diff --git a/README.md b/README.md
index e2b0357..890b035 100644
--- a/README.md
+++ b/README.md
@@ -85,7 +85,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for
 
 <details><summary><b><a href="https://github.com/mir-group/flare">FLARE</a></b> (🥇22 ·  ⭐ 290) - An open-source Python package for creating fast and accurate interatomic potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>C++</code> <code>ML-IAP</code></summary>
 
-- [GitHub](https://github.com/mir-group/flare) (👨‍💻 42 · 🔀 67 · 📥 8 · 📦 11 · 📋 220 - 16% open · ⏱️ 30.09.2024):
+- [GitHub](https://github.com/mir-group/flare) (👨‍💻 42 · 🔀 67 · 📥 8 · 📦 12 · 📋 220 - 16% open · ⏱️ 12.10.2024):
 
 	```
 	git clone https://github.com/mir-group/flare
@@ -93,19 +93,19 @@ _Projects that focus on enabling active learning, iterative learning schemes for
 </details>
 <details><summary><b><a href="https://github.com/zincware/IPSuite">IPSuite</a></b> (🥈17 ·  ⭐ 18) - A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials. <code><a href="http://bit.ly/2M0xmjV">EPL-2.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>workflows</code> <a href="https://en.wikipedia.org/wiki/High-throughput_computing"><code>HTC</code></a> <a href="https://www.go-fair.org/fair-principles/"><code>FAIR</code></a></summary>
 
-- [GitHub](https://github.com/zincware/IPSuite) (👨‍💻 8 · 🔀 10 · 📦 6 · 📋 130 - 51% open · ⏱️ 19.09.2024):
+- [GitHub](https://github.com/zincware/IPSuite) (👨‍💻 8 · 🔀 10 · 📦 7 · 📋 130 - 51% open · ⏱️ 19.09.2024):
 
 	```
 	git clone https://github.com/zincware/IPSuite
 	```
-- [PyPi](https://pypi.org/project/ipsuite) (📥 150 / month · ⏱️ 08.08.2024):
+- [PyPi](https://pypi.org/project/ipsuite) (📥 290 / month · ⏱️ 08.08.2024):
 	```
 	pip install ipsuite
 	```
 </details>
-<details><summary><b><a href="https://github.com/ulissigroup/finetuna">Finetuna</a></b> (🥉10 ·  ⭐ 42) - Active Learning for Machine Learning Potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/ulissigroup/finetuna">Finetuna</a></b> (🥉10 ·  ⭐ 43) - Active Learning for Machine Learning Potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/ulissigroup/finetuna) (👨‍💻 11 · 🔀 11 · 📋 20 - 25% open · ⏱️ 15.05.2024):
+- [GitHub](https://github.com/ulissigroup/finetuna) (👨‍💻 11 · 🔀 11 · 📦 1 · 📋 20 - 25% open · ⏱️ 15.05.2024):
 
 	```
 	git clone https://github.com/ulissigroup/finetuna
@@ -113,7 +113,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for
 </details>
 <details><summary>Show 3 hidden projects...</summary>
 
-- <b><a href="https://github.com/mir-group/flare_pp">flare++</a></b> (🥈13 ·  ⭐ 35 · 💀) - A many-body extension of the FLARE code. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>C++</code> <code>ML-IAP</code>
+- <b><a href="https://github.com/mir-group/flare_pp">flare++</a></b> (🥈14 ·  ⭐ 35 · 💀) - A many-body extension of the FLARE code. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>C++</code> <code>ML-IAP</code>
 - <b><a href="https://github.com/ACEsuit/ACEHAL">ACEHAL</a></b> (🥉5 ·  ⭐ 11 · 💀) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. <code>Unlicensed</code> <code>Julia</code>
 - <b><a href="https://github.com/nec-research/alebrew">ALEBREW</a></b> (🥉3 ·  ⭐ 9 · 💤) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. <code><a href="https://github.com/nec-research/alebrew/blob/main/LICENSE.txt">Custom</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a>
 </details>
@@ -137,44 +137,44 @@ _Projects that collect atomistic ML resources or foster communication within com
 
 🔗&nbsp;<b><a href="https://mattermodeling.stackexchange.com/questions/tagged/machine-learning">Matter Modeling Stack Exchange - Machine Learning</a></b>  - Forum StackExchange, site Matter Modeling, ML-tagged questions.
 
-<details><summary><b><a href="https://github.com/ml-tooling/best-of-ml-python">Best-of Machine Learning with Python</a></b> (🥇22 ·  ⭐ 16K) - A ranked list of awesome machine learning Python libraries. Updated weekly. <code><a href="https://tldrlegal.com/search?q=CC-BY-4.0">CC-BY-4.0</a></code> <code>general-ml</code> <code>Python</code></summary>
+<details><summary><b><a href="https://github.com/ml-tooling/best-of-ml-python">Best-of Machine Learning with Python</a></b> (🥇23 ·  ⭐ 16K) - A ranked list of awesome machine learning Python libraries. Updated weekly. <code><a href="https://tldrlegal.com/search?q=CC-BY-4.0">CC-BY-4.0</a></code> <code>general-ml</code> <code>Python</code></summary>
 
-- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨‍💻 47 · 🔀 2.3K · 📋 59 - 42% open · ⏱️ 26.09.2024):
+- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨‍💻 47 · 🔀 2.4K · 📋 61 - 44% open · ⏱️ 10.10.2024):
 
 	```
 	git clone https://github.com/ml-tooling/best-of-ml-python
 	```
 </details>
-<details><summary><b><a href="https://github.com/naganandy/graph-based-deep-learning-literature">Graph-based Deep Learning Literature</a></b> (🥇19 ·  ⭐ 4.7K) - links to conference publications in graph-based deep learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>general-ml</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+<details><summary><b><a href="https://github.com/naganandy/graph-based-deep-learning-literature">Graph-based Deep Learning Literature</a></b> (🥇19 ·  ⭐ 4.8K) - links to conference publications in graph-based deep learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>general-ml</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/naganandy/graph-based-deep-learning-literature) (👨‍💻 12 · 🔀 770 · ⏱️ 09.09.2024):
+- [GitHub](https://github.com/naganandy/graph-based-deep-learning-literature) (👨‍💻 12 · 🔀 770 · ⏱️ 09.10.2024):
 
 	```
 	git clone https://github.com/naganandy/graph-based-deep-learning-literature
 	```
 </details>
-<details><summary><b><a href="https://github.com/materialsproject/matbench">MatBench</a></b> (🥇18 ·  ⭐ 110 · 💤) - Matbench: Benchmarks for materials science property prediction. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code></summary>
+<details><summary><b><a href="https://github.com/janosh/matbench-discovery">MatBench Discovery</a></b> (🥇19 ·  ⭐ 94) - An evaluation framework for machine learning models simulating high-throughput materials discovery. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code></summary>
 
-- [GitHub](https://github.com/materialsproject/matbench) (👨‍💻 25 · 🔀 46 · 📦 16 · 📋 65 - 60% open · ⏱️ 20.01.2024):
+- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 8 · 🔀 13 · 📦 3 · 📋 39 - 10% open · ⏱️ 15.10.2024):
 
 	```
-	git clone https://github.com/materialsproject/matbench
+	git clone https://github.com/janosh/matbench-discovery
 	```
-- [PyPi](https://pypi.org/project/matbench) (📥 410 / month · 📦 2 · ⏱️ 27.07.2022):
+- [PyPi](https://pypi.org/project/matbench-discovery) (📥 1.9K / month · ⏱️ 11.09.2024):
 	```
-	pip install matbench
+	pip install matbench-discovery
 	```
 </details>
-<details><summary><b><a href="https://github.com/janosh/matbench-discovery">MatBench Discovery</a></b> (🥇18 ·  ⭐ 92) - An evaluation framework for machine learning models simulating high-throughput materials discovery. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code></summary>
+<details><summary><b><a href="https://github.com/materialsproject/matbench">MatBench</a></b> (🥈18 ·  ⭐ 120 · 💤) - Matbench: Benchmarks for materials science property prediction. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code></summary>
 
-- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 8 · 🔀 12 · 📦 2 · 📋 39 - 10% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/materialsproject/matbench) (👨‍💻 25 · 🔀 46 · 📦 17 · 📋 65 - 60% open · ⏱️ 20.01.2024):
 
 	```
-	git clone https://github.com/janosh/matbench-discovery
+	git clone https://github.com/materialsproject/matbench
 	```
-- [PyPi](https://pypi.org/project/matbench-discovery) (📥 1.7K / month · ⏱️ 11.09.2024):
+- [PyPi](https://pypi.org/project/matbench) (📥 630 / month · 📦 2 · ⏱️ 27.07.2022):
 	```
-	pip install matbench-discovery
+	pip install matbench
 	```
 </details>
 <details><summary><b><a href="https://github.com/openml/OpenML">OpenML</a></b> (🥈17 ·  ⭐ 660) - Open Machine Learning. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>datasets</code></summary>
@@ -187,7 +187,7 @@ _Projects that collect atomistic ML resources or foster communication within com
 </details>
 <details><summary><b><a href="https://huggingface.co/GT4SD">GT4SD - Generative Toolkit for Scientific Discovery</a></b> (🥈15 ·  ⭐ 340) - Gradio apps of generative models in GT4SD. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a> <code>model-repository</code></summary>
 
-- [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 68 · 📋 110 - 12% open · ⏱️ 12.09.2024):
+- [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 69 · 📋 110 - 12% open · ⏱️ 12.09.2024):
 
 	```
 	git clone https://github.com/GT4SD/gt4sd-core
@@ -201,23 +201,15 @@ _Projects that collect atomistic ML resources or foster communication within com
 	git clone https://github.com/divelab/AIRS
 	```
 </details>
-<details><summary><b><a href="https://github.com/Eipgen/Neural-Network-Models-for-Chemistry">Neural-Network-Models-for-Chemistry</a></b> (🥈11 ·  ⭐ 78) - A collection of Nerual Network Models for chemistry. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+<details><summary><b><a href="https://github.com/Eipgen/Neural-Network-Models-for-Chemistry">Neural-Network-Models-for-Chemistry</a></b> (🥈10 ·  ⭐ 84) - A collection of Nerual Network Models for chemistry. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 10 · 📋 2 - 50% open · ⏱️ 20.09.2024):
+- [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 12 · 📋 2 - 50% open · ⏱️ 20.09.2024):
 
 	```
 	git clone https://github.com/Eipgen/Neural-Network-Models-for-Chemistry
 	```
 </details>
-<details><summary><b><a href="https://github.com/neurreps/awesome-neural-geometry">Awesome Neural Geometry</a></b> (🥈9 ·  ⭐ 910) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. <code>Unlicensed</code> <code>educational</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
-
-- [GitHub](https://github.com/neurreps/awesome-neural-geometry) (👨‍💻 12 · 🔀 57 · ⏱️ 25.09.2024):
-
-	```
-	git clone https://github.com/neurreps/awesome-neural-geometry
-	```
-</details>
-<details><summary><b><a href="https://next-gen.materialsproject.org/materials/gnome">GNoME Explorer</a></b> (🥈9 ·  ⭐ 870) - Graph Networks for Materials Exploration Database. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>datasets</code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a></summary>
+<details><summary><b><a href="https://next-gen.materialsproject.org/materials/gnome">GNoME Explorer</a></b> (🥈9 ·  ⭐ 880) - Graph Networks for Materials Exploration Database. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>datasets</code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a></summary>
 
 - [GitHub](https://github.com/google-deepmind/materials_discovery) (👨‍💻 2 · 🔀 140 · 📋 22 - 81% open · ⏱️ 04.09.2024):
 
@@ -225,15 +217,15 @@ _Projects that collect atomistic ML resources or foster communication within com
 	git clone https://github.com/google-deepmind/materials_discovery
 	```
 </details>
-<details><summary><b><a href="https://github.com/tilde-lab/awesome-materials-informatics">Awesome Materials Informatics</a></b> (🥈9 ·  ⭐ 370) - Curated list of known efforts in materials informatics, i.e. in modern materials science. <code><a href="https://github.com/tilde-lab/awesome-materials-informatics#license">Custom</a></code></summary>
+<details><summary><b><a href="https://github.com/tilde-lab/awesome-materials-informatics">Awesome Materials Informatics</a></b> (🥈9 ·  ⭐ 380) - Curated list of known efforts in materials informatics, i.e. in modern materials science. <code><a href="https://github.com/tilde-lab/awesome-materials-informatics#license">Custom</a></code></summary>
 
-- [GitHub](https://github.com/tilde-lab/awesome-materials-informatics) (👨‍💻 19 · 🔀 81 · ⏱️ 18.09.2024):
+- [GitHub](https://github.com/tilde-lab/awesome-materials-informatics) (👨‍💻 19 · 🔀 82 · ⏱️ 18.09.2024):
 
 	```
 	git clone https://github.com/tilde-lab/awesome-materials-informatics
 	```
 </details>
-<details><summary><b><a href="https://molformer.res.ibm.com/">MoLFormers UI</a></b> (🥈9 ·  ⭐ 250 · 💤) - A family of foundation models trained on chemicals. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <a href="https://en.wikipedia.org/wiki/Language_model"><code>language-models</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a></summary>
+<details><summary><b><a href="https://molformer.res.ibm.com/">MoLFormers UI</a></b> (🥈9 ·  ⭐ 260 · 💤) - A family of foundation models trained on chemicals. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <a href="https://en.wikipedia.org/wiki/Language_model"><code>language-models</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a></summary>
 
 - [GitHub](https://github.com/IBM/molformer) (👨‍💻 5 · 🔀 41 · 📋 19 - 47% open · ⏱️ 16.10.2023):
 
@@ -241,7 +233,15 @@ _Projects that collect atomistic ML resources or foster communication within com
 	git clone https://github.com/IBM/molformer
 	```
 </details>
-<details><summary><b><a href="https://github.com/sherrylixuecheng/AI_for_Science_paper_collection">AI for Science paper collection</a></b> (🥈9 ·  ⭐ 57 · 🐣) - List the AI for Science papers accepted by top conferences. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
+<details><summary><b><a href="https://github.com/neurreps/awesome-neural-geometry">Awesome Neural Geometry</a></b> (🥉8 ·  ⭐ 920) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. <code>Unlicensed</code> <code>educational</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+
+- [GitHub](https://github.com/neurreps/awesome-neural-geometry) (👨‍💻 12 · 🔀 57 · ⏱️ 25.09.2024):
+
+	```
+	git clone https://github.com/neurreps/awesome-neural-geometry
+	```
+</details>
+<details><summary><b><a href="https://github.com/sherrylixuecheng/AI_for_Science_paper_collection">AI for Science paper collection</a></b> (🥉8 ·  ⭐ 61 · 🐣) - List the AI for Science papers accepted by top conferences. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
 - [GitHub](https://github.com/sherrylixuecheng/AI_for_Science_paper_collection) (👨‍💻 5 · 🔀 6 · ⏱️ 14.09.2024):
 
@@ -257,15 +257,15 @@ _Projects that collect atomistic ML resources or foster communication within com
 	git clone https://github.com/tilde-lab/optimade.science
 	```
 </details>
-<details><summary><b><a href="https://github.com/yuanqidu/awesome-graph-generation">Awesome-Graph-Generation</a></b> (🥉7 ·  ⭐ 270 · 💤) - A curated list of up-to-date graph generation papers and resources. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+<details><summary><b><a href="https://github.com/yuanqidu/awesome-graph-generation">Awesome-Graph-Generation</a></b> (🥉7 ·  ⭐ 280) - A curated list of up-to-date graph generation papers and resources. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/yuanqidu/awesome-graph-generation) (👨‍💻 4 · 🔀 17 · ⏱️ 17.03.2024):
+- [GitHub](https://github.com/yuanqidu/awesome-graph-generation) (👨‍💻 4 · 🔀 18 · ⏱️ 14.10.2024):
 
 	```
 	git clone https://github.com/yuanqidu/awesome-graph-generation
 	```
 </details>
-<details><summary><b><a href="https://github.com/smsharma/awesome-neural-sbi">Awesome Neural SBI</a></b> (🥉7 ·  ⭐ 85) - Community-sourced list of papers and resources on neural simulation-based inference. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>
+<details><summary><b><a href="https://github.com/smsharma/awesome-neural-sbi">Awesome Neural SBI</a></b> (🥉7 ·  ⭐ 87) - Community-sourced list of papers and resources on neural simulation-based inference. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>
 
 - [GitHub](https://github.com/smsharma/awesome-neural-sbi) (👨‍💻 3 · 🔀 6 · 📋 2 - 50% open · ⏱️ 17.06.2024):
 
@@ -273,7 +273,7 @@ _Projects that collect atomistic ML resources or foster communication within com
 	git clone https://github.com/smsharma/awesome-neural-sbi
 	```
 </details>
-<details><summary><b><a href="https://github.com/kdmsit/Awesome-Crystal-GNNs">Awesome-Crystal-GNNs</a></b> (🥉7 ·  ⭐ 60) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/kdmsit/Awesome-Crystal-GNNs">Awesome-Crystal-GNNs</a></b> (🥉7 ·  ⭐ 64) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
 - [GitHub](https://github.com/kdmsit/Awesome-Crystal-GNNs) (👨‍💻 2 · 🔀 8 · ⏱️ 16.06.2024):
 
@@ -283,7 +283,7 @@ _Projects that collect atomistic ML resources or foster communication within com
 </details>
 <details><summary><b><a href="https://github.com/sedaoturak/data-resources-for-materials-science">The Collection of Database and Dataset Resources in Materials Science</a></b> (🥉6 ·  ⭐ 260) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. <code>Unlicensed</code> <code>datasets</code></summary>
 
-- [GitHub](https://github.com/sedaoturak/data-resources-for-materials-science) (👨‍💻 2 · 🔀 42 · 📋 2 - 50% open · ⏱️ 07.06.2024):
+- [GitHub](https://github.com/sedaoturak/data-resources-for-materials-science) (👨‍💻 2 · 🔀 43 · 📋 2 - 50% open · ⏱️ 07.06.2024):
 
 	```
 	git clone https://github.com/sedaoturak/data-resources-for-materials-science
@@ -293,7 +293,7 @@ _Projects that collect atomistic ML resources or foster communication within com
 
 - <b><a href="https://github.com/ncfrey/resources">A Highly Opinionated List of Open-Source Materials Informatics Resources</a></b> (🥉7 ·  ⭐ 120 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md">MADICES Awesome Interoperability</a></b> (🥉7 ·  ⭐ 1) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code>
-- <b><a href="https://github.com/AlexDuvalinho/geometric-gnns">Geometric-GNNs</a></b> (🥉4 ·  ⭐ 92 · 💤) - List of Geometric GNNs for 3D atomic systems. <code>Unlicensed</code> <code>datasets</code> <code>educational</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
+- <b><a href="https://github.com/AlexDuvalinho/geometric-gnns">Geometric-GNNs</a></b> (🥉4 ·  ⭐ 93 · 💤) - List of Geometric GNNs for 3D atomic systems. <code>Unlicensed</code> <code>datasets</code> <code>educational</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
 - <b><a href="https://thismaterialdoesnotexist.com/">Does this material exist?</a></b> (🥉4 ·  ⭐ 15) - Vote on whether you think predicted crystal structures could be synthesised. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>for-fun</code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a>
 - <b><a href="https://github.com/topics/materials-informatics">GitHub topic materials-informatics</a></b> (🥉1) - GitHub topic materials-informatics. <code>Unlicensed</code>
 - <b><a href="https://ma.issp.u-tokyo.ac.jp/en/">MateriApps</a></b> (🥉1) - A Portal Site of Materials Science Simulation. <code>Unlicensed</code>
@@ -320,7 +320,7 @@ _Datasets, databases and trained models for atomistic ML._
 
 🔗&nbsp;<b><a href="https://doi.org/10.6084/m9.figshare.19658538">HME21 Dataset</a></b>  - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a>
 
-🔗&nbsp;<b><a href="https://pages.nist.gov/jarvis_leaderboard/">JARVIS-Leaderboard</a></b> ( ⭐ 58)  - Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w. <code>model-repository</code> <code>benchmarking</code> <code>community-resource</code> <code>educational</code>
+🔗&nbsp;<b><a href="https://pages.nist.gov/jarvis_leaderboard/">JARVIS-Leaderboard</a></b> ( ⭐ 59)  - A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w. <code>model-repository</code> <code>benchmarking</code> <code>community-resource</code> <code>educational</code>
 
 🔗&nbsp;<b><a href="https://materialsproject.org/ml/charge_densities">Materials Project - Charge Densities</a></b>  - Materials Project has started offering charge density information available for download via their public API.
 
@@ -344,35 +344,35 @@ _Datasets, databases and trained models for atomistic ML._
 
 <details><summary><b><a href="https://github.com/Materials-Consortia/optimade-python-tools">OPTIMADE Python tools</a></b> (🥇27 ·  ⭐ 68) - Tools for implementing and consuming OPTIMADE APIs in Python. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 60 · 📋 450 - 23% open · ⏱️ 30.09.2024):
+- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 61 · 📋 460 - 23% open · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/Materials-Consortia/optimade-python-tools
 	```
-- [PyPi](https://pypi.org/project/optimade) (📥 8.8K / month · 📦 4 · ⏱️ 16.09.2024):
+- [PyPi](https://pypi.org/project/optimade) (📥 11K / month · 📦 4 · ⏱️ 15.10.2024):
 	```
 	pip install optimade
 	```
-- [Conda](https://anaconda.org/conda-forge/optimade) (📥 92K · ⏱️ 16.09.2024):
+- [Conda](https://anaconda.org/conda-forge/optimade) (📥 94K · ⏱️ 16.10.2024):
 	```
 	conda install -c conda-forge optimade
 	```
 </details>
-<details><summary><b><a href="https://github.com/materialsproject/MPContribs">MPContribs</a></b> (🥇23 ·  ⭐ 35) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/materialsproject/MPContribs">MPContribs</a></b> (🥇25 ·  ⭐ 35 · 📈) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/materialsproject/MPContribs) (👨‍💻 25 · 🔀 20 · 📦 39 · 📋 99 - 21% open · ⏱️ 30.09.2024):
+- [GitHub](https://github.com/materialsproject/MPContribs) (👨‍💻 25 · 🔀 20 · 📦 40 · 📋 100 - 21% open · ⏱️ 17.10.2024):
 
 	```
 	git clone https://github.com/materialsproject/MPContribs
 	```
-- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 2.7K / month · 📦 3 · ⏱️ 20.06.2024):
+- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 8.9K / month · 📦 3 · ⏱️ 17.10.2024):
 	```
 	pip install mpcontribs-client
 	```
 </details>
-<details><summary><b><a href="https://github.com/FAIR-Chem/fairchem">FAIR Chemistry datasets</a></b> (🥇21 ·  ⭐ 770) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a></summary>
+<details><summary><b><a href="https://github.com/FAIR-Chem/fairchem">FAIR Chemistry datasets</a></b> (🥇21 ·  ⭐ 780) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a></summary>
 
-- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 230 · 📋 210 - 6% open · ⏱️ 01.10.2024):
+- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 240 · 📋 230 - 13% open · ⏱️ 14.10.2024):
 
 	```
 	git clone https://github.com/FAIR-Chem/fairchem
@@ -386,14 +386,14 @@ _Datasets, databases and trained models for atomistic ML._
 	git clone https://github.com/Materials-Consortia/OPTIMADE
 	```
 </details>
-<details><summary><b><a href="https://github.com/jla-gardner/load-atoms">load-atoms</a></b> (🥈15 ·  ⭐ 38) - download and manipulate atomistic datasets. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>data-structures</code></summary>
+<details><summary><b><a href="https://github.com/jla-gardner/load-atoms">load-atoms</a></b> (🥈16 ·  ⭐ 38) - download and manipulate atomistic datasets. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>data-structures</code></summary>
 
-- [GitHub](https://github.com/jla-gardner/load-atoms) (👨‍💻 3 · 🔀 2 · 📦 3 · 📋 31 - 3% open · ⏱️ 16.09.2024):
+- [GitHub](https://github.com/jla-gardner/load-atoms) (👨‍💻 3 · 🔀 2 · 📦 4 · 📋 31 - 3% open · ⏱️ 16.10.2024):
 
 	```
 	git clone https://github.com/jla-gardner/load-atoms
 	```
-- [PyPi](https://pypi.org/project/load-atoms) (📥 1K / month · ⏱️ 16.09.2024):
+- [PyPi](https://pypi.org/project/load-atoms) (📥 2.7K / month · ⏱️ 04.10.2024):
 	```
 	pip install load-atoms
 	```
@@ -406,9 +406,9 @@ _Datasets, databases and trained models for atomistic ML._
 	git clone https://github.com/divelab/AIRS
 	```
 </details>
-<details><summary><b><a href="https://github.com/openmm/spice-dataset">SPICE</a></b> (🥈10 ·  ⭐ 150) - A collection of QM data for training potential functions. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a></summary>
+<details><summary><b><a href="https://github.com/openmm/spice-dataset">SPICE</a></b> (🥈11 ·  ⭐ 150) - A collection of QM data for training potential functions. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a></summary>
 
-- [GitHub](https://github.com/openmm/spice-dataset) (👨‍💻 1 · 🔀 9 · 📥 260 · 📋 64 - 26% open · ⏱️ 19.08.2024):
+- [GitHub](https://github.com/openmm/spice-dataset) (👨‍💻 1 · 🔀 9 · 📥 260 · 📋 65 - 26% open · ⏱️ 19.08.2024):
 
 	```
 	git clone https://github.com/openmm/spice-dataset
@@ -430,7 +430,7 @@ _Datasets, databases and trained models for atomistic ML._
 	git clone https://github.com/HSE-LAMBDA/ai4material_design
 	```
 </details>
-<details><summary><b><a href="https://github.com/deepmodeling/AIS-Square">AIS Square</a></b> (🥉7 ·  ⭐ 10 · 💤) - A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the.. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>community-resource</code> <code>model-repository</code></summary>
+<details><summary><b><a href="https://github.com/deepmodeling/AIS-Square">AIS Square</a></b> (🥉7 ·  ⭐ 12 · 💤) - A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the.. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>community-resource</code> <code>model-repository</code></summary>
 
 - [GitHub](https://github.com/deepmodeling/AIS-Square) (👨‍💻 8 · 🔀 8 · 📋 6 - 83% open · ⏱️ 06.12.2023):
 
@@ -440,7 +440,7 @@ _Datasets, databases and trained models for atomistic ML._
 </details>
 <details><summary><b><a href="https://github.com/Jesperkemist/perovskitedatabase">The Perovskite Database Project</a></b> (🥉5 ·  ⭐ 58 · 💤) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. <code>Unlicensed</code> <code>community-resource</code></summary>
 
-- [GitHub](https://github.com/Jesperkemist/perovskitedatabase) (👨‍💻 2 · 🔀 18 · ⏱️ 07.03.2024):
+- [GitHub](https://github.com/Jesperkemist/perovskitedatabase) (👨‍💻 2 · 🔀 20 · ⏱️ 07.03.2024):
 
 	```
 	git clone https://github.com/Jesperkemist/perovskitedatabase
@@ -461,7 +461,7 @@ _Datasets, databases and trained models for atomistic ML._
 - <b><a href="https://github.com/isayev/ANI1_dataset">ANI-1 Dataset</a></b> (🥉8 ·  ⭐ 96 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/deepchem/moleculenet">MoleculeNet Leaderboard</a></b> (🥉8 ·  ⭐ 89 · 💀) -  <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>benchmarking</code>
 - <b><a href="https://github.com/learningmatter-mit/geom">GEOM</a></b> (🥉7 ·  ⭐ 200 · 💀) - GEOM: Energy-annotated molecular conformations. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a>
-- <b><a href="https://github.com/aiqm/ANI1x_datasets">ANI-1x Datasets</a></b> (🥉6 ·  ⭐ 55 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/aiqm/ANI1x_datasets">ANI-1x Datasets</a></b> (🥉6 ·  ⭐ 57 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/isayev/COMP6">COMP6 Benchmark dataset</a></b> (🥉6 ·  ⭐ 39 · 💀) - COMP6 Benchmark dataset for ML potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/drcassar/SciGlass">SciGlass</a></b> (🥉5 ·  ⭐ 10 · 💀) - The database contains a vast set of data on the properties of glass materials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python">GDB-9-Ex9 and ORNL_AISD-Ex</a></b> (🥉5 ·  ⭐ 6 · 💀) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. <code>Unlicensed</code>
@@ -482,41 +482,41 @@ _Projects that focus on providing data structures used in atomistic machine lear
 
 <details><summary><b><a href="https://github.com/deepmodeling/dpdata">dpdata</a></b> (🥇24 ·  ⭐ 200) - A Python package for manipulating atomistic data of software in computational science. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
 
-- [GitHub](https://github.com/deepmodeling/dpdata) (👨‍💻 61 · 🔀 130 · 📦 120 · 📋 120 - 28% open · ⏱️ 20.09.2024):
+- [GitHub](https://github.com/deepmodeling/dpdata) (👨‍💻 61 · 🔀 130 · 📦 130 · 📋 120 - 28% open · ⏱️ 20.09.2024):
 
 	```
 	git clone https://github.com/deepmodeling/dpdata
 	```
-- [PyPi](https://pypi.org/project/dpdata) (📥 43K / month · 📦 40 · ⏱️ 20.09.2024):
+- [PyPi](https://pypi.org/project/dpdata) (📥 50K / month · 📦 40 · ⏱️ 20.09.2024):
 	```
 	pip install dpdata
 	```
-- [Conda](https://anaconda.org/deepmodeling/dpdata) (📥 230 · ⏱️ 27.09.2023):
+- [Conda](https://anaconda.org/deepmodeling/dpdata) (📥 240 · ⏱️ 27.09.2023):
 	```
 	conda install -c deepmodeling dpdata
 	```
 </details>
 <details><summary><b><a href="https://github.com/metatensor/metatensor">Metatensor</a></b> (🥈21 ·  ⭐ 52) - Self-describing sparse tensor data format for atomistic machine learning and beyond. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>Rust</code> <code>C-lang</code> <code>C++</code> <code>Python</code></summary>
 
-- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 22 · 🔀 15 · 📥 28K · 📦 11 · 📋 210 - 34% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 22 · 🔀 16 · 📥 28K · 📦 13 · 📋 210 - 34% open · ⏱️ 11.10.2024):
 
 	```
 	git clone https://github.com/lab-cosmo/metatensor
 	```
 </details>
-<details><summary><b><a href="https://github.com/materialsproject/pyrho">mp-pyrho</a></b> (🥉17 ·  ⭐ 36 · 💤) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. <code><a href="https://github.com/materialsproject/pyrho">Custom</a></code> <code>ML-DFT</code></summary>
+<details><summary><b><a href="https://github.com/materialsproject/pyrho">mp-pyrho</a></b> (🥉17 ·  ⭐ 37 · 💤) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. <code><a href="https://github.com/materialsproject/pyrho">Custom</a></code> <code>ML-DFT</code></summary>
 
-- [GitHub](https://github.com/materialsproject/pyrho) (👨‍💻 8 · 🔀 7 · 📦 24 · 📋 4 - 25% open · ⏱️ 23.02.2024):
+- [GitHub](https://github.com/materialsproject/pyrho) (👨‍💻 8 · 🔀 7 · 📦 25 · 📋 4 - 25% open · ⏱️ 23.02.2024):
 
 	```
 	git clone https://github.com/materialsproject/pyrho
 	```
-- [PyPi](https://pypi.org/project/mp-pyrho) (📥 18K / month · 📦 3 · ⏱️ 23.02.2024):
+- [PyPi](https://pypi.org/project/mp-pyrho) (📥 19K / month · 📦 3 · ⏱️ 23.02.2024):
 	```
 	pip install mp-pyrho
 	```
 </details>
-<details><summary><b><a href="https://github.com/dmlc/dlpack">dlpack</a></b> (🥉15 ·  ⭐ 890) - common in-memory tensor structure. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>C++</code></summary>
+<details><summary><b><a href="https://github.com/dmlc/dlpack">dlpack</a></b> (🥉15 ·  ⭐ 900) - common in-memory tensor structure. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>C++</code></summary>
 
 - [GitHub](https://github.com/dmlc/dlpack) (👨‍💻 24 · 🔀 130 · 📋 71 - 40% open · ⏱️ 28.09.2024):
 
@@ -536,15 +536,15 @@ _Projects and models that focus on quantities of DFT, such as density functional
 
 <details><summary><b><a href="https://github.com/google-research/google-research/tree/master/jax_dft">JAX-DFT</a></b> (🥇25 ·  ⭐ 34K) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/google-research/google-research) (👨‍💻 800 · 🔀 7.8K · 📋 1.8K - 81% open · ⏱️ 03.10.2024):
+- [GitHub](https://github.com/google-research/google-research) (👨‍💻 810 · 🔀 7.9K · 📋 1.8K - 81% open · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/google-research/google-research
 	```
 </details>
-<details><summary><b><a href="https://github.com/mala-project/mala">MALA</a></b> (🥇17 ·  ⭐ 81) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
+<details><summary><b><a href="https://github.com/mala-project/mala">MALA</a></b> (🥇19 ·  ⭐ 81 · 📈) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
 
-- [GitHub](https://github.com/mala-project/mala) (👨‍💻 44 · 🔀 23 · 📦 1 · 📋 270 - 15% open · ⏱️ 04.07.2024):
+- [GitHub](https://github.com/mala-project/mala) (👨‍💻 44 · 🔀 24 · 📦 2 · 📋 280 - 14% open · ⏱️ 14.10.2024):
 
 	```
 	git clone https://github.com/mala-project/mala
@@ -566,9 +566,9 @@ _Projects and models that focus on quantities of DFT, such as density functional
 	git clone https://github.com/andreagrisafi/SALTED
 	```
 </details>
-<details><summary><b><a href="https://github.com/mzjb/DeepH-pack">DeepH-pack</a></b> (🥈12 ·  ⭐ 220) - Deep neural networks for density functional theory Hamiltonian. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>Julia</code></summary>
+<details><summary><b><a href="https://github.com/mzjb/DeepH-pack">DeepH-pack</a></b> (🥈12 ·  ⭐ 230) - Deep neural networks for density functional theory Hamiltonian. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>Julia</code></summary>
 
-- [GitHub](https://github.com/mzjb/DeepH-pack) (👨‍💻 8 · 🔀 44 · 📋 51 - 25% open · ⏱️ 22.05.2024):
+- [GitHub](https://github.com/mzjb/DeepH-pack) (👨‍💻 8 · 🔀 44 · 📋 51 - 25% open · ⏱️ 07.10.2024):
 
 	```
 	git clone https://github.com/mzjb/DeepH-pack
@@ -582,7 +582,7 @@ _Projects and models that focus on quantities of DFT, such as density functional
 	git clone https://github.com/deepmodeling/deepks-kit
 	```
 </details>
-<details><summary><b><a href="https://github.com/XanaduAI/GradDFT">Grad DFT</a></b> (🥈10 ·  ⭐ 74 · 💤) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
+<details><summary><b><a href="https://github.com/XanaduAI/GradDFT">Grad DFT</a></b> (🥈10 ·  ⭐ 75 · 💤) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
 - [GitHub](https://github.com/XanaduAI/GradDFT) (👨‍💻 4 · 🔀 6 · 📋 54 - 20% open · ⏱️ 13.02.2024):
 
@@ -590,15 +590,15 @@ _Projects and models that focus on quantities of DFT, such as density functional
 	git clone https://github.com/XanaduAI/GradDFT
 	```
 </details>
-<details><summary><b><a href="https://github.com/QuantumLab-ZY/HamGNN">HamGNN</a></b> (🥈8 ·  ⭐ 55) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <code>magnetism</code> <code>C-lang</code></summary>
+<details><summary><b><a href="https://github.com/QuantumLab-ZY/HamGNN">HamGNN</a></b> (🥈8 ·  ⭐ 56) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <code>magnetism</code> <code>C-lang</code></summary>
 
-- [GitHub](https://github.com/QuantumLab-ZY/HamGNN) (👨‍💻 2 · 🔀 15 · 📋 29 - 79% open · ⏱️ 26.09.2024):
+- [GitHub](https://github.com/QuantumLab-ZY/HamGNN) (👨‍💻 2 · 🔀 15 · 📋 31 - 80% open · ⏱️ 26.09.2024):
 
 	```
 	git clone https://github.com/QuantumLab-ZY/HamGNN
 	```
 </details>
-<details><summary><b><a href="https://github.com/AIforGreatGood/charge3net">ChargE3Net</a></b> (🥈7 ·  ⭐ 29) - Higher-order equivariant neural networks for charge density prediction in materials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+<details><summary><b><a href="https://github.com/AIforGreatGood/charge3net">ChargE3Net</a></b> (🥈7 ·  ⭐ 30) - Higher-order equivariant neural networks for charge density prediction in materials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
 - [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 2 · 🔀 8 · 📋 5 - 40% open · ⏱️ 15.08.2024):
 
@@ -634,12 +634,12 @@ _Projects and models that focus on quantities of DFT, such as density functional
 
 - <b><a href="https://github.com/google-deepmind/deepmind-research/tree/master/density_functional_approximation_dm21">DM21</a></b> (🥇20 ·  ⭐ 13K · 💀) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described.. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code>
 - <b><a href="https://github.com/semodi/neuralxc">NeuralXC</a></b> (🥈10 ·  ⭐ 33 · 💀) - Implementation of a machine learned density functional. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
+- <b><a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians</a></b> (🥈10 ·  ⭐ 13 · 💀) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>Julia</code>
 - <b><a href="https://github.com/biklooost/PROPhet">PROPhet</a></b> (🥈9 ·  ⭐ 63 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>single-paper</code> <code>C++</code>
-- <b><a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians</a></b> (🥈9 ·  ⭐ 12 · 💀) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>Julia</code>
 - <b><a href="https://github.com/gomes-lab/Mat2Spec">Mat2Spec</a></b> (🥈7 ·  ⭐ 27 · 💀) - Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Spectroscopy"><code>spectroscopy</code></a>
 - <b><a href="https://github.com/semodi/libnxc">Libnxc</a></b> (🥈7 ·  ⭐ 16 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. <code><a href="http://bit.ly/3postzC">MPL-2.0</a></code> <code>C++</code> <code>Fortran</code>
-- <b><a href="https://github.com/Xiaoxun-Gong/DeepH-E3">DeepH-E3</a></b> (🥉6 ·  ⭐ 70 · 💀) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>magnetism</code>
-- <b><a href="https://github.com/peterbjorgensen/DeepDFT">DeepDFT</a></b> (🥉6 ·  ⭐ 57 · 💀) - Official implementation of DeepDFT model. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/Xiaoxun-Gong/DeepH-E3">DeepH-E3</a></b> (🥉6 ·  ⭐ 73 · 💀) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>magnetism</code>
+- <b><a href="https://github.com/peterbjorgensen/DeepDFT">DeepDFT</a></b> (🥉6 ·  ⭐ 58 · 💀) - Official implementation of DeepDFT model. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/pedersor/ksr_dft">KSR-DFT</a></b> (🥉6 ·  ⭐ 4 · 💀) - Kohn-Sham regularizer for machine-learned DFT functionals. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code>
 - <b><a href="https://github.com/mzjb/xDeepH">xDeepH</a></b> (🥉5 ·  ⭐ 32 · 💀) - Extended DeepH (xDeepH) method for magnetic materials. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>magnetism</code> <code>Julia</code>
 - <b><a href="https://github.com/MihailBogojeski/ml-dft">ML-DFT</a></b> (🥉5 ·  ⭐ 23 · 💀) - A package for density functional approximation using machine learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
@@ -648,8 +648,8 @@ _Projects and models that focus on quantities of DFT, such as density functional
 - <b><a href="https://github.com/siddarthachar/deepcdp">DeepCDP</a></b> (🥉3 ·  ⭐ 6 · 💀) - DeepCDP: Deep learning Charge Density Prediction. <code>Unlicensed</code>
 - <b><a href="https://github.com/emotionor/APET">APET</a></b> (🥉3 ·  ⭐ 4 · 💀) - Atomic Positional Embedding-based Transformer. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Density_of_states"><code>density-of-states</code></a> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a>
 - <b><a href="https://github.com/foxjas/CSNN">CSNN</a></b> (🥉3 ·  ⭐ 2 · 💀) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
+- <b><a href="https://github.com/mala-project/malada">MALADA</a></b> (🥉3 ·  ⭐ 1 · 💀) - MALA Data Acquisition: Helpful tools to build data for MALA. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
 - <b><a href="https://github.com/brunocuevas/a3md">A3MD</a></b> (🥉2 ·  ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <code>single-paper</code>
-- <b><a href="https://github.com/mala-project/malada">MALADA</a></b> (🥉2 ·  ⭐ 1 · 💀) - MALA Data Acquisition: Helpful tools to build data for MALA. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
 - <b><a href="https://gitlab.com/jmargraf/kdf">kdft</a></b> (🥉1 ·  ⭐ 2 · 💀) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. <code>Unlicensed</code>
 - <b><a href="https://github.com/StefanoSanvitoGroup/MLdensity">MLDensity</a></b> ( ⭐ 2 · 💀) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure.. <code>Unlicensed</code>
 </details>
@@ -667,7 +667,7 @@ _Tutorials, guides, cookbooks, recipes, etc._
 
 🔗&nbsp;<b><a href="https://www.elsevier.com/books-and-journals/book-companion/9780323900492">Quantum Chemistry in the Age of Machine Learning</a></b>  - Book, 2022.
 
-<details><summary><b><a href="https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks">jarvis-tools-notebooks</a></b> (🥇12 ·  ⭐ 62) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. <code><a href="https://tldrlegal.com/search?q=NIST">NIST</a></code></summary>
+<details><summary><b><a href="https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks">jarvis-tools-notebooks</a></b> (🥇11 ·  ⭐ 63) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. <code><a href="https://tldrlegal.com/search?q=NIST">NIST</a></code></summary>
 
 - [GitHub](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks) (👨‍💻 5 · 🔀 26 · ⏱️ 14.08.2024):
 
@@ -675,9 +675,9 @@ _Tutorials, guides, cookbooks, recipes, etc._
 	git clone https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks
 	```
 </details>
-<details><summary><b><a href="https://github.com/schwallergroup/ai4chem_course">AI4Chemistry course</a></b> (🥈10 ·  ⭐ 130) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>chemistry</code></summary>
+<details><summary><b><a href="https://github.com/schwallergroup/ai4chem_course">AI4Chemistry course</a></b> (🥈10 ·  ⭐ 140) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>chemistry</code></summary>
 
-- [GitHub](https://github.com/schwallergroup/ai4chem_course) (👨‍💻 6 · 🔀 31 · 📋 4 - 25% open · ⏱️ 02.05.2024):
+- [GitHub](https://github.com/schwallergroup/ai4chem_course) (👨‍💻 6 · 🔀 33 · 📋 4 - 25% open · ⏱️ 02.05.2024):
 
 	```
 	git clone https://github.com/schwallergroup/ai4chem_course
@@ -693,13 +693,13 @@ _Tutorials, guides, cookbooks, recipes, etc._
 </details>
 <details><summary><b><a href="https://github.com/ceriottm/iam-notebooks">iam-notebooks</a></b> (🥈9 ·  ⭐ 26) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/ceriottm/iam-notebooks) (👨‍💻 6 · 🔀 5 · ⏱️ 26.06.2024):
+- [GitHub](https://github.com/ceriottm/iam-notebooks) (👨‍💻 6 · 🔀 5 · ⏱️ 09.10.2024):
 
 	```
 	git clone https://github.com/ceriottm/iam-notebooks
 	```
 </details>
-<details><summary><b><a href="https://github.com/anthony-wang/BestPractices">BestPractices</a></b> (🥈8 ·  ⭐ 170 · 💤) - Things that you should (and should not) do in your Materials Informatics research. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/anthony-wang/BestPractices">BestPractices</a></b> (🥈8 ·  ⭐ 180 · 💤) - Things that you should (and should not) do in your Materials Informatics research. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
 - [GitHub](https://github.com/anthony-wang/BestPractices) (👨‍💻 3 · 🔀 70 · 📋 7 - 71% open · ⏱️ 17.11.2023):
 
@@ -709,13 +709,13 @@ _Tutorials, guides, cookbooks, recipes, etc._
 </details>
 <details><summary><b><a href="https://github.com/lab-cosmo/atomistic-cookbook">COSMO Software Cookbook</a></b> (🥈8 ·  ⭐ 16) - A cookbook wtih recipes for atomic-scale modeling of materials and molecules. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
 
-- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (👨‍💻 10 · 🔀 1 · 📋 12 - 16% open · ⏱️ 03.10.2024):
+- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (👨‍💻 11 · 🔀 1 · 📋 12 - 8% open · ⏱️ 14.10.2024):
 
 	```
 	git clone https://github.com/lab-cosmo/software-cookbook
 	```
 </details>
-<details><summary><b><a href="https://github.com/ilyes319/mace-tutorials">MACE-tutorials</a></b> (🥉6 ·  ⭐ 38) - Another set of tutorials for the MACE interatomic potential by one of the authors. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a></summary>
+<details><summary><b><a href="https://github.com/ilyes319/mace-tutorials">MACE-tutorials</a></b> (🥉7 ·  ⭐ 38) - Another set of tutorials for the MACE interatomic potential by one of the authors. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a></summary>
 
 - [GitHub](https://github.com/ilyes319/mace-tutorials) (👨‍💻 2 · 🔀 10 · ⏱️ 16.07.2024):
 
@@ -725,19 +725,19 @@ _Tutorials, guides, cookbooks, recipes, etc._
 </details>
 <details><summary>Show 18 hidden projects...</summary>
 
-- <b><a href="https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb">Geometric GNN Dojo</a></b> (🥇12 ·  ⭐ 460 · 💀) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
+- <b><a href="https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb">Geometric GNN Dojo</a></b> (🥇12 ·  ⭐ 470 · 💀) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
 - <b><a href="https://github.com/deepchem/DeepLearningLifeSciences">DeepLearningLifeSciences</a></b> (🥇12 ·  ⭐ 350 · 💀) - Example code from the book Deep Learning for the Life Sciences. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://dmol.pub/">Deep Learning for Molecules and Materials Book</a></b> (🥈11 ·  ⭐ 610 · 💀) - Deep learning for molecules and materials book. <code><a href="https://github.com/whitead/dmol-book/blob/main/LICENSE">Custom</a></code>
+- <b><a href="https://dmol.pub/">Deep Learning for Molecules and Materials Book</a></b> (🥇11 ·  ⭐ 610 · 💀) - Deep learning for molecules and materials book. <code><a href="https://github.com/whitead/dmol-book/blob/main/LICENSE">Custom</a></code>
 - <b><a href="https://github.com/Materials-Consortia/optimade-tutorial-exercises">OPTIMADE Tutorial Exercises</a></b> (🥈9 ·  ⭐ 15 · 💀) - Tutorial exercises for the OPTIMADE API. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code>
 - <b><a href="https://github.com/rdkit/rdkit-tutorials">RDKit Tutorials</a></b> (🥈8 ·  ⭐ 260 · 💀) - Tutorials to learn how to work with the RDKit. <code><a href="https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE">Custom</a></code>
 - <b><a href="https://github.com/aimat-lab/MAChINE">MAChINE</a></b> (🥉7 ·  ⭐ 1 · 💀) - Client-Server Web App to introduce usage of ML in materials science to beginners. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/WardLT/applied-ai-for-materials">Applied AI for Materials</a></b> (🥉6 ·  ⭐ 58 · 💀) - Course materials for Applied AI for Materials Science and Engineering. <code>Unlicensed</code>
+- <b><a href="https://github.com/WardLT/applied-ai-for-materials">Applied AI for Materials</a></b> (🥉6 ·  ⭐ 59 · 💀) - Course materials for Applied AI for Materials Science and Engineering. <code>Unlicensed</code>
 - <b><a href="https://github.com/ulissigroup/ml_catalysis_tutorials">ML for catalysis tutorials</a></b> (🥉6 ·  ⭐ 8 · 💀) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/deepmodeling/AI4Science101">AI4Science101</a></b> (🥉5 ·  ⭐ 83 · 💀) - AI for Science. <code>Unlicensed</code>
+- <b><a href="https://github.com/deepmodeling/AI4Science101">AI4Science101</a></b> (🥉5 ·  ⭐ 84 · 💀) - AI for Science. <code>Unlicensed</code>
 - <b><a href="https://github.com/CompPhysVienna/MLSummerSchoolVienna2022">Machine Learning for Materials Hard and Soft</a></b> (🥉5 ·  ⭐ 34 · 💀) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. <code>Unlicensed</code>
 - <b><a href="https://github.com/Teoroo-CMC/DoE_Course_Material">Data Handling, DoE and Statistical Analysis for Material Chemists</a></b> (🥉5 ·  ⭐ 1 · 💀) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code>
 - <b><a href="https://github.com/BingqingCheng/ML-in-chemistry-101">ML-in-chemistry-101</a></b> (🥉4 ·  ⭐ 68 · 💀) - The course materials for Machine Learning in Chemistry 101. <code>Unlicensed</code>
-- <b><a href="https://github.com/gabor1/chemrev-gpr">chemrev-gpr</a></b> (🥉4 ·  ⭐ 6 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. <code>Unlicensed</code>
+- <b><a href="https://github.com/gabor1/chemrev-gpr">chemrev-gpr</a></b> (🥉4 ·  ⭐ 7 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. <code>Unlicensed</code>
 - <b><a href="https://github.com/ai4chemmat/ai4chemmat.github.io">AI4ChemMat Hands-On Series</a></b> (🥉4 ·  ⭐ 1) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab. <code><a href="http://bit.ly/3postzC">MPL-2.0</a></code>
 - <b><a href="https://github.com/Teoroo-CMC/PiNN_lab">PiNN Lab</a></b> (🥉3 ·  ⭐ 2 · 💀) - Material for running a lab session on atomic neural networks. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code>
 - <b><a href="https://github.com/bfocassio/MLDensity_tutorial">MLDensity_tutorial</a></b> (🥉2 ·  ⭐ 9 · 💀) - Tutorial files to work with ML for the charge density in molecules and solids. <code>Unlicensed</code>
@@ -752,21 +752,21 @@ _Tutorials, guides, cookbooks, recipes, etc._
 
 _Projects that focus on explainability and model interpretability in atomistic ML._
 
-<details><summary><b><a href="https://github.com/ur-whitelab/exmol">exmol</a></b> (🥇18 ·  ⭐ 280 · 💤) - Explainer for black box models that predict molecule properties. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/ur-whitelab/exmol">exmol</a></b> (🥇19 ·  ⭐ 280 · 💤) - Explainer for black box models that predict molecule properties. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/ur-whitelab/exmol) (👨‍💻 7 · 🔀 40 · 📦 20 · 📋 69 - 15% open · ⏱️ 04.12.2023):
+- [GitHub](https://github.com/ur-whitelab/exmol) (👨‍💻 7 · 🔀 41 · 📦 21 · 📋 69 - 15% open · ⏱️ 04.12.2023):
 
 	```
 	git clone https://github.com/ur-whitelab/exmol
 	```
-- [PyPi](https://pypi.org/project/exmol) (📥 1K / month · 📦 1 · ⏱️ 03.06.2022):
+- [PyPi](https://pypi.org/project/exmol) (📥 2.3K / month · 📦 1 · ⏱️ 03.06.2022):
 	```
 	pip install exmol
 	```
 </details>
-<details><summary><b><a href="https://github.com/aimat-lab/graph_attention_student">MEGAN: Multi Explanation Graph Attention Student</a></b> (🥉5 ·  ⭐ 5) - Minimal implementation of graph attention student model architecture. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+<details><summary><b><a href="https://github.com/aimat-lab/graph_attention_student">MEGAN: Multi Explanation Graph Attention Student</a></b> (🥉6 ·  ⭐ 5) - Minimal implementation of graph attention student model architecture. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨‍💻 2 · 🔀 1 · 📋 3 - 33% open · ⏱️ 19.08.2024):
+- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨‍💻 2 · 🔀 1 · 📋 3 - 33% open · ⏱️ 07.10.2024):
 
 	```
 	git clone https://github.com/aimat-lab/graph_attention_student
@@ -800,66 +800,66 @@ _Projects and models that focus on quantities of electronic structure methods, w
 
 _General tools for atomistic machine learning._
 
-<details><summary><b><a href="https://github.com/rdkit/rdkit">RDKit</a></b> (🥇35 ·  ⭐ 2.6K) -  <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>C++</code></summary>
+<details><summary><b><a href="https://github.com/deepchem/deepchem">DeepChem</a></b> (🥇35 ·  ⭐ 5.4K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 230 · 🔀 860 · 📥 1.1K · 📦 3 · 📋 3.4K - 29% open · ⏱️ 03.10.2024):
+- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 250 · 🔀 1.7K · 📦 440 · 📋 1.9K - 34% open · ⏱️ 17.10.2024):
 
 	```
-	git clone https://github.com/rdkit/rdkit
+	git clone https://github.com/deepchem/deepchem
 	```
-- [PyPi](https://pypi.org/project/rdkit) (📥 2.4M / month · 📦 730 · ⏱️ 07.08.2024):
+- [PyPi](https://pypi.org/project/deepchem) (📥 89K / month · 📦 13 · ⏱️ 17.10.2024):
 	```
-	pip install rdkit
+	pip install deepchem
 	```
-- [Conda](https://anaconda.org/rdkit/rdkit) (📥 2.6M · ⏱️ 16.06.2023):
+- [Conda](https://anaconda.org/conda-forge/deepchem) (📥 110K · ⏱️ 05.04.2024):
 	```
-	conda install -c rdkit rdkit
+	conda install -c conda-forge deepchem
+	```
+- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 7.8K · ⭐ 5 · ⏱️ 17.10.2024):
+	```
+	docker pull deepchemio/deepchem
 	```
 </details>
-<details><summary><b><a href="https://github.com/deepchem/deepchem">DeepChem</a></b> (🥇34 ·  ⭐ 5.4K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/rdkit/rdkit">RDKit</a></b> (🥇35 ·  ⭐ 2.6K) -  <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>C++</code></summary>
 
-- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 250 · 🔀 1.7K · 📦 430 · 📋 1.9K - 33% open · ⏱️ 20.09.2024):
+- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 240 · 🔀 870 · 📥 1.1K · 📦 3 · 📋 3.3K - 28% open · ⏱️ 17.10.2024):
 
 	```
-	git clone https://github.com/deepchem/deepchem
-	```
-- [PyPi](https://pypi.org/project/deepchem) (📥 44K / month · 📦 13 · ⏱️ 20.09.2024):
-	```
-	pip install deepchem
+	git clone https://github.com/rdkit/rdkit
 	```
-- [Conda](https://anaconda.org/conda-forge/deepchem) (📥 110K · ⏱️ 05.04.2024):
+- [PyPi](https://pypi.org/project/rdkit) (📥 2.4M / month · 📦 750 · ⏱️ 07.08.2024):
 	```
-	conda install -c conda-forge deepchem
+	pip install rdkit
 	```
-- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 7.7K · ⭐ 5 · ⏱️ 20.09.2024):
+- [Conda](https://anaconda.org/rdkit/rdkit) (📥 2.6M · ⏱️ 16.06.2023):
 	```
-	docker pull deepchemio/deepchem
+	conda install -c rdkit rdkit
 	```
 </details>
-<details><summary><b><a href="https://github.com/hackingmaterials/matminer">Matminer</a></b> (🥇28 ·  ⭐ 470) - Data mining for materials science. <code><a href="https://github.com/hackingmaterials/matminer/blob/main/LICENSE">Custom</a></code></summary>
+<details><summary><b><a href="https://github.com/hackingmaterials/matminer">Matminer</a></b> (🥇29 ·  ⭐ 480) - Data mining for materials science. <code><a href="https://github.com/hackingmaterials/matminer/blob/main/LICENSE">Custom</a></code></summary>
 
-- [GitHub](https://github.com/hackingmaterials/matminer) (👨‍💻 55 · 🔀 190 · 📦 320 · 📋 230 - 12% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/hackingmaterials/matminer) (👨‍💻 56 · 🔀 190 · 📦 320 · 📋 230 - 13% open · ⏱️ 11.10.2024):
 
 	```
 	git clone https://github.com/hackingmaterials/matminer
 	```
-- [PyPi](https://pypi.org/project/matminer) (📥 14K / month · 📦 58 · ⏱️ 27.03.2024):
+- [PyPi](https://pypi.org/project/matminer) (📥 20K / month · 📦 60 · ⏱️ 06.10.2024):
 	```
 	pip install matminer
 	```
-- [Conda](https://anaconda.org/conda-forge/matminer) (📥 71K · ⏱️ 28.03.2024):
+- [Conda](https://anaconda.org/conda-forge/matminer) (📥 72K · ⏱️ 06.10.2024):
 	```
 	conda install -c conda-forge matminer
 	```
 </details>
-<details><summary><b><a href="https://github.com/libAtoms/QUIP">QUIP</a></b> (🥈27 ·  ⭐ 350 · 📈) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_engineering"><code>rep-eng</code></a> <code>Fortran</code></summary>
+<details><summary><b><a href="https://github.com/libAtoms/QUIP">QUIP</a></b> (🥈26 ·  ⭐ 350 · 📉) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_engineering"><code>rep-eng</code></a> <code>Fortran</code></summary>
 
-- [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 85 · 🔀 120 · 📥 670 · 📦 42 · 📋 470 - 22% open · ⏱️ 27.09.2024):
+- [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 85 · 🔀 120 · 📥 680 · 📦 43 · 📋 470 - 22% open · ⏱️ 27.09.2024):
 
 	```
 	git clone https://github.com/libAtoms/QUIP
 	```
-- [PyPi](https://pypi.org/project/quippy-ase) (📥 9.5K / month · 📦 4 · ⏱️ 15.01.2023):
+- [PyPi](https://pypi.org/project/quippy-ase) (📥 12K / month · 📦 4 · ⏱️ 15.01.2023):
 	```
 	pip install quippy-ase
 	```
@@ -870,35 +870,35 @@ _General tools for atomistic machine learning._
 </details>
 <details><summary><b><a href="https://github.com/materialsvirtuallab/maml">MAML</a></b> (🥈24 ·  ⭐ 360) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
 
-- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 33 · 🔀 78 · 📦 10 · 📋 71 - 12% open · ⏱️ 18.09.2024):
+- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 33 · 🔀 78 · 📦 11 · 📋 71 - 12% open · ⏱️ 10.10.2024):
 
 	```
 	git clone https://github.com/materialsvirtuallab/maml
 	```
-- [PyPi](https://pypi.org/project/maml) (📥 520 / month · 📦 2 · ⏱️ 13.06.2024):
+- [PyPi](https://pypi.org/project/maml) (📥 1K / month · 📦 2 · ⏱️ 13.06.2024):
 	```
 	pip install maml
 	```
 </details>
-<details><summary><b><a href="https://github.com/usnistgov/jarvis">JARVIS-Tools</a></b> (🥈23 ·  ⭐ 300) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. <code><a href="https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst">Custom</a></code></summary>
+<details><summary><b><a href="https://github.com/usnistgov/jarvis">JARVIS-Tools</a></b> (🥈23 ·  ⭐ 310) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. <code><a href="https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst">Custom</a></code></summary>
 
-- [GitHub](https://github.com/usnistgov/jarvis) (👨‍💻 15 · 🔀 120 · 📦 99 · 📋 90 - 50% open · ⏱️ 07.09.2024):
+- [GitHub](https://github.com/usnistgov/jarvis) (👨‍💻 15 · 🔀 120 · 📦 100 · 📋 90 - 50% open · ⏱️ 07.09.2024):
 
 	```
 	git clone https://github.com/usnistgov/jarvis
 	```
-- [PyPi](https://pypi.org/project/jarvis-tools) (📥 20K / month · 📦 31 · ⏱️ 07.09.2024):
+- [PyPi](https://pypi.org/project/jarvis-tools) (📥 26K / month · 📦 31 · ⏱️ 07.09.2024):
 	```
 	pip install jarvis-tools
 	```
-- [Conda](https://anaconda.org/conda-forge/jarvis-tools) (📥 78K · ⏱️ 07.09.2024):
+- [Conda](https://anaconda.org/conda-forge/jarvis-tools) (📥 80K · ⏱️ 07.09.2024):
 	```
 	conda install -c conda-forge jarvis-tools
 	```
 </details>
-<details><summary><b><a href="https://github.com/uw-cmg/MAST-ML">MAST-ML</a></b> (🥈20 ·  ⭐ 100 · 📈) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/uw-cmg/MAST-ML">MAST-ML</a></b> (🥈20 ·  ⭐ 100) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨‍💻 19 · 🔀 58 · 📥 120 · 📦 43 · 📋 220 - 14% open · ⏱️ 27.09.2024):
+- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨‍💻 19 · 🔀 59 · 📥 120 · 📦 44 · 📋 220 - 14% open · ⏱️ 09.10.2024):
 
 	```
 	git clone https://github.com/uw-cmg/MAST-ML
@@ -906,42 +906,42 @@ _General tools for atomistic machine learning._
 </details>
 <details><summary><b><a href="https://github.com/scikit-learn-contrib/scikit-matter">Scikit-Matter</a></b> (🥈18 ·  ⭐ 76) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>scikit-learn</code></summary>
 
-- [GitHub](https://github.com/scikit-learn-contrib/scikit-matter) (👨‍💻 15 · 🔀 20 · 📦 10 · 📋 70 - 20% open · ⏱️ 06.08.2024):
+- [GitHub](https://github.com/scikit-learn-contrib/scikit-matter) (👨‍💻 15 · 🔀 20 · 📦 11 · 📋 70 - 20% open · ⏱️ 09.10.2024):
 
 	```
 	git clone https://github.com/scikit-learn-contrib/scikit-matter
 	```
-- [PyPi](https://pypi.org/project/skmatter) (📥 2.2K / month · ⏱️ 24.08.2023):
+- [PyPi](https://pypi.org/project/skmatter) (📥 3.1K / month · ⏱️ 24.08.2023):
 	```
 	pip install skmatter
 	```
-- [Conda](https://anaconda.org/conda-forge/skmatter) (📥 2.2K · ⏱️ 24.08.2023):
+- [Conda](https://anaconda.org/conda-forge/skmatter) (📥 2.3K · ⏱️ 24.08.2023):
 	```
 	conda install -c conda-forge skmatter
 	```
 </details>
-<details><summary><b><a href="https://github.com/dralgroup/mlatom">MLatom</a></b> (🥈16 ·  ⭐ 48) - AI-enhanced computational chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-DFT</code> <code>ML-ESM</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Spectroscopy"><code>spectroscopy</code></a> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>structure-optimization</code></a></summary>
+<details><summary><b><a href="https://github.com/yoshida-lab/XenonPy">XenonPy</a></b> (🥉16 ·  ⭐ 140) - XenonPy is a Python Software for Materials Informatics. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
 
-- [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 3 · 🔀 9 · 📋 4 - 25% open · ⏱️ 23.09.2024):
+- [GitHub](https://github.com/yoshida-lab/XenonPy) (👨‍💻 10 · 🔀 57 · 📥 1.4K · 📋 87 - 24% open · ⏱️ 21.04.2024):
 
 	```
-	git clone https://github.com/dralgroup/mlatom
+	git clone https://github.com/yoshida-lab/XenonPy
 	```
-- [PyPi](https://pypi.org/project/mlatom) (📥 1.6K / month · ⏱️ 23.09.2024):
+- [PyPi](https://pypi.org/project/xenonpy) (📥 1.9K / month · 📦 1 · ⏱️ 31.10.2022):
 	```
-	pip install mlatom
+	pip install xenonpy
 	```
 </details>
-<details><summary><b><a href="https://github.com/yoshida-lab/XenonPy">XenonPy</a></b> (🥉15 ·  ⭐ 140) - XenonPy is a Python Software for Materials Informatics. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
+<details><summary><b><a href="https://github.com/dralgroup/mlatom">MLatom</a></b> (🥉16 ·  ⭐ 53) - AI-enhanced computational chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-DFT</code> <code>ML-ESM</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Spectroscopy"><code>spectroscopy</code></a> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>structure-optimization</code></a></summary>
 
-- [GitHub](https://github.com/yoshida-lab/XenonPy) (👨‍💻 10 · 🔀 57 · 📥 1.4K · 📋 87 - 24% open · ⏱️ 21.04.2024):
+- [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 3 · 🔀 10 · 📋 4 - 25% open · ⏱️ 09.10.2024):
 
 	```
-	git clone https://github.com/yoshida-lab/XenonPy
+	git clone https://github.com/dralgroup/mlatom
 	```
-- [PyPi](https://pypi.org/project/xenonpy) (📥 670 / month · 📦 1 · ⏱️ 31.10.2022):
+- [PyPi](https://pypi.org/project/mlatom) (📥 2.6K / month · ⏱️ 09.10.2024):
 	```
-	pip install xenonpy
+	pip install mlatom
 	```
 </details>
 <details><summary><b><a href="https://github.com/divelab/AIRS">Artificial Intelligence for Science (AIRS)</a></b> (🥉13 ·  ⭐ 500) - Artificial Intelligence Research for Science (AIRS). <code><a href="https://tldrlegal.com/search?q=GPL-3.0%20license">GPL-3.0 license</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-DFT</code> <code>ML-WFT</code> <a href="https://en.wikipedia.org/wiki/Biomolecule"><code>biomolecules</code></a></summary>
@@ -962,12 +962,12 @@ _General tools for atomistic machine learning._
 </details>
 <details><summary>Show 10 hidden projects...</summary>
 
-- <b><a href="https://github.com/qmlcode/qml">QML</a></b> (🥈16 ·  ⭐ 200 · 💀) - QML: Quantum Machine Learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/hackingmaterials/automatminer">Automatminer</a></b> (🥉15 ·  ⭐ 140 · 💀) - An automatic engine for predicting materials properties. <code><a href="https://github.com/hackingmaterials/automatminer/blob/main/LICENSE">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Automated_machine_learning"><code>autoML</code></a>
+- <b><a href="https://github.com/qmlcode/qml">QML</a></b> (🥈17 ·  ⭐ 200 · 💀) - QML: Quantum Machine Learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/hackingmaterials/automatminer">Automatminer</a></b> (🥉16 ·  ⭐ 140 · 💀) - An automatic engine for predicting materials properties. <code><a href="https://github.com/hackingmaterials/automatminer/blob/main/LICENSE">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Automated_machine_learning"><code>autoML</code></a>
 - <b><a href="https://github.com/ulissigroup/amptorch">AMPtorch</a></b> (🥉11 ·  ⭐ 59 · 💀) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code>
 - <b><a href="https://github.com/Mariewelt/OpenChem">OpenChem</a></b> (🥉10 ·  ⭐ 670 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/deepchem/jaxchem">JAXChem</a></b> (🥉7 ·  ⭐ 79 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/ulissigroup/uncertainty_benchmarking">uncertainty_benchmarking</a></b> (🥉7 ·  ⭐ 39 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. <code>Unlicensed</code> <code>benchmarking</code> <code>probabilistic</code>
+- <b><a href="https://github.com/ulissigroup/uncertainty_benchmarking">uncertainty_benchmarking</a></b> (🥉7 ·  ⭐ 40 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. <code>Unlicensed</code> <code>benchmarking</code> <code>probabilistic</code>
 - <b><a href="https://github.com/deepchem/torchchem">torchchem</a></b> (🥉7 ·  ⭐ 35 · 💀) - An experimental repo for experimenting with PyTorch models. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/ACEsuit/ACEatoms.jl">ACEatoms</a></b> (🥉4 ·  ⭐ 2 · 💀) - Generic code for modelling atomic properties using ACE. <code><a href="https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md">Custom</a></code> <code>Julia</code>
 - <b><a href="https://bitbucket.org/wolverton/magpie/">Magpie</a></b> (🥉3) - Materials Agnostic Platform for Informatics and Exploration (Magpie). <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>Java</code>
@@ -983,41 +983,41 @@ _Projects that implement generative models for atomistic ML._
 
 <details><summary><b><a href="https://github.com/GT4SD/gt4sd-core">GT4SD</a></b> (🥇18 ·  ⭐ 340) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 68 · 📋 110 - 12% open · ⏱️ 12.09.2024):
+- [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 69 · 📋 110 - 12% open · ⏱️ 12.09.2024):
 
 	```
 	git clone https://github.com/GT4SD/gt4sd-core
 	```
-- [PyPi](https://pypi.org/project/gt4sd) (📥 2.1K / month · ⏱️ 12.09.2024):
+- [PyPi](https://pypi.org/project/gt4sd) (📥 4.9K / month · ⏱️ 12.09.2024):
 	```
 	pip install gt4sd
 	```
 </details>
-<details><summary><b><a href="https://github.com/microsoft/molecule-generation">MoLeR</a></b> (🥇15 ·  ⭐ 260 · 💤) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/microsoft/molecule-generation">MoLeR</a></b> (🥇15 ·  ⭐ 270 · 💤) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/microsoft/molecule-generation) (👨‍💻 5 · 🔀 42 · 📋 39 - 23% open · ⏱️ 03.01.2024):
+- [GitHub](https://github.com/microsoft/molecule-generation) (👨‍💻 5 · 🔀 42 · 📋 39 - 20% open · ⏱️ 03.01.2024):
 
 	```
 	git clone https://github.com/microsoft/molecule-generation
 	```
-- [PyPi](https://pypi.org/project/molecule-generation) (📥 320 / month · 📦 1 · ⏱️ 05.01.2024):
+- [PyPi](https://pypi.org/project/molecule-generation) (📥 500 / month · 📦 1 · ⏱️ 05.01.2024):
 	```
 	pip install molecule-generation
 	```
 </details>
-<details><summary><b><a href="https://github.com/hspark1212/MOFTransformer">PMTransformer</a></b> (🥇15 ·  ⭐ 85) - Universal Transfer Learning in Porous Materials, including MOFs. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
+<details><summary><b><a href="https://github.com/hspark1212/MOFTransformer">PMTransformer</a></b> (🥇15 ·  ⭐ 84) - Universal Transfer Learning in Porous Materials, including MOFs. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
 
-- [GitHub](https://github.com/hspark1212/MOFTransformer) (👨‍💻 2 · 🔀 12 · 📦 6 · ⏱️ 20.06.2024):
+- [GitHub](https://github.com/hspark1212/MOFTransformer) (👨‍💻 2 · 🔀 12 · 📦 7 · ⏱️ 20.06.2024):
 
 	```
 	git clone https://github.com/hspark1212/MOFTransformer
 	```
-- [PyPi](https://pypi.org/project/moftransformer) (📥 440 / month · 📦 1 · ⏱️ 20.06.2024):
+- [PyPi](https://pypi.org/project/moftransformer) (📥 1K / month · 📦 1 · ⏱️ 20.06.2024):
 	```
 	pip install moftransformer
 	```
 </details>
-<details><summary><b><a href="https://github.com/atomistic-machine-learning/schnetpack-gschnet">SchNetPack G-SchNet</a></b> (🥈12 ·  ⭐ 46 · 📉) - G-SchNet extension for SchNetPack. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/atomistic-machine-learning/schnetpack-gschnet">SchNetPack G-SchNet</a></b> (🥈12 ·  ⭐ 47) - G-SchNet extension for SchNetPack. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
 - [GitHub](https://github.com/atomistic-machine-learning/schnetpack-gschnet) (👨‍💻 3 · 🔀 8 · ⏱️ 05.09.2024):
 
@@ -1025,21 +1025,21 @@ _Projects that implement generative models for atomistic ML._
 	git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet
 	```
 </details>
-<details><summary><b><a href="https://github.com/RokasEl/simgen">SiMGen</a></b> (🥈9 ·  ⭐ 12 · 💤) - Zero Shot Molecular Generation via Similarity Kernels. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Visualization"><code>viz</code></a></summary>
+<details><summary><b><a href="https://github.com/RokasEl/simgen">SiMGen</a></b> (🥈9 ·  ⭐ 13 · 💤) - Zero Shot Molecular Generation via Similarity Kernels. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Visualization"><code>viz</code></a></summary>
 
-- [GitHub](https://github.com/RokasEl/simgen) (👨‍💻 4 · 🔀 2 · 📦 1 · 📋 4 - 25% open · ⏱️ 15.02.2024):
+- [GitHub](https://github.com/RokasEl/simgen) (👨‍💻 4 · 🔀 2 · 📦 2 · 📋 4 - 25% open · ⏱️ 15.02.2024):
 
 	```
 	git clone https://github.com/RokasEl/simgen
 	```
-- [PyPi](https://pypi.org/project/simgen) (📥 47 / month · ⏱️ 14.02.2024):
+- [PyPi](https://pypi.org/project/simgen) (📥 71 / month · ⏱️ 14.02.2024):
 	```
 	pip install simgen
 	```
 </details>
-<details><summary><b><a href="https://github.com/terraytherapeutics/COATI">COATI</a></b> (🥉5 ·  ⭐ 98 · 💤) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
+<details><summary><b><a href="https://github.com/terraytherapeutics/COATI">COATI</a></b> (🥉5 ·  ⭐ 100 · 💤) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/terraytherapeutics/COATI) (👨‍💻 5 · 🔀 5 · 📋 3 - 33% open · ⏱️ 23.03.2024):
+- [GitHub](https://github.com/terraytherapeutics/COATI) (👨‍💻 5 · 🔀 6 · 📋 3 - 33% open · ⏱️ 23.03.2024):
 
 	```
 	git clone https://github.com/terraytherapeutics/COATI
@@ -1048,7 +1048,7 @@ _Projects that implement generative models for atomistic ML._
 <details><summary>Show 8 hidden projects...</summary>
 
 - <b><a href="https://github.com/whitead/synspace">synspace</a></b> (🥈13 ·  ⭐ 35 · 💀) - Synthesis generative model. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/ehoogeboom/e3_diffusion_for_molecules">EDM</a></b> (🥈9 ·  ⭐ 430 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/ehoogeboom/e3_diffusion_for_molecules">EDM</a></b> (🥈9 ·  ⭐ 440 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/atomistic-machine-learning/G-SchNet">G-SchNet</a></b> (🥉8 ·  ⭐ 130 · 💀) - G-SchNet - a generative model for 3d molecular structures. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/tsudalab/bVAE-IM">bVAE-IM</a></b> (🥉8 ·  ⭐ 11 · 💀) - Implementation of Chemical Design with GPU-based Ising Machine. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Quantum_machine_learning"><code>QML</code></a> <code>single-paper</code>
 - <b><a href="https://github.com/atomistic-machine-learning/cG-SchNet">cG-SchNet</a></b> (🥉7 ·  ⭐ 52 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
@@ -1064,18 +1064,18 @@ _Projects that implement generative models for atomistic ML._
 
 _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics._
 
-<details><summary><b><a href="https://github.com/deepmodeling/deepmd-kit">DeePMD-kit</a></b> (🥇25 ·  ⭐ 1.5K · 📉) - A deep learning package for many-body potential energy representation and molecular dynamics. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>C++</code></summary>
+<details><summary><b><a href="https://github.com/deepmodeling/deepmd-kit">DeePMD-kit</a></b> (🥇26 ·  ⭐ 1.5K) - A deep learning package for many-body potential energy representation and molecular dynamics. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>C++</code></summary>
 
-- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 500 · 📥 40K · 📦 16 · 📋 790 - 12% open · ⏱️ 17.09.2024):
+- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 510 · 📥 41K · 📦 17 · 📋 810 - 12% open · ⏱️ 17.09.2024):
 
 	```
 	git clone https://github.com/deepmodeling/deepmd-kit
 	```
-- [PyPi](https://pypi.org/project/deepmd-kit) (📥 3.2K / month · 📦 4 · ⏱️ 25.09.2024):
+- [PyPi](https://pypi.org/project/deepmd-kit) (📥 10K / month · 📦 4 · ⏱️ 25.09.2024):
 	```
 	pip install deepmd-kit
 	```
-- [Conda](https://anaconda.org/deepmodeling/deepmd-kit) (📥 1.3K · ⏱️ 06.04.2024):
+- [Conda](https://anaconda.org/deepmodeling/deepmd-kit) (📥 1.4K · ⏱️ 06.04.2024):
 	```
 	conda install -c deepmodeling deepmd-kit
 	```
@@ -1086,72 +1086,64 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 </details>
 <details><summary><b><a href="https://github.com/aiqm/torchani">TorchANI</a></b> (🥇24 ·  ⭐ 460 · 💤) - Accurate Neural Network Potential on PyTorch. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/aiqm/torchani) (👨‍💻 19 · 🔀 130 · 📦 42 · 📋 170 - 13% open · ⏱️ 14.11.2023):
+- [GitHub](https://github.com/aiqm/torchani) (👨‍💻 19 · 🔀 120 · 📦 43 · 📋 170 - 13% open · ⏱️ 14.11.2023):
 
 	```
 	git clone https://github.com/aiqm/torchani
 	```
-- [PyPi](https://pypi.org/project/torchani) (📥 3K / month · 📦 4 · ⏱️ 14.11.2023):
+- [PyPi](https://pypi.org/project/torchani) (📥 4.9K / month · 📦 4 · ⏱️ 14.11.2023):
 	```
 	pip install torchani
 	```
-- [Conda](https://anaconda.org/conda-forge/torchani) (📥 490K · ⏱️ 11.09.2024):
+- [Conda](https://anaconda.org/conda-forge/torchani) (📥 510K · ⏱️ 11.09.2024):
 	```
 	conda install -c conda-forge torchani
 	```
 </details>
-<details><summary><b><a href="https://github.com/mir-group/nequip">NequIP</a></b> (🥇23 ·  ⭐ 610 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/mir-group/nequip">NequIP</a></b> (🥇23 ·  ⭐ 620) - NequIP is a code for building E(3)-equivariant interatomic potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 11 · 🔀 130 · 📦 25 · 📋 92 - 28% open · ⏱️ 09.07.2024):
+- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 11 · 🔀 140 · 📦 27 · 📋 95 - 26% open · ⏱️ 09.07.2024):
 
 	```
 	git clone https://github.com/mir-group/nequip
 	```
-- [PyPi](https://pypi.org/project/nequip) (📥 3K / month · 📦 1 · ⏱️ 09.07.2024):
+- [PyPi](https://pypi.org/project/nequip) (📥 4.2K / month · 📦 1 · ⏱️ 09.07.2024):
 	```
 	pip install nequip
 	```
-- [Conda](https://anaconda.org/conda-forge/nequip) (📥 6K · ⏱️ 10.07.2024):
+- [Conda](https://anaconda.org/conda-forge/nequip) (📥 6.2K · ⏱️ 10.07.2024):
 	```
 	conda install -c conda-forge nequip
 	```
 </details>
-<details><summary><b><a href="https://github.com/ACEsuit/mace">MACE</a></b> (🥇22 ·  ⭐ 490) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/ACEsuit/mace">MACE</a></b> (🥇22 ·  ⭐ 500) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 41 · 🔀 180 · 📋 280 - 25% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 42 · 🔀 190 · 📋 270 - 22% open · ⏱️ 10.10.2024):
 
 	```
 	git clone https://github.com/ACEsuit/mace
 	```
 </details>
-<details><summary><b><a href="https://github.com/torchmd/torchmd-net">TorchMD-NET</a></b> (🥇22 ·  ⭐ 320) - Training neural network potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <code>pretrained</code></summary>
+<details><summary><b><a href="https://github.com/torchmd/torchmd-net">TorchMD-NET</a></b> (🥇22 ·  ⭐ 330) - Training neural network potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <code>pretrained</code></summary>
 
 - [GitHub](https://github.com/torchmd/torchmd-net) (👨‍💻 16 · 🔀 71 · 📋 120 - 28% open · ⏱️ 28.08.2024):
 
 	```
 	git clone https://github.com/torchmd/torchmd-net
 	```
-- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 180K · ⏱️ 12.09.2024):
+- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 190K · ⏱️ 12.09.2024):
 	```
 	conda install -c conda-forge torchmd-net
 	```
 </details>
-<details><summary><b><a href="https://github.com/brucefan1983/GPUMD">GPUMD</a></b> (🥇21 ·  ⭐ 450) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>C++</code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a></summary>
+<details><summary><b><a href="https://github.com/deepmodeling/dpgen">DP-GEN</a></b> (🥇22 ·  ⭐ 300) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>workflows</code></summary>
 
-- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 34 · 🔀 120 · 📋 180 - 9% open · ⏱️ 01.10.2024):
-
-	```
-	git clone https://github.com/brucefan1983/GPUMD
-	```
-</details>
-<details><summary><b><a href="https://github.com/deepmodeling/dpgen">DP-GEN</a></b> (🥇21 ·  ⭐ 300) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>workflows</code></summary>
-
-- [GitHub](https://github.com/deepmodeling/dpgen) (👨‍💻 64 · 🔀 170 · 📥 1.8K · 📦 6 · 📋 300 - 11% open · ⏱️ 10.04.2024):
+- [GitHub](https://github.com/deepmodeling/dpgen) (👨‍💻 64 · 🔀 170 · 📥 1.8K · 📦 7 · 📋 300 - 12% open · ⏱️ 10.04.2024):
 
 	```
 	git clone https://github.com/deepmodeling/dpgen
 	```
-- [PyPi](https://pypi.org/project/dpgen) (📥 760 / month · 📦 1 · ⏱️ 10.04.2024):
+- [PyPi](https://pypi.org/project/dpgen) (📥 1.4K / month · 📦 1 · ⏱️ 10.04.2024):
 	```
 	pip install dpgen
 	```
@@ -1160,27 +1152,35 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	conda install -c deepmodeling dpgen
 	```
 </details>
-<details><summary><b><a href="https://github.com/FAIR-Chem/fairchem">fairchem</a></b> (🥈19 ·  ⭐ 770) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp). <code>Unlicensed</code> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a></summary>
+<details><summary><b><a href="https://github.com/brucefan1983/GPUMD">GPUMD</a></b> (🥈21 ·  ⭐ 460) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>C++</code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a></summary>
 
-- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 230 · 📋 210 - 6% open · ⏱️ 01.10.2024):
+- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 34 · 🔀 120 · 📋 180 - 9% open · ⏱️ 16.10.2024):
+
+	```
+	git clone https://github.com/brucefan1983/GPUMD
+	```
+</details>
+<details><summary><b><a href="https://github.com/FAIR-Chem/fairchem">fairchem</a></b> (🥈19 ·  ⭐ 780) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp). <code>Unlicensed</code> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a></summary>
+
+- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 240 · 📋 230 - 13% open · ⏱️ 14.10.2024):
 
 	```
 	git clone https://github.com/FAIR-Chem/fairchem
 	```
 </details>
-<details><summary><b><a href="https://github.com/apax-hub/apax">apax</a></b> (🥈18 ·  ⭐ 15) - A flexible and performant framework for training machine learning potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/apax-hub/apax">apax</a></b> (🥈19 ·  ⭐ 15) - A flexible and performant framework for training machine learning potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 7 · 🔀 2 · 📦 2 · 📋 120 - 10% open · ⏱️ 01.10.2024):
+- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 7 · 🔀 2 · 📦 3 · 📋 120 - 10% open · ⏱️ 17.10.2024):
 
 	```
 	git clone https://github.com/apax-hub/apax
 	```
-- [PyPi](https://pypi.org/project/apax) (📥 280 / month · ⏱️ 17.09.2024):
+- [PyPi](https://pypi.org/project/apax) (📥 1K / month · ⏱️ 17.09.2024):
 	```
 	pip install apax
 	```
 </details>
-<details><summary><b><a href="https://github.com/learningmatter-mit/NeuralForceField">Neural Force Field</a></b> (🥈17 ·  ⭐ 230) - Neural Network Force Field based on PyTorch. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code></summary>
+<details><summary><b><a href="https://github.com/learningmatter-mit/NeuralForceField">Neural Force Field</a></b> (🥈16 ·  ⭐ 230) - Neural Network Force Field based on PyTorch. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code></summary>
 
 - [GitHub](https://github.com/learningmatter-mit/NeuralForceField) (👨‍💻 41 · 🔀 48 · 📋 20 - 10% open · ⏱️ 24.09.2024):
 
@@ -1188,60 +1188,72 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	git clone https://github.com/learningmatter-mit/NeuralForceField
 	```
 </details>
-<details><summary><b><a href="https://github.com/MaterSim/PyXtal_FF">PyXtalFF</a></b> (🥈15 ·  ⭐ 85 · 💤) - Machine Learning Interatomic Potential Predictions. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/openkim/kliff">KLIFF</a></b> (🥈16 ·  ⭐ 34 · 📈) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. <code><a href="https://tldrlegal.com/search?q=LGPL-2.1">LGPL-2.1</a></code> <code>probabilistic</code> <code>workflows</code></summary>
 
-- [GitHub](https://github.com/MaterSim/PyXtal_FF) (👨‍💻 9 · 🔀 23 · 📋 63 - 19% open · ⏱️ 07.01.2024):
+- [GitHub](https://github.com/openkim/kliff) (👨‍💻 9 · 🔀 20 · 📦 4 · 📋 41 - 53% open · ⏱️ 08.10.2024):
 
 	```
-	git clone https://github.com/MaterSim/PyXtal_FF
+	git clone https://github.com/openkim/kliff
 	```
-- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 230 / month · ⏱️ 21.12.2022):
+- [PyPi](https://pypi.org/project/kliff) (📥 770 / month · ⏱️ 17.12.2023):
 	```
-	pip install pyxtal_ff
+	pip install kliff
+	```
+- [Conda](https://anaconda.org/conda-forge/kliff) (📥 110K · ⏱️ 10.09.2024):
+	```
+	conda install -c conda-forge kliff
 	```
 </details>
-<details><summary><b><a href="https://github.com/uf3/uf3">Ultra-Fast Force Fields (UF3)</a></b> (🥈15 ·  ⭐ 60) - UF3: a python library for generating ultra-fast interatomic potentials. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
+<details><summary><b><a href="https://github.com/MaterSim/PyXtal_FF">PyXtalFF</a></b> (🥈15 ·  ⭐ 85 · 💤) - Machine Learning Interatomic Potential Predictions. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/uf3/uf3) (👨‍💻 10 · 🔀 20 · 📦 1 · 📋 50 - 38% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/MaterSim/PyXtal_FF) (👨‍💻 9 · 🔀 23 · 📋 63 - 19% open · ⏱️ 07.01.2024):
 
 	```
-	git clone https://github.com/uf3/uf3
+	git clone https://github.com/MaterSim/PyXtal_FF
 	```
-- [PyPi](https://pypi.org/project/uf3) (📥 51 / month · ⏱️ 27.10.2023):
+- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 540 / month · ⏱️ 21.12.2022):
 	```
-	pip install uf3
+	pip install pyxtal_ff
 	```
 </details>
-<details><summary><b><a href="https://github.com/libAtoms/workflow">wfl</a></b> (🥈15 ·  ⭐ 31) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <code>workflows</code> <a href="https://en.wikipedia.org/wiki/High-throughput_computing"><code>HTC</code></a></summary>
+<details><summary><b><a href="https://github.com/libAtoms/workflow">wfl</a></b> (🥈15 ·  ⭐ 32) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <code>workflows</code> <a href="https://en.wikipedia.org/wiki/High-throughput_computing"><code>HTC</code></a></summary>
 
-- [GitHub](https://github.com/libAtoms/workflow) (👨‍💻 19 · 🔀 18 · 📦 1 · 📋 160 - 42% open · ⏱️ 03.09.2024):
+- [GitHub](https://github.com/libAtoms/workflow) (👨‍💻 19 · 🔀 18 · 📦 2 · 📋 160 - 42% open · ⏱️ 11.10.2024):
 
 	```
 	git clone https://github.com/libAtoms/workflow
 	```
 </details>
-<details><summary><b><a href="https://github.com/thorben-frank/mlff">So3krates (MLFF)</a></b> (🥈14 ·  ⭐ 79) - Build neural networks for machine learning force fields with JAX. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/thorben-frank/mlff">So3krates (MLFF)</a></b> (🥈14 ·  ⭐ 82) - Build neural networks for machine learning force fields with JAX. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 15 · 📋 9 - 33% open · ⏱️ 23.08.2024):
+- [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 16 · 📋 10 - 40% open · ⏱️ 23.08.2024):
 
 	```
 	git clone https://github.com/thorben-frank/mlff
 	```
 </details>
-<details><summary><b><a href="https://github.com/openkim/kliff">KLIFF</a></b> (🥈14 ·  ⭐ 34) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. <code><a href="https://tldrlegal.com/search?q=LGPL-2.1">LGPL-2.1</a></code> <code>probabilistic</code> <code>workflows</code></summary>
+<details><summary><b><a href="https://github.com/openmm/NNPOps">NNPOps</a></b> (🥈14 ·  ⭐ 81) - High-performance operations for neural network potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>C++</code></summary>
 
-- [GitHub](https://github.com/openkim/kliff) (👨‍💻 9 · 🔀 20 · 📦 3 · 📋 41 - 53% open · ⏱️ 06.07.2024):
+- [GitHub](https://github.com/openmm/NNPOps) (👨‍💻 9 · 🔀 18 · 📋 56 - 39% open · ⏱️ 10.07.2024):
 
 	```
-	git clone https://github.com/openkim/kliff
+	git clone https://github.com/openmm/NNPOps
 	```
-- [PyPi](https://pypi.org/project/kliff) (📥 450 / month · ⏱️ 17.12.2023):
+- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 250K · ⏱️ 11.09.2024):
 	```
-	pip install kliff
+	conda install -c conda-forge nnpops
 	```
-- [Conda](https://anaconda.org/conda-forge/kliff) (📥 110K · ⏱️ 10.09.2024):
+</details>
+<details><summary><b><a href="https://github.com/uf3/uf3">Ultra-Fast Force Fields (UF3)</a></b> (🥈14 ·  ⭐ 60) - UF3: a python library for generating ultra-fast interatomic potentials. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
+
+- [GitHub](https://github.com/uf3/uf3) (👨‍💻 10 · 🔀 20 · 📦 2 · 📋 50 - 38% open · ⏱️ 04.10.2024):
+
 	```
-	conda install -c conda-forge kliff
+	git clone https://github.com/uf3/uf3
+	```
+- [PyPi](https://pypi.org/project/uf3) (📥 100 / month · ⏱️ 27.10.2023):
+	```
+	pip install uf3
 	```
 </details>
 <details><summary><b><a href="https://github.com/deepmodeling/DMFF">DMFF</a></b> (🥈13 ·  ⭐ 150 · 💤) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
@@ -1252,16 +1264,16 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	git clone https://github.com/deepmodeling/DMFF
 	```
 </details>
-<details><summary><b><a href="https://github.com/openmm/NNPOps">NNPOps</a></b> (🥈13 ·  ⭐ 81) - High-performance operations for neural network potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>C++</code></summary>
+<details><summary><b><a href="https://cortner.github.io/ACEweb/software/">Pacemaker</a></b> (🥈13 ·  ⭐ 70) - Python package for fitting atomic cluster expansion (ACE) potentials. <code><a href="https://github.com/ICAMS/python-ace/blob/master/LICENSE.md">Custom</a></code></summary>
 
-- [GitHub](https://github.com/openmm/NNPOps) (👨‍💻 9 · 🔀 17 · 📋 55 - 38% open · ⏱️ 10.07.2024):
+- [GitHub](https://github.com/ICAMS/python-ace) (👨‍💻 6 · 🔀 17 · 📋 57 - 35% open · ⏱️ 10.10.2024):
 
 	```
-	git clone https://github.com/openmm/NNPOps
+	git clone https://github.com/ICAMS/python-ace
 	```
-- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 240K · ⏱️ 11.09.2024):
+- [PyPi](https://pypi.org/project/python-ace) (📥 29 / month · ⏱️ 24.10.2022):
 	```
-	conda install -c conda-forge nnpops
+	pip install python-ace
 	```
 </details>
 <details><summary><b><a href="https://github.com/isayev/ASE_ANI">ANI-1</a></b> (🥈12 ·  ⭐ 220 · 💤) - ANI-1 neural net potential with python interface (ASE). <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
@@ -1272,7 +1284,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	git clone https://github.com/isayev/ASE_ANI
 	```
 </details>
-<details><summary><b><a href="https://github.com/Teoroo-CMC/PiNN">PiNN</a></b> (🥈12 ·  ⭐ 100) - A Python library for building atomic neural networks. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
+<details><summary><b><a href="https://github.com/Teoroo-CMC/PiNN">PiNN</a></b> (🥈12 ·  ⭐ 110) - A Python library for building atomic neural networks. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
 
 - [GitHub](https://github.com/Teoroo-CMC/PiNN) (👨‍💻 5 · 🔀 32 · 📋 6 - 16% open · ⏱️ 27.06.2024):
 
@@ -1284,18 +1296,6 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	docker pull teoroo/pinn
 	```
 </details>
-<details><summary><b><a href="https://cortner.github.io/ACEweb/software/">Pacemaker</a></b> (🥈12 ·  ⭐ 69) - Python package for fitting atomic cluster expansion (ACE) potentials. <code><a href="https://github.com/ICAMS/python-ace/blob/master/LICENSE.md">Custom</a></code></summary>
-
-- [GitHub](https://github.com/ICAMS/python-ace) (👨‍💻 6 · 🔀 16 · 📋 53 - 30% open · ⏱️ 06.09.2024):
-
-	```
-	git clone https://github.com/ICAMS/python-ace
-	```
-- [PyPi](https://pypi.org/project/python-ace) (📥 17 / month · ⏱️ 24.10.2022):
-	```
-	pip install python-ace
-	```
-</details>
 <details><summary><b><a href="https://github.com/Teoroo-CMC/CCS">CCS_fit</a></b> (🥈12 ·  ⭐ 8 · 💤) - Curvature Constrained Splines. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code></summary>
 
 - [GitHub](https://github.com/Teoroo-CMC/CCS) (👨‍💻 8 · 🔀 11 · 📥 650 · 📋 14 - 57% open · ⏱️ 16.02.2024):
@@ -1303,14 +1303,14 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	```
 	git clone https://github.com/Teoroo-CMC/CCS
 	```
-- [PyPi](https://pypi.org/project/ccs_fit) (📥 1.4K / month · ⏱️ 16.02.2024):
+- [PyPi](https://pypi.org/project/ccs_fit) (📥 5.7K / month · ⏱️ 16.02.2024):
 	```
 	pip install ccs_fit
 	```
 </details>
 <details><summary><b><a href="https://github.com/ACEsuit/ACEfit.jl">ACEfit</a></b> (🥈12 ·  ⭐ 7) -  <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>Julia</code></summary>
 
-- [GitHub](https://github.com/ACEsuit/ACEfit.jl) (👨‍💻 8 · 🔀 6 · 📋 57 - 38% open · ⏱️ 14.09.2024):
+- [GitHub](https://github.com/ACEsuit/ACEfit.jl) (👨‍💻 8 · 🔀 7 · 📋 57 - 38% open · ⏱️ 14.09.2024):
 
 	```
 	git clone https://github.com/ACEsuit/ACEfit.jl
@@ -1326,7 +1326,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 </details>
 <details><summary><b><a href="https://calorine.materialsmodeling.org/">calorine</a></b> (🥉10 ·  ⭐ 12) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. <code><a href="https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE">Custom</a></code></summary>
 
-- [PyPi](https://pypi.org/project/calorine) (📥 2.2K / month · 📦 2 · ⏱️ 26.07.2024):
+- [PyPi](https://pypi.org/project/calorine) (📥 3.1K / month · 📦 2 · ⏱️ 26.07.2024):
 	```
 	pip install calorine
 	```
@@ -1370,7 +1370,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 </details>
 <details><summary><b><a href="https://github.com/ACEsuit/mace-jax">MACE-Jax</a></b> (🥉8 ·  ⭐ 60 · 💤) - Equivariant machine learning interatomic potentials in JAX. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/ACEsuit/mace-jax) (👨‍💻 2 · 🔀 5 · 📋 7 - 42% open · ⏱️ 04.10.2023):
+- [GitHub](https://github.com/ACEsuit/mace-jax) (👨‍💻 2 · 🔀 5 · 📋 8 - 50% open · ⏱️ 04.10.2023):
 
 	```
 	git clone https://github.com/ACEsuit/mace-jax
@@ -1402,26 +1402,26 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 </details>
 <details><summary><b><a href="https://github.com/mcaroba/turbogap">TurboGAP</a></b> (🥉8 ·  ⭐ 16) - The TurboGAP code. <code><a href="https://github.com/mcaroba/turbogap/blob/master/LICENSE.md">Custom</a></code> <code>Fortran</code></summary>
 
-- [GitHub](https://github.com/mcaroba/turbogap) (👨‍💻 8 · 🔀 9 · 📋 11 - 72% open · ⏱️ 30.09.2024):
+- [GitHub](https://github.com/mcaroba/turbogap) (👨‍💻 8 · 🔀 9 · 📋 11 - 72% open · ⏱️ 17.10.2024):
 
 	```
 	git clone https://github.com/mcaroba/turbogap
 	```
 </details>
-<details><summary><b><a href="https://github.com/RowleyGroup/MLXDM">MLXDM</a></b> (🥉8 ·  ⭐ 6) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>long-range</code></summary>
+<details><summary><b><a href="https://github.com/bigd4/PyNEP">PyNEP</a></b> (🥉7 ·  ⭐ 48) - A python interface of the machine learning potential NEP used in GPUMD. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/RowleyGroup/MLXDM) (👨‍💻 7 · 🔀 2 · ⏱️ 15.08.2024):
+- [GitHub](https://github.com/bigd4/PyNEP) (👨‍💻 7 · 🔀 16 · 📋 11 - 36% open · ⏱️ 01.06.2024):
 
 	```
-	git clone https://github.com/RowleyGroup/MLXDM
+	git clone https://github.com/bigd4/PyNEP
 	```
 </details>
-<details><summary><b><a href="https://github.com/bigd4/PyNEP">PyNEP</a></b> (🥉7 ·  ⭐ 46) - A python interface of the machine learning potential NEP used in GPUMD. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/RowleyGroup/MLXDM">MLXDM</a></b> (🥉7 ·  ⭐ 6) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>long-range</code></summary>
 
-- [GitHub](https://github.com/bigd4/PyNEP) (👨‍💻 7 · 🔀 16 · 📋 11 - 36% open · ⏱️ 01.06.2024):
+- [GitHub](https://github.com/RowleyGroup/MLXDM) (👨‍💻 7 · 🔀 2 · ⏱️ 15.08.2024):
 
 	```
-	git clone https://github.com/bigd4/PyNEP
+	git clone https://github.com/RowleyGroup/MLXDM
 	```
 </details>
 <details><summary><b><a href="https://cortner.github.io/ACEweb/software/">TensorPotential</a></b> (🥉6 ·  ⭐ 8) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. <code><a href="https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md">Custom</a></code></summary>
@@ -1440,7 +1440,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 	git clone https://github.com/mariogeiger/nequip-jax
 	```
 </details>
-<details><summary><b><a href="https://github.com/mariogeiger/allegro-jax">Allegro-JAX</a></b> ( ⭐ 17) - JAX implementation of the Allegro interatomic potential. <code>Unlicensed</code></summary>
+<details><summary><b><a href="https://github.com/mariogeiger/allegro-jax">Allegro-JAX</a></b> ( ⭐ 18) - JAX implementation of the Allegro interatomic potential. <code>Unlicensed</code></summary>
 
 - [GitHub](https://github.com/mariogeiger/allegro-jax) (👨‍💻 2 · 🔀 2 · 📋 2 - 50% open · ⏱️ 09.04.2024):
 
@@ -1451,23 +1451,23 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 <details><summary>Show 32 hidden projects...</summary>
 
 - <b><a href="https://github.com/materialsvirtuallab/megnet">MEGNet</a></b> (🥇23 ·  ⭐ 500 · 💀) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://en.wikipedia.org/wiki/Multifidelity_simulation"><code>multifidelity</code></a>
-- <b><a href="https://github.com/stefanch/sGDML">sGDML</a></b> (🥈16 ·  ⭐ 140 · 💀) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/stefanch/sGDML">sGDML</a></b> (🥈17 ·  ⭐ 140 · 💀) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/CompPhysVienna/n2p2">n2p2</a></b> (🥈14 ·  ⭐ 220 · 💀) - n2p2 - A Neural Network Potential Package. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>C++</code>
 - <b><a href="https://github.com/jparkhill/TensorMol">TensorMol</a></b> (🥈12 ·  ⭐ 270 · 💀) - Tensorflow + Molecules = TensorMol. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>single-paper</code>
 - <b><a href="https://github.com/MDIL-SNU/SIMPLE-NN">SIMPLE-NN</a></b> (🥈11 ·  ⭐ 47 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code>
-- <b><a href="https://github.com/aimat-lab/NNsForMD">NNsforMD</a></b> (🥉10 ·  ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/mir-group/allegro">Allegro</a></b> (🥉9 ·  ⭐ 330 · 💀) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/aimat-lab/NNsForMD">NNsforMD</a></b> (🥈11 ·  ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/mir-group/allegro">Allegro</a></b> (🥉10 ·  ⭐ 330 · 💀) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/atomistic-machine-learning/SchNet">SchNet</a></b> (🥉9 ·  ⭐ 220 · 💀) - SchNet - a deep learning architecture for quantum chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/TUM-DAML/gemnet_pytorch">GemNet</a></b> (🥉9 ·  ⭐ 180 · 💀) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. <code><a href="https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE">Custom</a></code>
-- <b><a href="https://github.com/aiqm/aimnet">AIMNet</a></b> (🥉8 ·  ⭐ 95 · 💀) - Atoms In Molecules Neural Network Potential. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>single-paper</code>
-- <b><a href="https://github.com/materialsvirtuallab/snap">SNAP</a></b> (🥉8 ·  ⭐ 36 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
+- <b><a href="https://github.com/aiqm/aimnet">AIMNet</a></b> (🥉8 ·  ⭐ 97 · 💀) - Atoms In Molecules Neural Network Potential. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>single-paper</code>
+- <b><a href="https://github.com/materialsvirtuallab/snap">SNAP</a></b> (🥉8 ·  ⭐ 37 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
 - <b><a href="https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks">Atomistic Adversarial Attacks</a></b> (🥉8 ·  ⭐ 31 · 💀) - Code for performing adversarial attacks on atomistic systems using NN potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code>
-- <b><a href="https://github.com/HSE-LAMBDA/MEGNetSparse">MEGNetSparse</a></b> (🥉8 ·  ⭐ 1 · 💀) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Crystallographic_defect"><code>material-defect</code></a>
+- <b><a href="https://github.com/MMunibas/Asparagus">Asparagus</a></b> (🥉8 ·  ⭐ 4 · 🐣) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>workflows</code> <code>sampling</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a>
+- <b><a href="https://github.com/HSE-LAMBDA/MEGNetSparse">MEGNetSparse</a></b> (🥉8 ·  ⭐ 1) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Crystallographic_defect"><code>material-defect</code></a>
 - <b><a href="https://github.com/MMunibas/PhysNet">PhysNet</a></b> (🥉7 ·  ⭐ 89 · 💀) - Code for training PhysNet models. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a>
-- <b><a href="https://github.com/MMunibas/Asparagus">Asparagus</a></b> (🥉7 ·  ⭐ 4 · 🐣) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>workflows</code> <code>sampling</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a>
 - <b><a href="https://gitlab.com/ashapeev/mlip-3">MLIP-3</a></b> (🥉6 ·  ⭐ 26 · 💀) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). <code><a href="http://bit.ly/3rqEWVr">BSD-2</a></code> <code>C++</code>
 - <b><a href="https://github.com/libAtoms/testing-framework">testing-framework</a></b> (🥉6 ·  ⭐ 11 · 💀) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of.. <code>Unlicensed</code> <code>benchmarking</code>
-- <b><a href="https://gitlab.com/PANNAdevs/panna">PANNA</a></b> (🥉6 ·  ⭐ 9 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>benchmarking</code>
+- <b><a href="https://gitlab.com/PANNAdevs/panna">PANNA</a></b> (🥉6 ·  ⭐ 10 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>benchmarking</code>
 - <b><a href="https://gitlab.com/zaverkin_v/gmnn">GN-MM</a></b> (🥉5 ·  ⭐ 10 · 💀) - The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Feature_engineering"><code>rep-eng</code></a> <code>magnetism</code>
 - <b><a href="https://github.com/Luthaf/alchemical-learning">Alchemical learning</a></b> (🥉5 ·  ⭐ 2 · 💀) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
 - <b><a href="https://github.com/ACEsuit/ACE1pack.jl">ACE1Pack.jl</a></b> (🥉5 ·  ⭐ 1 · 💀) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>Julia</code>
@@ -1491,99 +1491,91 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
 
 _Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML._
 
-<details><summary><b><a href="https://github.com/Future-House/paper-qa">paper-qa</a></b> (🥇29 ·  ⭐ 6K) - High accuracy RAG for answering questions from scientific documents with citations. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Large_language_model#Agency"><code>ai-agent</code></a></summary>
+<details><summary><b><a href="https://github.com/Future-House/paper-qa">paper-qa</a></b> (🥇30 ·  ⭐ 6.2K) - High accuracy RAG for answering questions from scientific documents with citations. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Large_language_model#Agency"><code>ai-agent</code></a></summary>
 
-- [GitHub](https://github.com/Future-House/paper-qa) (👨‍💻 25 · 🔀 550 · 📦 71 · 📋 210 - 31% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/Future-House/paper-qa) (👨‍💻 26 · 🔀 580 · 📦 74 · 📋 240 - 37% open · ⏱️ 17.10.2024):
 
 	```
 	git clone https://github.com/whitead/paper-qa
 	```
-- [PyPi](https://pypi.org/project/paper-qa) (📥 16K / month · 📦 8 · ⏱️ 27.09.2024):
+- [PyPi](https://pypi.org/project/paper-qa) (📥 16K / month · 📦 8 · ⏱️ 16.10.2024):
 	```
 	pip install paper-qa
 	```
 </details>
-<details><summary><b><a href="https://github.com/OpenBioML/chemnlp">OpenBioML ChemNLP</a></b> (🥇17 ·  ⭐ 150) - ChemNLP project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code></summary>
+<details><summary><b><a href="https://github.com/OpenBioML/chemnlp">OpenBioML ChemNLP</a></b> (🥇18 ·  ⭐ 150) - ChemNLP project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code></summary>
 
 - [GitHub](https://github.com/OpenBioML/chemnlp) (👨‍💻 27 · 🔀 46 · 📋 250 - 44% open · ⏱️ 19.08.2024):
 
 	```
 	git clone https://github.com/OpenBioML/chemnlp
 	```
-- [PyPi](https://pypi.org/project/chemnlp) (📥 98 / month · 📦 1 · ⏱️ 07.08.2023):
+- [PyPi](https://pypi.org/project/chemnlp) (📥 420 / month · 📦 1 · ⏱️ 07.08.2023):
 	```
 	pip install chemnlp
 	```
 </details>
-<details><summary><b><a href="https://github.com/ur-whitelab/chemcrow-public">ChemCrow</a></b> (🥈15 ·  ⭐ 600 · 💤) - Open source package for the accurate solution of reasoning-intensive chemical tasks. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Large_language_model#Agency"><code>ai-agent</code></a></summary>
+<details><summary><b><a href="https://github.com/ur-whitelab/chemcrow-public">ChemCrow</a></b> (🥇16 ·  ⭐ 600 · 💤) - Open source package for the accurate solution of reasoning-intensive chemical tasks. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Large_language_model#Agency"><code>ai-agent</code></a></summary>
 
-- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 87 · 📦 5 · 📋 22 - 36% open · ⏱️ 27.03.2024):
+- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 88 · 📦 6 · 📋 22 - 36% open · ⏱️ 27.03.2024):
 
 	```
 	git clone https://github.com/ur-whitelab/chemcrow-public
 	```
-- [PyPi](https://pypi.org/project/chemcrow) (📥 630 / month · ⏱️ 27.03.2024):
+- [PyPi](https://pypi.org/project/chemcrow) (📥 1.7K / month · ⏱️ 27.03.2024):
 	```
 	pip install chemcrow
 	```
 </details>
-<details><summary><b><a href="https://github.com/usnistgov/atomgpt">AtomGPT</a></b> (🥈13 ·  ⭐ 22) - AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design. <code><a href="https://github.com/usnistgov/atomgpt/blob/main/LICENSE.rst">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
-
-- [GitHub](https://github.com/usnistgov/atomgpt) (👨‍💻 2 · 🔀 3 · 📦 1 · ⏱️ 22.09.2024):
-
-	```
-	git clone https://github.com/usnistgov/atomgpt
-	```
-- [PyPi](https://pypi.org/project/atomgpt) (📥 260 / month · ⏱️ 22.09.2024):
-	```
-	pip install atomgpt
-	```
-</details>
-<details><summary><b><a href="https://github.com/kjappelbaum/gptchem">gptchem</a></b> (🥈12 ·  ⭐ 230 · 💤) - Use GPT-3 to solve chemistry problems. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/kjappelbaum/gptchem">gptchem</a></b> (🥈13 ·  ⭐ 230 · 💤) - Use GPT-3 to solve chemistry problems. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
 - [GitHub](https://github.com/kjappelbaum/gptchem) (👨‍💻 4 · 🔀 41 · 📋 21 - 90% open · ⏱️ 04.10.2023):
 
 	```
 	git clone https://github.com/kjappelbaum/gptchem
 	```
-- [PyPi](https://pypi.org/project/gptchem) (📥 120 / month · ⏱️ 04.10.2023):
+- [PyPi](https://pypi.org/project/gptchem) (📥 240 / month · ⏱️ 04.10.2023):
 	```
 	pip install gptchem
 	```
 </details>
-<details><summary><b><a href="https://github.com/usnistgov/chemnlp">NIST ChemNLP</a></b> (🥈11 ·  ⭐ 70) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>literature-data</code></summary>
+<details><summary><b><a href="https://github.com/Yeonghun1675/ChatMOF">ChatMOF</a></b> (🥈13 ·  ⭐ 63) - Predict and Inverse design for metal-organic framework with large-language models (llms). <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a></summary>
 
-- [GitHub](https://github.com/usnistgov/chemnlp) (👨‍💻 2 · 🔀 16 · 📦 3 · ⏱️ 19.08.2024):
+- [GitHub](https://github.com/Yeonghun1675/ChatMOF) (👨‍💻 1 · 🔀 8 · 📦 3 · ⏱️ 01.07.2024):
 
 	```
-	git clone https://github.com/usnistgov/chemnlp
+	git clone https://github.com/Yeonghun1675/ChatMOF
 	```
-- [PyPi](https://pypi.org/project/chemnlp) (📥 98 / month · 📦 1 · ⏱️ 07.08.2023):
+- [PyPi](https://pypi.org/project/chatmof) (📥 1.1K / month · ⏱️ 01.07.2024):
 	```
-	pip install chemnlp
+	pip install chatmof
 	```
 </details>
-<details><summary><b><a href="https://github.com/Yeonghun1675/ChatMOF">ChatMOF</a></b> (🥈11 ·  ⭐ 58) - Predict and Inverse design for metal-organic framework with large-language models (llms). <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a></summary>
+<details><summary><b><a href="https://github.com/usnistgov/atomgpt">AtomGPT</a></b> (🥈13 ·  ⭐ 26) - AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design. <code><a href="https://github.com/usnistgov/atomgpt/blob/main/LICENSE.rst">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
 
-- [GitHub](https://github.com/Yeonghun1675/ChatMOF) (👨‍💻 1 · 🔀 8 · 📦 2 · ⏱️ 01.07.2024):
+- [GitHub](https://github.com/usnistgov/atomgpt) (👨‍💻 2 · 🔀 3 · 📦 2 · ⏱️ 04.10.2024):
 
 	```
-	git clone https://github.com/Yeonghun1675/ChatMOF
+	git clone https://github.com/usnistgov/atomgpt
 	```
-- [PyPi](https://pypi.org/project/chatmof) (📥 340 / month · ⏱️ 01.07.2024):
+- [PyPi](https://pypi.org/project/atomgpt) (📥 630 / month · ⏱️ 22.09.2024):
 	```
-	pip install chatmof
+	pip install atomgpt
 	```
 </details>
-<details><summary><b><a href="https://github.com/chiang-yuan/llamp">LLaMP</a></b> (🥉10 ·  ⭐ 61) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Cheminformatics"><code>cheminformatics</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <a href="https://en.wikipedia.org/wiki/Language_model"><code>language-models</code></a> <code>Python</code> <code>general-tool</code></summary>
+<details><summary><b><a href="https://github.com/usnistgov/chemnlp">NIST ChemNLP</a></b> (🥈12 ·  ⭐ 71) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>literature-data</code></summary>
 
-- [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 6 · 🔀 7 · 📋 25 - 32% open · ⏱️ 10.09.2024):
+- [GitHub](https://github.com/usnistgov/chemnlp) (👨‍💻 2 · 🔀 16 · 📦 4 · ⏱️ 19.08.2024):
 
 	```
-	git clone https://github.com/chiang-yuan/llamp
+	git clone https://github.com/usnistgov/chemnlp
+	```
+- [PyPi](https://pypi.org/project/chemnlp) (📥 420 / month · 📦 1 · ⏱️ 07.08.2023):
+	```
+	pip install chemnlp
 	```
 </details>
-<details><summary><b><a href="https://github.com/IBM/molformer">MoLFormer</a></b> (🥉9 ·  ⭐ 250 · 💤) - Repository for MolFormer. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a></summary>
+<details><summary><b><a href="https://github.com/IBM/molformer">MoLFormer</a></b> (🥉9 ·  ⭐ 260 · 💤) - Repository for MolFormer. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Drug_design#Computer-aided_drug_design"><code>drug-discovery</code></a></summary>
 
 - [GitHub](https://github.com/IBM/molformer) (👨‍💻 5 · 🔀 41 · 📋 19 - 47% open · ⏱️ 16.10.2023):
 
@@ -1603,6 +1595,14 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
 	conda install -c msr-ai4science molskill
 	```
 </details>
+<details><summary><b><a href="https://github.com/chiang-yuan/llamp">LLaMP</a></b> (🥉9 ·  ⭐ 61) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Cheminformatics"><code>cheminformatics</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <a href="https://en.wikipedia.org/wiki/Language_model"><code>language-models</code></a> <code>Python</code> <code>general-tool</code></summary>
+
+- [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 6 · 🔀 7 · 📋 25 - 32% open · ⏱️ 14.10.2024):
+
+	```
+	git clone https://github.com/chiang-yuan/llamp
+	```
+</details>
 <details><summary><b><a href="https://github.com/lamalab-org/chemlift">chemlift</a></b> (🥉7 ·  ⭐ 31 · 💤) - Language-interfaced fine-tuning for chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
 - [GitHub](https://github.com/lamalab-org/chemlift) (👨‍💻 2 · 🔀 3 · 📋 18 - 61% open · ⏱️ 14.10.2023):
@@ -1611,17 +1611,17 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
 	git clone https://github.com/lamalab-org/chemlift
 	```
 </details>
-<details><summary><b><a href="https://github.com/vertaix/LLM-Prop">LLM-Prop</a></b> (🥉7 ·  ⭐ 27) - A repository for the LLM-Prop implementation. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/vertaix/LLM-Prop">LLM-Prop</a></b> (🥉7 ·  ⭐ 28) - A repository for the LLM-Prop implementation. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/vertaix/LLM-Prop) (👨‍💻 6 · 🔀 5 · 📋 2 - 50% open · ⏱️ 26.04.2024):
+- [GitHub](https://github.com/vertaix/LLM-Prop) (👨‍💻 6 · 🔀 6 · 📋 2 - 50% open · ⏱️ 26.04.2024):
 
 	```
 	git clone https://github.com/vertaix/LLM-Prop
 	```
 </details>
-<details><summary><b><a href="https://github.com/facebookresearch/crystal-text-llm">crystal-text-llm</a></b> (🥉5 ·  ⭐ 68) - Large language models to generate stable crystals. <code><a href="https://tldrlegal.com/search?q=CC-BY-NC-4.0">CC-BY-NC-4.0</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a></summary>
+<details><summary><b><a href="https://github.com/facebookresearch/crystal-text-llm">crystal-text-llm</a></b> (🥉5 ·  ⭐ 75) - Large language models to generate stable crystals. <code><a href="https://tldrlegal.com/search?q=CC-BY-NC-4.0">CC-BY-NC-4.0</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a></summary>
 
-- [GitHub](https://github.com/facebookresearch/crystal-text-llm) (👨‍💻 3 · 🔀 12 · 📋 9 - 77% open · ⏱️ 18.06.2024):
+- [GitHub](https://github.com/facebookresearch/crystal-text-llm) (👨‍💻 3 · 🔀 13 · 📋 11 - 81% open · ⏱️ 18.06.2024):
 
 	```
 	git clone https://github.com/facebookresearch/crystal-text-llm
@@ -1629,7 +1629,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
 </details>
 <details><summary><b><a href="https://github.com/CFN-softbio/SciBot">SciBot</a></b> (🥉5 ·  ⭐ 28) - SciBot is a simple demo of building a domain-specific chatbot for science. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Large_language_model#Agency"><code>ai-agent</code></a></summary>
 
-- [GitHub](https://github.com/CFN-softbio/SciBot) (👨‍💻 1 · 🔀 8 · 📦 1 · ⏱️ 03.09.2024):
+- [GitHub](https://github.com/CFN-softbio/SciBot) (👨‍💻 1 · 🔀 8 · 📦 2 · ⏱️ 03.09.2024):
 
 	```
 	git clone https://github.com/CFN-softbio/SciBot
@@ -1645,13 +1645,13 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
 </details>
 <details><summary>Show 7 hidden projects...</summary>
 
-- <b><a href="https://github.com/mcs07/ChemDataExtractor">ChemDataExtractor</a></b> (🥇16 ·  ⭐ 300 · 💀) - Automatically extract chemical information from scientific documents. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>literature-data</code>
+- <b><a href="https://github.com/mcs07/ChemDataExtractor">ChemDataExtractor</a></b> (🥇16 ·  ⭐ 310 · 💀) - Automatically extract chemical information from scientific documents. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>literature-data</code>
 - <b><a href="https://github.com/materialsintelligence/mat2vec">mat2vec</a></b> (🥈12 ·  ⭐ 620 · 💀) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
 - <b><a href="https://github.com/whitead/nlcc">nlcc</a></b> (🥈12 ·  ⭐ 44 · 💀) - Natural language computational chemistry command line interface. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>single-paper</code>
 - <b><a href="https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC">BERT-PSIE-TC</a></b> (🥉5 ·  ⭐ 12 · 💀) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>magnetism</code>
 - <b><a href="https://github.com/ShuHuang/chemdatawriter">ChemDataWriter</a></b> (🥉4 ·  ⭐ 14 · 💤) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>literature-data</code>
-- <b><a href="https://github.com/lamm-mit/Cephalo">Cephalo</a></b> (🥉4 ·  ⭐ 6 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <code>pretrained</code>
-- <b><a href="https://github.com/hoon-ock/CatBERTa">CatBERTa</a></b> (🥉3 ·  ⭐ 19 · 💤) - Large Language Model for Catalyst Property Prediction. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a>
+- <b><a href="https://github.com/hoon-ock/CatBERTa">CatBERTa</a></b> (🥉3 ·  ⭐ 20 · 💤) - Large Language Model for Catalyst Property Prediction. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a>
+- <b><a href="https://github.com/lamm-mit/Cephalo">Cephalo</a></b> (🥉3 ·  ⭐ 6 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <code>pretrained</code>
 </details>
 <br>
 
@@ -1663,21 +1663,21 @@ _Projects that implement materials discovery methods using atomistic ML._
 
 🔗&nbsp;<b><a href="https://www.microsoft.com/en-us/research/blog/mattergen-property-guided-materials-design/">MatterGen</a></b>  - A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687. <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Proprietary_software"><code>proprietary</code></a>
 
-<details><summary><b><a href="https://github.com/CompRhys/aviary">aviary</a></b> (🥇15 ·  ⭐ 47) - The Wren sits on its Roost in the Aviary. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/CompRhys/aviary">aviary</a></b> (🥇15 ·  ⭐ 48) - The Wren sits on its Roost in the Aviary. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/CompRhys/aviary) (👨‍💻 4 · 🔀 11 · 📋 29 - 13% open · ⏱️ 10.09.2024):
+- [GitHub](https://github.com/CompRhys/aviary) (👨‍💻 4 · 🔀 11 · 📋 29 - 13% open · ⏱️ 16.10.2024):
 
 	```
 	git clone https://github.com/CompRhys/aviary
 	```
 </details>
-<details><summary><b><a href="https://gitlab.com/cest-group/boss">BOSS</a></b> (🥇12 ·  ⭐ 20) - Bayesian Optimization Structure Search (BOSS). <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>probabilistic</code></summary>
+<details><summary><b><a href="https://gitlab.com/cest-group/boss">BOSS</a></b> (🥇14 ·  ⭐ 20) - Bayesian Optimization Structure Search (BOSS). <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>probabilistic</code></summary>
 
-- [PyPi](https://pypi.org/project/aalto-boss) (📥 5.2K / month · ⏱️ 20.07.2024):
+- [PyPi](https://pypi.org/project/aalto-boss) (📥 15K / month · ⏱️ 09.10.2024):
 	```
 	pip install aalto-boss
 	```
-- [GitLab](https://gitlab.com/cest-group/boss) (🔀 11 · 📋 30 - 3% open · ⏱️ 20.07.2024):
+- [GitLab](https://gitlab.com/cest-group/boss) (🔀 11 · 📋 31 - 6% open · ⏱️ 09.10.2024):
 
 	```
 	git clone https://gitlab.com/cest-group/boss
@@ -1685,7 +1685,7 @@ _Projects that implement materials discovery methods using atomistic ML._
 </details>
 <details><summary><b><a href="https://agox.gitlab.io/agox/">AGOX</a></b> (🥈10 ·  ⭐ 13) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>structure-optimization</code></a></summary>
 
-- [PyPi](https://pypi.org/project/agox) (📥 260 / month · ⏱️ 26.08.2024):
+- [PyPi](https://pypi.org/project/agox) (📥 1K / month · ⏱️ 26.08.2024):
 	```
 	pip install agox
 	```
@@ -1695,7 +1695,7 @@ _Projects that implement materials discovery methods using atomistic ML._
 	git clone https://gitlab.com/agox/agox
 	```
 </details>
-<details><summary><b><a href="https://github.com/google-deepmind/materials_discovery">Materials Discovery: GNoME</a></b> (🥈9 ·  ⭐ 870) - Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>datasets</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Proprietary_software"><code>proprietary</code></a></summary>
+<details><summary><b><a href="https://github.com/google-deepmind/materials_discovery">Materials Discovery: GNoME</a></b> (🥈9 ·  ⭐ 880) - Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>datasets</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Proprietary_software"><code>proprietary</code></a></summary>
 
 - [GitHub](https://github.com/google-deepmind/materials_discovery) (👨‍💻 2 · 🔀 140 · 📋 22 - 81% open · ⏱️ 04.09.2024):
 
@@ -1703,7 +1703,7 @@ _Projects that implement materials discovery methods using atomistic ML._
 	git clone https://github.com/google-deepmind/materials_discovery
 	```
 </details>
-<details><summary><b><a href="https://github.com/Minoru938/CSPML">CSPML (crystal structure prediction with machine learning-based element substitution)</a></b> (🥈7 ·  ⭐ 20) - Original implementation of CSPML. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>structure-prediction</code></summary>
+<details><summary><b><a href="https://github.com/Minoru938/CSPML">CSPML (crystal structure prediction with machine learning-based element substitution)</a></b> (🥉5 ·  ⭐ 20 · 📉) - Original implementation of CSPML. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>structure-prediction</code></summary>
 
 - [GitHub](https://github.com/Minoru938/CSPML) (🔀 9 · 📋 3 - 66% open · ⏱️ 25.09.2024):
 
@@ -1713,8 +1713,8 @@ _Projects that implement materials discovery methods using atomistic ML._
 </details>
 <details><summary>Show 6 hidden projects...</summary>
 
-- <b><a href="https://github.com/ulissigroup/CAMD">Computational Autonomy for Materials Discovery (CAMD)</a></b> (🥉6 ·  ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code>
-- <b><a href="https://gitlab.com/bigd4/magus">MAGUS</a></b> (🥉4 ·  ⭐ 60 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. <code>Unlicensed</code> <code>structure-prediction</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a>
+- <b><a href="https://github.com/ulissigroup/CAMD">Computational Autonomy for Materials Discovery (CAMD)</a></b> (🥈6 ·  ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code>
+- <b><a href="https://gitlab.com/bigd4/magus">MAGUS</a></b> (🥉4 ·  ⭐ 61 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. <code>Unlicensed</code> <code>structure-prediction</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a>
 - <b><a href="https://github.com/takahashi-akira-36m/ml_atomate">ML-atomate</a></b> (🥉4 ·  ⭐ 4 · 💤) - Machine learning-assisted Atomate code for autonomous computational materials screening. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <code>workflows</code>
 - <b><a href="https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks">closed-loop-acceleration-benchmarks</a></b> (🥉4 · 💀) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <code>single-paper</code>
 - <b><a href="https://github.com/MDIL-SNU/SPINNER">SPINNER</a></b> (🥉3 ·  ⭐ 12 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>C++</code> <code>structure-prediction</code>
@@ -1728,9 +1728,9 @@ _Projects that implement materials discovery methods using atomistic ML._
 
 _Projects that implement mathematical objects used in atomistic machine learning._
 
-<details><summary><b><a href="https://github.com/google-deepmind/kfac-jax">KFAC-JAX</a></b> (🥇18 ·  ⭐ 230) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
+<details><summary><b><a href="https://github.com/google-deepmind/kfac-jax">KFAC-JAX</a></b> (🥇19 ·  ⭐ 240) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 15 · 🔀 18 · 📦 10 · 📋 19 - 47% open · ⏱️ 27.09.2024):
+- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 15 · 🔀 20 · 📦 11 · 📋 19 - 47% open · ⏱️ 16.10.2024):
 
 	```
 	git clone https://github.com/google-deepmind/kfac-jax
@@ -1740,26 +1740,26 @@ _Projects that implement mathematical objects used in atomistic machine learning
 	pip install kfac-jax
 	```
 </details>
-<details><summary><b><a href="https://github.com/ziatdinovmax/gpax">gpax</a></b> (🥇17 ·  ⭐ 200) - Gaussian Processes for Experimental Sciences. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>
+<details><summary><b><a href="https://github.com/ziatdinovmax/gpax">gpax</a></b> (🥇18 ·  ⭐ 200) - Gaussian Processes for Experimental Sciences. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>
 
-- [GitHub](https://github.com/ziatdinovmax/gpax) (👨‍💻 6 · 🔀 24 · 📦 2 · 📋 40 - 20% open · ⏱️ 21.05.2024):
+- [GitHub](https://github.com/ziatdinovmax/gpax) (👨‍💻 6 · 🔀 25 · 📦 3 · 📋 40 - 20% open · ⏱️ 21.05.2024):
 
 	```
 	git clone https://github.com/ziatdinovmax/gpax
 	```
-- [PyPi](https://pypi.org/project/gpax) (📥 380 / month · ⏱️ 20.03.2024):
+- [PyPi](https://pypi.org/project/gpax) (📥 810 / month · ⏱️ 20.03.2024):
 	```
 	pip install gpax
 	```
 </details>
-<details><summary><b><a href="https://github.com/lab-cosmo/sphericart">SpheriCart</a></b> (🥇17 ·  ⭐ 70) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/lab-cosmo/sphericart">SpheriCart</a></b> (🥇18 ·  ⭐ 71) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 11 · 📥 86 · 📦 3 · 📋 41 - 56% open · ⏱️ 07.09.2024):
+- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 11 · 📥 87 · 📦 5 · 📋 41 - 56% open · ⏱️ 11.10.2024):
 
 	```
 	git clone https://github.com/lab-cosmo/sphericart
 	```
-- [PyPi](https://pypi.org/project/sphericart) (📥 980 / month · ⏱️ 04.09.2024):
+- [PyPi](https://pypi.org/project/sphericart) (📥 1.3K / month · ⏱️ 04.09.2024):
 	```
 	pip install sphericart
 	```
@@ -1806,48 +1806,48 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
 
 <details><summary><b><a href="https://github.com/jax-md/jax-md">JAX-MD</a></b> (🥇26 ·  ⭐ 1.2K) - Differentiable, Hardware Accelerated, Molecular Dynamics. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/jax-md/jax-md) (👨‍💻 34 · 🔀 190 · 📦 56 · 📋 150 - 46% open · ⏱️ 05.09.2024):
+- [GitHub](https://github.com/jax-md/jax-md) (👨‍💻 34 · 🔀 190 · 📦 59 · 📋 150 - 47% open · ⏱️ 05.09.2024):
 
 	```
 	git clone https://github.com/jax-md/jax-md
 	```
-- [PyPi](https://pypi.org/project/jax-md) (📥 3.4K / month · 📦 3 · ⏱️ 09.08.2023):
+- [PyPi](https://pypi.org/project/jax-md) (📥 4.8K / month · 📦 3 · ⏱️ 09.08.2023):
 	```
 	pip install jax-md
 	```
 </details>
-<details><summary><b><a href="https://github.com/luigibonati/mlcolvar">mlcolvar</a></b> (🥈19 ·  ⭐ 91 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>sampling</code></summary>
+<details><summary><b><a href="https://github.com/luigibonati/mlcolvar">mlcolvar</a></b> (🥈21 ·  ⭐ 89 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>sampling</code></summary>
 
-- [GitHub](https://github.com/luigibonati/mlcolvar) (👨‍💻 8 · 🔀 24 · 📦 2 · 📋 73 - 20% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/luigibonati/mlcolvar) (👨‍💻 8 · 🔀 24 · 📦 3 · 📋 74 - 17% open · ⏱️ 09.10.2024):
 
 	```
 	git clone https://github.com/luigibonati/mlcolvar
 	```
-- [PyPi](https://pypi.org/project/mlcolvar) (📥 240 / month · ⏱️ 12.06.2024):
+- [PyPi](https://pypi.org/project/mlcolvar) (📥 370 / month · ⏱️ 12.06.2024):
 	```
 	pip install mlcolvar
 	```
 </details>
-<details><summary><b><a href="https://github.com/FitSNAP/FitSNAP">FitSNAP</a></b> (🥈18 ·  ⭐ 150) - Software for generating machine-learning interatomic potentials for LAMMPS. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code></summary>
+<details><summary><b><a href="https://github.com/FitSNAP/FitSNAP">FitSNAP</a></b> (🥈20 ·  ⭐ 150 · 📈) - Software for generating machine-learning interatomic potentials for LAMMPS. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code></summary>
 
-- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 24 · 🔀 50 · 📥 11 · 📦 2 · 📋 73 - 21% open · ⏱️ 19.09.2024):
+- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 24 · 🔀 50 · 📥 11 · 📦 3 · 📋 73 - 21% open · ⏱️ 19.09.2024):
 
 	```
 	git clone https://github.com/FitSNAP/FitSNAP
 	```
-- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 8.5K · ⏱️ 16.06.2023):
+- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 8.8K · ⏱️ 16.06.2023):
 	```
 	conda install -c conda-forge fitsnap3
 	```
 </details>
 <details><summary><b><a href="https://github.com/openmm/openmm-torch">openmm-torch</a></b> (🥈16 ·  ⭐ 180) - OpenMM plugin to define forces with neural networks. <code><a href="https://github.com/openmm/openmm-torch#license">Custom</a></code> <code>ML-IAP</code> <code>C++</code></summary>
 
-- [GitHub](https://github.com/openmm/openmm-torch) (👨‍💻 8 · 🔀 23 · 📋 92 - 28% open · ⏱️ 23.08.2024):
+- [GitHub](https://github.com/openmm/openmm-torch) (👨‍💻 8 · 🔀 23 · 📋 93 - 29% open · ⏱️ 23.08.2024):
 
 	```
 	git clone https://github.com/openmm/openmm-torch
 	```
-- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 470K · ⏱️ 30.09.2024):
+- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 490K · ⏱️ 30.09.2024):
 	```
 	conda install -c conda-forge openmm-torch
 	```
@@ -1859,14 +1859,14 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
 	```
 	git clone https://github.com/openmm/openmm-ml
 	```
-- [Conda](https://anaconda.org/conda-forge/openmm-ml) (📥 5.3K · ⏱️ 07.06.2024):
+- [Conda](https://anaconda.org/conda-forge/openmm-ml) (📥 5.5K · ⏱️ 07.06.2024):
 	```
 	conda install -c conda-forge openmm-ml
 	```
 </details>
 <details><summary><b><a href="https://github.com/mir-group/pair_nequip">pair_nequip</a></b> (🥉10 ·  ⭐ 41) - LAMMPS pair style for NequIP. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
 
-- [GitHub](https://github.com/mir-group/pair_nequip) (👨‍💻 3 · 🔀 12 · 📋 30 - 33% open · ⏱️ 05.06.2024):
+- [GitHub](https://github.com/mir-group/pair_nequip) (👨‍💻 3 · 🔀 12 · 📋 31 - 35% open · ⏱️ 05.06.2024):
 
 	```
 	git clone https://github.com/mir-group/pair_nequip
@@ -1888,9 +1888,16 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
 	git clone https://github.com/mir-group/pair_allegro
 	```
 </details>
-<details><summary>Show 2 hidden projects...</summary>
+<details><summary><b><a href="https://github.com/initqp/somd">SOMD</a></b> (🥉5 ·  ⭐ 12) - Molecular dynamics package designed for the SIESTA DFT code. <code><a href="http://bit.ly/3pwmjO5">AGPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>
+
+- [GitHub](https://github.com/initqp/somd) (🔀 2 · ⏱️ 16.10.2024):
+
+	```
+	git clone https://github.com/initqp/somd
+	```
+</details>
+<details><summary>Show 1 hidden projects...</summary>
 
-- <b><a href="https://github.com/initqp/somd">SOMD</a></b> (🥉4 ·  ⭐ 12) - Molecular dynamics package designed for the SIESTA DFT code. <code><a href="http://bit.ly/3pwmjO5">AGPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a>
 - <b><a href="https://gitlab.com/ivannovikov/interface-lammps-mlip-3">interface-lammps-mlip-3</a></b> (🥉3 ·  ⭐ 5 · 💀) - An interface between LAMMPS and MLIP (version 3). <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code>
 </details>
 <br>
@@ -1916,7 +1923,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 
 <details><summary><b><a href="https://github.com/cdk/cdk">cdk</a></b> (🥇26 ·  ⭐ 490) - The Chemistry Development Kit. <code><a href="https://tldrlegal.com/search?q=LGPL-2.1">LGPL-2.1</a></code> <a href="https://en.wikipedia.org/wiki/Cheminformatics"><code>cheminformatics</code></a> <code>Java</code></summary>
 
-- [GitHub](https://github.com/cdk/cdk) (👨‍💻 160 · 🔀 160 · 📥 22K · 📋 290 - 10% open · ⏱️ 19.09.2024):
+- [GitHub](https://github.com/cdk/cdk) (👨‍💻 160 · 🔀 160 · 📥 22K · 📋 290 - 10% open · ⏱️ 06.10.2024):
 
 	```
 	git clone https://github.com/cdk/cdk
@@ -1937,7 +1944,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 	```
 	git clone https://github.com/SINGROUP/dscribe
 	```
-- [PyPi](https://pypi.org/project/dscribe) (📥 22K / month · 📦 35 · ⏱️ 28.05.2024):
+- [PyPi](https://pypi.org/project/dscribe) (📥 25K / month · 📦 35 · ⏱️ 28.05.2024):
 	```
 	pip install dscribe
 	```
@@ -1946,32 +1953,32 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 	conda install -c conda-forge dscribe
 	```
 </details>
-<details><summary><b><a href="https://github.com/ppdebreuck/modnet">MODNet</a></b> (🥇16 ·  ⭐ 77) - MODNet: a framework for machine learning materials properties. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code> <code>small-data</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a></summary>
+<details><summary><b><a href="https://github.com/ppdebreuck/modnet">MODNet</a></b> (🥇17 ·  ⭐ 78) - MODNet: a framework for machine learning materials properties. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code> <code>small-data</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a></summary>
 
-- [GitHub](https://github.com/ppdebreuck/modnet) (👨‍💻 10 · 🔀 32 · 📦 9 · 📋 53 - 49% open · ⏱️ 24.09.2024):
+- [GitHub](https://github.com/ppdebreuck/modnet) (👨‍💻 10 · 🔀 32 · 📦 10 · 📋 53 - 49% open · ⏱️ 24.09.2024):
 
 	```
 	git clone https://github.com/ppdebreuck/modnet
 	```
 </details>
-<details><summary><b><a href="https://github.com/rouyang2017/SISSO">SISSO</a></b> (🥈14 ·  ⭐ 240) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>Fortran</code></summary>
+<details><summary><b><a href="https://github.com/drcassar/glasspy">GlassPy</a></b> (🥈15 ·  ⭐ 27) - Python module for scientists working with glass materials. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code></summary>
 
-- [GitHub](https://github.com/rouyang2017/SISSO) (👨‍💻 3 · 🔀 78 · 📋 76 - 23% open · ⏱️ 20.09.2024):
+- [GitHub](https://github.com/drcassar/glasspy) (👨‍💻 2 · 🔀 7 · 📦 7 · 📋 15 - 46% open · ⏱️ 13.10.2024):
 
 	```
-	git clone https://github.com/rouyang2017/SISSO
+	git clone https://github.com/drcassar/glasspy
+	```
+- [PyPi](https://pypi.org/project/glasspy) (📥 910 / month · ⏱️ 05.09.2024):
+	```
+	pip install glasspy
 	```
 </details>
-<details><summary><b><a href="https://github.com/drcassar/glasspy">GlassPy</a></b> (🥈14 ·  ⭐ 26 · 💤) - Python module for scientists working with glass materials. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code></summary>
+<details><summary><b><a href="https://github.com/rouyang2017/SISSO">SISSO</a></b> (🥈14 ·  ⭐ 240) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>Fortran</code></summary>
 
-- [GitHub](https://github.com/drcassar/glasspy) (👨‍💻 1 · 🔀 7 · 📦 6 · 📋 13 - 53% open · ⏱️ 21.01.2024):
+- [GitHub](https://github.com/rouyang2017/SISSO) (👨‍💻 3 · 🔀 78 · 📋 76 - 23% open · ⏱️ 20.09.2024):
 
 	```
-	git clone https://github.com/drcassar/glasspy
-	```
-- [PyPi](https://pypi.org/project/glasspy) (📥 600 / month · ⏱️ 05.09.2024):
-	```
-	pip install glasspy
+	git clone https://github.com/rouyang2017/SISSO
 	```
 </details>
 <details><summary><b><a href="https://github.com/lab-cosmo/librascal">Librascal</a></b> (🥈13 ·  ⭐ 80 · 💤) - A scalable and versatile library to generate representations for atomic-scale learning. <code><a href="https://tldrlegal.com/search?q=LGPL-2.1">LGPL-2.1</a></code></summary>
@@ -1984,7 +1991,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 </details>
 <details><summary><b><a href="https://github.com/Luthaf/rascaline">Rascaline</a></b> (🥈12 ·  ⭐ 44) - Computing representations for atomistic machine learning. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>Rust</code> <code>C++</code></summary>
 
-- [GitHub](https://github.com/Luthaf/rascaline) (👨‍💻 14 · 🔀 13 · 📋 69 - 46% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/Luthaf/rascaline) (👨‍💻 14 · 🔀 13 · 📋 69 - 46% open · ⏱️ 10.10.2024):
 
 	```
 	git clone https://github.com/Luthaf/rascaline
@@ -1992,7 +1999,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 </details>
 <details><summary><b><a href="https://github.com/Rutgers-ZRG/libfp">fplib</a></b> (🥉11 ·  ⭐ 7) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>C-lang</code> <code>single-paper</code></summary>
 
-- [GitHub](https://github.com/Rutgers-ZRG/libfp) (🔀 1 · ⏱️ 26.09.2024):
+- [GitHub](https://github.com/Rutgers-ZRG/libfp) (🔀 1 · 📦 1 · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/zhuligs/fplib
@@ -2006,9 +2013,9 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 	git clone https://github.com/lab-cosmo/nice
 	```
 </details>
-<details><summary><b><a href="https://github.com/muhrin/milad">milad</a></b> (🥉5 ·  ⭐ 30) - Moment Invariants Local Atomic Descriptor. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a></summary>
+<details><summary><b><a href="https://github.com/muhrin/milad">milad</a></b> (🥉6 ·  ⭐ 30) - Moment Invariants Local Atomic Descriptor. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a></summary>
 
-- [GitHub](https://github.com/muhrin/milad) (👨‍💻 1 · 🔀 1 · 📦 2 · ⏱️ 20.08.2024):
+- [GitHub](https://github.com/muhrin/milad) (👨‍💻 1 · 🔀 1 · 📦 3 · ⏱️ 20.08.2024):
 
 	```
 	git clone https://github.com/muhrin/milad
@@ -2024,15 +2031,15 @@ _Projects that offer implementations of representations aka descriptors, fingerp
 </details>
 <details><summary>Show 14 hidden projects...</summary>
 
-- <b><a href="https://github.com/SUNCAT-Center/CatLearn">CatLearn</a></b> (🥇16 ·  ⭐ 100 · 💀) -  <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Surface_science"><code>surface-science</code></a>
+- <b><a href="https://github.com/SUNCAT-Center/CatLearn">CatLearn</a></b> (🥇17 ·  ⭐ 100 · 💀) -  <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://en.wikipedia.org/wiki/Surface_science"><code>surface-science</code></a>
+- <b><a href="https://github.com/sirmarcel/cmlkit">cmlkit</a></b> (🥈12 ·  ⭐ 34 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>benchmarking</code>
 - <b><a href="https://github.com/Kaaiian/CBFV">CBFV</a></b> (🥈12 ·  ⭐ 25 · 💀) - Tool to quickly create a composition-based feature vector. <code>Unlicensed</code>
 - <b><a href="https://github.com/capoe/benchml">BenchML</a></b> (🥈12 ·  ⭐ 15 · 💀) - ML benchmarking and pipeling framework. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>benchmarking</code>
-- <b><a href="https://github.com/sirmarcel/cmlkit">cmlkit</a></b> (🥉11 ·  ⭐ 34 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>benchmarking</code>
 - <b><a href="https://github.com/lantunes/skipatom">SkipAtom</a></b> (🥉9 ·  ⭐ 24 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://bitbucket.org/andrewpeterson/amp/">AMP</a></b> (🥉7 · 💀) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. <code>Unlicensed</code>
 - <b><a href="https://github.com/capoe/soapxx">SOAPxx</a></b> (🥉6 ·  ⭐ 7 · 💀) - A SOAP implementation. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <code>C++</code>
 - <b><a href="https://github.com/libAtoms/soap_turbo">soap_turbo</a></b> (🥉6 ·  ⭐ 5 · 💀) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. <code><a href="https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md">Custom</a></code> <code>Fortran</code>
 - <b><a href="https://github.com/ceriottm/lode">pyLODE</a></b> (🥉6 ·  ⭐ 3 · 💀) - Pythonic implementation of LOng Distance Equivariants. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a>
-- <b><a href="https://bitbucket.org/andrewpeterson/amp/">AMP</a></b> (🥉6 · 💀) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. <code>Unlicensed</code>
 - <b><a href="https://github.com/cnislab/MXenes4HER">MXenes4HER</a></b> (🥉5 ·  ⭐ 6 · 💀) - Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Catalysis"><code>catalysis</code></a> <code>scikit-learn</code> <code>single-paper</code>
 - <b><a href="https://gitlab.com/sissopp_developers/sissopp">SISSO++</a></b> (🥉5 ·  ⭐ 3 · 💀) - C++ Implementation of SISSO with python bindings. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>C++</code>
 - <b><a href="https://github.com/mm-tud/automl-materials">automl-materials</a></b> (🥉4 ·  ⭐ 5 · 💀) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Automated_machine_learning"><code>autoML</code></a> <code>benchmarking</code> <code>single-paper</code>
@@ -2049,12 +2056,12 @@ _General models that learn a representations aka embeddings of atomistic systems
 
 <details><summary><b><a href="https://github.com/dmlc/dgl">Deep Graph Library (DGL)</a></b> (🥇39 ·  ⭐ 13K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/dmlc/dgl) (👨‍💻 300 · 🔀 3K · 📦 300 · 📋 2.9K - 18% open · ⏱️ 25.09.2024):
+- [GitHub](https://github.com/dmlc/dgl) (👨‍💻 300 · 🔀 3K · 📦 310 · 📋 2.9K - 18% open · ⏱️ 16.10.2024):
 
 	```
 	git clone https://github.com/dmlc/dgl
 	```
-- [PyPi](https://pypi.org/project/dgl) (📥 110K / month · 📦 150 · ⏱️ 13.05.2024):
+- [PyPi](https://pypi.org/project/dgl) (📥 150K / month · 📦 150 · ⏱️ 13.05.2024):
 	```
 	pip install dgl
 	```
@@ -2065,60 +2072,72 @@ _General models that learn a representations aka embeddings of atomistic systems
 </details>
 <details><summary><b><a href="https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models">PyG Models</a></b> (🥇35 ·  ⭐ 21K) - Representation learning models implemented in PyTorch Geometric. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>general-ml</code></summary>
 
-- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨‍💻 520 · 🔀 3.6K · 📦 6.6K · 📋 3.7K - 27% open · ⏱️ 24.09.2024):
+- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨‍💻 520 · 🔀 3.6K · 📦 6.7K · 📋 3.7K - 28% open · ⏱️ 11.10.2024):
 
 	```
 	git clone https://github.com/pyg-team/pytorch_geometric
 	```
 </details>
-<details><summary><b><a href="https://github.com/e3nn/e3nn">e3nn</a></b> (🥇28 ·  ⭐ 950) - A modular framework for neural networks with Euclidean symmetry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/atomistic-machine-learning/schnetpack">SchNetPack</a></b> (🥇28 ·  ⭐ 780) - SchNetPack - Deep Neural Networks for Atomistic Systems. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+
+- [GitHub](https://github.com/atomistic-machine-learning/schnetpack) (👨‍💻 36 · 🔀 210 · 📦 92 · 📋 250 - 2% open · ⏱️ 24.09.2024):
+
+	```
+	git clone https://github.com/atomistic-machine-learning/schnetpack
+	```
+- [PyPi](https://pypi.org/project/schnetpack) (📥 1.6K / month · 📦 4 · ⏱️ 05.09.2024):
+	```
+	pip install schnetpack
+	```
+</details>
+<details><summary><b><a href="https://github.com/e3nn/e3nn">e3nn</a></b> (🥇27 ·  ⭐ 960 · 📉) - A modular framework for neural networks with Euclidean symmetry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 31 · 🔀 140 · 📦 310 · 📋 160 - 14% open · ⏱️ 25.08.2024):
+- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 31 · 🔀 140 · 📦 320 · 📋 160 - 14% open · ⏱️ 25.08.2024):
 
 	```
 	git clone https://github.com/e3nn/e3nn
 	```
-- [PyPi](https://pypi.org/project/e3nn) (📥 89K / month · 📦 4 · ⏱️ 13.04.2022):
+- [PyPi](https://pypi.org/project/e3nn) (📥 91K / month · 📦 4 · ⏱️ 13.04.2022):
 	```
 	pip install e3nn
 	```
-- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 22K · ⏱️ 18.06.2023):
+- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 23K · ⏱️ 18.06.2023):
 	```
 	conda install -c conda-forge e3nn
 	```
 </details>
-<details><summary><b><a href="https://github.com/atomistic-machine-learning/schnetpack">SchNetPack</a></b> (🥇28 ·  ⭐ 770) - SchNetPack - Deep Neural Networks for Atomistic Systems. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/materialsvirtuallab/matgl">MatGL (Materials Graph Library)</a></b> (🥇24 ·  ⭐ 260) - Graph deep learning library for materials. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://en.wikipedia.org/wiki/Multifidelity_simulation"><code>multifidelity</code></a></summary>
 
-- [GitHub](https://github.com/atomistic-machine-learning/schnetpack) (👨‍💻 36 · 🔀 210 · 📦 90 · 📋 250 - 2% open · ⏱️ 24.09.2024):
+- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 17 · 🔀 60 · 📦 50 · 📋 98 - 7% open · ⏱️ 16.10.2024):
 
 	```
-	git clone https://github.com/atomistic-machine-learning/schnetpack
+	git clone https://github.com/materialsvirtuallab/matgl
 	```
-- [PyPi](https://pypi.org/project/schnetpack) (📥 1.4K / month · 📦 4 · ⏱️ 05.09.2024):
+- [PyPi](https://pypi.org/project/m3gnet) (📥 2.5K / month · 📦 5 · ⏱️ 17.11.2022):
 	```
-	pip install schnetpack
+	pip install m3gnet
 	```
 </details>
-<details><summary><b><a href="https://github.com/materialsvirtuallab/matgl">MatGL (Materials Graph Library)</a></b> (🥇24 ·  ⭐ 260) - Graph deep learning library for materials. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://en.wikipedia.org/wiki/Multifidelity_simulation"><code>multifidelity</code></a></summary>
+<details><summary><b><a href="https://github.com/usnistgov/alignn">ALIGNN</a></b> (🥈22 ·  ⭐ 220) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. <code><a href="https://github.com/usnistgov/alignn/blob/main/LICENSE.rst">Custom</a></code></summary>
 
-- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 17 · 🔀 59 · 📦 46 · 📋 97 - 7% open · ⏱️ 03.10.2024):
+- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 79 · 📦 15 · 📋 65 - 61% open · ⏱️ 09.09.2024):
 
 	```
-	git clone https://github.com/materialsvirtuallab/matgl
+	git clone https://github.com/usnistgov/alignn
 	```
-- [PyPi](https://pypi.org/project/m3gnet) (📥 1.8K / month · 📦 5 · ⏱️ 17.11.2022):
+- [PyPi](https://pypi.org/project/alignn) (📥 12K / month · 📦 6 · ⏱️ 09.09.2024):
 	```
-	pip install m3gnet
+	pip install alignn
 	```
 </details>
 <details><summary><b><a href="https://github.com/e3nn/e3nn-jax">e3nn-jax</a></b> (🥈22 ·  ⭐ 180) - jax library for E3 Equivariant Neural Networks. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/e3nn/e3nn-jax) (👨‍💻 7 · 🔀 18 · 📦 38 · 📋 22 - 4% open · ⏱️ 28.09.2024):
+- [GitHub](https://github.com/e3nn/e3nn-jax) (👨‍💻 7 · 🔀 18 · 📦 41 · 📋 23 - 8% open · ⏱️ 28.09.2024):
 
 	```
 	git clone https://github.com/e3nn/e3nn-jax
 	```
-- [PyPi](https://pypi.org/project/e3nn-jax) (📥 3.6K / month · 📦 13 · ⏱️ 14.08.2024):
+- [PyPi](https://pypi.org/project/e3nn-jax) (📥 5.7K / month · 📦 13 · ⏱️ 14.08.2024):
 	```
 	pip install e3nn-jax
 	```
@@ -2131,31 +2150,31 @@ _General models that learn a representations aka embeddings of atomistic systems
 	git clone https://github.com/NVIDIA/DeepLearningExamples
 	```
 </details>
-<details><summary><b><a href="https://github.com/usnistgov/alignn">ALIGNN</a></b> (🥈21 ·  ⭐ 220) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. <code><a href="https://github.com/usnistgov/alignn/blob/main/LICENSE.rst">Custom</a></code></summary>
+<details><summary><b><a href="https://github.com/divelab/DIG">DIG: Dive into Graphs</a></b> (🥈21 ·  ⭐ 1.9K · 💤) - A library for graph deep learning research. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code></summary>
 
-- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 79 · 📦 14 · 📋 65 - 61% open · ⏱️ 09.09.2024):
+- [GitHub](https://github.com/divelab/DIG) (👨‍💻 50 · 🔀 280 · 📋 210 - 16% open · ⏱️ 04.02.2024):
 
 	```
-	git clone https://github.com/usnistgov/alignn
+	git clone https://github.com/divelab/DIG
 	```
-- [PyPi](https://pypi.org/project/alignn) (📥 3.3K / month · 📦 6 · ⏱️ 09.09.2024):
+- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 1.3K / month · ⏱️ 27.06.2022):
 	```
-	pip install alignn
+	pip install dive-into-graphs
 	```
 </details>
-<details><summary><b><a href="https://github.com/divelab/DIG">DIG: Dive into Graphs</a></b> (🥈20 ·  ⭐ 1.9K · 💤) - A library for graph deep learning research. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code></summary>
+<details><summary><b><a href="https://github.com/aimat-lab/gcnn_keras">kgcnn</a></b> (🥈19 ·  ⭐ 110) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/divelab/DIG) (👨‍💻 50 · 🔀 280 · 📋 210 - 16% open · ⏱️ 04.02.2024):
+- [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨‍💻 7 · 🔀 30 · 📦 19 · 📋 86 - 13% open · ⏱️ 06.05.2024):
 
 	```
-	git clone https://github.com/divelab/DIG
+	git clone https://github.com/aimat-lab/gcnn_keras
 	```
-- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 580 / month · ⏱️ 27.06.2022):
+- [PyPi](https://pypi.org/project/kgcnn) (📥 1.8K / month · 📦 3 · ⏱️ 27.02.2024):
 	```
-	pip install dive-into-graphs
+	pip install kgcnn
 	```
 </details>
-<details><summary><b><a href="https://github.com/deepmodeling/Uni-Mol">Uni-Mol</a></b> (🥈18 ·  ⭐ 680) - Official Repository for the Uni-Mol Series Methods. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code></summary>
+<details><summary><b><a href="https://github.com/deepmodeling/Uni-Mol">Uni-Mol</a></b> (🥈18 ·  ⭐ 690) - Official Repository for the Uni-Mol Series Methods. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code></summary>
 
 - [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨‍💻 17 · 🔀 120 · 📥 15K · 📋 160 - 41% open · ⏱️ 26.09.2024):
 
@@ -2163,38 +2182,26 @@ _General models that learn a representations aka embeddings of atomistic systems
 	git clone https://github.com/deepmodeling/Uni-Mol
 	```
 </details>
-<details><summary><b><a href="https://github.com/aimat-lab/gcnn_keras">kgcnn</a></b> (🥈18 ·  ⭐ 110) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/QUVA-Lab/escnn">escnn</a></b> (🥈17 ·  ⭐ 350) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. <code><a href="https://github.com/QUVA-Lab/escnn/blob/master/LICENSE">Custom</a></code></summary>
 
-- [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨‍💻 7 · 🔀 30 · 📦 18 · 📋 86 - 13% open · ⏱️ 06.05.2024):
+- [GitHub](https://github.com/QUVA-Lab/escnn) (👨‍💻 10 · 🔀 44 · 📋 75 - 50% open · ⏱️ 18.09.2024):
 
 	```
-	git clone https://github.com/aimat-lab/gcnn_keras
+	git clone https://github.com/QUVA-Lab/escnn
 	```
-- [PyPi](https://pypi.org/project/kgcnn) (📥 660 / month · 📦 3 · ⏱️ 27.02.2024):
+- [PyPi](https://pypi.org/project/escnn) (📥 1.7K / month · 📦 6 · ⏱️ 01.04.2022):
 	```
-	pip install kgcnn
+	pip install escnn
 	```
 </details>
 <details><summary><b><a href="https://github.com/IntelLabs/matsciml">matsciml</a></b> (🥈17 ·  ⭐ 140) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>workflows</code> <code>benchmarking</code></summary>
 
-- [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 12 · 🔀 19 · 📋 60 - 33% open · ⏱️ 01.10.2024):
+- [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 12 · 🔀 19 · 📋 61 - 32% open · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/IntelLabs/matsciml
 	```
 </details>
-<details><summary><b><a href="https://github.com/QUVA-Lab/escnn">escnn</a></b> (🥈16 ·  ⭐ 350) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. <code><a href="https://github.com/QUVA-Lab/escnn/blob/master/LICENSE">Custom</a></code></summary>
-
-- [GitHub](https://github.com/QUVA-Lab/escnn) (👨‍💻 10 · 🔀 44 · 📋 75 - 50% open · ⏱️ 18.09.2024):
-
-	```
-	git clone https://github.com/QUVA-Lab/escnn
-	```
-- [PyPi](https://pypi.org/project/escnn) (📥 1K / month · 📦 6 · ⏱️ 01.04.2022):
-	```
-	pip install escnn
-	```
-</details>
 <details><summary><b><a href="https://github.com/microsoft/Graphormer">Graphormer</a></b> (🥈15 ·  ⭐ 2.1K) - Graphormer is a general-purpose deep learning backbone for molecular modeling. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a> <code>pretrained</code></summary>
 
 - [GitHub](https://github.com/microsoft/Graphormer) (👨‍💻 14 · 🔀 330 · 📋 160 - 58% open · ⏱️ 28.05.2024):
@@ -2203,56 +2210,56 @@ _General models that learn a representations aka embeddings of atomistic systems
 	git clone https://github.com/microsoft/Graphormer
 	```
 </details>
-<details><summary><b><a href="https://github.com/ORNL/HydraGNN">HydraGNN</a></b> (🥈14 ·  ⭐ 61) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
+<details><summary><b><a href="https://github.com/ORNL/HydraGNN">HydraGNN</a></b> (🥈14 ·  ⭐ 64) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>
 
-- [GitHub](https://github.com/ORNL/HydraGNN) (👨‍💻 14 · 🔀 26 · 📦 1 · 📋 49 - 34% open · ⏱️ 27.09.2024):
+- [GitHub](https://github.com/ORNL/HydraGNN) (👨‍💻 14 · 🔀 26 · 📦 2 · 📋 49 - 34% open · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/ORNL/HydraGNN
 	```
 </details>
-<details><summary><b><a href="https://github.com/sparks-baird/CrabNet">Compositionally-Restricted Attention-Based Network (CrabNet)</a></b> (🥈13 ·  ⭐ 12) - Predict materials properties using only the composition information!. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/sparks-baird/CrabNet">Compositionally-Restricted Attention-Based Network (CrabNet)</a></b> (🥈14 ·  ⭐ 12) - Predict materials properties using only the composition information!. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/sparks-baird/CrabNet) (👨‍💻 6 · 🔀 5 · 📦 13 · 📋 18 - 83% open · ⏱️ 09.09.2024):
+- [GitHub](https://github.com/sparks-baird/CrabNet) (👨‍💻 6 · 🔀 5 · 📦 14 · 📋 18 - 83% open · ⏱️ 09.09.2024):
 
 	```
 	git clone https://github.com/sparks-baird/CrabNet
 	```
-- [PyPi](https://pypi.org/project/crabnet) (📥 660 / month · 📦 2 · ⏱️ 10.01.2023):
+- [PyPi](https://pypi.org/project/crabnet) (📥 2.4K / month · 📦 2 · ⏱️ 10.01.2023):
 	```
 	pip install crabnet
 	```
 </details>
-<details><summary><b><a href="https://github.com/lanl/hippynn">hippynn</a></b> (🥈12 ·  ⭐ 67) - python library for atomistic machine learning. <code><a href="https://github.com/lanl/hippynn/blob/main/LICENSE.txt">Custom</a></code> <code>workflows</code></summary>
+<details><summary><b><a href="https://github.com/lanl/hippynn">hippynn</a></b> (🥈12 ·  ⭐ 68) - python library for atomistic machine learning. <code><a href="https://github.com/lanl/hippynn/blob/main/LICENSE.txt">Custom</a></code> <code>workflows</code></summary>
 
-- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 14 · 🔀 23 · 📦 1 · 📋 18 - 33% open · ⏱️ 27.09.2024):
+- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 14 · 🔀 23 · 📦 2 · 📋 18 - 33% open · ⏱️ 09.10.2024):
 
 	```
 	git clone https://github.com/lanl/hippynn
 	```
 </details>
-<details><summary><b><a href="https://github.com/idocx/Atom2Vec">Atom2Vec</a></b> (🥈10 ·  ⭐ 35 · 💤) - Atom2Vec: a simple way to describe atoms for machine learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/vict0rsch/faenet">FAENet</a></b> (🥈12 ·  ⭐ 33 · 💤) - Frame Averaging Equivariant GNN for materials modeling. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/idocx/Atom2Vec) (👨‍💻 1 · 🔀 9 · 📦 2 · 📋 3 - 66% open · ⏱️ 23.02.2024):
+- [GitHub](https://github.com/vict0rsch/faenet) (👨‍💻 3 · 🔀 2 · 📦 3 · ⏱️ 12.10.2023):
 
 	```
-	git clone https://github.com/idocx/Atom2Vec
+	git clone https://github.com/vict0rsch/faenet
 	```
-- [PyPi](https://pypi.org/project/atom2vec) (📥 96 / month · ⏱️ 23.02.2024):
+- [PyPi](https://pypi.org/project/faenet) (📥 300 / month · ⏱️ 14.09.2023):
 	```
-	pip install atom2vec
+	pip install faenet
 	```
 </details>
-<details><summary><b><a href="https://github.com/vict0rsch/faenet">FAENet</a></b> (🥈10 ·  ⭐ 33 · 💤) - Frame Averaging Equivariant GNN for materials modeling. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/idocx/Atom2Vec">Atom2Vec</a></b> (🥈10 ·  ⭐ 35 · 💤) - Atom2Vec: a simple way to describe atoms for machine learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/vict0rsch/faenet) (👨‍💻 3 · 🔀 2 · 📦 2 · ⏱️ 12.10.2023):
+- [GitHub](https://github.com/idocx/Atom2Vec) (👨‍💻 1 · 🔀 9 · 📦 3 · 📋 3 - 66% open · ⏱️ 23.02.2024):
 
 	```
-	git clone https://github.com/vict0rsch/faenet
+	git clone https://github.com/idocx/Atom2Vec
 	```
-- [PyPi](https://pypi.org/project/faenet) (📥 120 / month · ⏱️ 14.09.2023):
+- [PyPi](https://pypi.org/project/atom2vec) (📥 310 / month · ⏱️ 23.02.2024):
 	```
-	pip install faenet
+	pip install atom2vec
 	```
 </details>
 <details><summary><b><a href="https://github.com/HSE-LAMBDA/ai4material_design">ai4material_design</a></b> (🥉9 ·  ⭐ 6 · 💤) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Crystallographic_defect"><code>material-defect</code></a></summary>
@@ -2265,7 +2272,7 @@ _General models that learn a representations aka embeddings of atomistic systems
 </details>
 <details><summary><b><a href="https://github.com/atomicarchitects/equiformer">Equiformer</a></b> (🥉8 ·  ⭐ 200) - [ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
 
-- [GitHub](https://github.com/atomicarchitects/equiformer) (👨‍💻 2 · 🔀 37 · 📋 14 - 42% open · ⏱️ 18.07.2024):
+- [GitHub](https://github.com/atomicarchitects/equiformer) (👨‍💻 2 · 🔀 37 · 📋 15 - 40% open · ⏱️ 18.07.2024):
 
 	```
 	git clone https://github.com/atomicarchitects/equiformer
@@ -2273,21 +2280,21 @@ _General models that learn a representations aka embeddings of atomistic systems
 </details>
 <details><summary><b><a href="https://github.com/atomicarchitects/equiformer_v2">EquiformerV2</a></b> (🥉8 ·  ⭐ 200) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/atomicarchitects/equiformer_v2) (👨‍💻 2 · 🔀 26 · 📋 18 - 83% open · ⏱️ 16.07.2024):
+- [GitHub](https://github.com/atomicarchitects/equiformer_v2) (👨‍💻 2 · 🔀 26 · 📋 18 - 77% open · ⏱️ 16.07.2024):
 
 	```
 	git clone https://github.com/atomicarchitects/equiformer_v2
 	```
 </details>
-<details><summary><b><a href="https://github.com/LLNL/graphite">graphite</a></b> (🥉8 ·  ⭐ 58) - A repository for implementing graph network models based on atomic structures. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/LLNL/graphite">graphite</a></b> (🥉8 ·  ⭐ 60) - A repository for implementing graph network models based on atomic structures. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/LLNL/graphite) (👨‍💻 2 · 🔀 9 · 📦 11 · 📋 4 - 75% open · ⏱️ 08.08.2024):
+- [GitHub](https://github.com/LLNL/graphite) (👨‍💻 2 · 🔀 9 · 📦 12 · 📋 4 - 75% open · ⏱️ 08.08.2024):
 
 	```
 	git clone https://github.com/llnl/graphite
 	```
 </details>
-<details><summary><b><a href="https://github.com/usccolumbia/deeperGATGNN">DeeperGATGNN</a></b> (🥉8 ·  ⭐ 46 · 💤) - Scalable graph neural networks for materials property prediction. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/usccolumbia/deeperGATGNN">DeeperGATGNN</a></b> (🥉8 ·  ⭐ 48 · 💤) - Scalable graph neural networks for materials property prediction. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
 - [GitHub](https://github.com/usccolumbia/deeperGATGNN) (👨‍💻 3 · 🔀 8 · 📋 12 - 33% open · ⏱️ 19.01.2024):
 
@@ -2295,7 +2302,7 @@ _General models that learn a representations aka embeddings of atomistic systems
 	git clone https://github.com/usccolumbia/deeperGATGNN
 	```
 </details>
-<details><summary><b><a href="https://github.com/Hongyu-yu/T-e3nn">T-e3nn</a></b> (🥉8 ·  ⭐ 8) - Time-reversal Euclidean neural networks based on e3nn. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>magnetism</code></summary>
+<details><summary><b><a href="https://github.com/Hongyu-yu/T-e3nn">T-e3nn</a></b> (🥉8 ·  ⭐ 9) - Time-reversal Euclidean neural networks based on e3nn. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>magnetism</code></summary>
 
 - [GitHub](https://github.com/Hongyu-yu/T-e3nn) (👨‍💻 26 · ⏱️ 29.09.2024):
 
@@ -2312,18 +2319,18 @@ _General models that learn a representations aka embeddings of atomistic systems
 - <b><a href="https://github.com/snap-stanford/pretrain-gnns">pretrained-gnns</a></b> (🥈10 ·  ⭐ 960 · 💀) - Strategies for Pre-training Graph Neural Networks. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>pretrained</code>
 - <b><a href="https://github.com/gasteigerjo/gdc">GDC</a></b> (🥈10 ·  ⭐ 270 · 💀) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a>
 - <b><a href="https://github.com/FabianFuchsML/se3-transformer-public">SE(3)-Transformers</a></b> (🥉9 ·  ⭐ 490 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>single-paper</code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a>
-- <b><a href="https://github.com/superlouis/GATGNN">GATGNN: Global Attention Graph Neural Network</a></b> (🥉9 ·  ⭐ 69 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/masashitsubaki/molecularGNN_smiles">molecularGNN_smiles</a></b> (🥉8 ·  ⭐ 290 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code>
+- <b><a href="https://github.com/superlouis/GATGNN">GATGNN: Global Attention Graph Neural Network</a></b> (🥉9 ·  ⭐ 71 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/masashitsubaki/molecularGNN_smiles">molecularGNN_smiles</a></b> (🥉8 ·  ⭐ 300 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code>
 - <b><a href="https://github.com/hyllios/CGAT">CGAT</a></b> (🥉8 ·  ⭐ 25 · 💀) - Crystal graph attention neural networks for materials prediction. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/learningmatter-mit/uvvisml">UVVisML</a></b> (🥉8 ·  ⭐ 22 · 💀) - Predict optical properties of molecules with machine learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Optical_properties"><code>optical-properties</code></a> <code>single-paper</code> <code>probabilistic</code>
+- <b><a href="https://github.com/learningmatter-mit/uvvisml">UVVisML</a></b> (🥉8 ·  ⭐ 23 · 💀) - Predict optical properties of molecules with machine learning. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Optical_properties"><code>optical-properties</code></a> <code>single-paper</code> <code>probabilistic</code>
 - <b><a href="https://github.com/tensorfieldnetworks/tensorfieldnetworks">tensorfieldnetworks</a></b> (🥉7 ·  ⭐ 150 · 💀) - Rotation- and translation-equivariant neural networks for 3D point clouds. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/atomistic-machine-learning/dtnn">DTNN</a></b> (🥉7 ·  ⭐ 76 · 💀) - Deep Tensor Neural Network. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/risilab/cormorant">Cormorant</a></b> (🥉7 ·  ⭐ 59 · 💀) - Codebase for Cormorant Neural Networks. <code><a href="https://github.com/risilab/cormorant/blob/master/LICENSE">Custom</a></code>
 - <b><a href="https://github.com/Open-Catalyst-Project/AdsorbML">AdsorbML</a></b> (🥉7 ·  ⭐ 36 · 💀) -  <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Surface_science"><code>surface-science</code></a> <code>single-paper</code>
 - <b><a href="https://github.com/emilemathieu/escnn_jax">escnn_jax</a></b> (🥉7 ·  ⭐ 26 · 💀) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. <code><a href="https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE">Custom</a></code>
 - <b><a href="https://github.com/aimat-lab/ML4pXRDs">ML4pXRDs</a></b> (🥉7 · 💀) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/X-ray_crystallography"><code>XRD</code></a> <code>single-paper</code>
-- <b><a href="https://github.com/ACEsuit/mace-layer">MACE-Layer</a></b> (🥉6 ·  ⭐ 33 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/pfnet-research/charge_transfer_nnp">charge_transfer_nnp</a></b> (🥉6 ·  ⭐ 29 · 💀) - Graph neural network potential with charge transfer. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a>
+- <b><a href="https://github.com/ACEsuit/mace-layer">MACE-Layer</a></b> (🥉6 ·  ⭐ 32 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/pfnet-research/charge_transfer_nnp">charge_transfer_nnp</a></b> (🥉6 ·  ⭐ 31 · 💀) - Graph neural network potential with charge transfer. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a>
 - <b><a href="https://github.com/learningmatter-mit/GLAMOUR">GLAMOUR</a></b> (🥉6 ·  ⭐ 21 · 💀) - Graph Learning over Macromolecule Representations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>single-paper</code>
 - <b><a href="https://github.com/risilab/Autobahn">Autobahn</a></b> (🥉5 ·  ⭐ 30 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
 - <b><a href="https://github.com/atomistic-machine-learning/field_schnet">FieldSchNet</a></b> (🥉5 ·  ⭐ 17 · 💀) - Deep neural network for molecules in external fields. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
@@ -2334,7 +2341,7 @@ _General models that learn a representations aka embeddings of atomistic systems
 - <b><a href="https://github.com/learningmatter-mit/per-site_painn">Per-site PAiNN</a></b> (🥉5 ·  ⭐ 1 · 💀) - Fork of PaiNN for PerovskiteOrderingGCNNs. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code> <code>pretrained</code> <code>single-paper</code>
 - <b><a href="https://github.com/gasteigerjo/gtn">Graph Transport Network</a></b> (🥉4 ·  ⭐ 16 · 💀) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. <code><a href="https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Transport_phenomena"><code>transport-phenomena</code></a>
 - <b><a href="https://github.com/sirmarcel/gkx">gkx: Green-Kubo Method in JAX</a></b> (🥉4 ·  ⭐ 4 · 💤) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Transport_phenomena"><code>transport-phenomena</code></a>
-- <b><a href="https://github.com/learningmatter-mit/atom_by_atom">atom_by_atom</a></b> (🥉3 ·  ⭐ 7 · 💤) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Surface_science"><code>surface-science</code></a> <code>single-paper</code>
+- <b><a href="https://github.com/learningmatter-mit/atom_by_atom">atom_by_atom</a></b> (🥉3 ·  ⭐ 8 · 💤) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Surface_science"><code>surface-science</code></a> <code>single-paper</code>
 - <b><a href="https://github.com/jeherr/element-encoder">Element encoder</a></b> (🥉3 ·  ⭐ 6 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>single-paper</code>
 - <b><a href="https://zenodo.org/record/7967079">Point Edge Transformer</a></b> (🥉2) - Smooth, exact rotational symmetrization for deep learning on point clouds. <code><a href="https://tldrlegal.com/search?q=CC-BY-4.0">CC-BY-4.0</a></code>
 - <b><a href="https://github.com/risilab/SphericalNet">SphericalNet</a></b> (🥉1 ·  ⭐ 3 · 💀) - Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in.. <code>Unlicensed</code>
@@ -2353,58 +2360,70 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large
 
 🔗&nbsp;<b><a href="https://www.microsoft.com/en-us/research/blog/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions/">MatterSim</a></b>  - A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967. <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Proprietary_software"><code>proprietary</code></a>
 
-<details><summary><b><a href="https://github.com/deepmodeling/deepmd-kit">DPA-2</a></b> (🥇24 ·  ⭐ 1.5K · 📉) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>ML-IAP</code> <code>pretrained</code> <code>workflows</code> <code>datasets</code></summary>
+<details><summary><b><a href="https://github.com/deepmodeling/deepmd-kit">DPA-2</a></b> (🥇24 ·  ⭐ 1.5K) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>ML-IAP</code> <code>pretrained</code> <code>workflows</code> <code>datasets</code></summary>
 
-- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 500 · 📥 40K · 📦 16 · 📋 790 - 12% open · ⏱️ 17.09.2024):
+- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 510 · 📥 41K · 📦 17 · 📋 810 - 12% open · ⏱️ 17.09.2024):
 
 	```
 	git clone https://github.com/deepmodeling/deepmd-kit
 	```
 </details>
-<details><summary><b><a href="https://github.com/CederGroupHub/chgnet">CHGNet</a></b> (🥈23 ·  ⭐ 230) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. <code><a href="https://github.com/CederGroupHub/chgnet/blob/main/LICENSE">Custom</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a> <code>magnetism</code> <code>structure-relaxation</code></summary>
+<details><summary><b><a href="https://github.com/CederGroupHub/chgnet">CHGNet</a></b> (🥈23 ·  ⭐ 240) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. <code><a href="https://github.com/CederGroupHub/chgnet/blob/main/LICENSE">Custom</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Electrostatics"><code>electrostatics</code></a> <code>magnetism</code> <code>structure-relaxation</code></summary>
 
-- [GitHub](https://github.com/CederGroupHub/chgnet) (👨‍💻 9 · 🔀 62 · 📦 32 · 📋 59 - 5% open · ⏱️ 16.09.2024):
+- [GitHub](https://github.com/CederGroupHub/chgnet) (👨‍💻 10 · 🔀 63 · 📦 36 · 📋 59 - 5% open · ⏱️ 16.10.2024):
 
 	```
 	git clone https://github.com/CederGroupHub/chgnet
 	```
-- [PyPi](https://pypi.org/project/chgnet) (📥 30K / month · 📦 21 · ⏱️ 16.09.2024):
+- [PyPi](https://pypi.org/project/chgnet) (📥 36K / month · 📦 21 · ⏱️ 16.09.2024):
 	```
 	pip install chgnet
 	```
 </details>
-<details><summary><b><a href="https://github.com/ACEsuit/mace-mp">MACE-MP</a></b> (🥉18 ·  ⭐ 460) - Pretrained foundation models for materials chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a></summary>
+<details><summary><b><a href="https://github.com/ACEsuit/mace-mp">MACE-MP</a></b> (🥈18 ·  ⭐ 500) - Pretrained foundation models for materials chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <code>pretrained</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a></summary>
 
-- [GitHub](https://github.com/ACEsuit/mace-mp) (👨‍💻 2 · 🔀 170 · 📥 28K · 📋 9 - 22% open · ⏱️ 24.04.2024):
+- [GitHub](https://github.com/ACEsuit/mace-mp) (👨‍💻 2 · 🔀 190 · 📥 30K · 📋 10 - 10% open · ⏱️ 24.04.2024):
 
 	```
 	git clone https://github.com/ACEsuit/mace-mp
 	```
-- [PyPi](https://pypi.org/project/mace-torch) (📥 11K / month · 📦 14 · ⏱️ 16.07.2024):
+- [PyPi](https://pypi.org/project/mace-torch) (📥 14K / month · 📦 14 · ⏱️ 16.07.2024):
 	```
 	pip install mace-torch
 	```
 </details>
-<details><summary><b><a href="https://github.com/MDIL-SNU/SevenNet">SevenNet</a></b> (🥉15 ·  ⭐ 110) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>pretrained</code></summary>
+<details><summary><b><a href="https://github.com/materialsvirtuallab/m3gnet">M3GNet</a></b> (🥈18 ·  ⭐ 240 · 📉) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>ML-IAP</code> <code>pretrained</code></summary>
 
-- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 11 · 🔀 14 · 📦 3 · 📋 22 - 50% open · ⏱️ 18.09.2024):
+- [GitHub](https://github.com/materialsvirtuallab/m3gnet) (👨‍💻 16 · 🔀 60 · 📦 27 · 📋 35 - 42% open · ⏱️ 04.10.2024):
 
 	```
-	git clone https://github.com/MDIL-SNU/SevenNet
+	git clone https://github.com/materialsvirtuallab/m3gnet
+	```
+- [PyPi](https://pypi.org/project/m3gnet) (📥 2.5K / month · 📦 5 · ⏱️ 17.11.2022):
+	```
+	pip install m3gnet
 	```
 </details>
-<details><summary><b><a href="https://github.com/orbital-materials/orb-models">Orb Models</a></b> (🥉14 ·  ⭐ 160 · 🐣) - ORB forcefield models from Orbital Materials. <code><a href="https://github.com/orbital-materials/orb-models/blob/main/LICENSE">Custom</a></code> <code>ML-IAP</code> <code>pretrained</code></summary>
+<details><summary><b><a href="https://github.com/orbital-materials/orb-models">Orb Models</a></b> (🥉14 ·  ⭐ 170 · 🐣) - ORB forcefield models from Orbital Materials. <code><a href="https://github.com/orbital-materials/orb-models/blob/main/LICENSE">Custom</a></code> <code>ML-IAP</code> <code>pretrained</code></summary>
 
-- [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 6 · 🔀 19 · 📦 1 · 📋 11 - 18% open · ⏱️ 02.10.2024):
+- [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 6 · 🔀 19 · 📦 2 · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/orbital-materials/orb-models
 	```
-- [PyPi](https://pypi.org/project/orb-models) (📥 1.2K / month · ⏱️ 13.09.2024):
+- [PyPi](https://pypi.org/project/orb-models) (📥 1.3K / month · ⏱️ 15.10.2024):
 	```
 	pip install orb-models
 	```
 </details>
+<details><summary><b><a href="https://github.com/MDIL-SNU/SevenNet">SevenNet</a></b> (🥉13 ·  ⭐ 110 · 📉) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>pretrained</code></summary>
+
+- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 12 · 🔀 13 · 📦 4 · 📋 22 - 50% open · ⏱️ 10.10.2024):
+
+	```
+	git clone https://github.com/MDIL-SNU/SevenNet
+	```
+</details>
 <details><summary><b><a href="https://github.com/facebookresearch/JMP">Joint Multidomain Pre-Training (JMP)</a></b> (🥉5 ·  ⭐ 38) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. <code><a href="https://tldrlegal.com/search?q=CC-BY-NC-4.0">CC-BY-NC-4.0</a></code> <code>pretrained</code> <code>ML-IAP</code> <code>general-tool</code></summary>
 
 - [GitHub](https://github.com/facebookresearch/JMP) (👨‍💻 1 · 🔀 6 · ⏱️ 07.05.2024):
@@ -2413,10 +2432,6 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large
 	git clone https://github.com/facebookresearch/JMP
 	```
 </details>
-<details><summary>Show 1 hidden projects...</summary>
-
-- <b><a href="https://github.com/materialsvirtuallab/m3gnet">M3GNet</a></b> (🥈19 ·  ⭐ 230 · 💀) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>ML-IAP</code> <code>pretrained</code>
-</details>
 <br>
 
 ## Unsupervised Learning
@@ -2425,21 +2440,21 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large
 
 _Projects that focus on unsupervised learning (USL) for atomistic ML, such as dimensionality reduction, clustering and visualization._
 
-<details><summary><b><a href="https://github.com/sissa-data-science/DADApy">DADApy</a></b> (🥇19 ·  ⭐ 100) - Distance-based Analysis of DAta-manifolds in python. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
+<details><summary><b><a href="https://github.com/sissa-data-science/DADApy">DADApy</a></b> (🥇20 ·  ⭐ 100) - Distance-based Analysis of DAta-manifolds in python. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>
 
-- [GitHub](https://github.com/sissa-data-science/DADApy) (👨‍💻 20 · 🔀 18 · 📦 8 · 📋 36 - 25% open · ⏱️ 16.09.2024):
+- [GitHub](https://github.com/sissa-data-science/DADApy) (👨‍💻 20 · 🔀 18 · 📦 10 · 📋 36 - 25% open · ⏱️ 16.09.2024):
 
 	```
 	git clone https://github.com/sissa-data-science/DADApy
 	```
-- [PyPi](https://pypi.org/project/dadapy) (📥 200 / month · ⏱️ 02.07.2024):
+- [PyPi](https://pypi.org/project/dadapy) (📥 440 / month · ⏱️ 02.07.2024):
 	```
 	pip install dadapy
 	```
 </details>
 <details><summary><b><a href="https://github.com/BingqingCheng/ASAP">ASAP</a></b> (🥈11 ·  ⭐ 140) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/BingqingCheng/ASAP) (👨‍💻 6 · 🔀 28 · 📦 6 · 📋 25 - 24% open · ⏱️ 27.06.2024):
+- [GitHub](https://github.com/BingqingCheng/ASAP) (👨‍💻 6 · 🔀 28 · 📦 7 · 📋 25 - 24% open · ⏱️ 27.06.2024):
 
 	```
 	git clone https://github.com/BingqingCheng/ASAP
@@ -2447,7 +2462,7 @@ _Projects that focus on unsupervised learning (USL) for atomistic ML, such as di
 </details>
 <details><summary>Show 5 hidden projects...</summary>
 
-- <b><a href="https://github.com/lab-cosmo/sketchmap">Sketchmap</a></b> (🥈9 ·  ⭐ 44 · 💀) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>C++</code>
+- <b><a href="https://github.com/lab-cosmo/sketchmap">Sketchmap</a></b> (🥈9 ·  ⭐ 46 · 💀) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>C++</code>
 - <b><a href="https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders">Coarse-Graining-Auto-encoders</a></b> (🥉5 ·  ⭐ 21 · 💀) - Implementation of coarse-graining Autoencoders. <code>Unlicensed</code> <code>single-paper</code>
 - <b><a href="https://github.com/mathsphy/paper-ml-robustness-material-property">paper-ml-robustness-material-property</a></b> (🥉5 ·  ⭐ 4 · 💀) - A critical examination of robustness and generalizability of machine learning prediction of materials properties. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>datasets</code> <code>single-paper</code>
 - <b><a href="https://github.com/Minoru938/KmdPlus">KmdPlus</a></b> (🥉4 ·  ⭐ 3) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
@@ -2461,69 +2476,69 @@ _Projects that focus on unsupervised learning (USL) for atomistic ML, such as di
 
 _Projects that focus on visualization (viz.) for atomistic ML._
 
-<details><summary><b><a href="https://github.com/materialsproject/crystaltoolkit">Crystal Toolkit</a></b> (🥇24 ·  ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/materialsproject/crystaltoolkit">Crystal Toolkit</a></b> (🥇24 ·  ⭐ 150) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/materialsproject/crystaltoolkit) (👨‍💻 28 · 🔀 57 · 📦 38 · 📋 110 - 47% open · ⏱️ 20.09.2024):
+- [GitHub](https://github.com/materialsproject/crystaltoolkit) (👨‍💻 28 · 🔀 57 · 📦 39 · 📋 110 - 47% open · ⏱️ 20.09.2024):
 
 	```
 	git clone https://github.com/materialsproject/crystaltoolkit
 	```
-- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 2K / month · 📦 8 · ⏱️ 04.09.2024):
+- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 4.5K / month · 📦 8 · ⏱️ 04.09.2024):
 	```
 	pip install crystal-toolkit
 	```
 </details>
-<details><summary><b><a href="https://github.com/janosh/pymatviz">pymatviz</a></b> (🥈21 ·  ⭐ 160) - A toolkit for visualizations in materials informatics. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>general-tool</code> <code>probabilistic</code></summary>
+<details><summary><b><a href="https://github.com/janosh/pymatviz">pymatviz</a></b> (🥈22 ·  ⭐ 160) - A toolkit for visualizations in materials informatics. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>general-tool</code> <code>probabilistic</code></summary>
 
-- [GitHub](https://github.com/janosh/pymatviz) (👨‍💻 8 · 🔀 14 · 📦 8 · 📋 47 - 25% open · ⏱️ 03.10.2024):
+- [GitHub](https://github.com/janosh/pymatviz) (👨‍💻 9 · 🔀 15 · 📦 9 · 📋 51 - 27% open · ⏱️ 16.10.2024):
 
 	```
 	git clone https://github.com/janosh/pymatviz
 	```
-- [PyPi](https://pypi.org/project/pymatviz) (📥 3.5K / month · 📦 2 · ⏱️ 01.09.2024):
+- [PyPi](https://pypi.org/project/pymatviz) (📥 4.5K / month · 📦 2 · ⏱️ 07.10.2024):
 	```
 	pip install pymatviz
 	```
 </details>
-<details><summary><b><a href="https://github.com/zincware/ZnDraw">ZnDraw</a></b> (🥈20 ·  ⭐ 30 · 📈) - A powerful tool for visualizing, modifying, and analysing atomistic systems. <code><a href="http://bit.ly/2M0xmjV">EPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>JavaScript</code></summary>
+<details><summary><b><a href="https://github.com/zincware/ZnDraw">ZnDraw</a></b> (🥈20 ·  ⭐ 30) - A powerful tool for visualizing, modifying, and analysing atomistic systems. <code><a href="http://bit.ly/2M0xmjV">EPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>JavaScript</code></summary>
 
-- [GitHub](https://github.com/zincware/ZnDraw) (👨‍💻 7 · 🔀 3 · 📦 4 · 📋 310 - 29% open · ⏱️ 17.09.2024):
+- [GitHub](https://github.com/zincware/ZnDraw) (👨‍💻 7 · 🔀 3 · 📦 6 · 📋 310 - 29% open · ⏱️ 12.10.2024):
 
 	```
 	git clone https://github.com/zincware/ZnDraw
 	```
-- [PyPi](https://pypi.org/project/zndraw) (📥 1.2K / month · 📦 2 · ⏱️ 26.08.2024):
+- [PyPi](https://pypi.org/project/zndraw) (📥 2.8K / month · 📦 2 · ⏱️ 26.08.2024):
 	```
 	pip install zndraw
 	```
 </details>
 <details><summary><b><a href="https://github.com/lab-cosmo/chemiscope">Chemiscope</a></b> (🥉18 ·  ⭐ 130) - An interactive structure/property explorer for materials and molecules. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>JavaScript</code></summary>
 
-- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 22 · 🔀 32 · 📥 310 · 📦 6 · 📋 120 - 29% open · ⏱️ 26.09.2024):
+- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 23 · 🔀 32 · 📥 330 · 📦 6 · 📋 130 - 27% open · ⏱️ 15.10.2024):
 
 	```
 	git clone https://github.com/lab-cosmo/chemiscope
 	```
-- [npm](https://www.npmjs.com/package/chemiscope) (📥 14 / month · 📦 3 · ⏱️ 15.03.2023):
+- [npm](https://www.npmjs.com/package/chemiscope) (📥 9 / month · 📦 3 · ⏱️ 15.03.2023):
 	```
 	npm install chemiscope
 	```
 </details>
-<details><summary><b><a href="https://github.com/janosh/elementari">Elementari</a></b> (🥉11 ·  ⭐ 140) - Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>JavaScript</code></summary>
+<details><summary><b><a href="https://github.com/janosh/elementari">Elementari</a></b> (🥉13 ·  ⭐ 140) - Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>JavaScript</code></summary>
 
-- [GitHub](https://github.com/janosh/elementari) (👨‍💻 2 · 🔀 12 · 📦 3 · 📋 7 - 28% open · ⏱️ 19.07.2024):
+- [GitHub](https://github.com/janosh/elementari) (👨‍💻 2 · 🔀 12 · 📦 3 · 📋 7 - 28% open · ⏱️ 07.10.2024):
 
 	```
 	git clone https://github.com/janosh/elementari
 	```
-- [npm](https://www.npmjs.com/package/elementari) (📥 120 / month · 📦 1 · ⏱️ 15.01.2024):
+- [npm](https://www.npmjs.com/package/elementari) (📥 210 / month · 📦 1 · ⏱️ 15.01.2024):
 	```
 	npm install elementari
 	```
 </details>
 <details><summary>Show 1 hidden projects...</summary>
 
-- <b><a href="https://github.com/usnistgov/atomvision">Atomvision</a></b> (🥉12 ·  ⭐ 29 · 💀) - Deep learning framework for atomistic image data. <code><a href="https://github.com/usnistgov/atomvision/blob/master/LICENSE.md">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Computer_vision"><code>computer-vision</code></a> <a href="https://en.wikipedia.org/wiki/Experimental_physics"><code>experimental-data</code></a> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
+- <b><a href="https://github.com/usnistgov/atomvision">Atomvision</a></b> (🥉13 ·  ⭐ 29 · 💀) - Deep learning framework for atomistic image data. <code><a href="https://github.com/usnistgov/atomvision/blob/master/LICENSE.md">Custom</a></code> <a href="https://en.wikipedia.org/wiki/Computer_vision"><code>computer-vision</code></a> <a href="https://en.wikipedia.org/wiki/Experimental_physics"><code>experimental-data</code></a> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>
 </details>
 <br>
 
@@ -2533,34 +2548,34 @@ _Projects that focus on visualization (viz.) for atomistic ML._
 
 _Projects and models that focus on quantities of wavefunction theory methods, such as Monte Carlo techniques like deep learning variational Monte Carlo (DL-VMC), quantum chemistry methods, etc._
 
-<details><summary><b><a href="https://github.com/deepqmc/deepqmc">DeepQMC</a></b> (🥇20 ·  ⭐ 350) - Deep learning quantum Monte Carlo for electrons in real space. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
+<details><summary><b><a href="https://github.com/deepqmc/deepqmc">DeepQMC</a></b> (🥇21 ·  ⭐ 350) - Deep learning quantum Monte Carlo for electrons in real space. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
 
-- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 59 · 📦 2 · 📋 46 - 8% open · ⏱️ 24.09.2024):
+- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 60 · 📦 3 · 📋 46 - 6% open · ⏱️ 24.09.2024):
 
 	```
 	git clone https://github.com/deepqmc/deepqmc
 	```
-- [PyPi](https://pypi.org/project/deepqmc) (📥 280 / month · ⏱️ 24.09.2024):
+- [PyPi](https://pypi.org/project/deepqmc) (📥 880 / month · ⏱️ 24.09.2024):
 	```
 	pip install deepqmc
 	```
 </details>
-<details><summary><b><a href="https://github.com/google-deepmind/ferminet">FermiNet</a></b> (🥈15 ·  ⭐ 720) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
+<details><summary><b><a href="https://github.com/google-deepmind/ferminet">FermiNet</a></b> (🥈15 ·  ⭐ 730) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code> <a href="https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)"><code>transformer</code></a></summary>
 
-- [GitHub](https://github.com/google-deepmind/ferminet) (👨‍💻 18 · 🔀 120 · 📋 50 - 2% open · ⏱️ 03.10.2024):
+- [GitHub](https://github.com/google-deepmind/ferminet) (👨‍💻 18 · 🔀 120 · 📋 53 - 1% open · ⏱️ 03.10.2024):
 
 	```
 	git clone https://github.com/google-deepmind/ferminet
 	```
 </details>
-<details><summary><b><a href="https://github.com/mdsunivie/deeperwin">DeepErwin</a></b> (🥉8 ·  ⭐ 48) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. <code><a href="https://github.com/mdsunivie/deeperwin/blob/master/LICENSE">Custom</a></code></summary>
+<details><summary><b><a href="https://github.com/mdsunivie/deeperwin">DeepErwin</a></b> (🥉9 ·  ⭐ 49) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. <code><a href="https://github.com/mdsunivie/deeperwin/blob/master/LICENSE">Custom</a></code></summary>
 
-- [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 7 · 🔀 6 · 📥 10 · ⏱️ 07.06.2024):
+- [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 7 · 🔀 7 · 📥 11 · ⏱️ 07.06.2024):
 
 	```
 	git clone https://github.com/mdsunivie/deeperwin
 	```
-- [PyPi](https://pypi.org/project/deeperwin) (📥 110 / month · ⏱️ 14.12.2021):
+- [PyPi](https://pypi.org/project/deeperwin) (📥 370 / month · ⏱️ 14.12.2021):
 	```
 	pip install deeperwin
 	```
diff --git a/history/2024-10-17_changes.md b/history/2024-10-17_changes.md
new file mode 100644
index 0000000..c2a6c68
--- /dev/null
+++ b/history/2024-10-17_changes.md
@@ -0,0 +1,20 @@
+## 📈 Trending Up
+
+_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._
+
+- <b><a href="https://github.com/materialsproject/MPContribs">MPContribs</a></b> (🥇25 ·  ⭐ 35 · 📈) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/luigibonati/mlcolvar">mlcolvar</a></b> (🥈21 ·  ⭐ 89 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>sampling</code>
+- <b><a href="https://github.com/FitSNAP/FitSNAP">FitSNAP</a></b> (🥈20 ·  ⭐ 150 · 📈) - Software for generating machine-learning interatomic potentials for LAMMPS. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code>
+- <b><a href="https://github.com/mala-project/mala">MALA</a></b> (🥇19 ·  ⭐ 81 · 📈) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
+- <b><a href="https://github.com/openkim/kliff">KLIFF</a></b> (🥈16 ·  ⭐ 34 · 📈) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. <code><a href="https://tldrlegal.com/search?q=LGPL-2.1">LGPL-2.1</a></code> <code>probabilistic</code> <code>workflows</code>
+
+## 📉 Trending Down
+
+_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._
+
+- <b><a href="https://github.com/e3nn/e3nn">e3nn</a></b> (🥇27 ·  ⭐ 960 · 📉) - A modular framework for neural networks with Euclidean symmetry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/libAtoms/QUIP">QUIP</a></b> (🥈26 ·  ⭐ 350 · 📉) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_engineering"><code>rep-eng</code></a> <code>Fortran</code>
+- <b><a href="https://github.com/materialsvirtuallab/m3gnet">M3GNet</a></b> (🥈18 ·  ⭐ 240 · 📉) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>ML-IAP</code> <code>pretrained</code>
+- <b><a href="https://github.com/MDIL-SNU/SevenNet">SevenNet</a></b> (🥉13 ·  ⭐ 110 · 📉) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>pretrained</code>
+- <b><a href="https://github.com/Minoru938/CSPML">CSPML (crystal structure prediction with machine learning-based element substitution)</a></b> (🥉5 ·  ⭐ 20 · 📉) - Original implementation of CSPML. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>structure-prediction</code>
+
diff --git a/history/2024-10-17_projects.csv b/history/2024-10-17_projects.csv
new file mode 100644
index 0000000..9e5d26b
--- /dev/null
+++ b/history/2024-10-17_projects.csv
@@ -0,0 +1,430 @@
+,name,resource,category,license,homepage,description,projectrank,show,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_latest_release_published_at,pypi_dependent_project_count,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,updated_github_id,trending,maven_id,maven_url,maven_latest_release_published_at,maven_dependent_project_count,npm_id,npm_url,npm_latest_release_published_at,npm_dependent_project_count,npm_monthly_downloads,gitlab_id,gitlab_url,docs_url,ignore
+0,AI for Science Map,True,community,GPL-3.0 license,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,Atomic Cluster Expansion,True,community,,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,CrystaLLM,True,community,https://materialis.ai/terms.html,https://crystallm.com,Generate a crystal structure from a composition.,0,True,"['language-models', 'generative', 'pretrained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,GAP-ML.org community homepage,True,community,,https://gap-ml.org/,,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,matsci.org,True,community,,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,Matter Modeling Stack Exchange - Machine Learning,True,community,,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,Alexandria Materials Database,True,datasets,CC-BY-4.0,https://alexandria.icams.rub.de/,"A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,Citrination Datasets,True,datasets,MIT,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,"['model-repository', 'pretrained', 'language-models']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,Graphs of Materials Project 20190401,True,datasets,MIT,https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992,The dataset used to train the MEGNet interatomic potential.,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,HME21 Dataset,True,datasets,,https://doi.org/10.6084/m9.figshare.19658538,High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP)..,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w.,0,True,"['model-repository', 'benchmarking', 'community', 'educational']",usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2024-10-12 19:11:30.000000,2024-10-12 18:57:04,867.0,51.0,43.0,5.0,328.0,18.0,2.0,59.0,2024-10-12 19:11:30.000,2024.10.10,29.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,Materials Project - Charge Densities,True,datasets,,https://materialsproject.org/ml/charge_densities,Materials Project has started offering charge density information available for download via their public API.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,Materials Project Trajectory (MPtrj) Dataset,True,datasets,MIT,https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842,The dataset used to train the CHGNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+16,matterverse.ai,True,datasets,,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,MPF.2021.2.8,True,datasets,,https://figshare.com/articles/dataset/MPF_2021_2_8/19470599,The dataset used to train the M3GNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,NRELMatDB,True,datasets,,https://materials.nrel.gov/,"Computational materials database with the specific focus on materials for renewable energy applications including, but..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,Quantum-Machine.org Datasets,True,datasets,,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+20,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+21,MoleculeNet,True,datasets,,https://moleculenet.org/,A Benchmark for Molecular Machine Learning.,0,True,['benchmarking'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,ZINC15,True,datasets,,https://zinc15.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+23,ZINC20,True,datasets,,https://zinc.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,AI for Science 101,True,educational,,https://ai4science101.github.io/,,0,True,"['community', 'rep-learn']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,AL4MS 2023 workshop tutorials,True,educational,,https://sites.utu.fi/al4ms2023/media-and-tutorials/,,0,True,['active-learning'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+26,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,MatterGen,True,materials-discovery,,https://www.microsoft.com/en-us/research/blog/mattergen-property-guided-materials-design/,A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687.,0,True,"['generative', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,IKS-PIML,True,ml-dft,,https://rodare.hzdr.de/record/2720,Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning..,0,True,"['neural-operator', 'pinn', 'datasets', 'single-paper']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+29,TeaNet,True,uip,,https://doi.org/10.24433/CO.0749085.v1,Universal neural network interatomic potential inspired by iterative electronic relaxations..,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,PreFerred Potential (PFP),True,uip,,https://www.nature.com/articles/s41467-022-30687-9#code-availability,Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9.,0,True,"['ml-iap', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,MatterSim,True,uip,,https://www.microsoft.com/en-us/research/blog/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions/,"A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967.",0,True,"['ml-iap', 'active-learning', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+32,Deep Graph Library (DGL),,rep-learn,Apache-2.0,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",39,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2024-10-16 04:52:59.000000,2024-10-16 04:52:59,4409.0,212.0,3010.0,175.0,5049.0,531.0,2356.0,13461.0,2024-09-03 04:16:25.000,2.4.0,453.0,295.0,dgl,dglteam/dgl,455.0,307.0,https://pypi.org/project/dgl,2024-05-13 01:10:39.000,148.0,151519.0,156947.0,https://anaconda.org/dglteam/dgl,2024-09-03 05:08:29.197,379984.0,1.0,,,,,,,,,,,,,,,,,,,,,
+33,PyG Models,,rep-learn,MIT,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,35,True,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2024-10-11 04:51:58.000000,2024-10-11 04:51:56,7631.0,55.0,3636.0,252.0,3139.0,1036.0,2659.0,21162.0,2024-09-26 07:09:50.000,2.6.1,42.0,520.0,,,6667.0,6667.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+34,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",35,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2024-10-17 17:51:02.906569,2024-10-17 17:25:03,10542.0,42.0,1675.0,143.0,2441.0,639.0,1238.0,5447.0,2024-04-03 16:21:23.000,2.8.0,930.0,248.0,deepchem,conda-forge/deepchem,454.0,441.0,https://pypi.org/project/deepchem,2024-10-17 17:36:05.000,13.0,89313.0,91505.0,https://anaconda.org/conda-forge/deepchem,2024-04-05 16:46:45.105,110330.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2024-10-17 17:51:02.906569,5.0,7753.0,,,,,,,,,,,,,,,,
+35,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,35,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2024-10-17 06:59:43.000000,2024-10-17 06:59:39,7896.0,109.0,873.0,81.0,3291.0,958.0,2386.0,2625.0,2024-09-27 11:53:59.000,Release_2024_09_1,100.0,235.0,rdkit,rdkit/rdkit,751.0,3.0,https://pypi.org/project/rdkit,2024-08-07 12:34:25.000,748.0,2388347.0,2409113.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2573931.0,1.0,,,,,,1096.0,,,,,,,,,,,,,,,
+36,paper-qa,,language-models,Apache-2.0,https://github.com/Future-House/paper-qa,High accuracy RAG for answering questions from scientific documents with citations.,30,True,['ai-agent'],whitead/paper-qa,https://github.com/Future-House/paper-qa,2023-02-05 01:07:25,2024-10-17 18:12:54.000000,2024-10-17 05:03:15,432.0,186.0,583.0,55.0,338.0,92.0,151.0,6208.0,2024-10-16 01:03:11.000,5.2.1,119.0,26.0,paper-qa,,82.0,74.0,https://pypi.org/project/paper-qa,2024-10-16 01:03:11.000,8.0,15988.0,15988.0,,,,1.0,,,,,,,Future-House/paper-qa,,,,,,,,,,,,,,
+37,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,29,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2024-10-14 08:06:22.000000,2024-10-11 14:20:38,4174.0,18.0,194.0,28.0,728.0,31.0,200.0,475.0,2024-10-06 12:46:05.000,0.9.3,72.0,56.0,matminer,conda-forge/matminer,383.0,323.0,https://pypi.org/project/matminer,2024-10-06 12:46:05.000,60.0,19546.0,21045.0,https://anaconda.org/conda-forge/matminer,2024-10-06 16:00:19.611,71973.0,1.0,,,,,,,,,,,,,,,,,,,,,
+38,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,28,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2024-09-30 13:38:02.000000,2024-09-24 14:00:57,1698.0,25.0,213.0,32.0,419.0,6.0,245.0,778.0,2024-09-05 11:35:29.000,2.1.1,12.0,36.0,schnetpack,,96.0,92.0,https://pypi.org/project/schnetpack,2024-09-05 11:35:29.000,4.0,1556.0,1556.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+39,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,27,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2024-08-25 22:32:06.000000,2024-08-25 22:32:06,2174.0,8.0,138.0,20.0,227.0,23.0,134.0,958.0,2024-07-27 03:01:58.000,0.5.2,29.0,31.0,e3nn,conda-forge/e3nn,321.0,317.0,https://pypi.org/project/e3nn,2022-04-13 19:24:30.000,4.0,91142.0,91938.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,23102.0,1.0,,,,,,,,-1.0,,,,,,,,,,,,,
+40,OPTIMADE Python tools,,datasets,MIT,https://github.com/Materials-Consortia/optimade-python-tools,Tools for implementing and consuming OPTIMADE APIs in Python.,27,True,,Materials-Consortia/optimade-python-tools,https://github.com/Materials-Consortia/optimade-python-tools,2018-06-05 21:00:07,2024-10-16 01:14:26.563000,2024-10-15 21:50:04,1679.0,46.0,42.0,7.0,1722.0,107.0,351.0,68.0,2024-10-15 20:37:15.000,1.1.5,111.0,28.0,optimade,conda-forge/optimade,65.0,61.0,https://pypi.org/project/optimade,2024-10-15 20:37:15.000,4.0,11016.0,13014.0,https://anaconda.org/conda-forge/optimade,2024-10-16 01:14:26.563,93944.0,1.0,,,,,,,,,,,,,,,,,,,,,
+41,DeePMD-kit,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,26,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-10-17 18:08:59.000000,2024-09-17 18:00:40,2534.0,1.0,507.0,47.0,2076.0,103.0,711.0,1469.0,2024-07-03 19:29:34.000,2.2.11,54.0,69.0,deepmd-kit,deepmodeling/deepmd-kit,21.0,17.0,https://pypi.org/project/deepmd-kit,2024-09-25 16:10:59.000,4.0,10027.0,10784.0,https://anaconda.org/deepmodeling/deepmd-kit,2024-04-06 21:22:08.456,1382.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2024-07-27 08:24:51.741318,1.0,2918.0,41262.0,,,,,,,,,,,,,,,
+42,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",26,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2024-09-05 09:24:47.000000,2024-09-05 09:24:47,922.0,15.0,190.0,47.0,171.0,73.0,81.0,1171.0,2023-08-09 23:18:24.000,0.2.8,38.0,34.0,jax-md,,62.0,59.0,https://pypi.org/project/jax-md,2023-08-09 23:18:24.000,3.0,4793.0,4793.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+43,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,26,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2024-10-06 10:32:05.000000,2024-10-06 10:32:05,17778.0,68.0,157.0,40.0,822.0,32.0,260.0,493.0,2023-08-21 19:50:47.000,cdk-2.9,20.0,165.0,,,16.0,,,,,,183.0,,,,1.0,,,,,,22156.0,,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,2023-08-21 08:05:58,16.0,,,,,,,,,
+44,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,26,True,"['md', 'ml-iap', 'rep-eng', 'lang-fortran']",libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2024-09-27 15:26:55.000000,2024-09-27 15:18:25,10866.0,5.0,122.0,26.0,177.0,104.0,362.0,351.0,2023-06-15 19:54:01.129,0.9.14,15.0,85.0,quippy-ase,,47.0,43.0,https://pypi.org/project/quippy-ase,2023-01-15 16:54:03.041,4.0,11598.0,11681.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9958.0,680.0,,-1.0,,,,,,,,,,,,,
+45,JAX-DFT,,ml-dft,Apache-2.0,https://github.com/google-research/google-research/tree/master/jax_dft,This library provides basic building blocks that can construct DFT calculations as a differentiable program.,25,True,,google-research/google-research,https://github.com/google-research/google-research,2018-10-04 18:42:48,2024-10-15 23:13:12.000000,2024-10-15 23:13:05,4683.0,104.0,7854.0,749.0,874.0,1477.0,330.0,34079.0,,,,807.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+46,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,25,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2024-08-20 17:56:51.000000,2024-05-28 18:24:28,1288.0,,87.0,19.0,27.0,12.0,93.0,397.0,2024-05-28 18:22:25.000,2.1.1,32.0,18.0,dscribe,conda-forge/dscribe,239.0,204.0,https://pypi.org/project/dscribe,2024-05-28 18:22:25.000,35.0,24994.0,27785.0,https://anaconda.org/conda-forge/dscribe,2024-05-28 23:16:49.298,142360.0,1.0,,,,,,,,,,,,,,,,,,,,,
+47,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,25,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2024-10-17 00:30:46.000000,2024-10-17 00:27:37,5640.0,78.0,20.0,10.0,1742.0,22.0,79.0,35.0,2024-10-17 00:30:46.000,5.9.0,163.0,25.0,mpcontribs-client,,43.0,40.0,https://pypi.org/project/mpcontribs-client,2024-10-17 00:30:46.000,3.0,8882.0,8882.0,,,,1.0,,,,,,,,2.0,,,,,,,,,,,,,
+48,DPA-2,,uip,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492.,24,True,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-10-17 18:08:59.000000,2024-09-17 18:00:40,2534.0,1.0,507.0,47.0,2076.0,103.0,711.0,1468.0,2024-07-03 19:22:15.000,2.2.11,50.0,69.0,,,17.0,17.0,,,,,699.0,,,,1.0,,,,,,41262.0,,,,,,,,,,,,,,,
+49,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,24,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2024-09-11 21:03:45.224000,2023-11-14 16:32:59,434.0,,125.0,30.0,484.0,24.0,150.0,461.0,2023-11-14 16:41:14.000,2.2.4,24.0,19.0,torchani,conda-forge/torchani,47.0,43.0,https://pypi.org/project/torchani,2023-11-14 16:41:14.000,4.0,4920.0,15157.0,https://anaconda.org/conda-forge/torchani,2024-09-11 21:03:45.224,511870.0,1.0,,,,,,,,,,,,,,,,,,,,,
+50,MAML,,general-tool,BSD-3-Clause,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",24,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2024-10-10 11:32:40.000000,2024-10-10 11:31:53,1769.0,22.0,78.0,21.0,597.0,9.0,62.0,365.0,2024-06-13 15:29:41.000,2024.6.13,16.0,33.0,maml,,13.0,11.0,https://pypi.org/project/maml,2024-06-13 15:29:41.000,2.0,1036.0,1036.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+51,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,24,True,['multifidelity'],materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2024-10-17 13:12:49.000000,2024-10-16 15:51:49,1084.0,93.0,60.0,11.0,285.0,7.0,91.0,264.0,2024-08-07 12:24:58.000,1.1.3,31.0,17.0,m3gnet,,55.0,50.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,2489.0,2489.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+52,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,A Python package for manipulating atomistic data of software in computational science.,24,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2024-10-15 21:59:33.000000,2024-09-20 18:23:06,761.0,29.0,130.0,9.0,511.0,33.0,82.0,196.0,2024-09-20 18:25:00.000,0.2.21,48.0,61.0,dpdata,deepmodeling/dpdata,166.0,126.0,https://pypi.org/project/dpdata,2024-09-20 18:25:00.000,40.0,50118.0,50124.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,235.0,1.0,,,,,,,,,,,,,,,,,,,,,
+53,Crystal Toolkit,,visualization,MIT,https://github.com/materialsproject/crystaltoolkit,Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials..,24,True,,materialsproject/crystaltoolkit,https://github.com/materialsproject/crystaltoolkit,2017-07-25 21:06:36,2024-10-15 20:42:31.000000,2024-09-20 00:46:12,3277.0,17.0,57.0,10.0,316.0,53.0,59.0,150.0,2024-09-04 19:26:59.000,2024.9.4rc0,60.0,28.0,crystal-toolkit,,47.0,39.0,https://pypi.org/project/crystal-toolkit,2024-09-04 19:29:25.000,8.0,4532.0,4532.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+54,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,23,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2024-10-17 15:23:59.000000,2024-10-10 16:10:01,509.0,14.0,2360.0,404.0,275.0,27.0,34.0,16348.0,2024-10-10 16:10:15.000,2024.10.10,100.0,47.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+55,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,False,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07.000000,2023-04-16 03:55:52,236.0,,146.0,17.0,141.0,27.0,57.0,718.0,2023-02-13 08:45:17.000,0.3.2,17.0,22.0,dgllife,,255.0,251.0,https://pypi.org/project/dgllife,2022-12-21 13:18:00.570,4.0,14103.0,14103.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+56,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,23,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2024-10-11 04:31:59.000000,2024-07-09 15:58:45,1873.0,,136.0,22.0,162.0,25.0,70.0,616.0,2024-07-09 16:05:06.000,0.6.1,16.0,11.0,nequip,conda-forge/nequip,28.0,27.0,https://pypi.org/project/nequip,2024-07-09 16:05:26.000,1.0,4200.0,4412.0,https://anaconda.org/conda-forge/nequip,2024-07-10 05:13:00.157,6166.0,1.0,,,,,,,,,,,,,,,,,,,,,
+57,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,23,False,['multifidelity'],materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000000,2023-04-27 02:39:17,1146.0,,152.0,25.0,314.0,21.0,57.0,502.0,2022-11-16 21:25:01.818,1.3.2,37.0,14.0,megnet,,90.0,86.0,https://pypi.org/project/megnet,2022-11-16 21:25:01.818,4.0,3635.0,3635.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+58,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,23,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2024-10-04 14:11:37.000000,2024-09-07 20:21:38,2108.0,2.0,124.0,26.0,237.0,45.0,45.0,306.0,2024-09-07 20:22:34.000,2024.8.30,110.0,15.0,jarvis-tools,conda-forge/jarvis-tools,133.0,102.0,https://pypi.org/project/jarvis-tools,2024-09-07 20:21:10.000,31.0,26061.0,27720.0,https://anaconda.org/conda-forge/jarvis-tools,2024-09-07 20:30:56.460,79650.0,2.0,,,,,,,,,,,,,,,,,,,,,
+59,CHGNet,,uip,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,23,True,"['ml-iap', 'md', 'pretrained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2024-10-16 10:11:18.000000,2024-10-16 10:11:18,427.0,18.0,63.0,6.0,98.0,3.0,56.0,237.0,2024-09-16 22:18:58.000,0.4.0,17.0,10.0,chgnet,,57.0,36.0,https://pypi.org/project/chgnet,2024-09-16 22:18:58.000,21.0,36499.0,36499.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+60,MACE,,ml-iap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,22,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2024-10-17 11:30:30.000000,2024-10-10 16:32:53,905.0,137.0,187.0,25.0,246.0,60.0,212.0,501.0,2024-10-02 18:02:28.000,0.3.7,8.0,42.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+61,TorchMD-NET,,ml-iap,MIT,https://github.com/torchmd/torchmd-net,Training neural network potentials.,22,True,"['md', 'rep-learn', 'transformer', 'pretrained']",torchmd/torchmd-net,https://github.com/torchmd/torchmd-net,2021-04-09 16:16:32,2024-09-12 06:52:04.422000,2024-08-28 14:56:04,1287.0,18.0,71.0,10.0,231.0,34.0,84.0,328.0,2024-08-29 06:54:59.000,2.4.1,27.0,16.0,,conda-forge/torchmd-net,,,,,,,15907.0,https://anaconda.org/conda-forge/torchmd-net,2024-09-12 06:52:04.422,190887.0,1.0,,,,,,,,,,,,,,,,,,,,,
+62,DP-GEN,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,22,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2024-10-08 00:47:54.000000,2024-04-10 06:31:36,2083.0,,172.0,13.0,848.0,36.0,264.0,301.0,2024-04-10 06:37:54.000,0.12.1,18.0,64.0,dpgen,deepmodeling/dpgen,8.0,7.0,https://pypi.org/project/dpgen,2024-04-10 06:37:54.000,1.0,1369.0,1404.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,208.0,1.0,,,,,,1798.0,,,,,,,,,,,,,,,
+63,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,22,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2024-10-12 04:39:53.000000,2024-10-12 04:39:53,4564.0,69.0,67.0,21.0,198.0,36.0,180.0,290.0,2024-03-25 15:48:12.000,1.3.0,6.0,42.0,,,12.0,12.0,,,,,0.0,,,,1.0,,,,,,8.0,,,,,,,,,,,,,,,
+64,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en.,22,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2024-09-09 21:38:54.000000,2024-09-09 21:38:06,717.0,10.0,79.0,11.0,109.0,40.0,25.0,225.0,2024-09-09 21:38:54.000,2024.8.30,48.0,7.0,alignn,,21.0,15.0,https://pypi.org/project/alignn,2024-09-09 21:37:57.000,6.0,12008.0,12008.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+65,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,22,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2024-09-28 21:53:27.000000,2024-09-28 21:53:25,1004.0,18.0,18.0,12.0,48.0,2.0,21.0,178.0,2024-08-14 05:14:56.000,0.20.7,43.0,7.0,e3nn-jax,,54.0,41.0,https://pypi.org/project/e3nn-jax,2024-08-14 05:15:15.000,13.0,5734.0,5734.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+66,pymatviz,,visualization,MIT,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,22,True,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2024-10-16 21:53:23.000000,2024-10-16 21:53:22,361.0,47.0,15.0,7.0,184.0,14.0,37.0,160.0,2024-10-07 20:15:15.000,0.12.0,29.0,9.0,pymatviz,,11.0,9.0,https://pypi.org/project/pymatviz,2024-10-07 20:15:15.000,2.0,4469.0,4469.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+67,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,21,True,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2024-08-12 14:01:29.000000,2024-04-04 13:37:56,1437.0,,3199.0,296.0,540.0,341.0,570.0,13386.0,,,,115.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+68,DIG: Dive into Graphs,,rep-learn,GPL-3.0,https://github.com/divelab/DIG,A library for graph deep learning research.,21,True,,divelab/DIG,https://github.com/divelab/DIG,2020-10-30 03:51:15,2024-07-15 07:18:56.000000,2024-02-04 20:37:53,1083.0,,281.0,31.0,41.0,34.0,176.0,1863.0,2023-04-07 20:33:15.000,1.1.0,10.0,50.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,2022-06-27 05:08:24.000,,1292.0,1292.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+69,FAIR Chemistry datasets,,datasets,MIT,https://github.com/FAIR-Chem/fairchem,"Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.",21,True,['catalysis'],FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-10-17 17:09:22.000000,2024-10-14 17:55:08,862.0,63.0,244.0,24.0,671.0,31.0,195.0,780.0,2024-09-14 00:47:10.000,fairchem_core-1.2.0,11.0,42.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+70,GPUMD,,ml-iap,GPL-3.0,https://github.com/brucefan1983/GPUMD,GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials..,21,True,"['md', 'lang-cpp', 'electrostatics']",brucefan1983/GPUMD,https://github.com/brucefan1983/GPUMD,2017-07-14 15:32:56,2024-10-16 20:16:24.000000,2024-10-16 20:16:21,4060.0,173.0,116.0,26.0,556.0,17.0,166.0,461.0,2024-08-18 13:16:02.000,3.9.5,42.0,34.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+71,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,21,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2024-09-24 11:12:20.000000,2024-09-24 11:10:06,1465.0,5.0,60.0,22.0,161.0,3.0,43.0,349.0,2024-09-24 11:12:20.000,1.2.0,12.0,13.0,deepqmc,,3.0,3.0,https://pypi.org/project/deepqmc,2024-09-24 11:12:20.000,,880.0,880.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+72,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,21,True,['sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2024-10-09 10:22:29.000000,2024-10-09 10:22:26,1138.0,49.0,24.0,7.0,85.0,13.0,61.0,89.0,2024-06-12 17:08:54.000,1.1.1,10.0,8.0,mlcolvar,,3.0,3.0,https://pypi.org/project/mlcolvar,2024-06-12 17:08:54.000,,370.0,370.0,,,,2.0,,,,,,,,2.0,,,,,,,,,,,,,
+73,Metatensor,,data-structures,BSD-3-Clause,https://github.com/metatensor/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,21,True,"['lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",lab-cosmo/metatensor,https://github.com/metatensor/metatensor,2022-03-01 15:58:28,2024-10-16 12:00:47.000000,2024-10-11 15:43:34,811.0,48.0,16.0,18.0,541.0,73.0,138.0,52.0,2024-10-14 08:58:15.000,metatensor-operations-v0.2.4,44.0,22.0,,,13.0,13.0,,,,,2355.0,,,,2.0,,,,,,28269.0,metatensor/metatensor,,,,,,,,,,,,,,
+74,DM21,,ml-dft,Apache-2.0,https://github.com/google-deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,20,False,,google-deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2024-08-30 23:52:15.000000,2023-06-02 17:04:50,369.0,,2586.0,325.0,237.0,266.0,139.0,13141.0,,,,92.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+75,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating machine-learning interatomic potentials for LAMMPS.,20,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2024-09-19 15:26:23.000000,2024-09-19 15:26:23,1399.0,15.0,50.0,7.0,184.0,16.0,57.0,149.0,2023-06-28 16:00:48.000,3.1.0,7.0,24.0,,conda-forge/fitsnap3,3.0,3.0,,,,,186.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,8760.0,2.0,,,,,,11.0,,2.0,,,,,,,,,,,,,
+76,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,20,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2024-09-24 09:05:59.000000,2024-09-16 13:39:30,873.0,49.0,18.0,7.0,112.0,9.0,27.0,105.0,2024-07-02 15:52:45.000,0.3.1,6.0,20.0,dadapy,,10.0,10.0,https://pypi.org/project/dadapy,2024-07-02 15:49:35.000,,440.0,440.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+77,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,20,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2024-10-09 01:19:32.000000,2024-10-09 01:19:32,3312.0,16.0,59.0,14.0,37.0,32.0,191.0,104.0,2024-09-27 21:44:25.000,3.2.1,8.0,19.0,,,44.0,44.0,,,,,2.0,,,,2.0,,,,,,122.0,,,,,,,,,,,,,,,
+78,ZnDraw,,visualization,EPL-2.0,https://github.com/zincware/ZnDraw,"A powerful tool for visualizing, modifying, and analysing atomistic systems.",20,True,"['md', 'generative', 'lang-js']",zincware/ZnDraw,https://github.com/zincware/ZnDraw,2023-04-12 15:01:21,2024-10-12 09:23:08.000000,2024-10-12 09:23:06,396.0,69.0,3.0,1.0,357.0,90.0,218.0,30.0,2024-08-26 14:32:18.000,0.4.7,56.0,7.0,zndraw,,8.0,6.0,https://pypi.org/project/zndraw,2024-08-26 14:33:31.000,2.0,2820.0,2820.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+79,Graph-based Deep Learning Literature,,community,MIT,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,19,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2024-10-09 14:53:41.000000,2024-10-09 14:53:38,7733.0,53.0,774.0,252.0,21.0,,14.0,4770.0,,,,12.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+80,fairchem,,ml-iap,,https://github.com/FAIR-Chem/fairchem,FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp).,19,True,"['pretrained', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-10-17 17:09:22.000000,2024-10-14 17:55:08,862.0,63.0,244.0,24.0,671.0,31.0,195.0,780.0,2024-09-14 00:47:10.000,fairchem_core-1.2.0,11.0,42.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+81,ATOM3D,,datasets,MIT,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,19,False,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02.000000,2023-03-02 18:20:29,798.0,,35.0,17.0,6.0,21.0,40.0,301.0,2022-07-20 00:58:03.115,0.2.6,15.0,10.0,atom3d,,42.0,42.0,https://pypi.org/project/atom3d,2022-07-20 00:58:03.115,,2745.0,2745.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+82,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2024-06-02 00:38:18.000000,2023-12-04 18:03:57,189.0,,41.0,8.0,77.0,11.0,58.0,283.0,2023-06-19 20:58:01.262,3.0.3,27.0,7.0,exmol,,22.0,21.0,https://pypi.org/project/exmol,2022-06-03 18:52:10.000,1.0,2269.0,2269.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+83,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,19,True,,google-deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2024-10-17 15:59:05.000000,2024-10-16 12:34:24,238.0,24.0,20.0,11.0,258.0,9.0,10.0,242.0,2024-04-04 10:59:13.000,0.0.6,5.0,15.0,kfac-jax,,12.0,11.0,https://pypi.org/project/kfac-jax,2024-04-04 10:59:13.000,1.0,1118.0,1118.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+84,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",19,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2024-05-06 10:08:41.000000,2024-05-06 10:08:14,3099.0,,30.0,7.0,30.0,12.0,74.0,108.0,2024-02-27 12:33:28.000,4.0.1,28.0,7.0,kgcnn,,22.0,19.0,https://pypi.org/project/kgcnn,2024-02-27 12:33:28.000,3.0,1755.0,1755.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+85,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,19,True,"['datasets', 'benchmarking', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2024-10-15 18:23:14.000000,2024-10-15 18:22:48,382.0,33.0,13.0,8.0,95.0,4.0,35.0,94.0,2024-09-11 19:00:12.000,1.3.1,10.0,8.0,matbench-discovery,,3.0,3.0,https://pypi.org/project/matbench-discovery,2024-09-11 19:00:12.000,,1891.0,1891.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+86,MALA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,19,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2024-10-14 14:59:43.000000,2024-10-14 14:58:52,2356.0,48.0,24.0,9.0,310.0,39.0,236.0,81.0,2024-02-01 08:57:56.000,1.2.1,9.0,44.0,,,2.0,2.0,,,,,,,,,1.0,,,,,,,,2.0,,,,,,,,,,,,,
+87,apax,,ml-iap,MIT,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,19,True,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2024-10-17 12:46:35.000000,2024-10-17 12:45:00,1843.0,276.0,2.0,4.0,232.0,13.0,110.0,15.0,2024-09-17 10:55:44.000,0.7.0,7.0,7.0,apax,,3.0,3.0,https://pypi.org/project/apax,2024-09-17 10:55:44.000,,1004.0,1004.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+88,Uni-Mol,,rep-learn,MIT,https://github.com/deepmodeling/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,18,True,['pretrained'],deepmodeling/Uni-Mol,https://github.com/deepmodeling/Uni-Mol,2022-05-22 13:26:41,2024-10-16 09:08:13.000000,2024-09-26 03:45:38,138.0,10.0,121.0,16.0,116.0,67.0,95.0,688.0,2024-07-06 07:05:10.000,0.2.1,3.0,17.0,,,,,,,,,642.0,,,,2.0,,,,,,15414.0,,,,,,,,,,,,,,,
+89,MACE-MP,,uip,MIT,https://github.com/ACEsuit/mace-mp,Pretrained foundation models for materials chemistry.,18,True,"['ml-iap', 'pretrained', 'rep-learn', 'md']",ACEsuit/mace-mp,https://github.com/ACEsuit/mace-mp,2024-01-11 10:55:55,2024-07-16 11:55:19.000000,2024-04-24 14:56:12,10.0,,187.0,11.0,1.0,1.0,9.0,497.0,2024-07-16 11:55:19.000,0.3.6,4.0,2.0,mace-torch,,14.0,,https://pypi.org/project/mace-torch,2024-07-16 11:55:19.000,14.0,13862.0,17211.0,,,,2.0,,,,,,30146.0,,,,,,,,,,,,,,,
+90,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",18,True,"['pretrained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-09-12 14:04:43.000000,2024-09-12 13:43:18,298.0,2.0,69.0,17.0,150.0,14.0,98.0,336.0,2024-06-13 15:18:45.000,1.4.1,86.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,2024-09-12 13:44:36.000,,4865.0,4865.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+91,M3GNet,,uip,BSD-3-Clause,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,18,True,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2024-10-07 23:34:28.000000,2024-10-04 19:40:10,263.0,2.0,60.0,11.0,37.0,15.0,20.0,235.0,2022-11-17 23:25:35.000,0.2.4,16.0,16.0,m3gnet,,32.0,27.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,2489.0,2489.0,,,,2.0,,,,,,,,-1.0,,,,,,,,,,,,,
+92,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,18,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2024-08-09 21:37:33.000000,2024-05-21 08:13:54,787.0,,25.0,7.0,69.0,8.0,32.0,205.0,2024-03-20 06:39:54.000,0.1.8,16.0,6.0,gpax,,3.0,3.0,https://pypi.org/project/gpax,2024-03-20 06:39:54.000,,810.0,810.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+93,OpenBioML ChemNLP,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,18,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2024-10-14 17:36:09.000000,2024-08-19 19:00:21,372.0,41.0,46.0,3.0,285.0,111.0,140.0,148.0,2023-08-07 12:49:57.000,2023.7.1,6.0,27.0,chemnlp,,1.0,,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,417.0,417.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+94,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,18,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2024-10-17 06:35:15.000000,2024-10-15 13:08:13,755.0,29.0,32.0,19.0,254.0,36.0,94.0,132.0,2024-10-15 13:11:45.000,0.8.2,21.0,23.0,,,9.0,6.0,,,,,14.0,,,,3.0,,,,,,331.0,,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,2023-03-15 15:39:26.701,3.0,9.0,,,,
+95,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,18,True,"['datasets', 'benchmarking', 'model-repository']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2024-08-20 17:26:52.000000,2024-01-20 09:41:36,772.0,,46.0,8.0,299.0,39.0,26.0,116.0,2022-07-27 04:40:26.000,0.6,5.0,25.0,matbench,,19.0,17.0,https://pypi.org/project/matbench,2022-07-27 04:44:21.961,2.0,631.0,631.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+96,Open Databases Integration for Materials Design (OPTIMADE),,datasets,CC-BY-4.0,https://github.com/Materials-Consortia/OPTIMADE,Specification of a common REST API for access to materials databases.,18,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2024-08-28 09:04:20.000000,2024-06-12 09:31:09,1786.0,,35.0,21.0,297.0,68.0,169.0,82.0,2024-06-10 16:32:29.000,1.2.0,9.0,21.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+97,Scikit-Matter,,general-tool,BSD-3-Clause,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,18,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2024-10-09 12:01:55.000000,2024-10-09 12:01:55,387.0,5.0,20.0,17.0,162.0,14.0,56.0,76.0,2023-08-24 17:18:49.000,0.2.0,7.0,15.0,skmatter,conda-forge/skmatter,11.0,11.0,https://pypi.org/project/skmatter,2023-08-24 17:18:19.000,,3146.0,3265.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,2275.0,2.0,,,,,,,,,,,,,,,,,,,,,
+98,SpheriCart,,math,MIT,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,18,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2024-10-16 15:36:48.000000,2024-10-11 13:46:56,381.0,18.0,11.0,5.0,117.0,23.0,18.0,71.0,2024-09-04 06:56:55.000,0.5.0,11.0,10.0,sphericart,,5.0,5.0,https://pypi.org/project/sphericart,2024-09-04 06:56:55.000,,1314.0,1318.0,,,,1.0,,,,,,87.0,,,,,,,,,,,,,,,
+99,OpenML,,community,BSD-3,https://github.com/openml/OpenML,Open Machine Learning.,17,True,['datasets'],openml/OpenML,https://github.com/openml/OpenML,2012-12-11 11:27:40,2024-10-17 06:48:46.000000,2024-09-08 12:35:46,2320.0,3.0,90.0,48.0,204.0,367.0,560.0,664.0,,,,35.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+100,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,17,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2024-09-18 09:29:54.000000,2024-09-18 09:29:54,246.0,2.0,44.0,19.0,33.0,38.0,37.0,353.0,2023-07-17 22:58:13.120,1.0.11,16.0,10.0,escnn,,6.0,,https://pypi.org/project/escnn,2022-04-01 11:46:00.000,6.0,1725.0,1725.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+101,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,17,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2024-04-12 13:38:21.000000,2018-09-10 11:14:35,75.0,,81.0,23.0,101.0,31.0,19.0,199.0,2018-03-02 11:36:41.000,0.4.0,34.0,2.0,qml,,32.0,32.0,https://pypi.org/project/qml,2018-08-13 10:37:42.000,,1131.0,1131.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+102,matsciml,,rep-learn,MIT,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery..,17,True,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2024-10-15 20:47:52.000000,2024-10-15 20:47:52,2547.0,232.0,19.0,5.0,243.0,20.0,41.0,144.0,2023-08-31 23:59:40.000,1.0.0,2.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+103,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,17,False,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:59:32.000000,2023-08-31 12:57:53,205.0,,36.0,8.0,12.0,11.0,11.0,141.0,2023-08-31 12:58:49.000,1.0.2,21.0,8.0,sgdml,,11.0,10.0,https://pypi.org/project/sgdml,2023-08-31 12:59:32.000,1.0,1156.0,1156.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+104,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,17,False,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2024-06-28 07:53:45.000000,2023-02-07 09:31:25,1960.0,,57.0,19.0,80.0,10.0,17.0,101.0,2020-03-27 09:27:26.000,0.6.2,27.0,22.0,catlearn,,7.0,6.0,https://pypi.org/project/catlearn,2020-03-27 09:27:26.000,1.0,1354.0,1354.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+105,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,17,True,"['pretrained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2024-09-24 09:57:16.000000,2024-09-24 09:57:16,282.0,5.0,32.0,7.0,176.0,26.0,27.0,78.0,2024-05-07 14:09:13.000,0.4.4,21.0,10.0,,,10.0,10.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+106,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,https://github.com/materialsproject/pyrho,Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.,17,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2024-10-14 00:17:28.000000,2024-02-23 02:53:46,287.0,,7.0,9.0,116.0,1.0,3.0,37.0,2024-02-23 02:55:26.000,0.4.4,28.0,8.0,mp-pyrho,,28.0,25.0,https://pypi.org/project/mp-pyrho,2024-02-23 02:55:26.000,3.0,18769.0,18769.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+107,IPSuite,,active-learning,EPL-2.0,https://github.com/zincware/IPSuite,A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.,17,True,"['ml-iap', 'md', 'workflows', 'htc', 'FAIR']",zincware/IPSuite,https://github.com/zincware/IPSuite,2023-03-01 16:34:45,2024-10-14 22:04:20.000000,2024-09-19 19:38:38,447.0,19.0,10.0,3.0,207.0,68.0,65.0,18.0,2024-08-08 20:37:20.000,0.1.3,7.0,8.0,ipsuite,,7.0,7.0,https://pypi.org/project/ipsuite,2024-08-08 20:37:48.000,,293.0,293.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+108,ChemCrow,,language-models,MIT,https://github.com/ur-whitelab/chemcrow-public,Open source package for the accurate solution of reasoning-intensive chemical tasks.,16,True,['ai-agent'],ur-whitelab/chemcrow-public,https://github.com/ur-whitelab/chemcrow-public,2023-06-04 15:59:05,2024-04-03 19:49:19.000000,2024-03-27 04:32:41,110.0,,88.0,18.0,23.0,8.0,14.0,605.0,2024-03-27 04:30:13.000,0.3.24,27.0,3.0,chemcrow,,6.0,6.0,https://pypi.org/project/chemcrow,2024-03-27 04:30:13.000,,1700.0,1700.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+109,ChemDataExtractor,,language-models,MIT,https://github.com/mcs07/ChemDataExtractor,Automatically extract chemical information from scientific documents.,16,False,['literature-data'],mcs07/ChemDataExtractor,https://github.com/mcs07/ChemDataExtractor,2016-10-02 23:50:01,2023-07-27 18:05:13.000000,2017-02-21 23:20:23,106.0,,108.0,18.0,16.0,21.0,9.0,308.0,2017-02-03 00:28:29.000,1.3.0,8.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,125.0,117.0,https://pypi.org/project/chemdataextractor,2017-02-03 00:12:36.000,8.0,1016.0,1081.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3172.0,1.0,,,,,,3074.0,,,,,,,,,,,,,,,
+110,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,16,True,['pretrained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2024-09-24 20:39:33.000000,2024-09-24 20:39:32,3124.0,4.0,48.0,7.0,6.0,2.0,18.0,234.0,2024-05-29 21:15:00.000,1.0.0,1.0,41.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+111,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,16,True,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2024-09-30 23:48:17.883000,2024-08-23 21:20:06,74.0,3.0,23.0,11.0,63.0,27.0,66.0,181.0,2023-10-09 08:49:10.000,1.4,16.0,8.0,,conda-forge/openmm-torch,,,,,,,10889.0,https://anaconda.org/conda-forge/openmm-torch,2024-09-30 23:48:17.883,490014.0,2.0,,,,,,,,,,,,,,,,,,,,,
+112,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,16,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2024-07-15 21:14:48.000000,2024-04-21 06:58:38,693.0,,57.0,11.0,184.0,21.0,66.0,136.0,2023-05-21 15:54:32.000,0.6.8,54.0,10.0,xenonpy,,1.0,,https://pypi.org/project/xenonpy,2022-10-31 15:40:18.355,1.0,1907.0,1925.0,,,,3.0,,,,,,1436.0,,,,,,,,,,,,,,,
+113,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,16,False,['automl'],hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000000,2022-01-06 19:39:49,1666.0,,50.0,12.0,233.0,41.0,138.0,135.0,2020-07-28 02:19:07.000,1.0.3.20200727,17.0,13.0,automatminer,,9.0,9.0,https://pypi.org/project/automatminer,2020-07-28 02:23:45.000,,1191.0,1191.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+114,MLatom,,general-tool,MIT,https://github.com/dralgroup/mlatom,AI-enhanced computational chemistry.,16,True,"['uip', 'ml-iap', 'md', 'ml-dft', 'ml-esm', 'transfer-learning', 'active-learning', 'spectroscopy', 'structure-optimization']",dralgroup/mlatom,https://github.com/dralgroup/mlatom,2023-08-16 13:47:48,2024-10-09 05:51:55.000000,2024-10-09 05:51:55,68.0,19.0,10.0,3.0,23.0,1.0,3.0,53.0,2024-09-23 01:35:47.000,3.11.0,19.0,3.0,mlatom,,,,https://pypi.org/project/mlatom,2024-10-09 00:56:21.000,,2627.0,2627.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+115,load-atoms,,datasets,MIT,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,16,True,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-10-16 10:37:59.000000,2024-10-16 10:37:59,280.0,9.0,2.0,1.0,37.0,1.0,30.0,38.0,2024-10-04 11:25:06.000,0.3.2,39.0,3.0,load-atoms,,4.0,4.0,https://pypi.org/project/load-atoms,2024-10-04 11:25:06.000,,2680.0,2680.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+116,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,16,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2024-10-14 00:02:59.000000,2024-10-08 03:16:39,1085.0,12.0,20.0,4.0,153.0,22.0,19.0,34.0,2023-12-17 02:12:19.000,0.4.3,18.0,9.0,kliff,conda-forge/kliff,4.0,4.0,https://pypi.org/project/kliff,2023-12-17 02:12:19.000,,774.0,3053.0,https://anaconda.org/conda-forge/kliff,2024-09-10 06:39:09.645,109412.0,2.0,,,,,,,,2.0,,,,,,,,,,,,,
+117,Graphormer,,rep-learn,MIT,https://github.com/microsoft/Graphormer,Graphormer is a general-purpose deep learning backbone for molecular modeling.,15,True,"['transformer', 'pretrained']",microsoft/Graphormer,https://github.com/microsoft/Graphormer,2021-05-27 05:31:18,2024-06-07 17:01:35.000000,2024-05-28 06:22:34,77.0,,331.0,31.0,46.0,92.0,66.0,2100.0,2024-04-03 08:23:10.000,dig-v1.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+118,dlpack,,data-structures,Apache-2.0,https://github.com/dmlc/dlpack,common in-memory tensor structure.,15,True,['lang-cpp'],dmlc/dlpack,https://github.com/dmlc/dlpack,2017-02-24 16:56:47,2024-09-28 19:37:33.000000,2024-09-28 19:37:03,76.0,1.0,130.0,48.0,78.0,29.0,42.0,896.0,2024-09-09 15:40:21.000,1.0,10.0,24.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+119,FermiNet,,ml-wft,Apache-2.0,https://github.com/google-deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,15,True,['transformer'],google-deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2024-10-03 13:12:01.000000,2024-10-03 13:11:21,243.0,14.0,125.0,34.0,30.0,1.0,52.0,730.0,,,,18.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+120,GT4SD - Generative Toolkit for Scientific Discovery,,community,MIT,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,15,True,"['generative', 'pretrained', 'drug-discovery', 'model-repository']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-09-12 14:04:43.000000,2024-09-12 13:43:18,298.0,2.0,69.0,17.0,150.0,14.0,98.0,336.0,2024-06-13 15:18:45.000,1.4.1,57.0,20.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+121,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,15,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2024-01-05 14:31:05.000000,2024-01-03 14:28:02,67.0,,42.0,11.0,37.0,8.0,31.0,267.0,2024-01-05 14:31:05.000,0.4.1,5.0,5.0,molecule-generation,,1.0,,https://pypi.org/project/molecule-generation,2024-01-05 14:31:05.000,1.0,497.0,497.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+122,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,15,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2024-02-15 16:12:06.000000,2024-01-07 14:27:45,561.0,,23.0,9.0,4.0,12.0,51.0,85.0,2023-06-09 17:17:24.000,0.2.3,19.0,9.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,2022-12-21 20:21:00.409,,539.0,539.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+123,PMTransformer,,generative,MIT,https://github.com/hspark1212/MOFTransformer,"Universal Transfer Learning in Porous Materials, including MOFs.",15,True,"['transfer-learning', 'pretrained', 'transformer']",hspark1212/MOFTransformer,https://github.com/hspark1212/MOFTransformer,2021-12-11 06:30:12,2024-06-20 07:01:44.000000,2024-06-20 06:57:57,410.0,,12.0,5.0,127.0,,37.0,84.0,2024-06-20 07:02:24.000,2.2.0,17.0,2.0,moftransformer,,8.0,7.0,https://pypi.org/project/moftransformer,2024-06-20 07:01:44.000,1.0,1004.0,1004.0,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+124,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,15,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2024-10-16 20:16:42.000000,2024-10-16 20:16:41,642.0,12.0,11.0,3.0,62.0,4.0,25.0,48.0,2024-07-22 19:03:03.000,1.0.0,5.0,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+125,wfl,,ml-iap,GPL-2.0,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,15,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2024-10-11 16:17:27.000000,2024-10-11 16:17:27,1196.0,97.0,18.0,9.0,187.0,67.0,92.0,32.0,2024-04-25 15:07:11.000,0.2.4,4.0,19.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+126,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,15,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-10-13 22:55:06.000000,2024-10-13 21:52:07,374.0,40.0,7.0,6.0,14.0,7.0,8.0,27.0,2024-10-13 22:55:06.000,0.5.3,15.0,2.0,glasspy,,7.0,7.0,https://pypi.org/project/glasspy,2024-09-05 19:43:43.000,,913.0,913.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+127,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,14,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000000,2022-05-10 13:22:20,45.0,,452.0,59.0,20.0,7.0,63.0,2504.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+128,SISSO,,rep-eng,Apache-2.0,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,14,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2024-09-20 09:23:54.000000,2024-09-20 09:23:54,199.0,33.0,78.0,6.0,3.0,18.0,58.0,239.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+129,n2p2,,ml-iap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,14,False,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2024-10-02 15:42:34.000000,2022-09-05 10:56:20,387.0,,76.0,12.0,53.0,68.0,85.0,223.0,2022-05-23 12:53:39.000,2.2.0,11.0,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+130,Orb Models,,uip,https://github.com/orbital-materials/orb-models/blob/main/LICENSE,https://github.com/orbital-materials/orb-models,ORB forcefield models from Orbital Materials.,14,True,"['ml-iap', 'pretrained']",orbital-materials/orb-models,https://github.com/orbital-materials/orb-models,2024-08-30 15:27:25,2024-10-15 20:43:32.000000,2024-10-15 18:56:59,20.0,20.0,19.0,6.0,18.0,,12.0,169.0,2024-10-15 20:43:32.000,0.4.0,5.0,6.0,orb-models,,2.0,2.0,https://pypi.org/project/orb-models,2024-10-15 20:43:32.000,,1251.0,1251.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+131,So3krates (MLFF),,ml-iap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,14,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2024-10-03 09:06:35.000000,2024-08-23 09:41:03,150.0,17.0,16.0,7.0,21.0,4.0,6.0,82.0,2024-06-24 11:09:20.000,0.3.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+132,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,14,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2024-09-11 06:01:38.318000,2024-07-10 15:29:02,95.0,,18.0,8.0,63.0,22.0,34.0,81.0,2023-07-26 11:21:58.000,0.6,7.0,9.0,,conda-forge/nnpops,,,,,,,7969.0,https://anaconda.org/conda-forge/nnpops,2024-09-11 06:01:38.318,247057.0,2.0,,,,,,,,,,,,,,,,,,,,,
+133,HydraGNN,,rep-learn,BSD-3,https://github.com/ORNL/HydraGNN,Distributed PyTorch implementation of multi-headed graph convolutional neural networks.,14,True,,ORNL/HydraGNN,https://github.com/ORNL/HydraGNN,2021-05-28 03:32:03,2024-10-15 01:02:51.000000,2024-10-15 01:02:51,699.0,28.0,26.0,10.0,247.0,17.0,32.0,64.0,2023-11-10 15:25:43.000,3.0,2.0,14.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+134,Ultra-Fast Force Fields (UF3),,ml-iap,Apache-2.0,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,14,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2024-10-10 19:03:09.000000,2024-10-04 15:08:06,731.0,6.0,20.0,6.0,84.0,19.0,31.0,60.0,2023-10-27 16:37:16.000,0.4.0,4.0,10.0,uf3,,2.0,2.0,https://pypi.org/project/uf3,2023-10-27 16:37:16.000,,102.0,102.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+135,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,14,False,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000000,2022-02-24 19:00:50,989.0,,7.0,7.0,29.0,8.0,17.0,35.0,2021-12-23 05:02:12.000,0.1.1,25.0,10.0,flare_pp,,2.0,,https://pypi.org/project/flare_pp,2021-12-23 05:02:12.000,2.0,1359.0,1359.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+136,BOSS,,materials-discovery,Apache-2.0,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,14,True,['probabilistic'],,,2020-02-12 08:48:33,2024-10-09 15:57:12.000000,,,,11.0,,,2.0,29.0,20.0,2024-07-20 17:27:04.000,1.11.0,50.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,2024-10-09 15:57:12.000,,14976.0,14976.0,,,,1.0,,,,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,,
+137,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,14,True,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2024-09-09 20:13:07.000000,2024-09-09 20:13:07,429.0,2.0,5.0,1.0,56.0,15.0,3.0,12.0,2023-06-07 01:07:33.000,2.0.8,37.0,6.0,crabnet,,16.0,14.0,https://pypi.org/project/crabnet,2023-01-10 04:27:02.444,2.0,2395.0,2395.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+138,QH9,,datasets,CC-BY-NC-SA-4.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9,A Quantum Hamiltonian Prediction Benchmark.,13,True,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,1.0,14.0,503.0,,,,29.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+139,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence Research for Science (AIRS).,13,True,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,1.0,14.0,503.0,,,,29.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+140,QHNet,,ml-dft,GPL-3.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence Research for Science (AIRS).,13,True,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,1.0,14.0,503.0,,,,29.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+141,AI for Science Resources,,community,GPL-3.0 license,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",13,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,1.0,14.0,502.0,,,,29.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+142,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,13,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2024-05-17 19:25:11.000000,2023-10-04 11:27:09,147.0,,41.0,9.0,5.0,19.0,2.0,226.0,2023-11-30 09:31:51.000,0.0.4,4.0,4.0,gptchem,,,,https://pypi.org/project/gptchem,2023-10-04 11:28:07.000,,241.0,241.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+143,DMFF,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,13,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2024-10-13 05:58:50.000000,2024-01-12 00:58:20,431.0,,42.0,9.0,162.0,10.0,16.0,152.0,2023-11-09 14:32:37.000,1.0.0,4.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+144,Elementari,,visualization,MIT,https://github.com/janosh/elementari,"Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,..",13,True,['lang-js'],janosh/elementari,https://github.com/janosh/elementari,2022-06-01 15:29:36,2024-10-07 16:33:57.000000,2024-10-07 00:37:04,181.0,1.0,12.0,5.0,45.0,2.0,5.0,136.0,2024-01-15 14:26:00.710,0.2.3,11.0,2.0,,,4.0,3.0,,,,,211.0,,,,3.0,,,,,,,,,,,,,elementari,https://www.npmjs.com/package/elementari,2024-01-15 14:26:00.710,1.0,211.0,,,,
+145,SevenNet,,uip,GPL-3.0,https://github.com/MDIL-SNU/SevenNet,SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that..,13,True,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2024-10-16 12:17:56.000000,2024-10-10 13:15:28,474.0,119.0,13.0,5.0,77.0,11.0,11.0,114.0,,,6.0,12.0,,,4.0,4.0,,,,,,,,,3.0,,,,,,,,-2.0,,,,,,,,,,,,,
+146,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,13,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2024-01-11 17:38:31.000000,2023-11-30 14:48:28,2931.0,,20.0,22.0,201.0,115.0,132.0,80.0,,,3.0,30.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+147,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,13,True,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2024-08-06 19:01:36.000000,2024-08-06 19:01:36,46.0,1.0,19.0,13.0,33.0,20.0,35.0,80.0,2024-06-06 16:49:09.000,1.2,6.0,5.0,,conda-forge/openmm-ml,,,,,,,212.0,https://anaconda.org/conda-forge/openmm-ml,2024-06-07 16:52:07.157,5516.0,3.0,,,,,,,,,,,,,,,,,,,,,
+148,Pacemaker,,ml-iap,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,13,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-10-10 13:48:29.000000,2024-10-10 13:48:28,166.0,7.0,17.0,5.0,24.0,20.0,37.0,70.0,2022-10-24 21:50:17.233,0.2.8,2.0,6.0,python-ace,,,,https://pypi.org/project/python-ace,2022-10-24 21:50:17.233,,29.0,29.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+149,ChatMOF,,language-models,MIT,https://github.com/Yeonghun1675/ChatMOF,Predict and Inverse design for metal-organic framework with large-language models (llms).,13,True,['generative'],Yeonghun1675/ChatMOF,https://github.com/Yeonghun1675/ChatMOF,2023-05-19 06:33:06,2024-07-01 05:01:35.000000,2024-07-01 04:57:36,72.0,,8.0,1.0,12.0,,5.0,63.0,2024-06-14 09:56:27.000,0.2.1,17.0,1.0,chatmof,,3.0,3.0,https://pypi.org/project/chatmof,2024-07-01 05:01:35.000,,1116.0,1116.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+150,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,13,False,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,3.0,1.0,35.0,2023-04-15 22:48:00.713,0.3.0,3.0,2.0,synspace,,22.0,21.0,https://pypi.org/project/synspace,2023-01-16 17:29:00.461,1.0,1397.0,1397.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+151,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,13,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2024-10-03 14:08:02.000000,2024-09-27 15:31:59,716.0,17.0,4.0,3.0,46.0,1.0,5.0,30.0,2024-09-26 12:33:31.000,3.0.1,3.0,17.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+152,Atomvision,,visualization,https://github.com/usnistgov/atomvision/blob/master/LICENSE.md,https://github.com/usnistgov/atomvision,Deep learning framework for atomistic image data.,13,False,"['computer-vision', 'experimental-data', 'rep-learn']",usnistgov/atomvision,https://github.com/usnistgov/atomvision,2021-09-16 20:33:46,2024-08-23 22:09:27.000000,2023-05-08 03:21:25,123.0,,17.0,10.0,15.0,4.0,4.0,29.0,2023-05-08 03:15:44.402,2023.5.6,6.0,3.0,atomvision,,3.0,3.0,https://pypi.org/project/atomvision,2023-05-08 03:15:44.402,,961.0,961.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+153,AtomGPT,,language-models,https://github.com/usnistgov/atomgpt/blob/main/LICENSE.rst,https://github.com/usnistgov/atomgpt,AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design.,13,True,"['generative', 'pretrained', 'transformer']",usnistgov/atomgpt,https://github.com/usnistgov/atomgpt,2023-07-17 02:20:53,2024-10-04 16:38:24.000000,2024-10-04 16:38:24,113.0,18.0,3.0,3.0,9.0,2.0,,26.0,2024-09-22 05:47:21.000,2024.9.18,3.0,2.0,atomgpt,,2.0,2.0,https://pypi.org/project/atomgpt,2024-09-22 05:47:21.000,,631.0,631.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+154,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",13,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2024-09-12 16:22:52.000000,2024-06-22 16:18:31,410.0,,5.0,4.0,41.0,17.0,34.0,12.0,2024-06-22 16:34:35.000,0.3.1,17.0,10.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+155,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000000,2021-09-06 05:23:38,25.0,,277.0,23.0,7.0,18.0,20.0,640.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+156,mat2vec,,language-models,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,False,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000000,2023-05-06 22:45:49,55.0,,174.0,40.0,7.0,6.0,18.0,617.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+157,Geometric GNN Dojo,,educational,MIT,https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb,"New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge.",12,False,['rep-learn'],chaitjo/geometric-gnn-dojo,https://github.com/chaitjo/geometric-gnn-dojo,2023-01-21 20:08:45,2024-05-22 11:06:03.000000,2023-06-18 23:17:32,26.0,,41.0,10.0,5.0,3.0,6.0,466.0,2023-06-18 23:20:44.000,0.2.0,2.0,3.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+158,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,12,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000000,2021-09-17 05:10:37,52.0,,151.0,25.0,15.0,11.0,10.0,350.0,2019-10-28 18:46:28.000,1.0,1.0,10.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+159,TensorMol,,ml-iap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000000,2018-03-30 12:26:14,1724.0,,74.0,45.0,8.0,18.0,19.0,271.0,2017-11-08 18:05:50.000,0.1,1.0,12.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+160,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,12,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2024-10-07 10:24:16.000000,2024-10-07 10:24:16,68.0,2.0,44.0,7.0,18.0,13.0,38.0,227.0,2023-07-11 08:13:06.000,0.2.2,2.0,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+161,ANI-1,,ml-iap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,True,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2024-03-11 21:50:26.000000,2024-03-11 21:50:26,112.0,,55.0,33.0,9.0,16.0,21.0,220.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+162,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,12,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2024-08-02 06:55:47.000000,2024-06-27 11:23:15,160.0,,32.0,6.0,15.0,1.0,5.0,106.0,2019-10-09 09:21:30.000,0.3.0,1.0,5.0,,,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2024-06-27 11:32:07.538231,,246.0,,,,,,,,,,,,,,,,
+163,NIST ChemNLP,,language-models,MIT,https://github.com/usnistgov/chemnlp,ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data.,12,True,['literature-data'],usnistgov/chemnlp,https://github.com/usnistgov/chemnlp,2022-08-10 11:43:44,2024-08-19 19:40:05.000000,2024-08-19 19:40:04,81.0,1.0,16.0,9.0,15.0,1.0,,71.0,2023-08-07 12:49:57.000,2023.7.1,6.0,2.0,chemnlp,,5.0,4.0,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,417.0,417.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+164,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,12,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2024-10-09 18:50:43.000000,2024-10-09 18:48:51,162.0,23.0,23.0,9.0,90.0,6.0,12.0,68.0,2024-01-29 22:04:53.000,hippynn-0.0.3,3.0,14.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+165,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp/blob/master/LICENSE,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2024-02-24 20:11:49.000000,2022-06-14 22:18:28,143.0,,27.0,7.0,19.0,2.0,6.0,57.0,2022-04-27 17:05:25.000,0.3.12,13.0,4.0,,,14.0,14.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+166,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,12,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2024-09-05 10:44:55.000000,2024-09-05 10:44:55,165.0,1.0,8.0,4.0,1.0,,15.0,47.0,2024-07-03 16:43:48.000,1.1.0,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+167,Rascaline,,rep-eng,BSD-3-Clause,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,12,True,"['lang-rust', 'lang-cpp']",Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2024-10-10 14:02:24.000000,2024-10-10 13:55:50,575.0,16.0,13.0,7.0,265.0,32.0,37.0,44.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+168,nlcc,,language-models,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,12,False,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000000,2023-02-04 03:06:33,144.0,,7.0,5.0,1.0,,9.0,44.0,2023-02-04 03:11:01.949,0.6.0,10.0,3.0,nlcc,,2.0,2.0,https://pypi.org/project/nlcc,2022-12-07 05:07:49.878,,496.0,496.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+169,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,12,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,34.0,,,25.0,1.0,cmlkit,,7.0,6.0,https://pypi.org/project/cmlkit,2022-03-25 22:27:16.000,1.0,1922.0,1922.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+170,FAENet,,rep-learn,MIT,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,12,True,,vict0rsch/faenet,https://github.com/vict0rsch/faenet,2023-02-10 22:10:27,2023-10-12 08:46:26.000000,2023-10-12 08:46:22,125.0,,2.0,3.0,5.0,,,33.0,2023-09-12 04:00:49.000,0.1.2,3.0,3.0,faenet,,3.0,3.0,https://pypi.org/project/faenet,2023-09-14 21:06:36.000,,296.0,296.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+171,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,12,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000000,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,25.0,2021-10-24 17:22:06.000,1.1.0,3.0,3.0,CBFV,,14.0,14.0,https://pypi.org/project/CBFV,2021-10-24 17:22:06.000,,308.0,308.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+172,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,12,False,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000000,2023-05-24 15:04:57,341.0,,6.0,5.0,9.0,3.0,10.0,15.0,2022-07-14 08:49:29.365,0.3.4,3.0,9.0,benchml,,,,https://pypi.org/project/benchml,2022-07-14 08:49:29.365,,427.0,427.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+173,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,12,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2024-05-14 08:53:09.000000,2024-02-16 09:31:25,762.0,,11.0,3.0,13.0,8.0,6.0,8.0,2024-02-16 09:31:34.000,0.22.5,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,2024-02-16 09:31:34.000,,5705.0,5733.0,,,,2.0,,,,,,649.0,,,,,,,,,,,,,,,
+174,ACEfit,,ml-iap,MIT,https://github.com/ACEsuit/ACEfit.jl,,12,True,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2024-09-14 11:29:30.000000,2024-09-14 11:17:37,266.0,45.0,7.0,4.0,31.0,22.0,35.0,7.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+175,Deep Learning for Molecules and Materials Book,,educational,https://github.com/whitead/dmol-book/blob/main/LICENSE,https://dmol.pub/,Deep learning for molecules and materials book.,11,False,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000000,2023-07-02 18:02:56,558.0,,114.0,16.0,92.0,28.0,130.0,611.0,,,,19.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+176,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000000,2021-12-08 19:49:36,160.0,,131.0,19.0,9.0,27.0,8.0,350.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+177,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,11,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2024-08-19 16:56:05.000000,2024-08-19 16:56:05,43.0,1.0,9.0,17.0,47.0,17.0,48.0,152.0,2024-04-15 20:17:14.000,2.0.1,8.0,1.0,,,,,,,,,9.0,,,,2.0,,,,,,263.0,,,,,,,,,,,,,,,
+178,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,11,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2024-06-27 12:53:17.000000,2024-06-27 12:53:00,763.0,,28.0,7.0,37.0,6.0,19.0,144.0,2023-08-30 13:54:23.000,1,1.0,6.0,,,7.0,7.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+179,tinker-hp,,ml-iap,https://github.com/TinkerTools/tinker-hp/blob/master/license-Tinker.pdf,https://github.com/TinkerTools/tinker-hp,Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs.,11,True,,TinkerTools/tinker-hp,https://github.com/TinkerTools/tinker-hp,2018-06-12 12:15:51,2024-09-11 10:37:13.000000,2024-09-11 10:36:50,570.0,18.0,22.0,13.0,2.0,3.0,17.0,80.0,2019-11-24 16:21:50.000,published-version-V1,1.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+180,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,11,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2024-08-14 02:50:36.000000,2024-08-14 02:50:35,753.0,40.0,26.0,4.0,47.0,,,63.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+181,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,11,False,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000000,2023-07-16 02:08:13,759.0,,32.0,10.0,99.0,7.0,26.0,59.0,2023-07-16 02:11:38.000,1.0,3.0,14.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+182,SIMPLE-NN,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000000,2022-01-27 05:04:05,586.0,,19.0,12.0,91.0,4.0,26.0,47.0,2021-09-23 01:41:42.000,1.1.1,9.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+183,NNsforMD,,ml-iap,MIT,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",11,False,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000000,2022-11-10 13:04:45,265.0,,6.0,3.0,,,,10.0,2022-04-12 15:15:00.183,2.0.0,5.0,2.0,pyNNsMD,,2.0,2.0,https://pypi.org/project/pyNNsMD,2022-04-12 15:15:00.183,,162.0,162.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+184,fplib,,rep-eng,MIT,https://github.com/Rutgers-ZRG/libfp,libfp is a library for calculating crystalline fingerprints and measuring similarities of materials.,11,True,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/Rutgers-ZRG/libfp,2015-09-07 08:18:27,2024-10-15 19:19:54.000000,2024-10-15 19:19:49,57.0,20.0,1.0,3.0,1.0,,3.0,7.0,2024-09-26 20:12:39.000,3.1.2,3.0,,,,1.0,1.0,,,,,,,,,3.0,,,,,,,Rutgers-ZRG/libfp,,,,,,,,,,,,,,
+185,pretrained-gnns,,rep-learn,MIT,https://github.com/snap-stanford/pretrain-gnns,Strategies for Pre-training Graph Neural Networks.,10,False,['pretrained'],snap-stanford/pretrain-gnns,https://github.com/snap-stanford/pretrain-gnns,2020-01-30 22:12:41,2023-07-29 06:21:39.000000,2023-07-29 06:21:39,13.0,,161.0,18.0,8.0,34.0,29.0,962.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+186,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,10,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36.000000,2022-04-27 19:27:40,444.0,,112.0,36.0,12.0,15.0,2.0,672.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+187,Allegro,,ml-iap,MIT,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,10,False,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2024-10-15 03:36:13.000000,2023-05-08 21:16:45,38.0,,44.0,20.0,5.0,22.0,15.0,331.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+188,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,False,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000000,2023-04-26 14:22:40,28.0,,42.0,3.0,1.0,,11.0,266.0,,,,3.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+189,AI4Chemistry course,,educational,MIT,https://github.com/schwallergroup/ai4chem_course,"EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course.",10,True,['chemistry'],schwallergroup/ai4chem_course,https://github.com/schwallergroup/ai4chem_course,2022-08-22 07:29:30,2024-05-02 20:41:12.000000,2024-05-02 20:41:12,232.0,,33.0,4.0,9.0,1.0,3.0,136.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+190,DeePKS-kit,,ml-dft,LGPL-3.0,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,10,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2024-09-28 05:44:36.000000,2024-04-13 03:44:40,384.0,,35.0,14.0,46.0,5.0,14.0,100.0,,,,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+191,Neural-Network-Models-for-Chemistry,,community,,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,A collection of Nerual Network Models for chemistry.,10,True,['rep-learn'],Eipgen/Neural-Network-Models-for-Chemistry,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,2022-05-23 06:35:09,2024-09-20 01:25:39.000000,2024-09-20 01:25:39,216.0,8.0,12.0,3.0,22.0,1.0,1.0,84.0,2024-07-17 02:01:45.000,0.0.5,5.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+192,Grad DFT,,ml-dft,Apache-2.0,https://github.com/XanaduAI/GradDFT,GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation..,10,True,,XanaduAI/GradDFT,https://github.com/XanaduAI/GradDFT,2023-05-15 16:18:25,2024-02-13 16:05:53.000000,2024-02-13 16:05:51,419.0,,6.0,5.0,43.0,11.0,43.0,75.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+193,Finetuna,,active-learning,MIT,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2024-05-15 17:26:24.000000,2024-05-15 17:25:23,1200.0,,11.0,3.0,40.0,5.0,15.0,43.0,,,,11.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+194,pair_nequip,,md,MIT,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,10,True,"['ml-iap', 'rep-learn']",mir-group/pair_nequip,https://github.com/mir-group/pair_nequip,2021-04-02 15:28:02,2024-06-05 17:06:39.000000,2024-06-05 17:06:39,101.0,,12.0,9.0,8.0,11.0,20.0,41.0,2022-05-20 00:39:04.000,0.5.2,4.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+195,Atom2Vec,,rep-learn,MIT,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,10,True,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2024-02-23 21:44:03.000000,2024-02-23 21:43:58,4.0,,9.0,1.0,1.0,2.0,1.0,35.0,2024-02-23 21:43:41.000,1.1.0,2.0,1.0,atom2vec,,3.0,3.0,https://pypi.org/project/atom2vec,2024-02-23 21:43:41.000,,312.0,312.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+196,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2024-06-17 22:55:40.000000,2021-07-05 21:36:23,337.0,,10.0,5.0,10.0,5.0,5.0,33.0,,,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+197,OpenKIM,,datasets,LGPL-2.1,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",10,False,"['model-repository', 'knowledge-base', 'pretrained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000000,2022-03-17 23:01:36,2371.0,,20.0,12.0,55.0,18.0,18.0,31.0,,,,24.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+198,PACE,,md,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",10,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2024-09-11 16:13:06.000000,2023-11-27 21:28:13,59.0,,10.0,6.0,16.0,2.0,6.0,24.0,2024-09-11 16:13:06.000,.2024.9.11,8.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+199,ACEhamiltonians,,ml-dft,MIT,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",10,False,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2024-10-14 12:57:35.000000,2023-04-12 15:04:14,33.0,,7.0,5.0,42.0,2.0,3.0,13.0,2024-02-07 16:35:47.000,0.1.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+200,AGOX,,materials-discovery,GPL-3.0,https://agox.gitlab.io/agox/,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,10,True,['structure-optimization'],,,2022-03-08 09:08:13,2024-08-26 18:44:50.000000,,,,5.0,,,13.0,11.0,13.0,2024-08-03 18:26:35.000,3.7.0,5.0,,agox,,,,https://pypi.org/project/agox,2024-08-26 18:44:50.000,,1028.0,1028.0,,,,2.0,,,,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,,
+201,calorine,,ml-iap,https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE,https://calorine.materialsmodeling.org/,A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264.,10,True,,,,2021-04-23 16:12:56,2024-07-26 09:35:09.000000,,,,4.0,,,9.0,77.0,12.0,2024-02-28 15:53:18.000,2.2.1,14.0,,calorine,,2.0,,https://pypi.org/project/calorine,2024-07-26 09:35:09.000,2.0,3073.0,3073.0,,,,3.0,,,,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,,
+202,GNoME Explorer,,community,Apache-2.0,https://next-gen.materialsproject.org/materials/gnome,Graph Networks for Materials Exploration Database.,9,True,"['datasets', 'materials-discovery']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-09-04 18:54:00.000000,2024-09-04 18:51:58,10.0,2.0,137.0,45.0,7.0,18.0,4.0,881.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+203,Materials Discovery: GNoME,,materials-discovery,Apache-2.0,https://github.com/google-deepmind/materials_discovery,"Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials.",9,True,"['uip', 'datasets', 'rep-learn', 'proprietary']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-09-04 18:54:00.000000,2024-09-04 18:51:58,10.0,2.0,137.0,45.0,7.0,18.0,4.0,881.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+204,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000000,2021-11-18 09:11:56,63.0,,68.0,17.0,5.0,11.0,17.0,490.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+205,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,9,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000000,2022-07-10 17:56:12,6.0,,113.0,8.0,,3.0,36.0,438.0,,,,1.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+206,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics#license,https://github.com/tilde-lab/awesome-materials-informatics,"Curated list of known efforts in materials informatics, i.e. in modern materials science.",9,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2024-09-18 13:34:19.000000,2024-09-18 13:34:19,139.0,1.0,82.0,17.0,54.0,,8.0,377.0,2023-03-02 19:56:59.000,2023.03.02,1.0,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+207,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000000,2023-10-03 09:57:19,103.0,,60.0,4.0,,1.0,30.0,289.0,,,,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+208,MoLFormers UI,,community,Apache-2.0,https://molformer.res.ibm.com/,A family of foundation models trained on chemicals.,9,True,"['transformer', 'language-models', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000000,2023-10-16 16:33:13,7.0,,41.0,10.0,3.0,9.0,10.0,258.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+209,MoLFormer,,language-models,Apache-2.0,https://github.com/IBM/molformer,Repository for MolFormer.,9,True,"['transformer', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000000,2023-10-16 16:33:13,7.0,,41.0,10.0,3.0,9.0,10.0,258.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+210,SchNet,,ml-iap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000000,2018-09-04 08:42:34,53.0,,65.0,16.0,,1.0,2.0,219.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+211,GemNet,,ml-iap,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",9,False,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000000,2023-04-26 14:20:12,36.0,,29.0,4.0,1.0,,14.0,179.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+212,MolSkill,,language-models,MIT,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,9,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-10-31 17:03:36.000000,2023-10-31 17:03:36,81.0,,9.0,6.0,8.0,2.0,4.0,103.0,2023-08-04 12:22:15.000,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,,,15.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,312.0,3.0,,,,,,,,,,,,,,,,,,,,,
+213,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,False,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000000,2022-10-03 21:57:33,99.0,,18.0,8.0,,3.0,3.0,71.0,2021-04-05 06:49:29.000,0.2,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+214,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2024-08-31 03:43:18.000000,2024-08-31 03:41:21,914.0,2.0,15.0,8.0,65.0,24.0,58.0,65.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+215,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,9.0,7.0,63.0,2018-04-15 16:55:15.000,1.2,3.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+216,LLaMP,,language-models,BSD-3-Clause,https://github.com/chiang-yuan/llamp,A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An..,9,True,"['materials-discovery', 'cheminformatics', 'generative', 'MD', 'multimodal', 'language-models', 'lang-py', 'general-tool']",chiang-yuan/llamp,https://github.com/chiang-yuan/llamp,2023-07-01 08:15:34,2024-10-14 03:45:00.000000,2024-10-14 03:44:53,375.0,6.0,7.0,1.0,30.0,8.0,17.0,61.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+217,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,9,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2024-06-07 15:52:47.000000,2024-06-07 15:52:33,66.0,,7.0,2.0,5.0,,11.0,49.0,2024-03-25 13:47:47.000,transferable_atomic_orbitals,6.0,7.0,deeperwin,,,,https://pypi.org/project/deeperwin,2021-12-14 11:03:19.657,,373.0,373.0,,,,3.0,,,,,,11.0,,,,,,,,,,,,,,,
+218,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,9,False,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2024-09-30 15:56:54.000000,2023-05-24 22:47:50,64.0,,10.0,31.0,1.0,4.0,5.0,46.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+219,DSECOP,,educational,CCO-1.0,https://github.com/GDS-Education-Community-of-Practice/DSECOP,This repository contains data science educational materials developed by DSECOP Fellows.,9,True,,GDS-Education-Community-of-Practice/DSECOP,https://github.com/GDS-Education-Community-of-Practice/DSECOP,2022-03-07 17:47:33,2024-06-26 14:49:22.000000,2024-06-26 14:49:19,555.0,,25.0,10.0,25.0,1.0,7.0,43.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+220,iam-notebooks,,educational,Apache-2.0,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,9,True,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2024-10-09 14:20:36.000000,2024-10-09 14:20:22,243.0,1.0,5.0,4.0,7.0,4.0,,26.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+221,lie-nn,,math,MIT,https://github.com/lie-nn/lie-nn,Tools for building equivariant polynomials on reductive Lie groups.,9,False,['rep-learn'],lie-nn/lie-nn,https://github.com/lie-nn/lie-nn,2022-04-01 18:02:49,2023-06-29 19:38:34.000000,2023-06-20 22:30:53,249.0,,1.0,8.0,3.0,1.0,,26.0,2023-06-20 22:31:12.000,0.0.0,1.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+222,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",9,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,24.0,2022-05-04 13:20:18.000,1.2.5,12.0,1.0,skipatom,conda-forge/skipatom,2.0,2.0,https://pypi.org/project/skipatom,2022-05-04 13:20:18.000,,333.0,392.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1605.0,3.0,,,,,,,,,,,,,,,,,,,,,
+223,ACE1.jl,,ml-iap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,9,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2024-09-11 23:05:47.000000,2024-09-11 23:05:45,561.0,3.0,7.0,5.0,31.0,22.0,24.0,20.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+224,Point Edge Transformer (PET),,ml-iap,MIT,https://github.com/spozdn/pet,Point Edge Transformer.,9,True,"['rep-learn', 'transformer']",spozdn/pet,https://github.com/spozdn/pet,2023-02-08 18:36:10,2024-10-17 13:23:23.000000,2024-07-02 10:29:58,201.0,,5.0,4.0,12.0,5.0,,18.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+225,OPTIMADE Tutorial Exercises,,educational,MIT,https://github.com/Materials-Consortia/optimade-tutorial-exercises,Tutorial exercises for the OPTIMADE API.,9,False,['datasets'],Materials-Consortia/optimade-tutorial-exercises,https://github.com/Materials-Consortia/optimade-tutorial-exercises,2021-08-25 17:33:15,2023-09-27 08:32:31.000000,2023-09-27 08:32:30,49.0,,7.0,12.0,15.0,,3.0,15.0,2023-06-12 07:47:14.000,2.0.1,5.0,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+226,SiMGen,,generative,MIT,https://github.com/RokasEl/simgen,Zero Shot Molecular Generation via Similarity Kernels.,9,True,['visualization'],RokasEl/simgen,https://github.com/RokasEl/simgen,2023-01-25 16:41:18,2024-06-13 15:43:18.000000,2024-02-15 10:31:41,257.0,,2.0,2.0,23.0,1.0,3.0,13.0,2024-02-14 10:35:02.000,0.1.0,2.0,4.0,simgen,,2.0,2.0,https://pypi.org/project/simgen,2024-02-14 11:08:25.000,,71.0,71.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+227,Materials Data Facility (MDF),,datasets,Apache-2.0,https://www.materialsdatafacility.org,"A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,..",9,True,,materials-data-facility/connect_client,https://github.com/materials-data-facility/connect_client,2018-09-12 20:49:58,2024-03-10 03:11:45.000000,2024-02-05 22:48:40,158.0,,1.0,4.0,35.0,1.0,6.0,10.0,2024-02-05 22:49:58.000,0.5.0,23.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+228,2DMD dataset,,datasets,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,True,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+229,ai4material_design,,rep-learn,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",9,True,"['pretrained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+230,Awesome Neural Geometry,,community,,https://github.com/neurreps/awesome-neural-geometry,"A curated collection of resources and research related to the geometry of representations in the brain, deep networks,..",8,True,"['educational', 'rep-learn']",neurreps/awesome-neural-geometry,https://github.com/neurreps/awesome-neural-geometry,2022-07-31 01:19:57,2024-09-25 13:12:36.000000,2024-09-25 13:12:36,126.0,2.0,57.0,29.0,13.0,,1.0,915.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+231,molecularGNN_smiles,,rep-learn,Apache-2.0,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",8,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000000,2020-11-28 02:04:45,79.0,,75.0,6.0,,6.0,1.0,295.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+232,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,False,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000000,2023-03-19 13:36:55,68.0,,73.0,17.0,7.0,5.0,1.0,261.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+233,QDF for molecule,,ml-esm,MIT,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",8,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000000,2021-02-20 03:46:09,20.0,,42.0,4.0,,1.0,3.0,203.0,,,,1.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,
+234,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,8,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2024-07-27 08:42:53.000000,2024-07-18 10:32:17,6.0,,37.0,5.0,2.0,6.0,9.0,202.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+235,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,8,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2024-07-31 23:38:48.000000,2024-07-16 05:51:23,16.0,,26.0,5.0,1.0,14.0,4.0,202.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+236,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,8,True,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25.000000,2023-11-17 02:58:25,17.0,,70.0,8.0,8.0,5.0,2.0,178.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+237,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,False,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000000,2023-03-24 12:05:41,64.0,,25.0,7.0,,,10.0,129.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+238,AIMNet,,ml-iap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,8,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000000,2019-11-21 23:49:00,7.0,,25.0,10.0,2.0,4.0,,97.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+239,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000000,2022-08-08 15:56:17,25.0,,18.0,12.0,2.0,8.0,3.0,96.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+240,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000000,2021-04-29 19:51:06,78.0,,19.0,5.0,15.0,24.0,5.0,89.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+241,AI for Science paper collection,,community,Apache-2.0,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,List the AI for Science papers accepted by top conferences.,8,True,,sherrylixuecheng/AI_for_Science_paper_collection,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,2024-06-28 16:20:57,2024-09-14 16:58:10.000000,2024-09-14 16:58:10,79.0,29.0,6.0,2.0,10.0,,,61.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+242,MACE-Jax,,ml-iap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,8,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-10-04 08:07:35.000000,2023-10-04 08:07:35,207.0,,5.0,11.0,1.0,4.0,4.0,60.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+243,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,8,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2024-08-08 04:10:45.000000,2024-08-08 04:10:44,30.0,2.0,9.0,5.0,4.0,3.0,1.0,60.0,,,,2.0,,,12.0,12.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+244,HamGNN,,ml-dft,GPL-3.0,https://github.com/QuantumLab-ZY/HamGNN,An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix.,8,True,"['rep-learn', 'magnetism', 'lang-c']",QuantumLab-ZY/HamGNN,https://github.com/QuantumLab-ZY/HamGNN,2023-07-14 12:20:27,2024-09-26 08:22:48.000000,2024-09-26 08:22:48,70.0,15.0,15.0,5.0,1.0,25.0,6.0,56.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+245,DeeperGATGNN,,rep-learn,MIT,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2024-01-19 18:11:52.000000,2024-01-19 18:11:38,25.0,,8.0,3.0,1.0,4.0,8.0,48.0,2022-03-08 02:14:28.000,1.0,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+246,GAP,,ml-iap,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,8,True,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2024-08-17 08:35:27.000000,2024-08-17 08:35:27,206.0,1.0,20.0,10.0,69.0,,,40.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+247,SIMPLE-NN v2,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab..,8,True,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-12-29 02:08:47.000000,2023-12-29 02:08:47,504.0,,17.0,5.0,88.0,4.0,9.0,40.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+248,SNAP,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000000,2020-06-30 05:20:37,38.0,,17.0,11.0,1.0,1.0,3.0,37.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+249,pair_allegro,,md,MIT,https://github.com/mir-group/pair_allegro,LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support.,8,True,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_allegro,2021-08-09 17:26:51,2024-08-28 14:05:40.000000,2024-06-05 17:00:50,101.0,,8.0,9.0,3.0,12.0,18.0,34.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+250,Atomistic Adversarial Attacks,,ml-iap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000000,2022-10-03 16:19:29,33.0,,7.0,5.0,1.0,,1.0,31.0,2021-07-19 18:09:36.000,1.0.1,1.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+251,ALF,,ml-iap,https://github.com/lanl/ALF/blob/main/LICENSE,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,8,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2024-10-11 06:03:08.000000,2024-08-08 16:59:10,149.0,1.0,12.0,8.0,27.0,,,30.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+252,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,8,False,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000000,2023-01-10 22:31:07,153.0,,9.0,3.0,1.0,,1.0,25.0,2023-07-18 12:04:35.000,0.1,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+253,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,False,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000000,2023-05-26 22:35:14,17.0,,7.0,4.0,1.0,,1.0,23.0,2022-02-06 18:14:14.000,0.0.2,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+254,GElib,,math,MPL-2.0,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,8,True,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2024-09-25 17:51:19.000000,2024-07-27 21:36:58,602.0,1.0,3.0,4.0,4.0,4.0,4.0,19.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+255,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/atomistic-cookbook,A cookbook wtih recipes for atomic-scale modeling of materials and molecules.,8,True,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/atomistic-cookbook,2023-05-23 10:33:47,2024-10-16 09:32:07.000000,2024-10-14 13:34:41,102.0,36.0,1.0,16.0,79.0,1.0,11.0,16.0,,,,11.0,,,,,,,,,,,,,2.0,,,,,,,lab-cosmo/atomistic-cookbook,,,,,,,,,,,,,,
+256,TurboGAP,,ml-iap,https://github.com/mcaroba/turbogap/blob/master/LICENSE.md,https://github.com/mcaroba/turbogap,The TurboGAP code.,8,True,['lang-fortran'],mcaroba/turbogap,https://github.com/mcaroba/turbogap,2021-05-02 09:19:05,2024-10-17 16:14:11.000000,2024-10-17 16:13:48,316.0,7.0,9.0,8.0,7.0,8.0,3.0,16.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+257,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,False,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000000,2023-07-11 04:39:24,39.0,,3.0,8.0,,,,11.0,2023-03-01 14:26:13.000,1.0.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+258,T-e3nn,,rep-learn,MIT,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,True,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2024-09-29 08:13:51.000000,2024-09-29 08:13:51,2146.0,1.0,,2.0,,,,9.0,,,,26.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+259,optimade.science,,community,MIT,https://optimade.science,A sky-scanner Optimade browser-only GUI.,8,True,['datasets'],tilde-lab/optimade.science,https://github.com/tilde-lab/optimade.science,2019-06-08 14:10:54,2024-06-10 12:03:39.000000,2024-06-10 12:03:39,247.0,,2.0,4.0,32.0,7.0,19.0,8.0,2023-03-02 20:13:25.000,2.0.0,1.0,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+260,Asparagus,,ml-iap,MIT,https://github.com/MMunibas/Asparagus,"Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175.",8,False,"['workflows', 'sampling', 'md']",MMunibas/Asparagus,https://github.com/MMunibas/Asparagus,2024-07-08 13:44:56,2024-10-17 15:03:00.000000,2024-10-17 15:02:53,44.0,40.0,3.0,1.0,4.0,,,4.0,,,2.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+261,MEGNetSparse,,ml-iap,MIT,https://github.com/HSE-LAMBDA/MEGNetSparse,"A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,..",8,False,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2024-10-17 14:05:03.000000,2024-10-17 14:04:59,24.0,5.0,1.0,2.0,,,,1.0,2023-08-21 17:11:01.000,0.0.10,9.0,2.0,MEGNetSparse,,2.0,2.0,https://pypi.org/project/MEGNetSparse,2023-08-21 17:11:01.000,,560.0,560.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+262,Awesome-Graph-Generation,,community,,https://github.com/yuanqidu/awesome-graph-generation,A curated list of up-to-date graph generation papers and resources.,7,True,['rep-learn'],yuanqidu/awesome-graph-generation,https://github.com/yuanqidu/awesome-graph-generation,2021-08-07 05:43:46,2024-10-14 12:01:41.000000,2024-10-14 12:01:35,85.0,1.0,18.0,7.0,2.0,,,280.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+263,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,7,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000000,2022-04-24 18:57:39,95.0,,26.0,10.0,,2.0,11.0,198.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+264,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,Rotation- and translation-equivariant neural networks for 3D point clouds.,7,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000000,2020-01-07 17:22:15,10.0,,32.0,9.0,2.0,1.0,2.0,153.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+265,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000000,2022-02-18 13:37:51,8.0,,22.0,9.0,,,,117.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+266,PhysNet,,ml-iap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,89.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+267,Awesome Neural SBI,,community,MIT,https://github.com/smsharma/awesome-neural-sbi,Community-sourced list of papers and resources on neural simulation-based inference.,7,True,['active-learning'],smsharma/awesome-neural-sbi,https://github.com/smsharma/awesome-neural-sbi,2023-01-20 19:48:13,2024-06-17 04:24:32.000000,2024-06-17 04:24:27,56.0,,6.0,6.0,2.0,1.0,1.0,87.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+268,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000000,2020-07-15 04:55:41,96.0,,10.0,7.0,13.0,1.0,1.0,79.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+269,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000000,2017-07-11 08:25:39,9.0,,31.0,14.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+270,Awesome-Crystal-GNNs,,community,MIT,https://github.com/kdmsit/Awesome-Crystal-GNNs,This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials.,7,True,,kdmsit/Awesome-Crystal-GNNs,https://github.com/kdmsit/Awesome-Crystal-GNNs,2022-11-15 11:12:18,2024-06-16 16:02:41.000000,2024-06-16 16:02:37,34.0,,8.0,4.0,,,,64.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+271,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+272,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,7,False,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000000,2023-03-24 12:09:56,28.0,,14.0,3.0,,,3.0,52.0,2022-02-21 13:36:41.000,1.0,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+273,PyNEP,,ml-iap,MIT,https://github.com/bigd4/PyNEP,A python interface of the machine learning potential NEP used in GPUMD.,7,True,,bigd4/PyNEP,https://github.com/bigd4/PyNEP,2022-03-21 06:27:13,2024-06-01 09:06:22.000000,2024-06-01 09:06:22,80.0,,16.0,2.0,15.0,4.0,7.0,48.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+274,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000000,2021-06-07 23:27:19,265.0,,7.0,6.0,1.0,,,40.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+275,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,7,True,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2024-09-14 17:54:11.000000,2024-07-16 12:45:42,7.0,,10.0,3.0,,1.0,,38.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+276,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,7,False,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2024-05-07 21:54:19.000000,2023-07-31 16:28:09,56.0,,5.0,7.0,11.0,3.0,1.0,36.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+277,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000000,2020-05-01 20:12:23,49.0,,13.0,8.0,27.0,5.0,1.0,35.0,,,,5.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+278,chemlift,,language-models,MIT,https://github.com/lamalab-org/chemlift,Language-interfaced fine-tuning for chemistry.,7,True,,lamalab-org/chemlift,https://github.com/lamalab-org/chemlift,2023-07-10 06:54:07,2023-11-30 10:47:50.000000,2023-10-14 16:50:14,36.0,,3.0,1.0,1.0,11.0,7.0,31.0,2023-11-30 19:42:07.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+279,ChargE3Net,,ml-dft,MIT,https://github.com/AIforGreatGood/charge3net,Higher-order equivariant neural networks for charge density prediction in materials.,7,True,['rep-learn'],AIforGreatGood/charge3net,https://github.com/AIforGreatGood/charge3net,2023-12-16 13:54:56,2024-08-15 14:35:44.000000,2024-08-15 14:35:27,12.0,4.0,8.0,5.0,1.0,2.0,3.0,30.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+280,LLM-Prop,,language-models,MIT,https://github.com/vertaix/LLM-Prop,A repository for the LLM-Prop implementation.,7,True,,vertaix/LLM-Prop,https://github.com/vertaix/LLM-Prop,2022-10-16 19:15:21,2024-04-26 14:20:54.000000,2024-04-26 14:20:54,175.0,,6.0,2.0,,1.0,1.0,28.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+281,Mat2Spec,,ml-dft,MIT,https://github.com/gomes-lab/Mat2Spec,Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings.,7,False,['spectroscopy'],gomes-lab/Mat2Spec,https://github.com/gomes-lab/Mat2Spec,2022-01-17 11:45:57,2022-04-17 17:12:29.000000,2022-04-17 17:12:29,8.0,,10.0,,,,,27.0,,,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+282,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,7,False,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000000,2023-06-28 14:39:56,203.0,,2.0,,,,,26.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+283,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,16.0,,,2.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+284,Q-stack,,ml-dft,MIT,https://github.com/lcmd-epfl/Q-stack,Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML).,7,True,"['excited-states', 'general-tool']",lcmd-epfl/Q-stack,https://github.com/lcmd-epfl/Q-stack,2021-10-20 15:33:26,2024-09-27 13:04:58.000000,2024-09-26 08:13:30,422.0,1.0,5.0,2.0,46.0,9.0,20.0,14.0,,,1.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+285,AIS Square,,datasets,LGPL-3.0,https://github.com/deepmodeling/AIS-Square,"A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the..",7,True,"['community', 'model-repository']",deepmodeling/AIS-Square,https://github.com/deepmodeling/AIS-Square,2022-09-13 09:52:30,2024-08-26 15:33:53.000000,2023-12-06 03:06:55,469.0,,8.0,8.0,210.0,5.0,1.0,12.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+286,NICE,,rep-eng,MIT,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,7,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2024-04-15 14:39:34.000000,2024-04-15 14:39:33,233.0,,3.0,17.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+287,MLXDM,,ml-iap,MIT,https://github.com/RowleyGroup/MLXDM,A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K.,7,True,['long-range'],RowleyGroup/MLXDM,https://github.com/RowleyGroup/MLXDM,2022-05-03 17:47:26,2024-08-15 21:32:50.000000,2024-08-15 21:32:12,53.0,7.0,2.0,5.0,,,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+288,MADICES Awesome Interoperability,,community,MIT,MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md,Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences..,7,False,['datasets'],MADICES/MADICES.github.io,https://github.com/MADICES/MADICES.github.io,2021-12-26 13:27:32,2024-07-10 07:36:51.000000,2024-07-10 07:35:07,217.0,,5.0,4.0,17.0,1.0,14.0,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+289,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,7,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12.000000,2023-09-29 10:20:31,1026.0,,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+290,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,7,False,,,,,2023-01-25 17:30:41.112000,,,,25.0,,,,,,2023-01-25 17:30:41.112,1.0.1,3.0,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,2023-01-25 17:30:41.112,,371.0,371.0,,,,3.0,,,,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/,
+291,ML4pXRDs,,rep-learn,MIT,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,7,False,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000000,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31.000,1.0,1.0,,,,,,,,,,0.0,,,,3.0,,,,,,4.0,,,,,,,,,,,,,,,
+292,The Collection of Database and Dataset Resources in Materials Science,,community,,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",6,True,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2024-06-07 15:51:11.000000,2024-06-07 15:51:11,30.0,,43.0,12.0,2.0,1.0,1.0,257.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+293,DeepH-E3,,ml-dft,MIT,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,6,False,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000000,2023-04-04 13:26:27,16.0,,18.0,6.0,,9.0,7.0,73.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+294,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000000,2022-03-12 02:26:41,107.0,,31.0,4.0,13.0,5.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+295,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,False,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000000,2023-02-28 15:37:37,128.0,,8.0,1.0,,,5.0,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+296,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000000,2022-04-11 17:25:55,12.0,,5.0,5.0,,4.0,3.0,57.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+297,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,39.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+298,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,False,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000000,2023-06-06 10:09:58,19.0,,8.0,5.0,2.0,1.0,,32.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+299,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000000,2022-04-06 01:53:22,1.0,,8.0,11.0,,,,31.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+300,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2024-08-20 12:50:12.000000,2024-08-20 12:50:10,111.0,1.0,1.0,4.0,,,,30.0,,,,1.0,,,3.0,3.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+301,MLIP-3,,ml-iap,BSD-2-Clause,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,6,False,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000000,,,,8.0,,,24.0,6.0,26.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,,
+302,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,False,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000000,2022-12-31 17:56:21,14.0,,7.0,3.0,1.0,1.0,8.0,21.0,2021-08-23 18:58:52.000,0.1,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+303,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,This package is deprecated. Functionalities are migrating to Porcupine.jl.,6,False,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000000,2023-09-27 18:34:44,62.0,,,4.0,,,,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+304,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000000,2021-08-30 17:05:32,162.0,,2.0,4.0,,2.0,,11.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+305,testing-framework,,ml-iap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+306,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,6,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2024-07-24 05:27:21.000000,2022-08-09 07:21:05,16.0,,3.0,9.0,2.0,1.0,,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+307,PANNA,,ml-iap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000000,,,,10.0,,,,,10.0,,,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,,
+308,ML for catalysis tutorials,,educational,MIT,https://github.com/ulissigroup/ml_catalysis_tutorials,A jupyter book repo for tutorial on how to use OCP ML models for catalysis.,6,False,,ulissigroup/ml_catalysis_tutorials,https://github.com/ulissigroup/ml_catalysis_tutorials,2022-10-28 20:37:30,2022-10-31 18:06:07.000000,2022-10-31 17:49:25,40.0,,1.0,4.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+309,TensorPotential,,ml-iap,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",6,True,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2024-09-12 10:19:56.000000,2024-09-12 10:19:56,22.0,4.0,4.0,2.0,2.0,,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+310,COSMO Toolbox,,math,,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2024-03-19 13:27:28.000000,2024-03-19 13:27:02,107.0,,6.0,27.0,1.0,,,7.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+311,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+312,Equisolve,,general-tool,BSD-3-Clause,https://github.com/lab-cosmo/equisolve,A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties..,6,True,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59.000000,2023-10-27 09:55:17,55.0,,1.0,17.0,43.0,19.0,4.0,5.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+313,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,True,['rep-learn'],aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-10-07 12:57:30.000000,2024-10-07 12:57:25,104.0,17.0,1.0,3.0,1.0,1.0,2.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+314,soap_turbo,,rep-eng,https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md,https://github.com/libAtoms/soap_turbo,soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP.,6,False,['lang-fortran'],libAtoms/soap_turbo,https://github.com/libAtoms/soap_turbo,2021-03-19 15:20:25,2024-09-04 11:20:17.000000,2023-05-24 09:42:00,36.0,,8.0,8.0,1.0,5.0,3.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+315,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",6,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2024-05-24 05:53:06.000000,2024-05-24 05:53:06,63.0,,4.0,23.0,,,,4.0,,,,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,True
+316,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,6,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2024-09-17 06:23:54.000000,2024-08-09 03:21:10,381.0,10.0,4.0,2.0,8.0,1.0,1.0,4.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/,
+317,KSR-DFT,,ml-dft,Apache-2.0,https://github.com/pedersor/ksr_dft,Kohn-Sham regularizer for machine-learned DFT functionals.,6,False,,pedersor/ksr_dft,https://github.com/pedersor/ksr_dft,2023-03-01 17:24:48,2023-03-04 07:20:22.000000,2023-03-04 07:20:18,466.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+318,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000000,2023-07-05 09:57:14,241.0,,1.0,2.0,,1.0,,3.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+319,ACEpsi.jl,,ml-wft,MIT,https://github.com/ACEsuit/ACEpsi.jl,ACE wave function parameterizations.,6,False,"['rep-eng', 'lang-julia']",ACEsuit/ACEpsi.jl,https://github.com/ACEsuit/ACEpsi.jl,2022-10-21 03:51:18,2024-04-12 06:18:19.000000,2023-10-05 21:21:35,162.0,,,4.0,16.0,5.0,4.0,2.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+320,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+321,COATI,,generative,Apache 2.0,https://github.com/terraytherapeutics/COATI,COATI: multi-modal contrastive pre-training for representing and traversing chemical space.,5,True,"['drug-discovery', 'multimodal', 'pretrained', 'rep-learn']",terraytherapeutics/COATI,https://github.com/terraytherapeutics/COATI,2023-08-11 14:56:39,2024-03-23 18:06:26.000000,2024-03-23 18:06:26,16.0,,6.0,2.0,7.0,1.0,2.0,100.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+322,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2024-04-11 02:15:55.000000,2022-09-04 02:06:18,139.0,,13.0,9.0,29.0,2.0,1.0,84.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+323,crystal-text-llm,,language-models,CC-BY-NC-4.0,https://github.com/facebookresearch/crystal-text-llm,Large language models to generate stable crystals.,5,True,['materials-discovery'],facebookresearch/crystal-text-llm,https://github.com/facebookresearch/crystal-text-llm,2024-02-05 22:29:12,2024-06-18 17:10:52.000000,2024-06-18 17:10:52,13.0,,13.0,4.0,3.0,9.0,2.0,75.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+324,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000000,2019-09-17 14:31:19,2.0,,19.0,5.0,,1.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+325,The Perovskite Database Project,,datasets,,https://github.com/Jesperkemist/perovskitedatabase,"Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form..",5,True,['community'],Jesperkemist/perovskitedatabase,https://github.com/Jesperkemist/perovskitedatabase,2021-01-17 14:26:45,2024-03-07 11:09:21.000000,2024-03-07 11:09:17,44.0,,20.0,3.0,7.0,1.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+326,Joint Multidomain Pre-Training (JMP),,uip,CC-BY-NC-4.0,https://github.com/facebookresearch/JMP,Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction.,5,True,"['pretrained', 'ml-iap', 'general-tool']",facebookresearch/JMP,https://github.com/facebookresearch/JMP,2024-03-14 23:10:10,2024-06-20 04:11:08.000000,2024-05-07 08:19:12,1.0,,6.0,3.0,1.0,2.0,,38.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+327,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000000,2022-07-22 08:10:24,49.0,,20.0,1.0,14.0,,,34.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+328,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,5,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000000,2023-06-14 11:44:46,4.0,,4.0,3.0,,2.0,,32.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+329,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,30.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+330,SciBot,,language-models,,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,5,True,['ai-agent'],CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2024-09-03 15:21:15.000000,2024-09-03 15:20:54,23.0,1.0,8.0,6.0,,,,28.0,,,,1.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+331,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,5,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000000,2020-09-18 16:36:30,9.0,,7.0,2.0,,1.0,1.0,23.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+332,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,Implementation of coarse-graining Autoencoders.,5,False,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,21.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+333,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,MIT,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,5,True,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2024-09-25 07:36:12.000000,2024-09-25 07:36:11,24.0,1.0,9.0,2.0,,2.0,1.0,20.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,-2.0,,,,,,,,,,,,,
+334,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,5,True,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2024-07-23 13:03:45.000000,2024-07-23 13:03:44,26.0,1.0,13.0,3.0,10.0,2.0,5.0,19.0,2020-12-17 16:51:47.000,2020.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+335,NequIP-JAX,,ml-iap,,https://github.com/mariogeiger/nequip-jax,JAX implementation of the NequIP interatomic potential.,5,True,,mariogeiger/nequip-jax,https://github.com/mariogeiger/nequip-jax,2023-03-08 04:18:28,2023-11-01 20:35:48.000000,2023-11-01 20:35:44,39.0,,3.0,1.0,2.0,2.0,,18.0,2023-06-22 22:36:36.000,1.1.0,3.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+336,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,Deep neural network for molecules in external fields.,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000000,2022-05-19 09:28:38,26.0,,4.0,2.0,1.0,1.0,,17.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+337,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,5,True,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2024-01-08 09:21:11.000000,2024-01-08 09:21:11,53.0,,4.0,2.0,,2.0,,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+338,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04.000,1.0.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+339,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,False,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000000,2023-04-05 01:13:11,24.0,,1.0,1.0,,1.0,,13.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+340,BERT-PSIE-TC,,language-models,MIT,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,5,False,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000000,2023-08-18 12:48:31,36.0,,3.0,1.0,,,,12.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+341,SOMD,,md,AGPL-3.0,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,5,True,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2024-10-16 19:13:50.000000,2024-10-16 19:11:24,304.0,3.0,2.0,1.0,11.0,,1.0,12.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+342,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,5,False,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000000,2023-09-21 21:50:43,121.0,,7.0,5.0,15.0,4.0,6.0,11.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+343,QMLearn,,ml-esm,MIT,http://qmlearn.rutgers.edu/,Quantum Machine Learning by learning one-body reduced density matrices in the AO basis...,5,False,,,,2022-02-15 13:42:13,2022-02-15 13:42:13.000000,,,,3.0,,,,,11.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,pavanello-research-group/qmlearn,https://gitlab.com/pavanello-research-group/qmlearn,,
+344,InfGCN for Electron Density Estimation,,ml-dft,MIT,https://github.com/ccr-cheng/InfGCN-pytorch,Official implementation of the NeurIPS 23 spotlight paper of InfGCN.,5,True,"['rep-learn', 'neural-operator']",ccr-cheng/infgcn-pytorch,https://github.com/ccr-cheng/InfGCN-pytorch,2023-10-01 21:21:40,2023-12-05 01:31:19.000000,2023-12-05 01:31:14,3.0,,3.0,1.0,,,3.0,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+345,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,5,False,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000000,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,10.0,2023-08-27 13:48:09.000,2.0.1,1.0,2.0,,,,,,,,,1.0,,,,3.0,,,,,,22.0,,,,,,,,,,,,,,,
+346,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem).,5,True,['rep-learn'],ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000000,2023-11-29 15:07:42,96.0,,2.0,2.0,16.0,1.0,3.0,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+347,GN-MM,,ml-iap,MIT,https://gitlab.com/zaverkin_v/gmnn,The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic..,5,False,"['active-learning', 'md', 'rep-eng', 'magnetism']",,,2021-09-19 15:56:31,2021-09-19 15:56:31.000000,,,,4.0,,,,,10.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,zaverkin_v/gmnn,https://gitlab.com/zaverkin_v/gmnn,,
+348,MAPI_LLM,,language-models,MIT,https://github.com/maykcaldas/MAPI_LLM,A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J.,5,True,"['ai-agent', 'dataset']",maykcaldas/MAPI_LLM,https://github.com/maykcaldas/MAPI_LLM,2023-03-30 04:24:54,2024-04-20 03:16:17.000000,2024-04-11 22:22:28,31.0,,2.0,1.0,7.0,,,9.0,2023-06-29 18:48:44.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+349,EGraFFBench,,rep-learn,,https://github.com/M3RG-IITD/MDBENCHGNN,,5,False,"['single-paper', 'benchmarking', 'ml-iap']",M3RG-IITD/MDBENCHGNN,https://github.com/M3RG-IITD/MDBENCHGNN,2023-07-06 18:15:34,2023-11-19 05:16:12.000000,2023-11-19 05:14:44,161.0,,,,,4.0,,8.0,2023-07-16 05:46:38.000,0.1.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+350,GDB-9-Ex9 and ORNL_AISD-Ex,,datasets,,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-..,5,False,,ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,2023-01-06 18:09:54,2023-08-11 16:49:35.000000,2023-08-11 16:49:35,47.0,,6.0,6.0,13.0,2.0,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+351,MXenes4HER,,rep-eng,GPL-3.0,https://github.com/cnislab/MXenes4HER,Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B.,5,False,"['materials-discovery', 'catalysis', 'scikit-learn', 'single-paper']",cnislab/MXenes4HER,https://github.com/cnislab/MXenes4HER,2022-11-28 09:27:36,2023-02-27 18:08:05.000000,2023-02-27 18:08:05,67.0,,3.0,1.0,1.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+352,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,False,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+353,q-pac,,ml-esm,MIT,https://gitlab.com/jmargraf/qpac,Kernel charge equilibration method.,5,False,['electrostatics'],,,2020-11-15 20:11:27,2020-11-15 20:11:27.000000,,,,4.0,,,2.0,,4.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,jmargraf/qpac,https://gitlab.com/jmargraf/qpac,,
+354,paper-ml-robustness-material-property,,unsupervised,BSD-3-Clause,https://github.com/mathsphy/paper-ml-robustness-material-property,A critical examination of robustness and generalizability of machine learning prediction of materials properties.,5,False,"['datasets', 'single-paper']",mathsphy/paper-ml-robustness-material-property,https://github.com/mathsphy/paper-ml-robustness-material-property,2023-02-21 02:38:13,2023-04-13 01:18:02.000000,2023-04-13 01:18:02,3.0,,3.0,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+355,rho_learn,,ml-dft,MIT,https://github.com/m-stack-org/rho_learn,A proof-of-concept workflow for torch-based electron density learning.,5,False,,jwa7/rho_learn,https://github.com/m-stack-org/rho_learn,2023-02-14 15:46:26,2023-04-03 07:03:02.000000,2023-03-27 16:58:46,98.0,,2.0,1.0,1.0,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,m-stack-org/rho_learn,,,,,,,,,,,,,,
+356,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,5,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000000,,,,3.0,,,3.0,19.0,3.0,,,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,,
+357,halex,,ml-esm,,https://github.com/ecignoni/halex,Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.,5,False,['excited-states'],ecignoni/halex,https://github.com/ecignoni/halex,2023-09-04 06:54:15,2024-02-08 10:20:53.000000,2024-02-08 10:20:49,169.0,,,3.0,,1.0,,3.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,,
+358,Alchemical learning,,ml-iap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000000,2023-04-07 10:19:10,120.0,,1.0,7.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+359,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+360,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,GPL-3.0,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",5,False,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000000,2023-06-26 12:48:15,157.0,,15.0,2.0,1.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+361,ACE1Pack.jl,,ml-iap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",5,False,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000000,2023-08-21 15:48:54,547.0,,,1.0,,,,1.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl,
+362,Per-Site CGCNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pretrained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+363,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,False,"['probabilistic', 'pretrained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000000,2023-06-05 17:30:34,123.0,,1.0,,,,,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+364,Geometric-GNNs,,community,,https://github.com/AlexDuvalinho/geometric-gnns,List of Geometric GNNs for 3D atomic systems.,4,False,"['datasets', 'educational', 'rep-learn']",AlexDuvalinho/geometric-gnns,https://github.com/AlexDuvalinho/geometric-gnns,2023-08-31 09:10:32,2024-02-29 16:25:54.000000,2024-02-29 16:25:53,37.0,,7.0,1.0,3.0,,1.0,93.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+365,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000000,2020-10-19 08:10:30,13.0,,17.0,2.0,,,,68.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+366,MAGUS,,materials-discovery,,https://gitlab.com/bigd4/magus,Machine learning And Graph theory assisted Universal structure Searcher.,4,False,"['structure-prediction', 'active-learning']",,,2023-01-31 09:00:23,2023-01-31 09:00:23.000000,,,,15.0,,,,,61.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,,
+367,Allegro-Legato,,ml-iap,MIT,https://github.com/ibayashi-hikaru/allegro-legato,An extension of Allegro with enhanced robustness and time-to-failure.,4,False,['md'],ibayashi-hikaru/allegro-legato,https://github.com/ibayashi-hikaru/allegro-legato,2023-01-17 19:46:10,2023-08-03 22:25:11.000000,2023-08-03 22:24:35,82.0,,1.0,1.0,,,,19.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+368,glp,,ml-iap,MIT,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,4,False,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2024-04-09 12:06:56.000000,2024-03-20 09:00:27,11.0,,1.0,2.0,3.0,,,17.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+369,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,16.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+370,Does this material exist?,,community,MIT,https://thismaterialdoesnotexist.com/,Vote on whether you think predicted crystal structures could be synthesised.,4,False,"['for-fun', 'materials-discovery']",ml-evs/this-material-does-not-exist,https://github.com/ml-evs/this-material-does-not-exist,2023-12-01 18:16:28,2024-07-29 09:50:18.000000,2024-04-10 12:32:06,16.0,,3.0,2.0,2.0,2.0,,15.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+371,ChemDataWriter,,language-models,MIT,https://github.com/ShuHuang/chemdatawriter,ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.,4,False,['literature-data'],ShuHuang/chemdatawriter,https://github.com/ShuHuang/chemdatawriter,2023-09-22 10:05:25,2023-10-07 04:23:47.000000,2023-10-07 04:07:59,9.0,,1.0,2.0,,1.0,,14.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+372,paper-data-redundancy,,datasets,BSD-3-Clause,https://github.com/mathsphy/paper-data-redundancy,Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data.,4,False,"['small-data', 'single-paper']",mathsphy/paper-data-redundancy,https://github.com/mathsphy/paper-data-redundancy,2023-06-10 15:00:28,2024-09-23 13:37:50.000000,2024-09-23 13:37:49,18.0,1.0,,1.0,,,,8.0,2023-10-11 14:09:07.000,1.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+373,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000000,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+374,Mapping out phase diagrams with generative classifiers,,generative,MIT,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,Repository for our ``Mapping out phase diagrams with generative models paper.,4,False,['phase-transition'],arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,2023-06-07 21:43:14,2023-06-27 08:12:29.000000,2023-06-27 08:12:29,39.0,,2.0,1.0,,,,7.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+375,automl-materials,,rep-eng,MIT,https://github.com/mm-tud/automl-materials,AutoML for Regression Tasks on Small Tabular Data in Materials Design.,4,False,"['automl', 'benchmarking', 'single-paper']",mm-tud/automl-materials,https://github.com/mm-tud/automl-materials,2022-10-07 09:49:18,2022-11-15 15:22:54.000000,2022-11-15 15:22:45,6.0,,1.0,2.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+376,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,4,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2024-03-20 09:05:20.000000,2024-03-20 09:05:14,3.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+377,ML-atomate,,materials-discovery,GPL-3.0,https://github.com/takahashi-akira-36m/ml_atomate,Machine learning-assisted Atomate code for autonomous computational materials screening.,4,False,"['active-learning', 'workflows']",takahashi-akira-36m/ml_atomate,https://github.com/takahashi-akira-36m/ml_atomate,2023-09-21 08:45:10,2023-11-17 09:54:23.000000,2023-11-17 09:51:02,6.0,,1.0,1.0,,,,4.0,2023-09-29 03:52:46.000,stam_m_2023_fix,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+378,KmdPlus,,unsupervised,MIT,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",4,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2024-09-25 07:36:49.000000,2024-09-25 07:36:48,8.0,1.0,1.0,1.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+379,Visual Graph Datasets,,datasets,MIT,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,4,False,"['xai', 'rep-learn']",aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2024-06-26 14:06:17.000000,2024-06-26 14:06:13,53.0,,2.0,3.0,,1.0,,2.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+380,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+381,OPTIMADE providers dashboard,,datasets,,https://www.optimade.org/providers-dashboard/,A dashboard of known providers.,4,False,,Materials-Consortia/providers-dashboard,https://github.com/Materials-Consortia/providers-dashboard,2020-06-17 16:15:07,2024-10-17 06:34:06.000000,2024-10-01 19:09:06,140.0,2.0,3.0,19.0,142.0,10.0,18.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+382,AI4ChemMat Hands-On Series,,educational,MPL-2.0,https://github.com/ai4chemmat/ai4chemmat.github.io,Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.,4,False,,ai4chemmat/ai4chemmat.github.io,https://github.com/ai4chemmat/ai4chemmat.github.io,2023-03-24 21:25:21,2024-04-24 16:32:18.000000,2024-04-24 16:32:18,40.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+383,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000000,2023-07-19 13:25:49,46.0,,1.0,3.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+384,ACE Workflows,,ml-iap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,4,False,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00.000000,2023-10-12 18:00:39,45.0,,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+385,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,,
+386,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+387,PeriodicPotentials,,ml-iap,MIT,https://github.com/AaltoRSE/PeriodicPotentials,A Periodic table app that displays potentials based on the selected elements.,4,False,"['community', 'visualization', 'lang-js']",AaltoRSE/PeriodicPotentials,https://github.com/AaltoRSE/PeriodicPotentials,2022-10-14 09:03:59,2022-10-18 17:10:22.000000,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+388,CatBERTa,,language-models,,https://github.com/hoon-ock/CatBERTa,Large Language Model for Catalyst Property Prediction.,3,False,"['transformer', 'catalysis']",hoon-ock/CatBERTa,https://github.com/hoon-ock/CatBERTa,2023-05-19 18:23:17,2024-03-08 02:59:22.000000,2024-03-08 02:59:22,93.0,,2.0,1.0,2.0,,,20.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+389,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,3,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2024-07-20 05:12:50.000000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,12.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+390,ALEBREW,,active-learning,https://github.com/nec-research/alebrew/blob/main/LICENSE.txt,https://github.com/nec-research/alebrew,Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic..,3,False,"['ml-iap', 'md']",nec-research/alebrew,https://github.com/nec-research/alebrew,2024-02-27 07:32:23,2024-03-17 13:51:57.000000,2024-03-17 13:51:52,2.0,,3.0,1.0,,1.0,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+391,atom_by_atom,,rep-learn,,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,3,False,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-10-19 15:59:08.000000,2023-10-19 15:35:49,74.0,,,2.0,,,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+392,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000000,2023-06-16 20:38:23,96.0,,2.0,2.0,27.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+393,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000000,2020-01-09 15:54:26,8.0,,2.0,4.0,,,1.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+394,Cephalo,,language-models,Apache-2.0,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,3,False,"['generative', 'multimodal', 'pretrained']",lamm-mit/Cephalo,https://github.com/lamm-mit/Cephalo,2024-05-28 12:29:13,2024-07-23 09:27:58.000000,2024-07-23 09:27:57,24.0,1.0,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+395,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,3,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000000,,,,6.0,,,4.0,1.0,5.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,,
+396,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+397,APET,,ml-dft,GPL-3.0,https://github.com/emotionor/APET,Atomic Positional Embedding-based Transformer.,3,False,"['density-of-states', 'transformer']",emotionor/APET,https://github.com/emotionor/APET,2023-03-06 01:53:16,2024-04-24 03:43:39.000000,2023-09-28 03:16:11,11.0,,,2.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+398,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2024-01-05 12:59:56.000000,2024-01-05 12:59:09,19.0,,,2.0,,,,3.0,,,,,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+399,torch_spex,,math,,https://github.com/lab-cosmo/torch_spex,Spherical expansions in PyTorch.,3,False,,lab-cosmo/torch_spex,https://github.com/lab-cosmo/torch_spex,2023-03-28 09:48:36,2024-03-28 05:33:01.000000,2024-03-28 05:33:01,77.0,,2.0,18.0,35.0,7.0,2.0,3.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+400,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,Material for running a lab session on atomic neural networks.,3,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000000,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+401,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000000,2022-10-11 04:27:40,6.0,,,1.0,,,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+402,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+403,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2024-10-17 07:39:59.000000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+404,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,3,False,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000000,2023-06-14 19:05:47,25.0,,,1.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+405,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+406,PyFLAME,,ml-iap,,https://gitlab.com/flame-code/PyFLAME,An automated approach for developing neural network interatomic potentials with FLAME..,3,False,"['active-learning', 'structure-prediction', 'structure-optimization', 'rep-eng', 'lang-fortran']",,,2021-04-07 09:16:07,2021-04-07 09:16:07.000000,,,,4.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,flame-code/PyFLAME,https://gitlab.com/flame-code/PyFLAME,,
+407,nep-data,,datasets,,https://gitlab.com/brucefan1983/nep-data,Data related to the NEP machine-learned potential of GPUMD.,2,False,"['ml-iap', 'md', 'transport-phenomena']",,,2021-11-22 19:43:01,2021-11-22 19:43:01.000000,,,,4.0,,,1.0,1.0,13.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,brucefan1983/nep-data,https://gitlab.com/brucefan1983/nep-data,,
+408,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+409,SingleNN,,ml-iap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+410,A3MD,,ml-dft,,https://github.com/brunocuevas/a3md,MPNN-like + Analytic Density Model = Accurate electron densities.,2,False,"['rep-learn', 'single-paper']",brunocuevas/a3md,https://github.com/brunocuevas/a3md,2021-06-02 07:23:17,2021-12-02 17:10:39.000000,2021-12-02 17:10:34,4.0,,1.0,2.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+411,tmQM_wB97MV Dataset,,datasets,,https://github.com/ulissigroup/tmQM_wB97MV,Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV..,2,False,"['catalysis', 'rep-learn']",ulissigroup/tmqm_wB97MV,https://github.com/ulissigroup/tmQM_wB97MV,2023-07-17 21:40:20,2024-04-09 22:01:26.000000,2024-04-09 22:01:26,17.0,,1.0,3.0,,,2.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+412,LAMMPS-style pair potentials with GAP,,educational,,https://github.com/victorprincipe/pair_potentials,A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD..,2,False,"['ml-iap', 'md', 'rep-eng']",victorprincipe/pair_potentials,https://github.com/victorprincipe/pair_potentials,2022-09-21 09:45:03,2022-10-03 08:06:22.000000,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+413,AisNet,,ml-iap,MIT,https://github.com/loilisxka/AisNet,A Universal Interatomic Potential Neural Network with Encoded Local Environment Features..,2,False,,loilisxka/AisNet,https://github.com/loilisxka/AisNet,2022-10-11 05:54:59,2022-10-11 06:02:47.000000,2022-10-11 05:58:06,2.0,,,1.0,,,,3.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+414,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000000,2023-11-28 11:17:01,24.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+415,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000000,2022-12-22 21:45:40,19.0,,,2.0,,,,2.0,2022-08-18 05:25:24.000,1.0.0,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+416,RuNNer,,ml-iap,GPL-3.0,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/,
+417,Point Edge Transformer,,rep-learn,CC-BY-4.0,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+418,nnp-pre-training,,ml-iap,,https://github.com/jla-gardner/nnp-pre-training,Synthetic pre-training for neural-network interatomic potentials.,1,False,"['pretrained', 'md']",jla-gardner/nnp-pre-training,https://github.com/jla-gardner/nnp-pre-training,2023-07-12 11:58:29,2023-12-19 12:08:14.000000,2023-12-19 12:08:14,11.0,,,1.0,,,,6.0,2023-12-19 12:02:35.000,1.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+419,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+420,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,1,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000000,2023-07-08 15:48:37,109.0,,,1.0,,,1.0,2.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+421,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,,
+422,mag-ace,,ml-iap,,https://github.com/mttrin93/mag-ace,Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.,1,False,"['magnetism', 'md', 'lang-fortran']",mttrin93/mag-ace,https://github.com/mttrin93/mag-ace,2023-12-26 19:00:40,2023-12-26 22:34:27.000000,2023-12-26 22:34:27,7.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+423,mlp,,ml-iap,,https://github.com/cesmix-mit/MLP,Proper orthogonal descriptors for efficient and accurate interatomic potentials...,1,False,['lang-julia'],cesmix-mit/mlp,https://github.com/cesmix-mit/MLP,2022-02-25 23:03:09,2022-10-22 19:01:45.000000,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+424,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,GitHub topic materials-informatics.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+425,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,A Portal Site of Materials Science Simulation.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,
+426,Allegro-JAX,,ml-iap,,https://github.com/mariogeiger/allegro-jax,JAX implementation of the Allegro interatomic potential.,0,True,,mariogeiger/allegro-jax,https://github.com/mariogeiger/allegro-jax,2023-07-02 19:00:00,2024-04-09 18:44:30.000000,2024-04-09 18:44:30,7.0,,2.0,2.0,1.0,1.0,1.0,18.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+427,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+428,MLDensity,,ml-dft,,https://github.com/StefanoSanvitoGroup/MLdensity,Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,https://github.com/StefanoSanvitoGroup/MLdensity,2023-01-31 20:44:45,2024-05-27 12:28:57.000000,2023-02-22 19:25:51,14.0,,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/latest-changes.md b/latest-changes.md
index 4266c47..c2a6c68 100644
--- a/latest-changes.md
+++ b/latest-changes.md
@@ -2,19 +2,19 @@
 
 _Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._
 
-- <b><a href="https://github.com/libAtoms/QUIP">QUIP</a></b> (🥈27 ·  ⭐ 350 · 📈) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_engineering"><code>rep-eng</code></a> <code>Fortran</code>
-- <b><a href="https://github.com/materialsproject/crystaltoolkit">Crystal Toolkit</a></b> (🥇24 ·  ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/uw-cmg/MAST-ML">MAST-ML</a></b> (🥈20 ·  ⭐ 100 · 📈) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/zincware/ZnDraw">ZnDraw</a></b> (🥈20 ·  ⭐ 30 · 📈) - A powerful tool for visualizing, modifying, and analysing atomistic systems. <code><a href="http://bit.ly/2M0xmjV">EPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <code>JavaScript</code>
-- <b><a href="https://github.com/luigibonati/mlcolvar">mlcolvar</a></b> (🥈19 ·  ⭐ 91 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>sampling</code>
+- <b><a href="https://github.com/materialsproject/MPContribs">MPContribs</a></b> (🥇25 ·  ⭐ 35 · 📈) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/luigibonati/mlcolvar">mlcolvar</a></b> (🥈21 ·  ⭐ 89 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>sampling</code>
+- <b><a href="https://github.com/FitSNAP/FitSNAP">FitSNAP</a></b> (🥈20 ·  ⭐ 150 · 📈) - Software for generating machine-learning interatomic potentials for LAMMPS. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code>
+- <b><a href="https://github.com/mala-project/mala">MALA</a></b> (🥇19 ·  ⭐ 81 · 📈) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code>
+- <b><a href="https://github.com/openkim/kliff">KLIFF</a></b> (🥈16 ·  ⭐ 34 · 📈) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. <code><a href="https://tldrlegal.com/search?q=LGPL-2.1">LGPL-2.1</a></code> <code>probabilistic</code> <code>workflows</code>
 
 ## 📉 Trending Down
 
 _Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._
 
-- <b><a href="https://github.com/deepchem/deepchem">DeepChem</a></b> (🥇34 ·  ⭐ 5.4K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/deepmodeling/deepmd-kit">DeePMD-kit</a></b> (🥇25 ·  ⭐ 1.5K · 📉) - A deep learning package for many-body potential energy representation and molecular dynamics. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>C++</code>
-- <b><a href="https://github.com/deepmodeling/deepmd-kit">DPA-2</a></b> (🥇24 ·  ⭐ 1.5K · 📉) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. <code><a href="http://bit.ly/37RvQcA">LGPL-3.0</a></code> <code>ML-IAP</code> <code>pretrained</code> <code>workflows</code> <code>datasets</code>
-- <b><a href="https://github.com/mir-group/nequip">NequIP</a></b> (🥇23 ·  ⭐ 610 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
-- <b><a href="https://github.com/atomistic-machine-learning/schnetpack-gschnet">SchNetPack G-SchNet</a></b> (🥈12 ·  ⭐ 46 · 📉) - G-SchNet extension for SchNetPack. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/e3nn/e3nn">e3nn</a></b> (🥇27 ·  ⭐ 960 · 📉) - A modular framework for neural networks with Euclidean symmetry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
+- <b><a href="https://github.com/libAtoms/QUIP">QUIP</a></b> (🥈26 ·  ⭐ 350 · 📉) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. <code><a href="http://bit.ly/2KucAZR">GPL-2.0</a></code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_engineering"><code>rep-eng</code></a> <code>Fortran</code>
+- <b><a href="https://github.com/materialsvirtuallab/m3gnet">M3GNet</a></b> (🥈18 ·  ⭐ 240 · 📉) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>ML-IAP</code> <code>pretrained</code>
+- <b><a href="https://github.com/MDIL-SNU/SevenNet">SevenNet</a></b> (🥉13 ·  ⭐ 110 · 📉) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. <code><a href="http://bit.ly/2M0xdwT">GPL-3.0</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>pretrained</code>
+- <b><a href="https://github.com/Minoru938/CSPML">CSPML (crystal structure prediction with machine learning-based element substitution)</a></b> (🥉5 ·  ⭐ 20 · 📉) - Original implementation of CSPML. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>structure-prediction</code>