUI4cA+hsvCSQYzRtCsTGAllZwOrVuqPxBr9fiKh4wTPwKXSGwXbHLmB3YAJIjhEIsBphNb9fiKh4XbrIBXvXLpm8QKHZswfYv1+qYElJuqPRg13A7sAEkBxFVSMWLtQbhxdkZQGrVsk5E0AqTOXKQJs2cs52Fzp109W2LRATozcWXdgF7A5MAMlRevaU4/z5euPwgt9/l7GU1asDTZrojoacjO3OOqy6swvYLZgAkqMkJ8sfjx07pCuFyi74QhQI6I2FnI0JoHWYALIL2C2YAJKjVKlijpvhxSg0vBBRSakEcPly4NQpvbG4GSeACHYBuwMTQHKcXr3kOG+e3jjcjhciKqlGjYCEBCA7G1i6VHc07rVjh2xnGRUFtGunOxp9WAF0ByaA5DjsjgrdmTPmUjpMAKk4gQDbnRVU8ty+PRAdrTcWnTgG0B2YAJLjqAvRypXA8eNaQ3Gt1atlG7hatYAGDXRHQ27ABDB0rLoLVgDdgQkgOU5iItCwoYwf4XqAZcMJIFRaaujFggUcu1VWTAAFxwC6AxNAciRWI0LDCxGVVlISUKkScOwYsHat7mjcJzcXSE2Vc7+3O3YBuwMTQHIkJoChYQJIpVWunLkQO9td6W3ZAqSny9g/tbC2X7EL2B2YAJIjqe6ohQtlLBuV3KlTsgg0wASQSocz8MtO3XR16CCzgP2MXcDuwASQHKlNG9nB4sQJYMUK3dG4y8qV8oe3bl1Z2oOopC66SI6zZ8uadlRyrLqb2AXsDkwAyZEiI4FLLpHzadP0xuI2nABCZdWzp3Rh7toFbNqkOxp3YQJoYhewOzABJMdKSZEjE8DS4YWIyiomxhx/y3ZXcjk5sosKwHYHsAvYLZgAkmOpBHDePG5PVRoqAezaVW8c5E6q3U2dqjcON9m4UYarVKwItGqlOxr9WAF0ByaA5FjNmgH16wNZWRyUXlLHjwPr18t55856YyF3UgngzJmcgFVS6qarUycz+fEzjgF0ByaA5FiBALuBS2vFChm8X78+UKeO7mjIjTp1kglY6enmunZUNA67yIsVQHdgAkiOxgSwdHgholBFRgJ9+8o5213JsN3lxTGA7sAEkBytXz85rlgBHDqkNxY34IWIrMAbr5I7e9ZcqortTrAL2B2YAJKj1akDtGsn5zNm6I3FDZgAkhVUArhgAXDypN5YnG7dOuD0aaBKFRm3TOwCdgsmgOR46mL0889643C6Y8fMtds4AYRCccEFQIMGMgFr5kzd0Tibuunq3NmsfPkdu4Ddgb+u5HhDhsjxu++A7Gy9sTiZWoescWMgNlZvLORugYDZ7r78Um8sTseqe37sAnYHJoDkeBddJF3BR48C06frjsa5eCEiK113nRy//VYqgVQwtrv82AXsDkwAyfEiI4E//lHOP/9cbyxOxgsRWalnTyA+XpaD4aLQBcvKAlatknO2OxO7gN2BCSC5gqpGfPMNqxGFYQJIVoqIAK69Vs5541Ww338HMjNl3cQmTXRH4xysALoDE0ByBVWNOHaMS1MU5PBhYNs2Oe/USW8s5B3B3cCZmVpDcaTgm65AQG8sTsIxgO7ABJBcITISuOYaOWc1Ij+1Y0OzZlKNILJC9+5AvXpARgYwZYruaJyHVfeCsQLoDkwAyTWCqxFnzmgNxXGWLpUjl38hK0VEmONvP/tMbyxOxHZXMI4BdAcmgOQaPXsCDRvKoPS339YdjbMsWSLH5GS9cZD3DB0qx88/B7Zu1RuLk5w+DaxeLedsd3mxC9gdfJ8Avvnmm2jUqBEqVKiA5ORkLFFXUnKciAjgscfkfPx44MQJvfE4hWEAixfLebduemMh7+naFRgwQLY8e/pp3dE4x4oVkuDUrQskJuqOxlnYBewOvk4AJ02ahPvvvx/jxo3D8uXLkZSUhAEDBuDAgQO6Q6NCDB8ONG0KHDgAvPGG7micYfduYP9++aPbsaPuaMiLnn1Wjh9/LFufkVl179aNE0DOxy5gd/B1Avjyyy/jjjvuwIgRI9C6dWu8/fbbqFixIt577z3doVEhoqKAp56S8+efl1nBfqcuRO3bAzExemMhb+rSBbjySrmgP/mk7micITgBpLzYBewOvk0As7KykJqaihS10SyAiIgIpKSkYOHChRojo+LceCPQurUkfyNGAEeO6I5IL3b/Ujg884xUur78Enj9dVZ32O4Kxy5gdyinOwBdDh06hJycHNSpUyfP43Xq1MH69esL/JrMzExkBi2GlZGRYWuMVLDISODFF4HBg2VG8OLFwLhxQI0a8vmqVYGaNYGEBFnCwuvdM5wAQuHQrh0wciTwzjvAPfdI2xs5Uqo9kZHS/mrUkAWRq1XTHa29Dh0yJ8R07ao3FidSCeCxY4Uv2xUbC1xyiVktpPDzbQJYFuPHj8fTHAXtCIMGAQsXAsOGAevXA6NGFfy82rXlDn3QINnVIC4uvHHaLSfHXIuMlQiy2z//KUMNHnoImDlTPgrSogVw4YWydueAAUD58uGN025q+ZcWLbjuZkGio+WYlgZcf33hz/vyS3N9Vwo/3yaAtWrVQmRkJPbv35/n8f3796Nu3boFfs3YsWNx//33n/t3RkYG6tevb2ucVLhu3YDly4EJE4A5c+Sx3Fzg+HHpFt6zRyaL/PijfNx9tySCY8cCPXrojd0q69YBJ08ClSsDLVvqjoa8LiIC+POfJal78klg7155PDtbqj2HDsmEpA0b5OODD6QqOGwY8PDDspuPF3D8X9E6dgTuuAPYtKngz69bJ78n27eHNSw6j28TwPLly6Nz586YPn06rrzySgBAbm4upk+fjjFjxhT4NdHR0YhWtzbkCDExhS9Ncfq0bNQ+d650QyxbZiaD/ftL4uj2WbNqHFKXLma3C5HdmjYFPvmk4M8dPCgVsqlTgUmTgH37gFdflbU7R42SxFEN13Arjv8rWrlywL/+VfjnR44E3n1X/kaTPr7ufb///vvx7rvv4oMPPsC6detw11134eTJkxgxYoTu0MgCMTHSDfXQQ3JBWr8euP12+eM0ZYqM3Xn8cXfvccpKBDlNXBxw2WXAK68Au3YBP/8sFfczZyQRbNMG+OEH3VGWnWGw3YVKrVbABFAvXyeA119/PV588UU8+eST6NChA1auXIlffvkl38QQ8oYWLeSuc+NGGQ+YkwP87W9SPSusq8LpeCEiJ4uMBAYOBObNA379FWjeXCqCQ4YAt97qzi0dt20DDh+WcY1JSbqjcScmgM7g6wQQAMaMGYMdO3YgMzMTixcvRjKnUnpe48bSJfzFF1KtWLNGZtAWNqDdqU6dAn77Tc75a0tOFgjIsIuVK4EHH5R/T5wI9Osn43TdRN10dehgTnag0qlYUY5MAPXyfQJI/vXHP8pensnJwNGjcoF6/33dUZWc2ooqPl6WuyFyupgY4IUXZAhG9erAggVSvS5k5S1HYtU9dKoCeOqU3jj8jgkg+VrdulL5u/FG2ev01ltl5qIbBA9E9/pah+QtKSnAokXABRcAO3YAffu6ZxgGJ4CEjl3AzsAEkHwvJkZmNI4ZIwO8b70V+PRT3VEVj5UIcrMWLWQtz7ZtZVxg374yvs7JsrNl6SmA7S4UTACdgQkgEaSC9tprsjxBbi5w880yaN3JmACS29WqBUybJmtY7t4tlcHDh3VHVbg1a2TiSrVqQLNmuqNxL44BdAYmgET/ExEBvPWWJH85OcANNzi3W+rgQbNa0qWL3liIQlGnDjBjhkzO2roVuO46GY7hROqmq2tXbmEWCo4BdAb+ChMFiYiQpWK6d5edDa64AnDils/qQtSyJbeiIveLjwe+/x6oVEmSwQcf1B1RwdT4P866Dw27gJ2BCSDReaKjga++AhISZMuiESNkbKCTsPuXvKZtW+Cjj+T8tdcK32lEJ7Y7azABdAYmgEQFiI8HvvkGiIoCvv4a+PBD3RHlxQsRedFVVwFPPCHno0fLTiJOcfw4sHatnHftqjcWt2MC6AxMAIkK0a0b8Mwzcn733cDOnXrjUbgVFXnZk09KF2t6uszIz83VHZFITZW2V7++3CBS2alJIBwDqBcTQKIiPPSQjAfMyJCuYCdcjLZuBY4cka2o2rfXHQ2RtcqVk4p7TIzMEH7rLd0RCd50WYcVQGdgAkhUhMhIWRi6YkUZnP6f/+iOyByIzq2oyKuaNweef17OH37YGV3BXADaOsEJoNPGV/sJE0CiYjRrBvztb3L+17/KtnE6LVokR16IyMv+/Gfgooukm/Chh/TGYhhsd1ZSCWBuriyuTXowASQqgdGjgdatgUOHgHHj9MaycKEce/TQGweRnSIigDfekOOkScCsWfpi2bUL2LtXegQ4ASR0KgEE2A2sExNAohKIigJef13O//lP4Lff9MRx+jSwcqWcd++uJwaicElKAkaNkvO779a3QLS66UpKkrUKKTTly5sLaXMiiD5MAIlKqF8/4JprZJeQ++7TE8OyZXIRrFsXaNhQTwxE4fTMM0DNmnLT9e67emJQCSBvuqwRCHAiiBMwASQqhZdekrvX6dOBmTPD//7BF6JAIPzvTxRusbHmckz/9396EgYmgNZjAqgfE0CiUmjYEBg5Us6feCL8M9h4ISI/uuMOaXv79gFvvx3e9z5zBlixQs7Z7qzDBFA/JoBEpfTXvwIVKgDz5wNTpoTvfQ2DCSD5U/ny5g4hEyYAJ0+G771TU2Wmau3aQOPG4Xtfr1MJIMcA6sMEkKiU4uNliQogvFXAbduA/ftlodzOncPznkROccstQNOmwIEDwD/+Eb73XbBAjhx2YS21GwgrgPowASQqg0cekT9gS5cCP/0UnvdU1b9OnfIuo0DkB1FRsk0cIItEnzgRnvflskv2YBewfkwAicqgdm2zCvjii+F5T3b/kt8NHQpccIFshfj++/a/H4dd2IcJoH5MAInK6J57pDt21ixg+XL7348XIvK7yEhzCaZXX5Ulmey0YweQlibtvEsXe9/Lb5gA6scEkKiMEhOB666T85dftve9Tp4EVq2ScyaA5GfDhgE1agBbtgDff2/ve6mbrg4dOOzCamoMICeB6MMEkCgEDzwgx0mTgN277XufZcuk2pGQANSvb9/7EDldpUrAXXfJ+Usv2fterLrbhxVA/ZgAEoWgUyegTx/ZneONN+x7Hy4ATWQaM0aWhpk/H1i82L73YQJoHyaA+jEBJAqRqgL+61/2/THjTEQiU3w8cNNNcq726LbaqVPmvttsd9ZjAqgfE0CiEF12GdCoEXDsGPDll9a/vmHkXYuMiIDRo+X41VfA4cPWv77adzs+HmjQwPrX9zsuBK0fE0CiEEVEALffLuf/+pf1r79lC3DokHR5depk/esTuVHnzkDHjkBmJvDRR9a/Podd2IsLQevHBJDIAiNGyBIV8+YB69ZZ+9rBC0BHR1v72kRuFQjIHsEA8O671u/Iw/F/9mIXsH5MAIkskJAADB4s5+++a+1r80JEVLCbbpJK0tq15jAJK3ABaPsxAdSPCSCRRUaOlOMHHwBnzlj3urwQERWsWjXghhvk3MrhF9u2yZ7DUVHcd9suTAD1YwJIZJEBA2SNviNHgG++seY1T5wAVq+WcyaARPmpbuDPPwfS0615TXXT1bEjUKGCNa9JeXESiH5MAIksEhkJDB8u5x9/bM1rLl0K5OZKYpmYaM1rEnlJcjLQurVU3b/6yprX5LJL9uMkEP2YABJZaOhQOf76K3DwYOivx+5foqIFAma7++QTa16T7c5+7ALWjwkgkYVatJAxQzk50iUVqrlz5dizZ+ivReRValHomTOBPXtCe62MDHMBaLY7+zAB1I8JIJHFrKpG5OTIVlcAcNFFob0WkZc1aiTJmmEAn30W2mstWCDDLho3BurVsyQ8KgDHAOrHBJDIYjfcIItDL1wIbN1a9tdZtQo4fhyoWhVo3966+Ii8yKobL1V17907tNehonEMoH5MAIksFh8P9O0r5//9b9lfJ7j7NzIy9LiIvOzaa4Fy5YAVK0JbjF21O1bd7cUuYP2YABLZILgaUdYdCubMkSMvRETFq1ULGDhQzstaBTxzBli8WM7Z7uwVnABavYsLlQwTQCIbXHWV7N27fn3ZqhGGwa4ootJSi0KXdTmYpUuBrCygTh2gWTPr4qL8VAIIWLtwPpUcE0AiG1SrBqSkyHlZLkYbN8oyMtHRQJcu1sZG5FWDB8vuHevXy/ZwpRXc/RsIWBsb5RWcALIbWA8mgEQ2ueYaOX79dem/VnX/XnihJIFEVLxq1YBLL5XzUNodq+72i4qSMZsAE0BdmAAS2WTIEJm8sXJl6WcDcyA6UdlcfbUcS1t5z8mRJWAAtrtw4UQQvZgAEtmkVi3g4ovlvLTVCCaARGVzxRWyDFNpb7yCl11q18628CgIE0C9mAAS2UhVI0qTAO7aBWzfLtVDbkVFVDrBN17ffFPyr1M3Xb16cdmlcOFi0HoxASSy0VVXyXHhwpJvUaUuRB07AlWq2BMXkZep8bel6QbmskvhxwqgXkwAiWyUkGBW8b79tmRfw+5fotBceaUcFy4E9u0r/vnByy6x3YUPdwPRiwkgkc3UxWjy5JI9n+v/EYWmXj2gWzc5/+mn4p+vll2qUIHLLoUTK4B6MQEkstnll8txxgzg5Mmin3v4MPD773Leq5e9cRF5mWp3P/5Y/HNV929yMpddCieOAdSLCSCRzVq3Bho2BDIzJQksyrx5cmzVSgazE1HZqARw6lRpe0Vh968erADqxQSQyGaBgHkxKq4bmN2/RNbo2BGoW1eq7qrCVxi2Oz2YAOrlywRw+/btuO2229C4cWPExMSgadOmGDduHLKysnSHRh41eLAcJ08ueuNzzkQkskZERMluvLjskj6cBKKXLxPA9evXIzc3F++88w5+//13vPLKK3j77bfx17/+VXdo5FF9+sjd7u7dwOrVBT/nxAlg+XI5ZwJIFLrgcYCF3XgFL7tUuXJ44iLBCqBe5XQHoMPAgQMxcODAc/9u0qQJNmzYgLfeegsvvviixsjIq2JigH795EI0eTKQlJT/OYsWyXZUDRsCDRqEP0Yir0lJkT1nt2yRmb4tWuR/Drt/9eEkEL18WQEsSHp6OmrWrFnkczIzM5GRkZHng6ikiuuOYvcvkbWqVDF3BWG7cx5WAPViAghg8+bNeOONN3DnnXcW+bzx48ejWrVq5z7q168fpgjJC1QCuGgRcPRo/s/PnClHdcEiotCpdvfzz/k/t38/sHatnDMBDD+OAdTLUwngo48+ikAgUOTH+vXr83zNnj17MHDgQFx77bW44447inz9sWPHIj09/dzHrl277Px2yGPq15flXXJz8y8Hc/IksHixnPftG/7YiLxqwAA5zp2bP9GYNUuOHToAsbHhjIoAVgB189QYwAceeADDhw8v8jlNmjQ5d753715ccskl6NGjB/71r38V+/rR0dGI5iqhFIL+/YF164ApU8z9SgFg/nwgO1vG/jVurC8+Iq9p2RJITJQJWHPnShtU1I3YJZfoic3vOAZQL08lgHFxcYiLiyvRc/fs2YNLLrkEnTt3xsSJExER4aliKDnUpZcCr70mCaBhyBqBgNn927ev+RgRhS4QkHY3caK0u+AEMLjdUfixAqiXL7OePXv2oE+fPmjQoAFefPFFHDx4EGlpaUhLS9MdGnncxRfLrMTt22VmosJKBJF9VNI3dar52O7dwKZNsl4gx//pwQRQL09VAEtq6tSp2Lx5MzZv3ozExMQ8nzOKWqWXKESVKwM9egCzZ0s14oILgPR0YNky+TwTQCLr9esnx9WrgbQ02SFEVf86dwaqVdMXm59xEohevqwADh8+HIZhFPhBZLfzqxFz58rEkAsukIkiRGStuDigUyc5nzZNjuz+1Y8VQL18mQAS6XTppXKcMUMmfrD7l8h+qt1NmSJHtjv9OAlEL192ARPp1KkTUKOGrAV41VXA0qXyOCsRRPbp3x/4+9+Bb78Frr0W2LEDKFcO6NVLd2T+pRLAffuAP/2p8OcNHFj056lsmAAShVlkJFCrliSAwbsTdOumLyYir+vRQ2YEHz8OfPmlPFatGlCpkt64/Cw7W46nTwOffFL48/bsYQJoByaARBpkZsoxNlYmgZw9K7OCg5apJCILbdwoSy9FRMgN2IEDwJkzsv92ZKTu6PypQgU5RkQAdeoU/rz27cMTj99wDCBRmG3fDuzcKRedLVuAm26Sx9WgdCKynpr80b+/VJSqVpUdeFau1BqWr2VlyTExEdi7t/CP117TG6dXMQEkCjN1IUpOli4oNQidCSCRfdSs+0svlbF/vXvLv9nu9DlzRo7cYEsPJoBEYRZ8IQLMBHDpUhmfRETWysyUtTeB/O2OCaA+KgFUXcEUXkwAicIoNxeYPl3O1YWoYUPZ/zcnB5g3T19sRF61YIFMNKhTB2jbVh5TCeDcuTIGl8JPjYVmAqgHE0CiMFq5Ejh8GKhSJe+s3z595DhrloagiDxODbtISTH32k5KkuWYjh8Hli/XF5ufsQtYLyaARGGkun/79JE9gRV2RxHZ5/xhF4DMPL34Yjlnu9ODXcB6MQEkCqOCLkSAmQCmpsqyMERkjSNHzL22U1Lyfo43XnqxC1gvJoBEYXL6tDnG7/wEMDFR9gLOzZUxSURkjRkzZP2/1q2BevXyfk4lgPPmmYsSU/iwAqgXE0CiMJk1S+54ExOBFi3yf15djDgOkMg6v/wix/NvugCgTRtZFPrkSbNKSOHDMYB6MQEkChN1IRo0yByIHkytS8YKIJE1DCNvuztfRARw0UVyznYXfqwA6sUEkChMfv5ZjgVdiADzQpSaCpw4EZ6YiLxszRrZ9SMmxpzwcT7V7ubMCV9cJDgGUC8mgERhsGULsGmT7EDQr1/Bz2nYEKhfX9YDXLQovPEReZG66brkksKTDJUAzp8vY3ApfNgFrBcTQKIwUN1QPXvKHqSFYTcwkXVUuxs4sPDndOgAVK4MHDsmFUMKH3YB68UEkCgMiuv+VTgeicgax4+bs+6LanflygE9esg52114sQtYLyaARDY7c0aWogBKngAuWgRkZdkbF5GXTZ8uS7tccIF8FIU3XnqwAqgXE0Aim82ZI2sAJiQA7doV/dxWrYDYWHk+t6ciKjtVdS+q+1cJTgANw76YKC+OAdSLCSCRzYIvRAUt/xIsEAB69ZJzzkokKpviln85X7dusjXj3r3A1q32xkYmdgHrxQSQyEaGAfzwg5xfdlnJvobdUUSh+e03YOdOSSz69Cn++TExQNeucs52Fz7sAtaLCSCRjdavlyVgypcH+vcv2deomcDz5nFZCqKyUDddKSlAxYol+xrOwA8/dgHrxQSQyEbqQnTJJUCVKiX7mo4d5aJ17Biwbp1toRF51vffy3HIkJJ/jRp6MX++9fFQwVgB1IsJIJGNVAL4hz+U/GvKlTO7oxYutD4mIi9LSwOWLJHzwYNL/nUXXijHDRuAI0esj4vy4xhAvZgAEtnk0CFgwQI5L82FCAC6d5cjE0Ci0pk8WY5dugDx8SX/uthYoHlzOedOPOHBLmC9mAAS2eSnn2QMX/v2ss1baTABJCqbslTdFba78GIXsF5MAIlsoi5EpRmHpKgL0bp1wNGj1sVE5GWnTwNTp8p5KO2OCWB4sAtYLyaARDbIzDTXIStLJSIuzty9YPFi6+Ii8rIZM4BTp4D69YGkpNJ/vUoAFy8GcnKsjY3yYwVQLyaARDaYMQM4cQKoW1fGIpWFuhipcYREVLRvv5Xj4MHFL7pekDZtZLb+iRPAmjWWhkYF4BhAvZgAEtng66/leNVVQEQZWxm7o4hK7uxZMwG85pqyvUZkJJCcLOdsd/ZjBVAvJoBEFgu+EF19ddlfh91RRCU3b57MvK9Z01zUuSx44xUeZ8+aC90zAdSDCSCRxYIvRBdfXPbXadsWqFQJOH4cWLvWuviIvOirr+R4xRWyr29ZMQEMD1X9A9gFrAsTQCKLWXUhKldONqkHeDEiKkpurjnsoqzdv4paEHrTJrmRI3swAdSPCSCRhYIvRKF0/yqsRhAVb8kSYO9emcCRkhLaa9WoAbRsKedcENo+agmYqCgZe0nhxwSQyELBF6JLLw399ZgAEhVPVd0HD7ammsR2Zz/OANaPCSCRhay+EAXvT3r4cOivR+Q1hmFd96/CBNB+nAGsHxNAIovk5gKffy7nVl2IatXi/qRERVm2DNi6FahYERg40JrXDJ6Bf/asNa9JeXEXEP2YABJZZOFCYOdO6f697DLrXpfVCKLCffqpHIcMkVnzVmjdGqhaVXYV+e03a16T8mIFUD8mgEQWUReiq64CYmKse10mgEQFy8kBJk2S8xtvtO51IyK4ILTdOAZQPyaARBY4exb44gs5v+EGa19bJYBLlnBBaKJg8+bJpKvq1YEBA6x9bd542YsVQP2YABJZYMYM4MABIDY29GUozsf9SYkKpqruV19tfSWJCaC9OAZQPyaARBZQF6Jrrw1t8eeCREZyQWii82VnA19+KedWdv8qqgt4yxa5uSNrsQtYPyaARCE6c8ZchsKOCxHAagTR+aZOlaWR6tQBLrnE+tevUQNo1UrOOQPfeuwC1o8JIFGIfvwRyMgAEhOBXr3seQ8mgER5ffSRHK+7zr6dJNju7MMuYP2YABKFaOJEOd58s8wetAP3JyUyHTsGfPONnA8bZt/79OghxwUL7HsPv2IFUD8mgEQh2LcP+OUXOR8+3L73qVnTXBB66VL73ofIDT77TCpI7doBnTrZ9z5qHGBqKmfgW41jAPVjAkgUgo8/lh1AevQwEzS7qIkgS5bY+z5ETvf++3IcPhwIBOx7n1atZHHpkyeBdevsex8/YhewfkwAicrIMMzuXzurfwoTQCJJxBYvlnF/Q4fa+16RkUCXLnLOdmctdgHrxwSQqIyWLpWLUUyMDES3W3ACaBj2vx+RE6nq32WXyQxgu/HGyx7sAtaPCSBRGanq39VXA9Wq2f9+SUmyxuChQ8D27fa/H5HTnD1rzv4dMSI878kE0B6sAOrn+wQwMzMTHTp0QCAQwMqVK3WHQy5x/LiM/wPCdyGqUEGSQIAXI/Kn77+XiVdxccDll4fnPVUCuHo1cPp0eN7TDzgGUD/fJ4APP/wwEhISdIdBLvPJJ7I1W/PmQN++4XtfViPIz956S4633QaULx+e96xfX7qac3KAFSvC855+wC5g/XydAP7888+YMmUKXnzxRd2hkIsYhnkhuusue2chnk8lgFwKhvxm40Zg2jRpb3feGb73DQTY7uzALmD9fJsA7t+/H3fccQc++ugjVKxYsURfk5mZiYyMjDwf5D8LFkh3UEyMvYvQFkRdiFJTZTwUkV+8/bYcL7sMaNQovO/Nyrv12AWsny8TQMMwMHz4cIwaNQpd1Bz/Ehg/fjyqVat27qN+/fo2RklOpap/N9wg+4WGU4sWQJUqwKlTwNq14X1vIl1OnzZn/951V/jfnwmg9VgB1M9TCeCjjz6KQCBQ5Mf69evxxhtv4Pjx4xg7dmypXn/s2LFIT08/97Fr1y6bvhNyqoMHgS++kPM//zn87x8RAXTtKue8GJFfTJoEHD0qlb+BA8P//qpOsHkzcORI+N/fizgGUL9yugOw0gMPPIDhxazI26RJE8yYMQMLFy5E9Hm/eV26dMHQoUPxwQcfFPi10dHR+b6G/OWf/wSysiQJK0Xx2FLdugEzZkgCePvtemIgChfDAF5+Wc5HjZLFmcOtZk2gWTPZi3vpUmDAgPDH4DWsAOrnqQQwLi4OcXFxxT7v9ddfx7PPPnvu33v37sWAAQMwadIkJKvNH4nOc/o08I9/yPkDD+iLg91R5CdTpwK//SZbso0cqS+Obt0kAVyyhAmgFTgGUD9PJYAl1aBBgzz/rly5MgCgadOmSExM1BESucCHH8oizI0aAddcoy8O1QW8Zo3sUVqpkr5YiOymFmm4447wj7kN1rWrLP/EGy9rsAtYP0+NASSyS04O8NJLcn7ffUA5jbdO9eoB8fFcl4y8b+VKqQBGRgL33KM3Fm7FaC12AevHBBBAo0aNYBgGOnTooDsUcqgffpDun+rVgVtv1RtL8LpkrEaQl6mbrmuvDf/SL+fr0EFu/A4cAHbu1BuLF7ALWD8mgETFMAxgwgQ5v+su4H8jBrRiAkhet20b8Nlncq5zzK0SEwO0by/nbHehYxewfkwAiYrx88/A4sVyAbj7bt3RCCaA5HXPPiuLnV96qb4Z9+dju7MOu4D1YwJIVATDAJ58Us7HjAHq1tUbj6IuiNu2ycQUIi/ZvBlQq3E984zeWIJxSzhr5OSYOxkxAdSHCSBREX74QbZdq1QJeOgh3dGYqleXXUEAXozIe/7v/yRJGDQIuPBC3dGYVAK4bJnER2Wjxv8BTAB1YgJIVAjDAMaNk/O//AUowRKTYcXuKPKijRuBjz+W86ef1hvL+Vq2lDHAJ08C69bpjsa9VPcvwDGAOjEBJCrEpEmyDEXlysCDD+qOJj8mgORFjz0G5OYCgweba146RWSkOfyC7a7sVAIYGal3SS2/YwJIVIBTp4CHH5bzhx8GYmP1xlMQrktGXjN7NvDll7Ln9d/+pjuagvHGK3RcAsYZmAASFeDFF4Fdu4AGDZxZ/QOApCQgKkomgWzbpjsaotDk5AD33ivnd9xhLrniNCoBXLxYbxxuxiVgnIEJINF5du8G/v53OX/+eVn+xYmioyUJBDgRhNxv4kQZclGtmkwCcSrVLf3bb7I/OJUel4BxBiaAROd56CHpAu7VC7juOt3RFI3LUpAXHDkiY/8AmXjltAlXwerXB+rUkYrlypW6o3EndgE7AxNAoiDffSe7D0REAK++KtuuOZmqRjABJDe77z7ZYq1lS2D0aN3RFC0QYLsLFSuAzsAEkOh/jhwBRo2S84ceAjp31htPSagKYGoq1yUjd5o8GfjwQ0ms3nsPKF9ed0TF40SQ0HAMoDMwAST6n/vuA9LSpArx1FO6oymZFi24Lhm517FjwMiRcn7//UD37lrDKTFWAEPDLmBnYAJIBOCLL6QKEREhg9Hd8ocpMtKsVLIaQW5iGFJx37sXaNbM2RM/zqfWAty4UZJYKh12ATsDE0DyvbVrgREj5Pzhh5219VRJcCIIudFrr8li6+XKAe+/79zZ9gWpVQto0kTOly3TG4sbsQvYGZgAkq9lZABXXSVdqH37uqsKoajuKFYAyS3mzDHX13zpJaBHD73xlAXbXdmxAugMTADJt7KzgaFDpRsnMRH49FN3bkukKoCrV+fdY5PIibZskeWVcnKAm26SfbbdiJX3suMYQGdgAki+lJMD3Hwz8OOP0g3x5ZdA7dq6oyqbBg1k3bSzZ4FVq3RHQ1S4nTul0r5/v+z08a9/OX+ppcJwIkjZsQvYGZgAku/k5gK33y7jj6KigK+/BpKTdUdVdoEAl6Ug59u3D+jXT5LA5s2BX38FKlXSHVXZdeokk8b27JGJLFRy7AJ2BiaA5CsZGcCVV8qg88hI6fa97DLdUYWO1QhysuXL5SZr82agUSNg2jSgbl3dUYWmUiWgTRs5Z7srHXYBOwMTQPKNDRtkhu8PP0jXwyefANdcozsqa3BAOjnVp5/Ktoq7dknlb/p02U7NC9juyoYVQGdgAkied+KE7DOalCSLJderB8ydC1x/ve7IrKMuRBs2AOnpemMhAuR38fLLZaLH6dPAoEHA4sXm8ilewIkgZcMxgM7ABJA8yTBkQsSjj8ois889J90O/fvLul0qYfKKuDjpWgO4Lhnpk5UlE6uGDgXatgV++knG2T7xhFTeq1fXHaG1godeGIbeWNyEFUBncOGiF0SmU6eAl18GFi6Uf+fkyGDznTvzrtDfpIk8b8gQ9846LE63bsD27XIx6tdPdzTkZZs3A+PHy9aJgFT4du2SDzW+CwAGD5Z1/po31xOn3dq1kyrWsWPyf9Ksme6InCEnR7bW3LKl4M+vXClHJoB6MQEk1/ruO+Cee4AdOwr+fHS0dEHdeCPwhz94v7uha1fg88/ZHUX2OX0amDAB+Pvf8yZ6werWleEVN91kdpF6VVQU0LEjsGiRtDsmgGLuXOCNN4p/XmKi/bFQ4ZgAkiu99x5w221y3qCB7CpQpYpU9+rWlUHmjRoBFStqDTOsuBQM2ckwgD/+Ubp1AeDSS+XmKhAAypeXi3n9+tIeIyP1xhpO3bpJArhkiSS9JIvrA5Ic3313wc+pVQsYODB8MVF+TADJdY4elT17AeDPfwaef97d64lZRa1Ltnu3dIPHx+uOiLzk++8l+YuOBj7+WGbQe3U4RWlwCab8Nm+W40UXAcOHaw2FisBJIOQ6Tz0FHD4sa3C99hqTP6VyZaBVKznnxYislJkJ3H+/nD/wgFQCmfwJlQAuXy7bS5KZAF5wgd44qGhMAMlV1q4F3nxTzl991Z1799qJy1KQHV59Fdi6VarKY8fqjsZZmjUDqlWTma2//647GmdgAugOTADJVe6/X2aYXXEFkJKiOxrn4cK0ZLX9+4Fnn5Xzv/9dKs1kiogAunSRc7Y7GSvKBNAdmACSa6xfL/uHRkYCL76oOxpnUhXAZcu4LhlZ4z//kcXUu3SR9f0oP1beTfv2yWzxyEhzbVJyJiaA5BoTJ8px0CDeWRamXTuZkXnkiHTZEYXCMMx2N3q0VLsoP04EManqX6NGskwOORebM7nC2bPAhx/K+a236o3FycqXl6UXAHZHUejmzZMLeuXKMvGDCqYqgGvWyOL0fsbuX/dgAkiu8OuvsutArVqyuDMVjtUIsoqq/l13Hcf+FaVePZkgk5MDrFihOxq9mAC6BxNAcoX33pPjn/4kVS4qHCeCkBVOnJCdZQBgxAi9sbgB251gAugeTADJ8Q4dko3kAXb/loTqjlq+XLrOicriiy+AkydlmZOePXVH43ycCCKYALoHE0ByvE8/lQVWO3eWSQ5UtObNgapVZSbe2rW6oyG3+uADOY4YwUWfS4JDL7gEjNswASTH+/ZbOXKfzZLhumQUqkOHgLlz5ZztrmRUm9u8WWbh+9GBA8Dx43LD0Lix7mioOEwAydGOHgVmz5bzK67QG4ubsBpBofjpJyA3F0hKAho21B2NO9SsaVa9li3TG4suqvrXoIHsGU3OxgSQHO2XX2RmXZs2QNOmuqNxDzUeiRVAKovvv5cjb7pKx+/tjt2/7sIE0IFycuSDgO++k+OQIXrjcBtVAfztNxkLSFRSZ87IjRfAdldafq+8MwF0FyaADnTvvVI+b9YMuPNO6QL147ZeWVnAzz/LOS9EpZOYCNSp4791ydatAx57DOjQAahUCahQoXQfVapIFeeZZ4Dt23V/N3rMnCmzfxMSgE6ddEfjLsFLwfjlb7ZhyN/p4cOB11+Xx+rW1RoSlRATQAeaNk0u3Js3A//6F9CnD9C7N5Caqjuy8JozB8jIAGrXNrtWqGQCAX8tS3HgADBypAwVeO45YNUq2ZEhM7N0HydOyP/XuHEy5OCee2RChJ+o7t8hQzj7t7Q6dpQ9cNPSgD17dEdjv8WLZXWGyy6TWeMZGfK4X5JftyunOwDKb8YM2YD9q6+kCy8nR7Zk6tIFeOQRucD5YU9OdSH6wx/88f1arWtXWT/R6wng5MkyU1VdfACgSRNg8ODST2BYuxaYMgXYtUsmQbz+ulzYPv8c6N/f2ridyDDyJoBUOhUrAm3byg3I0qVSifei3FzgwQeBV14xHytXThbpP3WKM4DdggmgA8XHA48/Lh+bNgE9ephViL//Hdi5U7Zo8vIsK16IQueHAelffCHJn1rwOjJSfm8uuyy015040Vx0PD1dth/897+BYcNCe12nW74c2LtXus8vuUR3NO7UrZskgEuWAFddpTsa6509C9xxB/D+++ZjjRoBixbJkKXvvpPhO+R8rKs4XLNmUhGsWVP+HQjIwsiXXebtwf3r1wM7dkiSm5KiOxp3UuuSbdoky+l4zYcfAjfckHe3kw8/DD35A2TxYzWeCZD3GD4ceOml0F/bydSY20svlTGRVHpengiSnQ0MHSrJnxoekJAg40br1DGLEkwA3YEJoAu0ayddeYGAVMYqVpSk8LbbvDvWYupUOV50kXy/VHqxsebSOV5bl2z+fKnQ5eaaF53x461dtPgvf5FuLgCoVk2ODz4oVUevUu3OD93ddgkee5ubqzcWqz3+uAyHKFdOrj3lywO//ioVQMDcp50JoDswAXSJHj3MDdkTE6UBfvqpjAf0InUhuvRSvXG4nRerEYcOSeUvJwdo2VImb7RqBTzwgPXvNW6cVDjS0839cIcN8+aErOPHgYUL5ZwJYNm1aQPExMiY1E2bdEdjncmTgeefl/O4ODnef7+MeVSYALoLE0AXee45WaZi40az0vH44+ZWaV6RlSVdCgAvRKEKXpbCC3JzJQHbvVsGmqt1x156CYiKsv79Klc2L3orVsi4uNOnZYHktDTr30+n2bOli69xYy66Hopy5WQ2MOCddrdrF3DLLXJ+0UXAvn2yOsPYsXmfxwTQXZgAukidOsCTT8r5r78Cd90l57fd5q2L0aJFsg5ZXBzQvr3uaNzNa0vB/POfsk1ZhQrS7XT2LDBwIDBokH3vedNNQPfuMruxbl2gdWtZ4mPkSG8NwWD3r3W81O4MQ5K/I0dka8DVq+Xxv/0NqFo173NVApiZGd4YqWx8nQBOnjwZycnJiImJQY0aNXDllVfqDqlYd98tS1zs3y+l944dpWHeead3LkZTpsjx0ku5/EuoOnaU/8O9e92/LtnBg1LxBmQs3syZ8r3ZPTEjEDCXu/jiC5kcEhUl43I/+sje9w6n4HZHofHS0IvPPwdmzZJu7b59ZThE+/bmkKRgrAC6i28vr1999RVuvvlmjBgxAqtWrcL8+fNxk5UjyG1SvrwMTgekGvL++3Ix+v5771yMOP7POpUqmWN03H4xeuwxufh06mQuizRkiFTk7JacDPTqJRXHOXOAp5+Wx+++W7qj3W73bpl5HxEhF3kKjaoArljh7mTo5ElzItTDD0syCAAPPSRLLp2PCaC7+DIBPHv2LO655x688MILGDVqFJo3b47WrVvjuuuu0x1aiQwfLjNjf/9dqn/BF6N9+7SGFrIjR8wZq0wAreGFakRqqqzDB8hYWHWzM2ZM+GJQ7/XOO7JDSLdukpB6ofqubrq6dgVq1NAbixc0bSr/j5mZwJo1uqMpuwkTzPG2zZtLL0Lt2sC11xb8fC4D4y6+TACXL1+OPXv2ICIiAh07dkR8fDwGDRqENS5pqdWrmwNy33hD7sa6dJGL0V//qjW0kM2YIQP9W7cG6tXTHY03uH1BaMOQmxvDAP70J2DDBqlMtGoV3mrV1VfLjOD9+2Xi1QcfSPX9p5+AH38MXxx2YPevtQIB90/A2rYNeOEFOX/5ZbnxAWTsa2GbELAC6C6+TAC3bt0KAHjqqafw+OOP48cff0SNGjXQp08fHDlypNCvy8zMREZGRp4PXVQ14ttvZXzXP/4h/37/fff+wQGA6dPlyAuRddSFaNkyd65L9uuvwIIFUvUePx548015fMyY8O5VGxUFjBol5//4hyxBc9998u/77nPvwHfDkBsvgO3OSm6vvD/3nPxO9+snFcA5c6Tb9847C/8aJoDu4qkE8NFHH0UgECjyY/369cj931XwsccewzXXXIPOnTtj4sSJCAQC+KKIVV7Hjx+PatWqnfuoX79+uL61fNq0kSUpcnPlziw52awK3n23Oy/0gLn8C8chWadtW5k1e+yYuWyKWxgG8H//J+d33SV79W7cKLMP1e97ON1xhySCCxfKtmmPPy4zg7dsAV59NfzxWGHtWuDAARnkn5ysOxrvcHMFcOdOqXAD0v7++U85v/rqovc35ixgd/FUAvjAAw9g3bp1RX40adIE8fHxAIDWQaPHo6Oj0aRJE+zcubPQ1x87dizS09PPfezatcv276koahmYTz6RC+WECbJu2eLFwMcfaw2tTPbule69iAigd2/d0XhHVJRMnABkiR03mTVLqn/R0bLQ88SJ8viwYfK7Hm5168pFEJB2V6WKtDsAePZZd47BVTddPXt6e3/xcFPJ9O+/y6LQbvL887ImZN++QIcOsukAAIweXfTXsQLoLp5KAOPi4tCyZcsiP8qXL4/OnTsjOjoaGzZsOPe12dnZ2L59Oxo2bFjo60dHR6Nq1ap5PnQaPFgugjt2SNIXH28uk/H44+67C5s1S44dO8o4R7JO9+5yVDs9uMWzz8rx9tul6vf99/LvP/1JX0w33ijHSZOk0n7zzTLO8sQJs1rpJioBvOQSvXF4Td26slalYbirCrhvnznh6vHHZYzr8eNA/fqyCHRRmAC6i6cSwJKqWrUqRo0ahXHjxmHKlCnYsGED7vpfOe3awqY3OVBMjOxIAMjFCJDu33r1ZOV2NWjXLVQC2KePzii8yY0J4IIFMjYtKkqWoPj+e1mMuWlTs3tNh4EDZW/gPXskxogIc7eQd98F/jfE2BVyc2UHEIAJoB3c2O5eekmKBz16yN9iVf274Ybi12XlLGB38WUCCAAvvPACbrjhBtx8883o2rUrduzYgRkzZqCGy9ZAuP56OX7+ufwxj4kxdwv529+kKuEWrETYR12IfvtN7ubd4OWX5XjLLUCDBnkvROGc/HG+6GhArRmvbrwuvlh20Dh71lyWyQ3WrAEOH5b1Irt00R2N97gtAczIMAsHjz8ufyvUDHdV+S4KK4Du4tsEMCoqCi+++CL279+PjIwMTJ06FW3atNEdVqn17y/ViL17gXnz5LERI6RKcuCA7FrgBrt3ywSFyMjiuxmo9BISJInKzXXHrMTdu809ru+9V9aH/OUX+XdJLkR2UzdeX3wB5OTI+d/+JsePPpJxX26gbrp69bJnL2W/UwngokXumJj30UdSNGjRQird334r1cAWLWQsYHGYALqLbxNAr4iONgelq2pEVBTwzDNy/sILwNGjemIrDXUh6tw5//6SZA03VSPeeUcSq4svllnMX38tg9LbtZMZ8LqlpAA1a8qagKoLtUsXaYuGYVbhnY5Vd3slJUmvzNGjMnvdyQzDXGJp9Gipsquq+403lqzqzlnA7sIE0ANUNeLLL6ULCpBusrZtZekPtUagk/FCZD+3JIBZWTKWDjBnHQZfiJwgKgq45ho5VzdegEwCCQQkYXX6uvI5ORz/Z7eoKLNr3entbtYsYN06GQ5wyy2y97baIaak7Y4VQHdhAugBffsCsbHS5Tt/vjwWEWHOCH71VeeP+1IJICeA2Cc4AXTy1mVffSWVtYQEGWt38KA5QeiGG3RGlpe68frmG7N7r3Vr4I9/lPPnntMTV0mtWiU3iFWqmMsEkfVUu1uwQG8cxVHVv5tvlmFF334rNwmdOsk2cCXBBNBdmAB6QFQUcNllcv7TT+bjf/yjNNwjR4C339YTW0ls3y4f5crJWCSyR4cOsiD0kSPO7o5SF6I77zS3WsvNlfgbN9YaWh69e8twhYMHzf2rAeCxx+Q4aZKz/59VUn3RRdL2yB5uqLwHj7lVVfcffpDjVVeV/HWYALoLE0CPKCgBjIwExo6V85deAk6fDn9cJaEuRF276lnc1y/Kl5cxloBzL0Zr10oVu1w52XUDMNf+GzJEX1wFiYqSSVhA3naXlCRrdObmmotEOxGHXYSHSgDXrpX92p1o4kSp9vXuLUOHTp0yu39L0+64DIy7MAH0iAEDpNt3zRrZxkcZOhRo2FC61NTink7DC1H4OL0aobafuvxyWdg8MxOYMkUe+8Mf9MVVmMsvl+PkyXkfV1XAjz6S6rbTnD0re7sCbHd2q1NHKteGIQv2O41hmO3u9tvlOH06cOaMrBzQrl3JX4sVQHdhAugRNWrIwp1A3mpEVBTwyCNy/uKL5iQRpzAMjv8LJycngGfPSsIEyFZvgFSHT5yQ8YBOHKc2cKAcly2TmyzlwguBfv3ke3rlFT2xFWXFClnzrXr1ki3vQaFxcrubP1/2sq5c2VxRQnX/DhlSujU3OQvYXZgAekhB3cAAMHw4EBcnlcEvvwx7WEXaulV2LYmKkr1IyV7qQrRmjfP2J502Tbahio01K2uq+3fw4OJ3IdChbl2zW12tU6ioG6///EfGXTqJuunq3VuGipC9nJwAqurfH/8oM4Bzc80EsLRVd1YA3cWBf1KprNRFU5XvlZgYYMwYOX/hBWfNAFXj/5KTgYoVtYbiC/HxMiTAifuTvv++HG+6SS4khpG3EuFUhXUDp6QA7dsDJ086b1tG1e7Y/RseTl0Q+tQpcxmj4cPlmJoKpKXJ7PCLLy7d66kEMDvbWdcZKhgTQA9p1072AT51ylzfS/nzn2UG6PLl+T+nE8f/hZ8TqxHHjpmzENWFaNUqqQ7HxMhSR06lKu9TpsiFTwkEgAcflPPXX3dOt1h2NjB3rpxz2EV4tG8vv8fp6cD69bqjMX37rSwR1qiRuQOTqroPGGBO6igplQACedsCORMTQA8JBMyL0fnViFq1ZIs4QMYCOkHw+D8mgOGjxoo6KQGcNEkSpLZtgY4d5TG1B2n//nLxdKouXaR9pafnX+vt+uvlpiwtDfjvf/XEd77UVBlXWbOmJCZkv6gooFs3OXdSu1NV92HDzCEWqt2VpeoenDCyG9j5mAB6zKBBcpw2Lf/n7rtPksTJk2XFd902bZI9jMuXl0HzFB5O7I5SydEtt5iDzn/9VY7qpsapIiOlWgLkb3flywP33CPnL73kjG4xddN18cXOHFfpVU6rvO/fL8OFAFn8GZAblZUrpQ2qCU6lEVwBZALofGz+HtOnjzTedetkQH2wZs1kZwUAePnlcEeWn7oQde/u7AqP1zhtf9I9e8wuSbXTR0aGeaFUa+05Wb9+cpwxI//nRo6U8VS//24mtTqx6q6H0xLAL7+UG8CuXYGmTeUxdQPTqZNMHCytyEjzpsIpQx6ocEwAPaZGDbMLTf2hD6bGJH34odzt6cSB6Ho4bX/SL76QyljPnkD9+vLYzJmyMG3z5jI+yenU7/CSJfm3XaxWzVzUWvfwi6wsc7tItrvwUr0ca9fKmFfdPvtMjsHbK6o1N0O56eJMYPdgAuhBasB8QdWIHj3kTjQry9xySweO/9PLSdUINQtR7a0LmJUyN1T/AElSGzeWdf/mzcv/+XvukerI9OmyBp8uS5fKJLFatWTfYgqf2rXNSpvuBaF37zZ/T6+9Vo65uUwA/YYJoAcVlQACwAMPyPGf/5QlKnRYv17GoFSoIEvAUHg5JQHcvl3GIgYCsg6ZYsWFKNyKancNGgDXXSfnL70UvpjOF7zoOsf/hZ9T2t0XX8ixVy+z6v7bb/I3uVIlM86yYALoHvwT4EG9esleqtu2ycf5rrwSaNJEFqdVs8DCTV2IevQo/VIDFDr1B/733/XuT/r553K8+GJZoxCQXQm2bJHfYTctU1LSG6/PPpPlbXRg1V0vpySAqvs3uOqubrr69AntbzITQPdgAuhBVaqYSw4UNA4wMhK4/345f+UVGWsVbrwQ6RW8P+miRfriUN2/weOQ1Cb0PXrI77JbqN/lFSsK3vmjc2d5Tk6OrAsYbpmZ5jI1bHd6BM/A1/F3F5CiwJIlUgG2o+qukkcmgM7HBNCjiqtGDB8uE0a2bDHXfQqX3FxOAHGCXr3kWNCYtXDYskUWJo+MNPcgBdzZ/QtIBbNVK0mqC1tsXd14vftu/skidlu0SHYIqlsXaNkyvO9Non17uanJyJAuVx1U92+fPvK7AMi4UDUTP9R2x/2A3YMJoEcFJ4AFrT1WqZIsTwGEf0mYtWuBQ4dk67euXcP73mRSK/+rP/zh9vXXcuzTx1xy4uxZc20ytyWAQPE3XpddJjOb09OBiRPDFxdg3nSppaIo/CIjzYXYdbe74Orf3LmSsNWvD7RoEdrrswvYPZgAelT37lKK37cP2LCh4OeMGSPjrObMkUpMuKju31698i4cSuGlEsDFi/XcrX/zjRyDq3+pqVIdqV5d1iJzm+ISwIgIc2Ho114Lbzdg8AQQ0kfnjdeePdLeAwFzTVjAvOlKSQn95oAJoHswAfSoChXMdacK+0OTmGjOTHzllfDEBfBC5BQtWshyIGfOSOIVTnv3mgPhgy9Ewb8bkZHhjckKvXvLce1a4PDhgp8zbJgMv9i6Ffjhh/DEdfq0+f/NYRd6BSeA4d4ZRu233b27OekKMNudFXtuMwF0DyaAHqb+0BQ1xuu+++T42WdyUbZbbq45PooXIr0CAXMcYLirEcEXooQE83FVOXPr70atWjIOEDAXXD5fpUrAnXfKebhuvBYulAtyQoLsCET6dO0qi7Gnpck42HBS3b/BVfdjx8weICvaHRNA92AC6GElGeTfpYs87+zZ8CwMvWqVzJCsXFlmRZJeurqjCroQBe9SYUUlQpeStLtwD78ITqw5/k+vmBhz7HM4292hQ+bN91VXmY/PmSM35s2bA/Xqhf4+nAXsHkwAPax7dxlztHVr0dU9VQV8+22ZDWYntddknz5yF0x6qQRw/ny5CITD4cPmhITgC9GSJfL7FxcHtGkTnljsUJIEsF698A6/UO0uJcX+96LilaR3xmo//CBjTjt0kHVgFauX5OIsYPdgAuhhVavKsgNA0X9orrhC1oQ7cgT46CN7Y+KFyFk6dpQuyWPHZFHocFAXoqQkc2sswKxSuX2WqkoAly2TsXeFCR5+sWePffEcOyZbwAFAv372vQ+VnI7Ke0FVd8D6YRfsAnYPJoAeV5I7zchI4O675fyVV+yrBGVmmn/wmAA6Q7ly5uK04boYqfF/wdU/wNqB6Do1bixj7bKzpapZmHANv5g9W9p0ixbmtl+kV8+ecpOzaZOMBbTbyZPmAuvB7e7QIWD1ajm3alIeE0D3YALocSVd7PfWW2WB0g0bgF9+sSeWhQulIlK3Ljeid5JwViPOnDEvREOGmI97aZZq8OSa4tqdqgK+8459wy9YdXee6tWBdu3kPBzdwFOnyg1448Z5h1eoMYFt2sjuQFZgAugeTAA9rmdPOa5aJeurFaZqVeD22+XcrjFJ6kLUr5+7u/i8Ri1dMnu2/ctSzJwpiU5iooxFUhYulAtUfLwMRne7kiaAwcMvPvzQnliC2x05R3C7s5va7ekPf8j7t9eOWfdMAN2DCaDH1asnF5jc3OI3IL/7bpk0Mm2aPdsUsRLhTBdeaC4avmmTve+l1r0bPDjvhSi4+9cLNwcqAVywoOjFniMjzYWhX33V+uEXu3cD69dLu+a6m86ifh5qQpRdcnPzJoDB7Bh2wQTQPZgA+kBJZ5w1amQOEH71VWtj4EB056pQwRwHqC4IdjCMwi9Ec+bI8eKL7Xv/cCrNnq+33ioVeDuGX6gdHrp0kcWnyTnU7/qaNcDBg/a9z7JlwP798jumqo6AvOe6dXKurhFWUMvAcBaw8zEB9IGSdkcB5pikTz4BDhywLoZZszgQ3cnCUY1YtQrYtUv2gA6uOGRmyvZUgLUXIp2C93wtrt1VqWLf8AtW3Z2rVi1zHKCd3cCq6j5gQN6tN9XvZevWEotVWAF0DyaAPqCqO0uXyozD4p7brZtclN96y7oYeCFytuAE0K5xgOpClJIiVUdl2TL5fYuLC30jeidR7W7RouKf+5e/WD/8wjDY7pwuHDdewcMugqlJX1bfdDEBdA8mgD7QurWU/0+eLH6tt0DArAK++abM2rRC8Gbj5DzJyZKUpaVJV6Qd1IXo/O5fdSHq1csb4/+U0iSAwcMvrKoCrlsnP8+YGDMWchY1+cKuoRc7d0rlPSICuOyyvJ9jAkhMAH0gIkKqekDJLkbXXCOzNA8eBD7+OPT350B05wseB2hHNSItzRwDevnleT+nLkTB45O8QLW5LVtKNsYrePjFvn2hv7+q/vXqlbfiSs6hfufXrrV2yI0yebIcu3fP28174gSwYkXeGKzCBNA9mAD6xIUXyrEkCWBUFHDvvXL+wgtFz2IsieCB6NWrh/ZaZB87u6PUhahrV1nqRcnJMff/9cr4P6V6daBVKzlXYxyL0r27fGRlAa+9Fvr7s/vX+WJjzd2a7BgHWFjVfeFCaXsNG1o/JpsJoHswAfQJVd0pbikYZeRIuYBt3Ah8911o780LkTuo7ig7xgEWdiFaswZITwcqV5at4bymNO0uEAAefVTO33pL/l/KKjvbTOTZ7pzNrm7gkyfNdf4KG3Zhx00XZwG7BxNAn0hOluOGDbLobHGqVAFGj5bzCRPKnhBwILp7dOsmXYX795vLQ1ghePePwgai9+gh29J5TWkq74D8/7RuLcvHvP122d936VLg+HGgZs28C26T86jKu9UJYPDuH6oSrdiZALIC6B5MAH0iNhZo1kzOi9qfNNjdd0tCsHRp2bsF167lQHS3iI42d45RlQMrzJhR8O4fgL0XIidQCeCSJSUbShERATz8sJy/+mrZJ2EF7/4Rwb/yjta7t1R/168H9u617nWDq+7Bk6uysswbEiaA/sY/DT5S2mpE7dqySC0A/P3vZXtPNf7voos4EN0NVJVWVeysUNjuH4bh/QSwdWuppp84ITdDJXHjjZIsp6UBH31UtvflrHv3qFkT6NxZzlXiHqrcXHPc7fndv6mpcmNRqxbQsqU17xeMCaB7MAH0kdKOAwSABx+URW1//dWcNVYa3IfUXS69VI4zZxa/ZmRJFLX7x9atMts1KsqcMes1kZHm91bSdle+PPDAA3L+/POln4R14oT5Xmx37qDanVUJ4NKlBe/+Adi/7BITQPdgAugjqgK4eHHJ9xxt3Bi47jo5f/750r0fB6K7T4cOUpE4frzkQwWKsmqVLAN0/u4fgHkh6tpVhgh4VWkr74DsDFKjBrB5M/DNN6V7v7lzpe01agQ0aVK6ryU91N/HadOsmYClbrrO3/0DsL/qzgTQPZgA+ki7dnIhTk+X8SYl9cgjcvz8c6nalNSiRZJIxMZyILpbREaaVSMruoEL2/0D8H73r6ISwNJU3itXlt1BABl+UZqk4Ndf5ZiS4q2Ftb2sRw+5Cdq3r/jF+kuisN0/cnPtX3ZJJYCcBex8TAB9pFw5c6yJWpS3JJKSgIED5Y/Hiy+W/Ot+/lmOAwZwILqbBFcjQqXGIZ1/IQL8kwAGz8DPyCj51/3lL5IULFtWukk5qt0NGlTyryG9KlQw20Go7W73bqm8BwL5d//4/Xfg6FGgUiWgY8fQ3qcwahkYVgCdj5dln+naVY6lSQABswo4caKMLSmJn36SIy9E7qLGI6kKblkdOmR2I5//O5CWBmzaJBcpNfPYq+LiZMFdw5AB+CVVqxZw221yXtJJWFu2yNqd5cpx2IXbqHYXauX9l1/k2K1b3t0/APOmq3t3+5ZdYhewezAB9BmVAC5bVrqvu/hiqWScOVOyvUr37jXvQgcMKH2cpE/jxjJ27OzZ0HYnmDJFkp527WRWa7B58+TYrp0/docpa7t74AHplp86tWRjMlX1r2dPmQBA7qES9tmzQ0ueiqoAh6PqzgTQPZgA+kyXLnJcubJ0DTQQAB5/XM7/8Q+p7hRF3YV27SoVEHIXK6oRJbkQeW3/38KodlfaynujRsDNN8v5008X/3z1f35+1x85X/v28rfy5MnSTRgKlp1tdiGf3+6Cl12ys90xAXQPJoA+07SpVFwyM0s/2Pjyy2UM4cmTwEsvFf1cjkNyN5UATplStq/PzTUnIxSUjPhl/J9S1gogADz2mFQBf/qp6ATyzBlzNwm2O/eJiDCrgGVtdwsWyDjTuDjzpkPZvh3Ys0eWXVLjUu3ABNA9mAD6TCBQ9mpEIACMGyfnb7xReBUwO9usHPFC5E79+knSsX49sG1b6b8+NRU4eFC6IXv0yPu5jAwZHgD4JwHs1EmO27YVXz0/3wUXAH/6k5wXVQWcPRs4fRqoVw9o27ZscZJeAwfKUd1Al1ZRE+/UTVeXLvYuu8RZwO7BBNCHyjoRBJDZnJ06SRXw5ZcLfs7ChbLUTGxs/rtQcofq1c3ErSwXI/U1KSlScQi2YIFUCJs2BeLjQwrTNapXB5o3l/OyVgEjImRWdWFfHzzpisu/uJMaL718uUyUKi3d4/8AzgJ2EyaAPqSSsrJciAIB4Mkn5fz114EDB/I/J/guNDKybDGSfqrrNpQEUOeFyGlCaXfNmgFDh8q5an/n47AL96tTx/w9UeOoS2rPHmD1avkb3b9//s+Hq92pCmBubul3saHw8m0CuHHjRlxxxRWoVasWqlatil69emGmGkDjcaoC+Ntv0mVUWkOGyB+pkyeB557L/3m1CCkHorub+vlNny7jy0rq8GHZbQYwu7SCqYVoe/UKLT63CaXyDkjiV66cJHrqYq5s2CDL6kRFcfkXt1PtTlV0S6qo5V8OHZLfESD/kAyrBe88wiqgs/k2ARw8eDDOnj2LGTNmIDU1FUlJSRg8eDDSylJ3d5nERKB2bbk7U2OxSiMQMBO/t94CduwwP7dli0wuiYxkAuh27drJeLLTp0u3HExRy79kZ5vLmXh9/b/zhVIBBGQsoFoXcOzYvLuDfP+9HC+5hMu/uJ2q4E6ZUrr9uIuqAKtdaFq1kq0e7cQE0D18mQAeOnQImzZtwqOPPor27dujWbNmmDBhAk6dOoU1a9boDs92gUDo1YiUFLnYZGXlHZiuqn8XXyx7mZJ7BQLmxaQ03cBFXYhWrZKEsmZNc0ycX3TsKOP49u6Vj7J44gnZNWL+/LwVIpUADhkSepykV9euMn46Pb3k2wcWN/FuwQI52l39A/KO+WUC6Gw2rQXubLGxsWjRogU+/PBDdOrUCdHR0XjnnXdQu3ZtdFZ7pRUgMzMTmUFTm9LT0wEAGaXZ38kh2rWTAeXz5wPDhpXtNR57TJadeP99YNQooGVL4Ouv5XP9+5du2ytypj59gH//WxL7Z54p/vm5uWZi0rt3/t8BtaVZ587AiROWhuoKLVoA69ZJRfXyy0v/9VWqACNHyvjbRx6RC/rRo2a3ep8+bHde0K+f7L3+zTeyFWdx5s+Xn7u6sTr/d2DOHDl26BCe34/ISOlhOnTInBTiNOq6bZRmo22PCRg+/e53796NK6+8EsuXL0dERARq166NyZMno2MRGyQ+9dRTeLokq7ESERGR4+3atQuJ549V8QlPJYCPPvoo/l7Mppnr1q1DixYtcOWVVyI7OxuPPfYYYmJi8O9//xvff/89li5divhC1qY4vwKYm5uLI0eOIDY2FgGL113IyMhA/fr1sWvXLlT12aAev37vfv2+AX7v/N75vfuJE753wzBw/PhxJCQkIOL8RRN9wlMJ4MGDB3H48OEin9OkSRPMnTsX/fv3x9GjR/P88jVr1gy33XYbHn30UbtDLVZGRgaqVauG9PR0X/5x8OP37tfvG+D3zu+d37uf+Pl7dxJPjQGMi4tDXAk2nj116hQA5Mv6IyIikJuba0tsRERERE7hy7pn9+7dUaNGDQwbNgyrVq3Cxo0b8dBDD2Hbtm24vCwjs4mIiIhcxJcJYK1atfDLL7/gxIkT6Nu3L7p06YJ58+bhu+++Q1JJplyFQXR0NMaNG4dop06hspFfv3e/ft8Av3d+7/ze/cTP37uTeGoMIBEREREVz5cVQCIiIiI/YwJIRERE5DNMAImIiIh8hgkgERERkc8wAXSgN998E40aNUKFChWQnJyMJUuW6A7JcuPHj0fXrl1RpUoV1K5dG1deeSU2bNiQ5zl9+vRBIBDI8zFq1ChNEVvnqaeeyvd9tWzZ8tznz5w5g9GjRyM2NhaVK1fGNddcg/3792uM2DqNGjXK970HAgGMHj0agLd+5nPmzMEf/vAHJCQkIBAI4Ntvv83zecMw8OSTTyI+Ph4xMTFISUnBpk2b8jznyJEjGDp0KKpWrYrq1avjtttuwwkXbKJc1PeenZ2NRx55BO3atUOlSpWQkJCAW265BXv37s3zGgX9rkyYMCHM30npFfdzHz58eL7va+DAgXme48WfO4AC234gEMALL7xw7jlu/bm7ERNAh5k0aRLuv/9+jBs3DsuXL0dSUhIGDBiAAwcO6A7NUrNnz8bo0aOxaNEiTJ06FdnZ2ejfvz9OnjyZ53l33HEH9u3bd+7j+eef1xSxtdq0aZPn+5o3b965z91333344Ycf8MUXX2D27NnYu3cvrr76ao3RWmfp0qV5vu+pU6cCAK699tpzz/HKz/zkyZNISkrCm2++WeDnn3/+ebz++ut4++23sXjxYlSqVAkDBgzAmTNnzj1n6NCh+P333zF16lT8+OOPmDNnDkaOHBmub6HMivreT506heXLl+OJJ57A8uXL8fXXX2PDhg0YMmRIvuc+88wzeX4X/vKXv4Qj/JAU93MHgIEDB+b5vj799NM8n/fizx1Anu953759eO+99xAIBHDNNdfkeZ4bf+6uZJCjdOvWzRg9evS5f+fk5BgJCQnG+PHjNUZlvwMHDhgAjNmzZ5977OKLLzbuuecefUHZZNy4cUZSUlKBnzt27JgRFRVlfPHFF+ceW7dunQHAWLhwYZgiDJ977rnHaNq0qZGbm2sYhnd/5gCMb7755ty/c3Nzjbp16xovvPDCuceOHTtmREdHG59++qlhGIaxdu1aA4CxdOnSc8/5+eefjUAgYOzZsydssYfq/O+9IEuWLDEAGDt27Dj3WMOGDY1XXnnF3uBsVtD3PmzYMOOKK64o9Gv89HO/4oorjL59++Z5zAs/d7dgBdBBsrKykJqaipSUlHOPRUREICUlBQsXLtQYmf3S09MBADVr1szz+CeffIJatWqhbdu2GDt27Llt/Nxu06ZNSEhIQJMmTTB06FDs3LkTAJCamors7Ow8vwMtW7ZEgwYNPPc7kJWVhY8//hi33norAoHAuce9+jMPtm3bNqSlpeX5OVerVg3Jycnnfs4LFy5E9erV0aVLl3PPSUlJQUREBBYvXhz2mO2Unp6OQCCA6tWr53l8woQJiI2NRceOHfHCCy/g7NmzegK02KxZs1C7dm20aNECd911V5497P3yc9+/fz8mT56M2267Ld/nvPpzdxpP7QXsdocOHUJOTg7q1KmT5/E6depg/fr1mqKyX25uLu6991707NkTbdu2Pff4TTfdhIYNGyIhIQGrV6/GI488gg0bNuDrr7/WGG3okpOT8f7776NFixbYt28fnn76aVx00UVYs2YN0tLSUL58+XwXwjp16iAtLU1PwDb59ttvcezYMQwfPvzcY179mZ9P/SwLauvqc2lpaahdu3aez5crVw41a9b01O/CmTNn8Mgjj+DGG29E1apVzz1+9913o1OnTqhZsyYWLFiAsWPHYt++fXj55Zc1Rhu6gQMH4uqrr0bjxo2xZcsW/PWvf8WgQYOwcOFCREZG+ubn/sEHH6BKlSr5hrd49efuREwASbvRo0djzZo1ecbBAcgz5qVdu3aIj49Hv379sGXLFjRt2jTcYVpm0KBB587bt2+P5ORkNGzYEJ9//jliYmI0RhZe//nPfzBo0CAkJCSce8yrP3MqWHZ2Nq677joYhoG33norz+fuv//+c+ft27dH+fLlceedd2L8+PGu3kLshhtuOHferl07tG/fHk2bNsWsWbPQr18/jZGF13vvvYehQ4eiQoUKeR736s/didgF7CC1atVCZGRkvhmf+/fvR926dTVFZa8xY8bgxx9/xMyZM5GYmFjkc5OTkwEAmzdvDkdoYVO9enU0b94cmzdvRt26dZGVlYVjx47leY7Xfgd27NiBadOm4fbbby/yeV79maufZVFtvW7duvkmf509exZHjhzxxO+CSv527NiBqVOn5qn+FSQ5ORlnz57F9u3bwxNgmDRp0gS1atU69zvu9Z87AMydOxcbNmwotv0D3v25OwETQAcpX748OnfujOnTp597LDc3F9OnT0f37t01RmY9wzAwZswYfPPNN5gxYwYaN25c7NesXLkSABAfH29zdOF14sQJbNmyBfHx8ejcuTOioqLy/A5s2LABO3fu9NTvwMSJE1G7dm1cfvnlRT7Pqz/zxo0bo27dunl+zhkZGVi8ePG5n3P37t1x7NgxpKamnnvOjBkzkJubey4xdiuV/G3atAnTpk1DbGxssV+zcuVKRERE5Osedbvdu3fj8OHD537HvfxzV/7zn/+gc+fOSEpKKva5Xv25O4LuWSiU12effWZER0cb77//vrF27Vpj5MiRRvXq1Y20tDTdoVnqrrvuMqpVq2bMmjXL2Ldv37mPU6dOGYZhGJs3bzaeeeYZY9myZca2bduM7777zmjSpInRu3dvzZGH7oEHHjBmzZplbNu2zZg/f76RkpJi1KpVyzhw4IBhGIYxatQoo0GDBsaMGTOMZcuWGd27dze6d++uOWrr5OTkGA0aNDAeeeSRPI977Wd+/PhxY8WKFcaKFSsMAMbLL79srFix4txM1wkTJhjVq1c3vvvuO2P16tXGFVdcYTRu3Ng4ffr0udcYOHCg0bFjR2Px4sXGvHnzjGbNmhk33nijrm+pxIr63rOysowhQ4YYiYmJxsqVK/O0/8zMTMMwDGPBggXGK6+8YqxcudLYsmWL8fHHHxtxcXHGLbfcovk7K15R3/vx48eNBx980Fi4cKGxbds2Y9q0aUanTp2MZs2aGWfOnDn3Gl78uSvp6elGxYoVjbfeeivf17v55+5GTAAd6I033jAaNGhglC9f3ujWrZuxaNEi3SFZDkCBHxMnTjQMwzB27txp9O7d26hZs6YRHR1tXHDBBcZDDz1kpKen6w3cAtdff70RHx9vlC9f3qhXr55x/fXXG5s3bz73+dOnTxt//vOfjRo1ahgVK1Y0rrrqKmPfvn0aI7bWr7/+agAwNmzYkOdxr/3MZ86cWeDv+LBhwwzDkKVgnnjiCaNOnTpGdHS00a9fv3z/J4cPHzZuvPFGo3LlykbVqlWNESNGGMePH9fw3ZROUd/7tm3bCm3/M2fONAzDMFJTU43k5GSjWrVqRoUKFYxWrVoZzz33XJ4kyamK+t5PnTpl9O/f34iLizOioqKMhg0bGnfccUe+G3wv/tyVd955x4iJiTGOHTuW7+vd/HN3o4BhGIatJUYiIiIichSOASQiIiLyGSaARERERD7DBJCIiIjIZ5gAEhEREfkME0AiIiIin2ECSEREROQzTACJiIiIfIYJIBEREZHPMAEkIiIi8hkmgEREREQ+wwSQiIiIyGeYABIRERH5DBNAIiIiIp9hAkhERETkM0wAiYiIiHyGCSARERGRzzABJCIiIvIZJoBEREREPsMEkIiIiMhnmAASERER+QwTQCIiIiKfYQJIRERE5DNMAImIiIh8hgkgERERkc8wASQiIiLyGSaARERERD7DBJCIiIjIZ5gAEhEREfkME0AiIiIin2ECSEREROQzTACJiIiIfOb/AXjLbZ1MdL0LAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "See also https://colab.research.google.com/github/knc6/jarvis-tools-notebooks/blob/master/jarvis-tools-notebooks/ThreeBodyTB_julia.ipynb" + ], + "metadata": { + "id": "Or7ud0yqC5Cj" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "529IJzQMgfMF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "rIpA3EcsgfJ6" + }, + "execution_count": 14, + "outputs": [] + } + ] +} \ No newline at end of file