From d32accbf2b4dd7e21f945e3f71641aa29e9075c4 Mon Sep 17 00:00:00 2001 From: Irratzo Date: Thu, 3 Oct 2024 18:31:55 +0000 Subject: [PATCH] Update best-of list for version 2024.10.03 --- README.md | 758 +++++++++++++++----------------- history/2024-10-03_changes.md | 20 + history/2024-10-03_projects.csv | 430 ++++++++++++++++++ latest-changes.md | 20 +- 4 files changed, 825 insertions(+), 403 deletions(-) create mode 100644 history/2024-10-03_changes.md create mode 100644 history/2024-10-03_projects.csv diff --git a/README.md b/README.md index 8cdbf76..e2b0357 100644 --- a/README.md +++ b/README.md @@ -83,9 +83,9 @@ The current focus of this list is more on simulation data rather than experiment _Projects that focus on enabling active learning, iterative learning schemes for atomistic ML._ -
FLARE (🥇22 · ⭐ 290 · 📈) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP +
FLARE (🥇22 · ⭐ 290) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP -- [GitHub](https://github.com/mir-group/flare) (👨‍💻 41 · 🔀 66 · 📥 8 · 📦 11 · 📋 220 - 16% open · ⏱️ 20.09.2024): +- [GitHub](https://github.com/mir-group/flare) (👨‍💻 42 · 🔀 67 · 📥 8 · 📦 11 · 📋 220 - 16% open · ⏱️ 30.09.2024): ``` git clone https://github.com/mir-group/flare @@ -98,7 +98,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for ``` git clone https://github.com/zincware/IPSuite ``` -- [PyPi](https://pypi.org/project/ipsuite) (📥 160 / month · ⏱️ 08.08.2024): +- [PyPi](https://pypi.org/project/ipsuite) (📥 150 / month · ⏱️ 08.08.2024): ``` pip install ipsuite ``` @@ -111,18 +111,11 @@ _Projects that focus on enabling active learning, iterative learning schemes for git clone https://github.com/ulissigroup/finetuna ```
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ACEHAL (🥉5 · ⭐ 11 · 💤) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed Julia - -- [GitHub](https://github.com/ACEsuit/ACEHAL) (👨‍💻 3 · 🔀 7 · 📋 10 - 40% open · ⏱️ 21.09.2023): - - ``` - git clone https://github.com/ACEsuit/ACEHAL - ``` -
-
Show 2 hidden projects... +
Show 3 hidden projects... - flare++ (🥈13 · ⭐ 35 · 💀) - A many-body extension of the FLARE code. MIT C++ ML-IAP -- ALEBREW (🥉3 · ⭐ 9) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD +- ACEHAL (🥉5 · ⭐ 11 · 💀) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed Julia +- ALEBREW (🥉3 · ⭐ 9 · 💤) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD

@@ -162,19 +155,19 @@ _Projects that collect atomistic ML resources or foster communication within com
MatBench (🥇18 · ⭐ 110 · 💤) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking model-repository -- [GitHub](https://github.com/materialsproject/matbench) (👨‍💻 25 · 🔀 45 · 📦 16 · 📋 65 - 60% open · ⏱️ 20.01.2024): +- [GitHub](https://github.com/materialsproject/matbench) (👨‍💻 25 · 🔀 46 · 📦 16 · 📋 65 - 60% open · ⏱️ 20.01.2024): ``` git clone https://github.com/materialsproject/matbench ``` -- [PyPi](https://pypi.org/project/matbench) (📥 420 / month · 📦 2 · ⏱️ 27.07.2022): +- [PyPi](https://pypi.org/project/matbench) (📥 410 / month · 📦 2 · ⏱️ 27.07.2022): ``` pip install matbench ```
MatBench Discovery (🥇18 · ⭐ 92) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository -- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 8 · 🔀 12 · 📦 2 · 📋 38 - 10% open · ⏱️ 23.09.2024): +- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 8 · 🔀 12 · 📦 2 · 📋 39 - 10% open · ⏱️ 02.10.2024): ``` git clone https://github.com/janosh/matbench-discovery @@ -192,7 +185,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/openml/OpenML ```
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GT4SD - Generative Toolkit for Scientific Discovery (🥈16 · ⭐ 330) - Gradio apps of generative models in GT4SD. MIT generative pretrained drug-discovery model-repository +
GT4SD - Generative Toolkit for Scientific Discovery (🥈15 · ⭐ 340) - Gradio apps of generative models in GT4SD. MIT generative pretrained drug-discovery model-repository - [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 68 · 📋 110 - 12% open · ⏱️ 12.09.2024): @@ -200,7 +193,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/GT4SD/gt4sd-core ```
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AI for Science Resources (🥈13 · ⭐ 490) - List of resources for AI4Science research, including learning resources. GPL-3.0 license +
AI for Science Resources (🥈13 · ⭐ 500) - List of resources for AI4Science research, including learning resources. GPL-3.0 license - [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): @@ -248,7 +241,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/IBM/molformer ```
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AI for Science paper collection (🥈9 · ⭐ 56 · 🐣) - List the AI for Science papers accepted by top conferences. Apache-2 +
AI for Science paper collection (🥈9 · ⭐ 57 · 🐣) - List the AI for Science papers accepted by top conferences. Apache-2 - [GitHub](https://github.com/sherrylixuecheng/AI_for_Science_paper_collection) (👨‍💻 5 · 🔀 6 · ⏱️ 14.09.2024): @@ -264,7 +257,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/tilde-lab/optimade.science ```
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Awesome-Graph-Generation (🥉7 · ⭐ 270) - A curated list of up-to-date graph generation papers and resources. Unlicensed rep-learn +
Awesome-Graph-Generation (🥉7 · ⭐ 270 · 💤) - A curated list of up-to-date graph generation papers and resources. Unlicensed rep-learn - [GitHub](https://github.com/yuanqidu/awesome-graph-generation) (👨‍💻 4 · 🔀 17 · ⏱️ 17.03.2024): @@ -272,7 +265,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/yuanqidu/awesome-graph-generation ```
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Awesome Neural SBI (🥉7 · ⭐ 84) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning +
Awesome Neural SBI (🥉7 · ⭐ 85) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning - [GitHub](https://github.com/smsharma/awesome-neural-sbi) (👨‍💻 3 · 🔀 6 · 📋 2 - 50% open · ⏱️ 17.06.2024): @@ -296,19 +289,12 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/sedaoturak/data-resources-for-materials-science ```
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Does this material exist? (🥉5 · ⭐ 15) - Vote on whether you think predicted crystal structures could be synthesised. MIT for-fun materials-discovery - -- [GitHub](https://github.com/ml-evs/this-material-does-not-exist) (👨‍💻 2 · 🔀 3 · ⏱️ 10.04.2024): - - ``` - git clone https://github.com/ml-evs/this-material-does-not-exist - ``` -
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Show 5 hidden projects... +
Show 6 hidden projects... -- MADICES Awesome Interoperability (🥉8 · ⭐ 1) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences.. MIT datasets - A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉7 · ⭐ 120 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT +- MADICES Awesome Interoperability (🥉7 · ⭐ 1) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences.. MIT datasets - Geometric-GNNs (🥉4 · ⭐ 92 · 💤) - List of Geometric GNNs for 3D atomic systems. Unlicensed datasets educational rep-learn +- Does this material exist? (🥉4 · ⭐ 15) - Vote on whether you think predicted crystal structures could be synthesised. MIT for-fun materials-discovery - GitHub topic materials-informatics (🥉1) - GitHub topic materials-informatics. Unlicensed - MateriApps (🥉1) - A Portal Site of Materials Science Simulation. Unlicensed
@@ -358,35 +344,35 @@ _Datasets, databases and trained models for atomistic ML._
OPTIMADE Python tools (🥇27 · ⭐ 68) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT -- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 60 · 📋 450 - 23% open · ⏱️ 23.09.2024): +- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 60 · 📋 450 - 23% open · ⏱️ 30.09.2024): ``` git clone https://github.com/Materials-Consortia/optimade-python-tools ``` -- [PyPi](https://pypi.org/project/optimade) (📥 8K / month · 📦 4 · ⏱️ 16.09.2024): +- [PyPi](https://pypi.org/project/optimade) (📥 8.8K / month · 📦 4 · ⏱️ 16.09.2024): ``` pip install optimade ``` -- [Conda](https://anaconda.org/conda-forge/optimade) (📥 91K · ⏱️ 16.09.2024): +- [Conda](https://anaconda.org/conda-forge/optimade) (📥 92K · ⏱️ 16.09.2024): ``` conda install -c conda-forge optimade ```
MPContribs (🥇23 · ⭐ 35) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT -- [GitHub](https://github.com/materialsproject/MPContribs) (👨‍💻 25 · 🔀 20 · 📦 39 · 📋 99 - 21% open · ⏱️ 25.09.2024): +- [GitHub](https://github.com/materialsproject/MPContribs) (👨‍💻 25 · 🔀 20 · 📦 39 · 📋 99 - 21% open · ⏱️ 30.09.2024): ``` git clone https://github.com/materialsproject/MPContribs ``` -- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 2.6K / month · 📦 3 · ⏱️ 20.06.2024): +- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 2.7K / month · 📦 3 · ⏱️ 20.06.2024): ``` pip install mpcontribs-client ```
FAIR Chemistry datasets (🥇21 · ⭐ 770) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis -- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 230 · 📋 200 - 6% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 230 · 📋 210 - 6% open · ⏱️ 01.10.2024): ``` git clone https://github.com/FAIR-Chem/fairchem @@ -407,12 +393,12 @@ _Datasets, databases and trained models for atomistic ML._ ``` git clone https://github.com/jla-gardner/load-atoms ``` -- [PyPi](https://pypi.org/project/load-atoms) (📥 760 / month · ⏱️ 16.09.2024): +- [PyPi](https://pypi.org/project/load-atoms) (📥 1K / month · ⏱️ 16.09.2024): ``` pip install load-atoms ```
-
QH9 (🥈13 · ⭐ 490) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT +
QH9 (🥈13 · ⭐ 500) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT - [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): @@ -420,9 +406,9 @@ _Datasets, databases and trained models for atomistic ML._ git clone https://github.com/divelab/AIRS ```
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SPICE (🥈11 · ⭐ 150) - A collection of QM data for training potential functions. MIT ML-IAP MD +
SPICE (🥈10 · ⭐ 150) - A collection of QM data for training potential functions. MIT ML-IAP MD -- [GitHub](https://github.com/openmm/spice-dataset) (👨‍💻 1 · 🔀 9 · 📥 250 · 📋 63 - 25% open · ⏱️ 19.08.2024): +- [GitHub](https://github.com/openmm/spice-dataset) (👨‍💻 1 · 🔀 9 · 📥 260 · 📋 64 - 26% open · ⏱️ 19.08.2024): ``` git clone https://github.com/openmm/spice-dataset @@ -452,7 +438,7 @@ _Datasets, databases and trained models for atomistic ML._ git clone https://github.com/deepmodeling/AIS-Square ```
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The Perovskite Database Project (🥉5 · ⭐ 58) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource +
The Perovskite Database Project (🥉5 · ⭐ 58 · 💤) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource - [GitHub](https://github.com/Jesperkemist/perovskitedatabase) (👨‍💻 2 · 🔀 18 · ⏱️ 07.03.2024): @@ -470,10 +456,10 @@ _Datasets, databases and trained models for atomistic ML._
Show 15 hidden projects... -- ATOM3D (🥈18 · ⭐ 300 · 💀) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking +- ATOM3D (🥈19 · ⭐ 300 · 💀) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking - OpenKIM (🥈10 · ⭐ 31 · 💀) - The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-.. LGPL-2.1 model-repository knowledge-base pretrained - ANI-1 Dataset (🥉8 · ⭐ 96 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT -- MoleculeNet Leaderboard (🥉8 · ⭐ 88 · 💀) - MIT benchmarking +- MoleculeNet Leaderboard (🥉8 · ⭐ 89 · 💀) - MIT benchmarking - GEOM (🥉7 · ⭐ 200 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery - ANI-1x Datasets (🥉6 · ⭐ 55 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT - COMP6 Benchmark dataset (🥉6 · ⭐ 39 · 💀) - COMP6 Benchmark dataset for ML potentials. MIT @@ -483,7 +469,7 @@ _Datasets, databases and trained models for atomistic ML._ - paper-data-redundancy (🥉4 · ⭐ 8) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data. BSD-3 small-data single-paper - Visual Graph Datasets (🥉4 · ⭐ 2) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT XAI rep-learn - OPTIMADE providers dashboard (🥉4 · ⭐ 1) - A dashboard of known providers. Unlicensed -- nep-data (🥉2 · ⭐ 12 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena +- nep-data (🥉2 · ⭐ 13 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena - tmQM_wB97MV Dataset (🥉2 · ⭐ 6) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV.. Unlicensed catalysis rep-learn

@@ -501,7 +487,7 @@ _Projects that focus on providing data structures used in atomistic machine lear ``` git clone https://github.com/deepmodeling/dpdata ``` -- [PyPi](https://pypi.org/project/dpdata) (📥 44K / month · 📦 40 · ⏱️ 20.09.2024): +- [PyPi](https://pypi.org/project/dpdata) (📥 43K / month · 📦 40 · ⏱️ 20.09.2024): ``` pip install dpdata ``` @@ -510,9 +496,9 @@ _Projects that focus on providing data structures used in atomistic machine lear conda install -c deepmodeling dpdata ```
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Metatensor (🥈21 · ⭐ 51) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3 Rust C-lang C++ Python +
Metatensor (🥈21 · ⭐ 52) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3 Rust C-lang C++ Python -- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 22 · 🔀 15 · 📥 27K · 📦 11 · 📋 210 - 34% open · ⏱️ 25.09.2024): +- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 22 · 🔀 15 · 📥 28K · 📦 11 · 📋 210 - 34% open · ⏱️ 02.10.2024): ``` git clone https://github.com/lab-cosmo/metatensor @@ -525,14 +511,14 @@ _Projects that focus on providing data structures used in atomistic machine lear ``` git clone https://github.com/materialsproject/pyrho ``` -- [PyPi](https://pypi.org/project/mp-pyrho) (📥 14K / month · 📦 3 · ⏱️ 23.02.2024): +- [PyPi](https://pypi.org/project/mp-pyrho) (📥 18K / month · 📦 3 · ⏱️ 23.02.2024): ``` pip install mp-pyrho ```
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dlpack (🥉16 · ⭐ 890) - common in-memory tensor structure. Apache-2 C++ +
dlpack (🥉15 · ⭐ 890) - common in-memory tensor structure. Apache-2 C++ -- [GitHub](https://github.com/dmlc/dlpack) (👨‍💻 23 · 🔀 130 · 📋 71 - 40% open · ⏱️ 26.03.2024): +- [GitHub](https://github.com/dmlc/dlpack) (👨‍💻 24 · 🔀 130 · 📋 71 - 40% open · ⏱️ 28.09.2024): ``` git clone https://github.com/dmlc/dlpack @@ -550,13 +536,13 @@ _Projects and models that focus on quantities of DFT, such as density functional
JAX-DFT (🥇25 · ⭐ 34K) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2 -- [GitHub](https://github.com/google-research/google-research) (👨‍💻 800 · 🔀 7.8K · 📋 1.8K - 81% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/google-research/google-research) (👨‍💻 800 · 🔀 7.8K · 📋 1.8K - 81% open · ⏱️ 03.10.2024): ``` git clone https://github.com/google-research/google-research ```
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MALA (🥇17 · ⭐ 81 · 📉) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3 +
MALA (🥇17 · ⭐ 81) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3 - [GitHub](https://github.com/mala-project/mala) (👨‍💻 44 · 🔀 23 · 📦 1 · 📋 270 - 15% open · ⏱️ 04.07.2024): @@ -564,7 +550,7 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/mala-project/mala ```
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QHNet (🥇13 · ⭐ 490) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn +
QHNet (🥇13 · ⭐ 500) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn - [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): @@ -574,7 +560,7 @@ _Projects and models that focus on quantities of DFT, such as density functional
SALTED (🥇13 · ⭐ 30) - Symmetry-Adapted Learning of Three-dimensional Electron Densities. GPL-3.0 -- [GitHub](https://github.com/andreagrisafi/SALTED) (👨‍💻 17 · 🔀 4 · 📋 6 - 16% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/andreagrisafi/SALTED) (👨‍💻 17 · 🔀 4 · 📋 6 - 16% open · ⏱️ 27.09.2024): ``` git clone https://github.com/andreagrisafi/SALTED @@ -588,7 +574,7 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/mzjb/DeepH-pack ```
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DeePKS-kit (🥈10 · ⭐ 99) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0 +
DeePKS-kit (🥈10 · ⭐ 100) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0 - [GitHub](https://github.com/deepmodeling/deepks-kit) (👨‍💻 7 · 🔀 35 · 📋 19 - 26% open · ⏱️ 13.04.2024): @@ -596,7 +582,7 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/deepmodeling/deepks-kit ```
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Grad DFT (🥈10 · ⭐ 73 · 💤) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2 +
Grad DFT (🥈10 · ⭐ 74 · 💤) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2 - [GitHub](https://github.com/XanaduAI/GradDFT) (👨‍💻 4 · 🔀 6 · 📋 54 - 20% open · ⏱️ 13.02.2024): @@ -612,20 +598,20 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/QuantumLab-ZY/HamGNN ```
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Q-stack (🥈8 · ⭐ 14) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT excited-states general-tool +
ChargE3Net (🥈7 · ⭐ 29) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn -- [GitHub](https://github.com/lcmd-epfl/Q-stack) (👨‍💻 7 · 🔀 5 · 📋 29 - 31% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 2 · 🔀 8 · 📋 5 - 40% open · ⏱️ 15.08.2024): ``` - git clone https://github.com/lcmd-epfl/Q-stack + git clone https://github.com/AIforGreatGood/charge3net ```
-
ChargE3Net (🥉7 · ⭐ 29) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn +
Q-stack (🥈7 · ⭐ 14) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT excited-states general-tool -- [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 2 · 🔀 8 · 📋 5 - 40% open · ⏱️ 15.08.2024): +- [GitHub](https://github.com/lcmd-epfl/Q-stack) (👨‍💻 7 · 🔀 5 · 📋 29 - 31% open · ⏱️ 26.09.2024): ``` - git clone https://github.com/AIforGreatGood/charge3net + git clone https://github.com/lcmd-epfl/Q-stack ```
InfGCN for Electron Density Estimation (🥉5 · ⭐ 11 · 💤) - Official implementation of the NeurIPS 23 spotlight paper of InfGCN. MIT rep-learn neural-operator @@ -636,27 +622,34 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/ccr-cheng/infgcn-pytorch ```
-
Show 21 hidden projects... +
charge-density-models (🥉5 · ⭐ 10 · 💤) - Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem). MIT rep-learn + +- [GitHub](https://github.com/ulissigroup/charge-density-models) (🔀 2 · 📋 4 - 25% open · ⏱️ 29.11.2023): + + ``` + git clone https://github.com/ulissigroup/charge-density-models + ``` +
+
Show 20 hidden projects... - DM21 (🥇20 · ⭐ 13K · 💀) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described.. Apache-2 - NeuralXC (🥈10 · ⭐ 33 · 💀) - Implementation of a machine learned density functional. BSD-3 -- PROPhet (🥈9 · ⭐ 62 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++ +- PROPhet (🥈9 · ⭐ 63 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++ - ACEhamiltonians (🥈9 · ⭐ 12 · 💀) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. MIT Julia -- Mat2Spec (🥉7 · ⭐ 27 · 💀) - Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings. MIT spectroscopy -- Libnxc (🥉7 · ⭐ 16 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran +- Mat2Spec (🥈7 · ⭐ 27 · 💀) - Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings. MIT spectroscopy +- Libnxc (🥈7 · ⭐ 16 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran - DeepH-E3 (🥉6 · ⭐ 70 · 💀) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. MIT magnetism - DeepDFT (🥉6 · ⭐ 57 · 💀) - Official implementation of DeepDFT model. MIT -- rho_learn (🥉6 · ⭐ 4) - A proof-of-concept framework for torch-based learning of the electron density and related scalar fields. MIT - KSR-DFT (🥉6 · ⭐ 4 · 💀) - Kohn-Sham regularizer for machine-learned DFT functionals. Apache-2 - xDeepH (🥉5 · ⭐ 32 · 💀) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia - ML-DFT (🥉5 · ⭐ 23 · 💀) - A package for density functional approximation using machine learning. MIT -- charge-density-models (🥉4 · ⭐ 10 · 💤) - Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem). MIT rep-learn +- rho_learn (🥉5 · ⭐ 4 · 💀) - A proof-of-concept workflow for torch-based electron density learning. MIT - gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper - DeepCDP (🥉3 · ⭐ 6 · 💀) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed -- APET (🥉3 · ⭐ 4 · 💤) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer +- APET (🥉3 · ⭐ 4 · 💀) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer - CSNN (🥉3 · ⭐ 2 · 💀) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3 -- MALADA (🥉3 · ⭐ 1 · 💀) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3 - A3MD (🥉2 · ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed rep-learn single-paper +- MALADA (🥉2 · ⭐ 1 · 💀) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3 - kdft (🥉1 · ⭐ 2 · 💀) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. Unlicensed - MLDensity ( ⭐ 2 · 💀) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure.. Unlicensed
@@ -706,14 +699,6 @@ _Tutorials, guides, cookbooks, recipes, etc._ git clone https://github.com/ceriottm/iam-notebooks ```
-
OPTIMADE Tutorial Exercises (🥈9 · ⭐ 14 · 💤) - Tutorial exercises for the OPTIMADE API. MIT datasets - -- [GitHub](https://github.com/Materials-Consortia/optimade-tutorial-exercises) (👨‍💻 6 · 🔀 7 · ⏱️ 27.09.2023): - - ``` - git clone https://github.com/Materials-Consortia/optimade-tutorial-exercises - ``` -
BestPractices (🥈8 · ⭐ 170 · 💤) - Things that you should (and should not) do in your Materials Informatics research. MIT - [GitHub](https://github.com/anthony-wang/BestPractices) (👨‍💻 3 · 🔀 70 · 📋 7 - 71% open · ⏱️ 17.11.2023): @@ -724,7 +709,7 @@ _Tutorials, guides, cookbooks, recipes, etc._
COSMO Software Cookbook (🥈8 · ⭐ 16) - A cookbook wtih recipes for atomic-scale modeling of materials and molecules. BSD-3 -- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (👨‍💻 10 · 🔀 1 · 📋 12 - 16% open · ⏱️ 23.09.2024): +- [GitHub](https://github.com/lab-cosmo/atomistic-cookbook) (👨‍💻 10 · 🔀 1 · 📋 12 - 16% open · ⏱️ 03.10.2024): ``` git clone https://github.com/lab-cosmo/software-cookbook @@ -738,13 +723,14 @@ _Tutorials, guides, cookbooks, recipes, etc._ git clone https://github.com/ilyes319/mace-tutorials ```
-
Show 17 hidden projects... +
Show 18 hidden projects... - Geometric GNN Dojo (🥇12 · ⭐ 460 · 💀) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn - DeepLearningLifeSciences (🥇12 · ⭐ 350 · 💀) - Example code from the book Deep Learning for the Life Sciences. MIT - Deep Learning for Molecules and Materials Book (🥈11 · ⭐ 610 · 💀) - Deep learning for molecules and materials book. Custom +- OPTIMADE Tutorial Exercises (🥈9 · ⭐ 15 · 💀) - Tutorial exercises for the OPTIMADE API. MIT datasets - RDKit Tutorials (🥈8 · ⭐ 260 · 💀) - Tutorials to learn how to work with the RDKit. Custom -- MAChINE (🥉7 · ⭐ 1 · 💤) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT +- MAChINE (🥉7 · ⭐ 1 · 💀) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT - Applied AI for Materials (🥉6 · ⭐ 58 · 💀) - Course materials for Applied AI for Materials Science and Engineering. Unlicensed - ML for catalysis tutorials (🥉6 · ⭐ 8 · 💀) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis. MIT - AI4Science101 (🥉5 · ⭐ 83 · 💀) - AI for Science. Unlicensed @@ -773,14 +759,14 @@ _Projects that focus on explainability and model interpretability in atomistic M ``` git clone https://github.com/ur-whitelab/exmol ``` -- [PyPi](https://pypi.org/project/exmol) (📥 890 / month · 📦 1 · ⏱️ 03.06.2022): +- [PyPi](https://pypi.org/project/exmol) (📥 1K / month · 📦 1 · ⏱️ 03.06.2022): ``` pip install exmol ```
-
MEGAN: Multi Explanation Graph Attention Student (🥉6 · ⭐ 5) - Minimal implementation of graph attention student model architecture. MIT rep-learn +
MEGAN: Multi Explanation Graph Attention Student (🥉5 · ⭐ 5) - Minimal implementation of graph attention student model architecture. MIT rep-learn -- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨‍💻 2 · 🔀 1 · ⏱️ 19.08.2024): +- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨‍💻 2 · 🔀 1 · 📋 3 - 33% open · ⏱️ 19.08.2024): ``` git clone https://github.com/aimat-lab/graph_attention_student @@ -814,89 +800,89 @@ _Projects and models that focus on quantities of electronic structure methods, w _General tools for atomistic machine learning._ -
DeepChem (🥇36 · ⭐ 5.4K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT +
RDKit (🥇35 · ⭐ 2.6K) - BSD-3 C++ -- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 250 · 🔀 1.7K · 📦 430 · 📋 1.9K - 33% open · ⏱️ 20.09.2024): +- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 230 · 🔀 860 · 📥 1.1K · 📦 3 · 📋 3.4K - 29% open · ⏱️ 03.10.2024): ``` - git clone https://github.com/deepchem/deepchem - ``` -- [PyPi](https://pypi.org/project/deepchem) (📥 40K / month · 📦 13 · ⏱️ 20.09.2024): + git clone https://github.com/rdkit/rdkit ``` - pip install deepchem +- [PyPi](https://pypi.org/project/rdkit) (📥 2.4M / month · 📦 730 · ⏱️ 07.08.2024): ``` -- [Conda](https://anaconda.org/conda-forge/deepchem) (📥 110K · ⏱️ 05.04.2024): - ``` - conda install -c conda-forge deepchem + pip install rdkit ``` -- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 7.6K · ⭐ 5 · ⏱️ 20.09.2024): +- [Conda](https://anaconda.org/rdkit/rdkit) (📥 2.6M · ⏱️ 16.06.2023): ``` - docker pull deepchemio/deepchem + conda install -c rdkit rdkit ```
-
RDKit (🥇35 · ⭐ 2.6K) - BSD-3 C++ +
DeepChem (🥇34 · ⭐ 5.4K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT -- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 230 · 🔀 860 · 📥 1.1K · 📦 3 · 📋 3.4K - 29% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 250 · 🔀 1.7K · 📦 430 · 📋 1.9K - 33% open · ⏱️ 20.09.2024): ``` - git clone https://github.com/rdkit/rdkit + git clone https://github.com/deepchem/deepchem ``` -- [PyPi](https://pypi.org/project/rdkit) (📥 1.8M / month · 📦 710 · ⏱️ 07.08.2024): +- [PyPi](https://pypi.org/project/deepchem) (📥 44K / month · 📦 13 · ⏱️ 20.09.2024): ``` - 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) (📥 7.7K · ⭐ 5 · ⏱️ 20.09.2024): + ``` + docker pull deepchemio/deepchem ```
Matminer (🥇28 · ⭐ 470) - Data mining for materials science. Custom -- [GitHub](https://github.com/hackingmaterials/matminer) (👨‍💻 54 · 🔀 190 · 📦 320 · 📋 230 - 12% open · ⏱️ 23.09.2024): +- [GitHub](https://github.com/hackingmaterials/matminer) (👨‍💻 55 · 🔀 190 · 📦 320 · 📋 230 - 12% open · ⏱️ 02.10.2024): ``` git clone https://github.com/hackingmaterials/matminer ``` -- [PyPi](https://pypi.org/project/matminer) (📥 13K / month · 📦 58 · ⏱️ 27.03.2024): +- [PyPi](https://pypi.org/project/matminer) (📥 14K / month · 📦 58 · ⏱️ 27.03.2024): ``` pip install matminer ``` -- [Conda](https://anaconda.org/conda-forge/matminer) (📥 70K · ⏱️ 28.03.2024): +- [Conda](https://anaconda.org/conda-forge/matminer) (📥 71K · ⏱️ 28.03.2024): ``` conda install -c conda-forge matminer ```
-
QUIP (🥈26 · ⭐ 350) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran +
QUIP (🥈27 · ⭐ 350 · 📈) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran -- [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 85 · 🔀 120 · 📥 670 · 📦 42 · 📋 470 - 22% open · ⏱️ 15.08.2024): +- [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 85 · 🔀 120 · 📥 670 · 📦 42 · 📋 470 - 22% open · ⏱️ 27.09.2024): ``` git clone https://github.com/libAtoms/QUIP ``` -- [PyPi](https://pypi.org/project/quippy-ase) (📥 7.3K / month · 📦 4 · ⏱️ 15.01.2023): +- [PyPi](https://pypi.org/project/quippy-ase) (📥 9.5K / month · 📦 4 · ⏱️ 15.01.2023): ``` pip install quippy-ase ``` -- [Docker Hub](https://hub.docker.com/r/libatomsquip/quip) (📥 9.9K · ⭐ 4 · ⏱️ 24.04.2023): +- [Docker Hub](https://hub.docker.com/r/libatomsquip/quip) (📥 10K · ⭐ 4 · ⏱️ 24.04.2023): ``` docker pull libatomsquip/quip ```
MAML (🥈24 · ⭐ 360) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3 -- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 33 · 🔀 77 · 📦 10 · 📋 71 - 12% open · ⏱️ 18.09.2024): +- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 33 · 🔀 78 · 📦 10 · 📋 71 - 12% open · ⏱️ 18.09.2024): ``` git clone https://github.com/materialsvirtuallab/maml ``` -- [PyPi](https://pypi.org/project/maml) (📥 420 / month · 📦 2 · ⏱️ 13.06.2024): +- [PyPi](https://pypi.org/project/maml) (📥 520 / month · 📦 2 · ⏱️ 13.06.2024): ``` pip install maml ```
-
JARVIS-Tools (🥈23 · ⭐ 300 · 📉) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom +
JARVIS-Tools (🥈23 · ⭐ 300) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom -- [GitHub](https://github.com/usnistgov/jarvis) (👨‍💻 15 · 🔀 120 · 📦 97 · 📋 90 - 50% open · ⏱️ 07.09.2024): +- [GitHub](https://github.com/usnistgov/jarvis) (👨‍💻 15 · 🔀 120 · 📦 99 · 📋 90 - 50% open · ⏱️ 07.09.2024): ``` git clone https://github.com/usnistgov/jarvis @@ -905,14 +891,14 @@ _General tools for atomistic machine learning._ ``` pip install jarvis-tools ``` -- [Conda](https://anaconda.org/conda-forge/jarvis-tools) (📥 77K · ⏱️ 07.09.2024): +- [Conda](https://anaconda.org/conda-forge/jarvis-tools) (📥 78K · ⏱️ 07.09.2024): ``` conda install -c conda-forge jarvis-tools ```
-
MAST-ML (🥈19 · ⭐ 100) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT +
MAST-ML (🥈20 · ⭐ 100 · 📈) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT -- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨‍💻 18 · 🔀 58 · 📥 120 · 📦 43 · 📋 220 - 14% open · ⏱️ 17.04.2024): +- [GitHub](https://github.com/uw-cmg/MAST-ML) (👨‍💻 19 · 🔀 58 · 📥 120 · 📦 43 · 📋 220 - 14% open · ⏱️ 27.09.2024): ``` git clone https://github.com/uw-cmg/MAST-ML @@ -925,7 +911,7 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/scikit-learn-contrib/scikit-matter ``` -- [PyPi](https://pypi.org/project/skmatter) (📥 1.8K / month · ⏱️ 24.08.2023): +- [PyPi](https://pypi.org/project/skmatter) (📥 2.2K / month · ⏱️ 24.08.2023): ``` pip install skmatter ``` @@ -934,31 +920,31 @@ _General tools for atomistic machine learning._ conda install -c conda-forge skmatter ```
-
XenonPy (🥈16 · ⭐ 130) - XenonPy is a Python Software for Materials Informatics. BSD-3 +
MLatom (🥈16 · ⭐ 48) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization -- [GitHub](https://github.com/yoshida-lab/XenonPy) (👨‍💻 10 · 🔀 57 · 📥 1.4K · 📋 87 - 24% open · ⏱️ 21.04.2024): +- [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 3 · 🔀 9 · 📋 4 - 25% open · ⏱️ 23.09.2024): ``` - git clone https://github.com/yoshida-lab/XenonPy + git clone https://github.com/dralgroup/mlatom ``` -- [PyPi](https://pypi.org/project/xenonpy) (📥 590 / month · 📦 1 · ⏱️ 31.10.2022): +- [PyPi](https://pypi.org/project/mlatom) (📥 1.6K / month · ⏱️ 23.09.2024): ``` - pip install xenonpy + pip install mlatom ```
-
MLatom (🥈16 · ⭐ 46) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization +
XenonPy (🥉15 · ⭐ 140) - XenonPy is a Python Software for Materials Informatics. BSD-3 -- [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) (📥 1.4K / month · ⏱️ 23.09.2024): +- [PyPi](https://pypi.org/project/xenonpy) (📥 670 / month · 📦 1 · ⏱️ 31.10.2022): ``` - pip install mlatom + pip install xenonpy ```
-
Artificial Intelligence for Science (AIRS) (🥉13 · ⭐ 490) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules +
Artificial Intelligence for Science (AIRS) (🥉13 · ⭐ 500) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules - [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): @@ -980,9 +966,9 @@ _General tools for atomistic machine learning._ - Automatminer (🥉15 · ⭐ 140 · 💀) - An automatic engine for predicting materials properties. Custom autoML - AMPtorch (🥉11 · ⭐ 59 · 💀) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. GPL-3.0 - OpenChem (🥉10 · ⭐ 670 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT -- JAXChem (🥉7 · ⭐ 77 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT +- JAXChem (🥉7 · ⭐ 79 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT - uncertainty_benchmarking (🥉7 · ⭐ 39 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. Unlicensed benchmarking probabilistic -- torchchem (🥉7 · ⭐ 34 · 💀) - An experimental repo for experimenting with PyTorch models. MIT +- torchchem (🥉7 · ⭐ 35 · 💀) - An experimental repo for experimenting with PyTorch models. MIT - ACEatoms (🥉4 · ⭐ 2 · 💀) - Generic code for modelling atomic properties using ACE. Custom Julia - Magpie (🥉3) - Materials Agnostic Platform for Informatics and Exploration (Magpie). MIT Java - quantum-structure-ml (🥉2 · ⭐ 2 · 💀) - Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification.. Unlicensed magnetism benchmarking @@ -995,14 +981,14 @@ _General tools for atomistic machine learning._ _Projects that implement generative models for atomistic ML._ -
GT4SD (🥇19 · ⭐ 330) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pretrained drug-discovery rep-learn +
GT4SD (🥇18 · ⭐ 340) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pretrained drug-discovery rep-learn - [GitHub](https://github.com/GT4SD/gt4sd-core) (👨‍💻 20 · 🔀 68 · 📋 110 - 12% open · ⏱️ 12.09.2024): ``` git clone https://github.com/GT4SD/gt4sd-core ``` -- [PyPi](https://pypi.org/project/gt4sd) (📥 1.6K / month · ⏱️ 12.09.2024): +- [PyPi](https://pypi.org/project/gt4sd) (📥 2.1K / month · ⏱️ 12.09.2024): ``` pip install gt4sd ``` @@ -1014,7 +1000,7 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/microsoft/molecule-generation ``` -- [PyPi](https://pypi.org/project/molecule-generation) (📥 310 / month · 📦 1 · ⏱️ 05.01.2024): +- [PyPi](https://pypi.org/project/molecule-generation) (📥 320 / month · 📦 1 · ⏱️ 05.01.2024): ``` pip install molecule-generation ``` @@ -1026,12 +1012,12 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/hspark1212/MOFTransformer ``` -- [PyPi](https://pypi.org/project/moftransformer) (📥 460 / month · 📦 1 · ⏱️ 20.06.2024): +- [PyPi](https://pypi.org/project/moftransformer) (📥 440 / month · 📦 1 · ⏱️ 20.06.2024): ``` pip install moftransformer ```
-
SchNetPack G-SchNet (🥈14 · ⭐ 46) - G-SchNet extension for SchNetPack. MIT +
SchNetPack G-SchNet (🥈12 · ⭐ 46 · 📉) - G-SchNet extension for SchNetPack. MIT - [GitHub](https://github.com/atomistic-machine-learning/schnetpack-gschnet) (👨‍💻 3 · 🔀 8 · ⏱️ 05.09.2024): @@ -1046,12 +1032,12 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/RokasEl/simgen ``` -- [PyPi](https://pypi.org/project/simgen) (📥 38 / month · ⏱️ 14.02.2024): +- [PyPi](https://pypi.org/project/simgen) (📥 47 / month · ⏱️ 14.02.2024): ``` pip install simgen ```
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COATI (🥉5 · ⭐ 98) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery multimodal pretrained rep-learn +
COATI (🥉5 · ⭐ 98 · 💤) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery multimodal pretrained rep-learn - [GitHub](https://github.com/terraytherapeutics/COATI) (👨‍💻 5 · 🔀 5 · 📋 3 - 33% open · ⏱️ 23.03.2024): @@ -1066,7 +1052,7 @@ _Projects that implement generative models for atomistic ML._ - G-SchNet (🥉8 · ⭐ 130 · 💀) - G-SchNet - a generative model for 3d molecular structures. MIT - bVAE-IM (🥉8 · ⭐ 11 · 💀) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper - cG-SchNet (🥉7 · ⭐ 52 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT -- rxngenerator (🥉7 · ⭐ 11 · 💀) - A generative model for molecular generation via multi-step chemical reactions. MIT +- rxngenerator (🥉6 · ⭐ 11 · 💀) - A generative model for molecular generation via multi-step chemical reactions. MIT - MolSLEPA (🥉5 · ⭐ 5 · 💀) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing. MIT XAI - Mapping out phase diagrams with generative classifiers (🥉4 · ⭐ 7 · 💀) - Repository for our ``Mapping out phase diagrams with generative models paper. MIT phase-transition
@@ -1078,14 +1064,14 @@ _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._ -
DeePMD-kit (🥇27 · ⭐ 1.5K) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++ +
DeePMD-kit (🥇25 · ⭐ 1.5K · 📉) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++ -- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 500 · 📥 40K · 📦 16 · 📋 780 - 12% open · ⏱️ 17.09.2024): +- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 500 · 📥 40K · 📦 16 · 📋 790 - 12% open · ⏱️ 17.09.2024): ``` git clone https://github.com/deepmodeling/deepmd-kit ``` -- [PyPi](https://pypi.org/project/deepmd-kit) (📥 2.9K / month · 📦 4 · ⏱️ 25.09.2024): +- [PyPi](https://pypi.org/project/deepmd-kit) (📥 3.2K / month · 📦 4 · ⏱️ 25.09.2024): ``` pip install deepmd-kit ``` @@ -1098,66 +1084,74 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc docker pull deepmodeling/deepmd-kit ```
-
NequIP (🥇24 · ⭐ 610) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +
TorchANI (🥇24 · ⭐ 460 · 💤) - Accurate Neural Network Potential on PyTorch. MIT -- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 11 · 🔀 130 · 📦 25 · 📋 92 - 28% open · ⏱️ 09.07.2024): +- [GitHub](https://github.com/aiqm/torchani) (👨‍💻 19 · 🔀 130 · 📦 42 · 📋 170 - 13% open · ⏱️ 14.11.2023): ``` - git clone https://github.com/mir-group/nequip + git clone https://github.com/aiqm/torchani ``` -- [PyPi](https://pypi.org/project/nequip) (📥 2.3K / month · 📦 1 · ⏱️ 09.07.2024): +- [PyPi](https://pypi.org/project/torchani) (📥 3K / month · 📦 4 · ⏱️ 14.11.2023): ``` - pip install nequip + pip install torchani ``` -- [Conda](https://anaconda.org/conda-forge/nequip) (📥 5.9K · ⏱️ 10.07.2024): +- [Conda](https://anaconda.org/conda-forge/torchani) (📥 490K · ⏱️ 11.09.2024): ``` - conda install -c conda-forge nequip + conda install -c conda-forge torchani ```
-
TorchANI (🥇24 · ⭐ 460 · 💤) - Accurate Neural Network Potential on PyTorch. MIT +
NequIP (🥇23 · ⭐ 610 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT -- [GitHub](https://github.com/aiqm/torchani) (👨‍💻 19 · 🔀 130 · 📦 42 · 📋 170 - 13% open · ⏱️ 14.11.2023): +- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 11 · 🔀 130 · 📦 25 · 📋 92 - 28% open · ⏱️ 09.07.2024): ``` - git clone https://github.com/aiqm/torchani + git clone https://github.com/mir-group/nequip ``` -- [PyPi](https://pypi.org/project/torchani) (📥 2.9K / month · 📦 4 · ⏱️ 14.11.2023): +- [PyPi](https://pypi.org/project/nequip) (📥 3K / month · 📦 1 · ⏱️ 09.07.2024): ``` - pip install torchani + pip install nequip ``` -- [Conda](https://anaconda.org/conda-forge/torchani) (📥 480K · ⏱️ 11.09.2024): +- [Conda](https://anaconda.org/conda-forge/nequip) (📥 6K · ⏱️ 10.07.2024): ``` - conda install -c conda-forge torchani + conda install -c conda-forge nequip ```
MACE (🥇22 · ⭐ 490) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT -- [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 39 · 🔀 180 · 📋 270 - 26% open · ⏱️ 18.09.2024): +- [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 41 · 🔀 180 · 📋 280 - 25% open · ⏱️ 02.10.2024): ``` git clone https://github.com/ACEsuit/mace ```
-
TorchMD-NET (🥇22 · ⭐ 320 · 📉) - Training neural network potentials. MIT MD rep-learn transformer pretrained +
TorchMD-NET (🥇22 · ⭐ 320) - Training neural network potentials. MIT MD rep-learn transformer pretrained - [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) (📥 170K · ⏱️ 12.09.2024): +- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 180K · ⏱️ 12.09.2024): ``` conda install -c conda-forge torchmd-net ```
-
DP-GEN (🥇22 · ⭐ 300) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0 workflows +
GPUMD (🥇21 · ⭐ 450) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics + +- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 34 · 🔀 120 · 📋 180 - 9% open · ⏱️ 01.10.2024): + + ``` + git clone https://github.com/brucefan1983/GPUMD + ``` +
+
DP-GEN (🥇21 · ⭐ 300) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0 workflows - [GitHub](https://github.com/deepmodeling/dpgen) (👨‍💻 64 · 🔀 170 · 📥 1.8K · 📦 6 · 📋 300 - 11% open · ⏱️ 10.04.2024): ``` git clone https://github.com/deepmodeling/dpgen ``` -- [PyPi](https://pypi.org/project/dpgen) (📥 650 / month · 📦 1 · ⏱️ 10.04.2024): +- [PyPi](https://pypi.org/project/dpgen) (📥 760 / month · 📦 1 · ⏱️ 10.04.2024): ``` pip install dpgen ``` @@ -1166,17 +1160,9 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc conda install -c deepmodeling dpgen ```
-
GPUMD (🥈21 · ⭐ 450 · 📉) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics - -- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 34 · 🔀 110 · 📋 190 - 12% open · ⏱️ 21.09.2024): - - ``` - git clone https://github.com/brucefan1983/GPUMD - ``` -
fairchem (🥈19 · ⭐ 770) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp). Unlicensed pretrained rep-learn catalysis -- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 230 · 📋 200 - 6% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 230 · 📋 210 - 6% open · ⏱️ 01.10.2024): ``` git clone https://github.com/FAIR-Chem/fairchem @@ -1184,57 +1170,57 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
apax (🥈18 · ⭐ 15) - A flexible and performant framework for training machine learning potentials. MIT -- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 7 · 🔀 2 · 📦 2 · 📋 120 - 12% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 7 · 🔀 2 · 📦 2 · 📋 120 - 10% open · ⏱️ 01.10.2024): ``` git clone https://github.com/apax-hub/apax ``` -- [PyPi](https://pypi.org/project/apax) (📥 240 / month · ⏱️ 17.09.2024): +- [PyPi](https://pypi.org/project/apax) (📥 280 / month · ⏱️ 17.09.2024): ``` pip install apax ```
Neural Force Field (🥈17 · ⭐ 230) - Neural Network Force Field based on PyTorch. MIT pretrained -- [GitHub](https://github.com/learningmatter-mit/NeuralForceField) (👨‍💻 41 · 🔀 47 · 📋 20 - 10% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/learningmatter-mit/NeuralForceField) (👨‍💻 41 · 🔀 48 · 📋 20 - 10% open · ⏱️ 24.09.2024): ``` git clone https://github.com/learningmatter-mit/NeuralForceField ```
-
wfl (🥈16 · ⭐ 31) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0 workflows HTC +
PyXtalFF (🥈15 · ⭐ 85 · 💤) - Machine Learning Interatomic Potential Predictions. MIT -- [GitHub](https://github.com/libAtoms/workflow) (👨‍💻 19 · 🔀 18 · 📦 1 · 📋 160 - 42% open · ⏱️ 03.09.2024): +- [GitHub](https://github.com/MaterSim/PyXtal_FF) (👨‍💻 9 · 🔀 23 · 📋 63 - 19% open · ⏱️ 07.01.2024): ``` - git clone https://github.com/libAtoms/workflow + git clone https://github.com/MaterSim/PyXtal_FF + ``` +- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 230 / month · ⏱️ 21.12.2022): + ``` + pip install pyxtal_ff ```
-
Ultra-Fast Force Fields (UF3) (🥈15 · ⭐ 59) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2 +
Ultra-Fast Force Fields (UF3) (🥈15 · ⭐ 60) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2 -- [GitHub](https://github.com/uf3/uf3) (👨‍💻 10 · 🔀 20 · 📦 1 · 📋 50 - 38% open · ⏱️ 06.09.2024): +- [GitHub](https://github.com/uf3/uf3) (👨‍💻 10 · 🔀 20 · 📦 1 · 📋 50 - 38% open · ⏱️ 02.10.2024): ``` git clone https://github.com/uf3/uf3 ``` -- [PyPi](https://pypi.org/project/uf3) (📥 45 / month · ⏱️ 27.10.2023): +- [PyPi](https://pypi.org/project/uf3) (📥 51 / month · ⏱️ 27.10.2023): ``` pip install uf3 ```
-
PyXtalFF (🥈14 · ⭐ 85 · 💤) - Machine Learning Interatomic Potential Predictions. MIT +
wfl (🥈15 · ⭐ 31) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0 workflows HTC -- [GitHub](https://github.com/MaterSim/PyXtal_FF) (👨‍💻 9 · 🔀 23 · 📋 63 - 19% open · ⏱️ 07.01.2024): +- [GitHub](https://github.com/libAtoms/workflow) (👨‍💻 19 · 🔀 18 · 📦 1 · 📋 160 - 42% open · ⏱️ 03.09.2024): ``` - git clone https://github.com/MaterSim/PyXtal_FF - ``` -- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 130 / month · ⏱️ 21.12.2022): - ``` - pip install pyxtal_ff + git clone https://github.com/libAtoms/workflow ```
-
So3krates (MLFF) (🥈14 · ⭐ 76) - Build neural networks for machine learning force fields with JAX. MIT +
So3krates (MLFF) (🥈14 · ⭐ 79) - Build neural networks for machine learning force fields with JAX. MIT - [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 15 · 📋 9 - 33% open · ⏱️ 23.08.2024): @@ -1249,18 +1235,18 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/openkim/kliff ``` -- [PyPi](https://pypi.org/project/kliff) (📥 280 / month · ⏱️ 17.12.2023): +- [PyPi](https://pypi.org/project/kliff) (📥 450 / month · ⏱️ 17.12.2023): ``` pip install kliff ``` -- [Conda](https://anaconda.org/conda-forge/kliff) (📥 100K · ⏱️ 10.09.2024): +- [Conda](https://anaconda.org/conda-forge/kliff) (📥 110K · ⏱️ 10.09.2024): ``` conda install -c conda-forge kliff ```
DMFF (🥈13 · ⭐ 150 · 💤) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0 -- [GitHub](https://github.com/deepmodeling/DMFF) (👨‍💻 14 · 🔀 41 · 📋 26 - 38% open · ⏱️ 12.01.2024): +- [GitHub](https://github.com/deepmodeling/DMFF) (👨‍💻 14 · 🔀 42 · 📋 26 - 38% open · ⏱️ 12.01.2024): ``` git clone https://github.com/deepmodeling/DMFF @@ -1273,12 +1259,12 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/openmm/NNPOps ``` -- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 230K · ⏱️ 11.09.2024): +- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 240K · ⏱️ 11.09.2024): ``` conda install -c conda-forge nnpops ```
-
ANI-1 (🥈12 · ⭐ 220) - ANI-1 neural net potential with python interface (ASE). MIT +
ANI-1 (🥈12 · ⭐ 220 · 💤) - ANI-1 neural net potential with python interface (ASE). MIT - [GitHub](https://github.com/isayev/ASE_ANI) (👨‍💻 6 · 🔀 55 · 📋 37 - 43% open · ⏱️ 11.03.2024): @@ -1293,7 +1279,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/Teoroo-CMC/PiNN ``` -- [Docker Hub](https://hub.docker.com/r/teoroo/pinn) (📥 240 · ⏱️ 27.06.2024): +- [Docker Hub](https://hub.docker.com/r/teoroo/pinn) (📥 250 · ⏱️ 27.06.2024): ``` docker pull teoroo/pinn ``` @@ -1305,11 +1291,23 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/ICAMS/python-ace ``` -- [PyPi](https://pypi.org/project/python-ace) (📥 14 / month · ⏱️ 24.10.2022): +- [PyPi](https://pypi.org/project/python-ace) (📥 17 / month · ⏱️ 24.10.2022): ``` pip install python-ace ```
+
CCS_fit (🥈12 · ⭐ 8 · 💤) - Curvature Constrained Splines. GPL-3.0 + +- [GitHub](https://github.com/Teoroo-CMC/CCS) (👨‍💻 8 · 🔀 11 · 📥 650 · 📋 14 - 57% open · ⏱️ 16.02.2024): + + ``` + git clone https://github.com/Teoroo-CMC/CCS + ``` +- [PyPi](https://pypi.org/project/ccs_fit) (📥 1.4K / month · ⏱️ 16.02.2024): + ``` + pip install ccs_fit + ``` +
ACEfit (🥈12 · ⭐ 7) - MIT Julia - [GitHub](https://github.com/ACEsuit/ACEfit.jl) (👨‍💻 8 · 🔀 6 · 📋 57 - 38% open · ⏱️ 14.09.2024): @@ -1326,9 +1324,9 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/TinkerTools/tinker-hp ```
-
calorine (🥈11 · ⭐ 12) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom +
calorine (🥉10 · ⭐ 12) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom -- [PyPi](https://pypi.org/project/calorine) (📥 1.6K / month · 📦 2 · ⏱️ 26.07.2024): +- [PyPi](https://pypi.org/project/calorine) (📥 2.2K / month · 📦 2 · ⏱️ 26.07.2024): ``` pip install calorine ``` @@ -1338,18 +1336,6 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://gitlab.com/materials-modeling/calorine ```
-
CCS_fit (🥈11 · ⭐ 8 · 💤) - Curvature Constrained Splines. GPL-3.0 - -- [GitHub](https://github.com/Teoroo-CMC/CCS) (👨‍💻 8 · 🔀 11 · 📥 640 · 📋 14 - 57% open · ⏱️ 16.02.2024): - - ``` - git clone https://github.com/Teoroo-CMC/CCS - ``` -- [PyPi](https://pypi.org/project/ccs_fit) (📥 1K / month · ⏱️ 16.02.2024): - ``` - pip install ccs_fit - ``` -
DimeNet (🥉9 · ⭐ 290 · 💤) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom - [GitHub](https://github.com/gasteigerjo/dimenet) (👨‍💻 2 · 🔀 60 · 📦 1 · 📋 31 - 3% open · ⏱️ 03.10.2023): @@ -1366,14 +1352,6 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/ACEsuit/ACE.jl ```
-
GAP (🥉9 · ⭐ 40) - Gaussian Approximation Potential (GAP). Custom - -- [GitHub](https://github.com/libAtoms/GAP) (👨‍💻 13 · 🔀 20 · ⏱️ 17.08.2024): - - ``` - git clone https://github.com/libAtoms/GAP - ``` -
ACE1.jl (🥉9 · ⭐ 20) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia - [GitHub](https://github.com/ACEsuit/ACE1.jl) (👨‍💻 9 · 🔀 7 · 📋 46 - 47% open · ⏱️ 11.09.2024): @@ -1390,7 +1368,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/spozdn/pet ```
-
MACE-Jax (🥉8 · ⭐ 59 · 💤) - Equivariant machine learning interatomic potentials in JAX. MIT +
MACE-Jax (🥉8 · ⭐ 60 · 💤) - Equivariant machine learning interatomic potentials in JAX. MIT - [GitHub](https://github.com/ACEsuit/mace-jax) (👨‍💻 2 · 🔀 5 · 📋 7 - 42% open · ⏱️ 04.10.2023): @@ -1398,6 +1376,14 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/ACEsuit/mace-jax ```
+
GAP (🥉8 · ⭐ 40) - Gaussian Approximation Potential (GAP). Custom + +- [GitHub](https://github.com/libAtoms/GAP) (👨‍💻 13 · 🔀 20 · ⏱️ 17.08.2024): + + ``` + git clone https://github.com/libAtoms/GAP + ``` +
SIMPLE-NN v2 (🥉8 · ⭐ 40 · 💤) - SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab.. GPL-3.0 - [GitHub](https://github.com/MDIL-SNU/SIMPLE-NN_v2) (👨‍💻 13 · 🔀 17 · 📋 13 - 30% open · ⏱️ 29.12.2023): @@ -1416,7 +1402,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
TurboGAP (🥉8 · ⭐ 16) - The TurboGAP code. Custom Fortran -- [GitHub](https://github.com/mcaroba/turbogap) (👨‍💻 8 · 🔀 9 · 📋 10 - 70% open · ⏱️ 11.09.2024): +- [GitHub](https://github.com/mcaroba/turbogap) (👨‍💻 8 · 🔀 9 · 📋 11 - 72% open · ⏱️ 30.09.2024): ``` git clone https://github.com/mcaroba/turbogap @@ -1446,7 +1432,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/ICAMS/TensorPotential ```
-
NequIP-JAX (🥉5 · ⭐ 17 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed +
NequIP-JAX (🥉5 · ⭐ 18 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed - [GitHub](https://github.com/mariogeiger/nequip-jax) (👨‍💻 2 · 🔀 3 · ⏱️ 01.11.2023): @@ -1469,24 +1455,24 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc - n2p2 (🥈14 · ⭐ 220 · 💀) - n2p2 - A Neural Network Potential Package. GPL-3.0 C++ - TensorMol (🥈12 · ⭐ 270 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper - SIMPLE-NN (🥈11 · ⭐ 47 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0 -- Allegro (🥉10 · ⭐ 330 · 💀) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT - NNsforMD (🥉10 · ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT +- Allegro (🥉9 · ⭐ 330 · 💀) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT - SchNet (🥉9 · ⭐ 220 · 💀) - SchNet - a deep learning architecture for quantum chemistry. MIT - GemNet (🥉9 · ⭐ 180 · 💀) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. Custom - AIMNet (🥉8 · ⭐ 95 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper - SNAP (🥉8 · ⭐ 36 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3 - Atomistic Adversarial Attacks (🥉8 · ⭐ 31 · 💀) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic - MEGNetSparse (🥉8 · ⭐ 1 · 💀) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect -- PhysNet (🥉7 · ⭐ 88 · 💀) - Code for training PhysNet models. MIT electrostatics +- PhysNet (🥉7 · ⭐ 89 · 💀) - Code for training PhysNet models. MIT electrostatics +- Asparagus (🥉7 · ⭐ 4 · 🐣) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. MIT workflows sampling MD - MLIP-3 (🥉6 · ⭐ 26 · 💀) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++ - testing-framework (🥉6 · ⭐ 11 · 💀) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of.. Unlicensed benchmarking - PANNA (🥉6 · ⭐ 9 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking -- Asparagus (🥉6 · ⭐ 4 · 🐣) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. MIT workflows sampling MD - GN-MM (🥉5 · ⭐ 10 · 💀) - The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic.. MIT active-learning MD rep-eng magnetism - Alchemical learning (🥉5 · ⭐ 2 · 💀) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3 - ACE1Pack.jl (🥉5 · ⭐ 1 · 💀) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. MIT Julia - Allegro-Legato (🥉4 · ⭐ 19 · 💀) - An extension of Allegro with enhanced robustness and time-to-failure. MIT MD -- glp (🥉4 · ⭐ 17) - tools for graph-based machine-learning potentials in jax. MIT +- glp (🥉4 · ⭐ 17 · 💤) - tools for graph-based machine-learning potentials in jax. MIT - ACE Workflows (🥉4 · 💤) - Workflow Examples for ACE Models. Unlicensed Julia workflows - PeriodicPotentials (🥉4 · 💀) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript - PyFLAME (🥉3 · 💀) - An automated approach for developing neural network interatomic potentials with FLAME.. Unlicensed active-learning structure-prediction structure-optimization rep-eng Fortran @@ -1505,14 +1491,14 @@ _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._ -
paper-qa (🥇29 · ⭐ 5.9K) - High accuracy RAG for answering questions from scientific documents with citations. Apache-2 ai-agent +
paper-qa (🥇29 · ⭐ 6K) - High accuracy RAG for answering questions from scientific documents with citations. Apache-2 ai-agent -- [GitHub](https://github.com/Future-House/paper-qa) (👨‍💻 25 · 🔀 550 · 📦 71 · 📋 200 - 30% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/Future-House/paper-qa) (👨‍💻 25 · 🔀 550 · 📦 71 · 📋 210 - 31% open · ⏱️ 02.10.2024): ``` git clone https://github.com/whitead/paper-qa ``` -- [PyPi](https://pypi.org/project/paper-qa) (📥 15K / month · 📦 8 · ⏱️ 24.09.2024): +- [PyPi](https://pypi.org/project/paper-qa) (📥 16K / month · 📦 8 · ⏱️ 27.09.2024): ``` pip install paper-qa ``` @@ -1524,19 +1510,19 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/OpenBioML/chemnlp ``` -- [PyPi](https://pypi.org/project/chemnlp) (📥 97 / month · 📦 1 · ⏱️ 07.08.2023): +- [PyPi](https://pypi.org/project/chemnlp) (📥 98 / month · 📦 1 · ⏱️ 07.08.2023): ``` pip install chemnlp ```
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ChemCrow (🥈15 · ⭐ 590) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent +
ChemCrow (🥈15 · ⭐ 600 · 💤) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent -- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 84 · 📦 5 · 📋 20 - 30% open · ⏱️ 27.03.2024): +- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 87 · 📦 5 · 📋 22 - 36% open · ⏱️ 27.03.2024): ``` git clone https://github.com/ur-whitelab/chemcrow-public ``` -- [PyPi](https://pypi.org/project/chemcrow) (📥 530 / month · ⏱️ 27.03.2024): +- [PyPi](https://pypi.org/project/chemcrow) (📥 630 / month · ⏱️ 27.03.2024): ``` pip install chemcrow ``` @@ -1548,19 +1534,19 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/usnistgov/atomgpt ``` -- [PyPi](https://pypi.org/project/atomgpt) (📥 230 / month · ⏱️ 22.09.2024): +- [PyPi](https://pypi.org/project/atomgpt) (📥 260 / month · ⏱️ 22.09.2024): ``` pip install atomgpt ```
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gptchem (🥈12 · ⭐ 220 · 💤) - Use GPT-3 to solve chemistry problems. MIT +
gptchem (🥈12 · ⭐ 230 · 💤) - Use GPT-3 to solve chemistry problems. MIT - [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) (📥 94 / month · ⏱️ 04.10.2023): +- [PyPi](https://pypi.org/project/gptchem) (📥 120 / month · ⏱️ 04.10.2023): ``` pip install gptchem ``` @@ -1572,12 +1558,12 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/usnistgov/chemnlp ``` -- [PyPi](https://pypi.org/project/chemnlp) (📥 97 / month · 📦 1 · ⏱️ 07.08.2023): +- [PyPi](https://pypi.org/project/chemnlp) (📥 98 / month · 📦 1 · ⏱️ 07.08.2023): ``` pip install chemnlp ```
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ChatMOF (🥈11 · ⭐ 57) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative +
ChatMOF (🥈11 · ⭐ 58) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative - [GitHub](https://github.com/Yeonghun1675/ChatMOF) (👨‍💻 1 · 🔀 8 · 📦 2 · ⏱️ 01.07.2024): @@ -1589,7 +1575,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce pip install chatmof ```
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LLaMP (🥉10 · ⭐ 59) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-tool +
LLaMP (🥉10 · ⭐ 61) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-tool - [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 6 · 🔀 7 · 📋 25 - 32% open · ⏱️ 10.09.2024): @@ -1612,7 +1598,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/microsoft/molskill ``` -- [Conda](https://anaconda.org/msr-ai4science/molskill) (📥 300 · ⏱️ 18.06.2023): +- [Conda](https://anaconda.org/msr-ai4science/molskill) (📥 310 · ⏱️ 18.06.2023): ``` conda install -c msr-ai4science molskill ``` @@ -1633,7 +1619,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce git clone https://github.com/vertaix/LLM-Prop ```
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crystal-text-llm (🥉5 · ⭐ 67) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery +
crystal-text-llm (🥉5 · ⭐ 68) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery - [GitHub](https://github.com/facebookresearch/crystal-text-llm) (👨‍💻 3 · 🔀 12 · 📋 9 - 77% open · ⏱️ 18.06.2024): @@ -1657,22 +1643,15 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce git clone https://github.com/maykcaldas/MAPI_LLM ```
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Cephalo (🥉5 · ⭐ 6 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2 generative multimodal pretrained - -- [GitHub](https://github.com/lamm-mit/Cephalo) (🔀 1 · ⏱️ 23.07.2024): - - ``` - git clone https://github.com/lamm-mit/Cephalo - ``` -
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Show 6 hidden projects... +
Show 7 hidden projects... - ChemDataExtractor (🥇16 · ⭐ 300 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data - mat2vec (🥈12 · ⭐ 620 · 💀) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn - nlcc (🥈12 · ⭐ 44 · 💀) - Natural language computational chemistry command line interface. MIT single-paper - BERT-PSIE-TC (🥉5 · ⭐ 12 · 💀) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT magnetism - ChemDataWriter (🥉4 · ⭐ 14 · 💤) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. MIT literature-data -- CatBERTa (🥉3 · ⭐ 19) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis +- Cephalo (🥉4 · ⭐ 6 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2 generative multimodal pretrained +- CatBERTa (🥉3 · ⭐ 19 · 💤) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis

@@ -1692,9 +1671,9 @@ _Projects that implement materials discovery methods using atomistic ML._ git clone https://github.com/CompRhys/aviary ```
-
BOSS (🥇13 · ⭐ 20) - Bayesian Optimization Structure Search (BOSS). Apache-2 probabilistic +
BOSS (🥇12 · ⭐ 20) - Bayesian Optimization Structure Search (BOSS). Apache-2 probabilistic -- [PyPi](https://pypi.org/project/aalto-boss) (📥 4K / month · ⏱️ 20.07.2024): +- [PyPi](https://pypi.org/project/aalto-boss) (📥 5.2K / month · ⏱️ 20.07.2024): ``` pip install aalto-boss ``` @@ -1706,7 +1685,7 @@ _Projects that implement materials discovery methods using atomistic ML._
AGOX (🥈10 · ⭐ 13) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization -- [PyPi](https://pypi.org/project/agox) (📥 230 / month · ⏱️ 26.08.2024): +- [PyPi](https://pypi.org/project/agox) (📥 260 / month · ⏱️ 26.08.2024): ``` pip install agox ``` @@ -1736,9 +1715,9 @@ _Projects that implement materials discovery methods using atomistic ML._ - Computational Autonomy for Materials Discovery (CAMD) (🥉6 · ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. Apache-2 - MAGUS (🥉4 · ⭐ 60 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning -- SPINNER (🥉4 · ⭐ 12 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction - ML-atomate (🥉4 · ⭐ 4 · 💤) - Machine learning-assisted Atomate code for autonomous computational materials screening. GPL-3.0 active-learning workflows - closed-loop-acceleration-benchmarks (🥉4 · 💀) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper +- SPINNER (🥉3 · ⭐ 12 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction - sl_discovery (🥉3 · ⭐ 5 · 💀) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper

@@ -1749,14 +1728,14 @@ _Projects that implement materials discovery methods using atomistic ML._ _Projects that implement mathematical objects used in atomistic machine learning._ -
KFAC-JAX (🥇19 · ⭐ 230) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2 +
KFAC-JAX (🥇18 · ⭐ 230) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2 -- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 15 · 🔀 18 · 📦 10 · 📋 19 - 47% open · ⏱️ 25.09.2024): +- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 15 · 🔀 18 · 📦 10 · 📋 19 - 47% open · ⏱️ 27.09.2024): ``` git clone https://github.com/google-deepmind/kfac-jax ``` -- [PyPi](https://pypi.org/project/kfac-jax) (📥 1K / month · 📦 1 · ⏱️ 04.04.2024): +- [PyPi](https://pypi.org/project/kfac-jax) (📥 1.1K / month · 📦 1 · ⏱️ 04.04.2024): ``` pip install kfac-jax ``` @@ -1768,19 +1747,19 @@ _Projects that implement mathematical objects used in atomistic machine learning ``` git clone https://github.com/ziatdinovmax/gpax ``` -- [PyPi](https://pypi.org/project/gpax) (📥 320 / month · ⏱️ 20.03.2024): +- [PyPi](https://pypi.org/project/gpax) (📥 380 / month · ⏱️ 20.03.2024): ``` pip install gpax ```
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SpheriCart (🥇17 · ⭐ 69) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT +
SpheriCart (🥇17 · ⭐ 70) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT -- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 11 · 📥 85 · 📦 3 · 📋 41 - 56% open · ⏱️ 07.09.2024): +- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 11 · 📥 86 · 📦 3 · 📋 41 - 56% open · ⏱️ 07.09.2024): ``` git clone https://github.com/lab-cosmo/sphericart ``` -- [PyPi](https://pypi.org/project/sphericart) (📥 1K / month · ⏱️ 04.09.2024): +- [PyPi](https://pypi.org/project/sphericart) (📥 980 / month · ⏱️ 04.09.2024): ``` pip install sphericart ``` @@ -1801,15 +1780,7 @@ _Projects that implement mathematical objects used in atomistic machine learning git clone https://github.com/risi-kondor/GElib ```
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EquivariantOperators.jl (🥉6 · ⭐ 19 · 💤) - This package is deprecated. Functionalities are migrating to Porcupine.jl. MIT Julia - -- [GitHub](https://github.com/aced-differentiate/EquivariantOperators.jl) (⏱️ 27.09.2023): - - ``` - git clone https://github.com/aced-differentiate/EquivariantOperators.jl - ``` -
-
COSMO Toolbox (🥉6 · ⭐ 7) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++ +
COSMO Toolbox (🥉6 · ⭐ 7 · 💤) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++ - [GitHub](https://github.com/lab-cosmo/toolbox) (👨‍💻 9 · 🔀 6 · ⏱️ 19.03.2024): @@ -1817,11 +1788,12 @@ _Projects that implement mathematical objects used in atomistic machine learning git clone https://github.com/lab-cosmo/toolbox ```
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Show 4 hidden projects... +
Show 5 hidden projects... - lie-nn (🥈9 · ⭐ 26 · 💀) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn +- EquivariantOperators.jl (🥉6 · ⭐ 19 · 💀) - This package is deprecated. Functionalities are migrating to Porcupine.jl. MIT Julia - cnine (🥉6 · ⭐ 4) - Cnine tensor library. Unlicensed C++ -- torch_spex (🥉3 · ⭐ 3) - Spherical expansions in PyTorch. Unlicensed +- torch_spex (🥉3 · ⭐ 3 · 💤) - Spherical expansions in PyTorch. Unlicensed - Wigner Kernels (🥉1 · ⭐ 2 · 💀) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking

@@ -1839,33 +1811,33 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/jax-md/jax-md ``` -- [PyPi](https://pypi.org/project/jax-md) (📥 3.3K / month · 📦 3 · ⏱️ 09.08.2023): +- [PyPi](https://pypi.org/project/jax-md) (📥 3.4K / month · 📦 3 · ⏱️ 09.08.2023): ``` pip install jax-md ```
-
FitSNAP (🥈18 · ⭐ 150) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0 +
mlcolvar (🥈19 · ⭐ 91 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT sampling -- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 24 · 🔀 51 · 📥 11 · 📦 2 · 📋 73 - 21% open · ⏱️ 19.09.2024): +- [GitHub](https://github.com/luigibonati/mlcolvar) (👨‍💻 8 · 🔀 24 · 📦 2 · 📋 73 - 20% open · ⏱️ 02.10.2024): ``` - git clone https://github.com/FitSNAP/FitSNAP + git clone https://github.com/luigibonati/mlcolvar ``` -- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 8.4K · ⏱️ 16.06.2023): +- [PyPi](https://pypi.org/project/mlcolvar) (📥 240 / month · ⏱️ 12.06.2024): ``` - conda install -c conda-forge fitsnap3 + pip install mlcolvar ```
-
mlcolvar (🥈17 · ⭐ 91) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT sampling +
FitSNAP (🥈18 · ⭐ 150) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0 -- [GitHub](https://github.com/luigibonati/mlcolvar) (👨‍💻 8 · 🔀 24 · 📦 2 · 📋 72 - 19% open · ⏱️ 23.09.2024): +- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 24 · 🔀 50 · 📥 11 · 📦 2 · 📋 73 - 21% open · ⏱️ 19.09.2024): ``` - git clone https://github.com/luigibonati/mlcolvar + git clone https://github.com/FitSNAP/FitSNAP ``` -- [PyPi](https://pypi.org/project/mlcolvar) (📥 190 / month · ⏱️ 12.06.2024): +- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 8.5K · ⏱️ 16.06.2023): ``` - pip install mlcolvar + conda install -c conda-forge fitsnap3 ```
openmm-torch (🥈16 · ⭐ 180) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++ @@ -1875,7 +1847,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/openmm/openmm-torch ``` -- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 460K · ⏱️ 03.06.2024): +- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 470K · ⏱️ 30.09.2024): ``` conda install -c conda-forge openmm-torch ``` @@ -1887,7 +1859,7 @@ _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) (📥 5.2K · ⏱️ 07.06.2024): +- [Conda](https://anaconda.org/conda-forge/openmm-ml) (📥 5.3K · ⏱️ 07.06.2024): ``` conda install -c conda-forge openmm-ml ``` @@ -1916,16 +1888,9 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach git clone https://github.com/mir-group/pair_allegro ```
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SOMD (🥉5 · ⭐ 12) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0 ML-IAP active-learning - -- [GitHub](https://github.com/initqp/somd) (🔀 2 · ⏱️ 17.08.2024): - - ``` - git clone https://github.com/initqp/somd - ``` -
-
Show 1 hidden projects... +
Show 2 hidden projects... +- SOMD (🥉4 · ⭐ 12) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0 ML-IAP active-learning - interface-lammps-mlip-3 (🥉3 · ⭐ 5 · 💀) - An interface between LAMMPS and MLIP (version 3). GPL-2.0

@@ -1991,7 +1956,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp
SISSO (🥈14 · ⭐ 240) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2 Fortran -- [GitHub](https://github.com/rouyang2017/SISSO) (👨‍💻 3 · 🔀 77 · 📋 76 - 23% open · ⏱️ 20.09.2024): +- [GitHub](https://github.com/rouyang2017/SISSO) (👨‍💻 3 · 🔀 78 · 📋 76 - 23% open · ⏱️ 20.09.2024): ``` git clone https://github.com/rouyang2017/SISSO @@ -2004,7 +1969,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp ``` git clone https://github.com/drcassar/glasspy ``` -- [PyPi](https://pypi.org/project/glasspy) (📥 760 / month · ⏱️ 05.09.2024): +- [PyPi](https://pypi.org/project/glasspy) (📥 600 / month · ⏱️ 05.09.2024): ``` pip install glasspy ``` @@ -2019,13 +1984,13 @@ _Projects that offer implementations of representations aka descriptors, fingerp
Rascaline (🥈12 · ⭐ 44) - Computing representations for atomistic machine learning. BSD-3 Rust C++ -- [GitHub](https://github.com/Luthaf/rascaline) (👨‍💻 14 · 🔀 13 · 📋 69 - 46% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/Luthaf/rascaline) (👨‍💻 14 · 🔀 13 · 📋 69 - 46% open · ⏱️ 02.10.2024): ``` git clone https://github.com/Luthaf/rascaline ```
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fplib (🥉10 · ⭐ 7 · 📈) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper +
fplib (🥉11 · ⭐ 7) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper - [GitHub](https://github.com/Rutgers-ZRG/libfp) (🔀 1 · ⏱️ 26.09.2024): @@ -2041,20 +2006,20 @@ _Projects that offer implementations of representations aka descriptors, fingerp git clone https://github.com/lab-cosmo/nice ```
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SA-GPR (🥉6 · ⭐ 19) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang +
milad (🥉5 · ⭐ 30) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative -- [GitHub](https://github.com/dilkins/TENSOAP) (👨‍💻 5 · 🔀 13 · 📋 7 - 28% open · ⏱️ 23.07.2024): +- [GitHub](https://github.com/muhrin/milad) (👨‍💻 1 · 🔀 1 · 📦 2 · ⏱️ 20.08.2024): ``` - git clone https://github.com/dilkins/TENSOAP + git clone https://github.com/muhrin/milad ```
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milad (🥉5 · ⭐ 30) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative +
SA-GPR (🥉5 · ⭐ 19) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang -- [GitHub](https://github.com/muhrin/milad) (👨‍💻 1 · 🔀 1 · 📦 2 · ⏱️ 20.08.2024): +- [GitHub](https://github.com/dilkins/TENSOAP) (👨‍💻 5 · 🔀 13 · 📋 7 - 28% open · ⏱️ 23.07.2024): ``` - git clone https://github.com/muhrin/milad + git clone https://github.com/dilkins/TENSOAP ```
Show 14 hidden projects... @@ -2093,14 +2058,14 @@ _General models that learn a representations aka embeddings of atomistic systems ``` pip install dgl ``` -- [Conda](https://anaconda.org/dglteam/dgl) (📥 370K · ⏱️ 03.09.2024): +- [Conda](https://anaconda.org/dglteam/dgl) (📥 380K · ⏱️ 03.09.2024): ``` conda install -c dglteam dgl ```
PyG Models (🥇35 · ⭐ 21K) - Representation learning models implemented in PyTorch Geometric. MIT general-ml -- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨‍💻 520 · 🔀 3.6K · 📦 6.5K · 📋 3.7K - 27% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/pyg-team/pytorch_geometric) (👨‍💻 520 · 🔀 3.6K · 📦 6.6K · 📋 3.7K - 27% open · ⏱️ 24.09.2024): ``` git clone https://github.com/pyg-team/pytorch_geometric @@ -2108,12 +2073,12 @@ _General models that learn a representations aka embeddings of atomistic systems
e3nn (🥇28 · ⭐ 950) - A modular framework for neural networks with Euclidean symmetry. MIT -- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 31 · 🔀 140 · 📦 300 · 📋 160 - 14% open · ⏱️ 25.08.2024): +- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 31 · 🔀 140 · 📦 310 · 📋 160 - 14% open · ⏱️ 25.08.2024): ``` git clone https://github.com/e3nn/e3nn ``` -- [PyPi](https://pypi.org/project/e3nn) (📥 94K / month · 📦 4 · ⏱️ 13.04.2022): +- [PyPi](https://pypi.org/project/e3nn) (📥 89K / month · 📦 4 · ⏱️ 13.04.2022): ``` pip install e3nn ``` @@ -2134,65 +2099,65 @@ _General models that learn a representations aka embeddings of atomistic systems pip install schnetpack ```
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MatGL (Materials Graph Library) (🥇24 · ⭐ 250) - Graph deep learning library for materials. BSD-3 multifidelity +
MatGL (Materials Graph Library) (🥇24 · ⭐ 260) - Graph deep learning library for materials. BSD-3 multifidelity -- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 17 · 🔀 59 · 📦 46 · 📋 97 - 7% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 17 · 🔀 59 · 📦 46 · 📋 97 - 7% open · ⏱️ 03.10.2024): ``` git clone https://github.com/materialsvirtuallab/matgl ``` -- [PyPi](https://pypi.org/project/m3gnet) (📥 1.7K / month · 📦 5 · ⏱️ 17.11.2022): +- [PyPi](https://pypi.org/project/m3gnet) (📥 1.8K / month · 📦 5 · ⏱️ 17.11.2022): ``` pip install m3gnet ```
e3nn-jax (🥈22 · ⭐ 180) - jax library for E3 Equivariant Neural Networks. Apache-2 -- [GitHub](https://github.com/e3nn/e3nn-jax) (👨‍💻 7 · 🔀 18 · 📦 38 · 📋 22 - 4% open · ⏱️ 14.08.2024): +- [GitHub](https://github.com/e3nn/e3nn-jax) (👨‍💻 7 · 🔀 18 · 📦 38 · 📋 22 - 4% open · ⏱️ 28.09.2024): ``` git clone https://github.com/e3nn/e3nn-jax ``` -- [PyPi](https://pypi.org/project/e3nn-jax) (📥 3.4K / month · 📦 13 · ⏱️ 14.08.2024): +- [PyPi](https://pypi.org/project/e3nn-jax) (📥 3.6K / month · 📦 13 · ⏱️ 14.08.2024): ``` pip install e3nn-jax ```
NVIDIA Deep Learning Examples for Tensor Cores (🥈21 · ⭐ 13K) - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and.. Custom educational drug-discovery -- [GitHub](https://github.com/NVIDIA/DeepLearningExamples) (👨‍💻 120 · 🔀 3.1K · 📋 880 - 35% open · ⏱️ 04.04.2024): +- [GitHub](https://github.com/NVIDIA/DeepLearningExamples) (👨‍💻 120 · 🔀 3.2K · 📋 910 - 37% open · ⏱️ 04.04.2024): ``` git clone https://github.com/NVIDIA/DeepLearningExamples ```
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DIG: Dive into Graphs (🥈21 · ⭐ 1.9K · 💤) - A library for graph deep learning research. GPL-3.0 +
ALIGNN (🥈21 · ⭐ 220) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom -- [GitHub](https://github.com/divelab/DIG) (👨‍💻 50 · 🔀 280 · 📋 210 - 16% open · ⏱️ 04.02.2024): +- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 79 · 📦 14 · 📋 65 - 61% open · ⏱️ 09.09.2024): ``` - git clone https://github.com/divelab/DIG + git clone https://github.com/usnistgov/alignn ``` -- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 540 / month · ⏱️ 27.06.2022): +- [PyPi](https://pypi.org/project/alignn) (📥 3.3K / month · 📦 6 · ⏱️ 09.09.2024): ``` - pip install dive-into-graphs + pip install alignn ```
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ALIGNN (🥈21 · ⭐ 220) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom +
DIG: Dive into Graphs (🥈20 · ⭐ 1.9K · 💤) - A library for graph deep learning research. GPL-3.0 -- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 79 · 📦 14 · 📋 64 - 62% 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) (📥 2.7K / month · 📦 6 · ⏱️ 09.09.2024): +- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 580 / month · ⏱️ 27.06.2022): ``` - pip install alignn + pip install dive-into-graphs ```
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Uni-Mol (🥈18 · ⭐ 670) - Official Repository for the Uni-Mol Series Methods. MIT pretrained +
Uni-Mol (🥈18 · ⭐ 680) - Official Repository for the Uni-Mol Series Methods. MIT pretrained -- [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨‍💻 17 · 🔀 120 · 📥 15K · 📋 160 - 40% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨‍💻 17 · 🔀 120 · 📥 15K · 📋 160 - 41% open · ⏱️ 26.09.2024): ``` git clone https://github.com/deepmodeling/Uni-Mol @@ -2200,32 +2165,24 @@ _General models that learn a representations aka embeddings of atomistic systems
kgcnn (🥈18 · ⭐ 110) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT -- [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨‍💻 7 · 🔀 29 · 📦 18 · 📋 86 - 13% open · ⏱️ 06.05.2024): +- [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨‍💻 7 · 🔀 30 · 📦 18 · 📋 86 - 13% open · ⏱️ 06.05.2024): ``` git clone https://github.com/aimat-lab/gcnn_keras ``` -- [PyPi](https://pypi.org/project/kgcnn) (📥 570 / month · 📦 3 · ⏱️ 27.02.2024): +- [PyPi](https://pypi.org/project/kgcnn) (📥 660 / month · 📦 3 · ⏱️ 27.02.2024): ``` pip install kgcnn ```
matsciml (🥈17 · ⭐ 140) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT workflows benchmarking -- [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 12 · 🔀 19 · 📋 59 - 35% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 12 · 🔀 19 · 📋 60 - 33% open · ⏱️ 01.10.2024): ``` git clone https://github.com/IntelLabs/matsciml ```
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Graphormer (🥈16 · ⭐ 2.1K) - Graphormer is a general-purpose deep learning backbone for molecular modeling. MIT transformer pretrained - -- [GitHub](https://github.com/microsoft/Graphormer) (👨‍💻 14 · 🔀 330 · 📋 160 - 58% open · ⏱️ 28.05.2024): - - ``` - git clone https://github.com/microsoft/Graphormer - ``` -
escnn (🥈16 · ⭐ 350) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom - [GitHub](https://github.com/QUVA-Lab/escnn) (👨‍💻 10 · 🔀 44 · 📋 75 - 50% open · ⏱️ 18.09.2024): @@ -2233,14 +2190,22 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/QUVA-Lab/escnn ``` -- [PyPi](https://pypi.org/project/escnn) (📥 970 / month · 📦 6 · ⏱️ 01.04.2022): +- [PyPi](https://pypi.org/project/escnn) (📥 1K / month · 📦 6 · ⏱️ 01.04.2022): ``` pip install escnn ```
+
Graphormer (🥈15 · ⭐ 2.1K) - Graphormer is a general-purpose deep learning backbone for molecular modeling. MIT transformer pretrained + +- [GitHub](https://github.com/microsoft/Graphormer) (👨‍💻 14 · 🔀 330 · 📋 160 - 58% open · ⏱️ 28.05.2024): + + ``` + git clone https://github.com/microsoft/Graphormer + ``` +
HydraGNN (🥈14 · ⭐ 61) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3 -- [GitHub](https://github.com/ORNL/HydraGNN) (👨‍💻 14 · 🔀 26 · 📦 1 · 📋 49 - 34% open · ⏱️ 21.09.2024): +- [GitHub](https://github.com/ORNL/HydraGNN) (👨‍💻 14 · 🔀 26 · 📦 1 · 📋 49 - 34% open · ⏱️ 27.09.2024): ``` git clone https://github.com/ORNL/HydraGNN @@ -2253,14 +2218,14 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/sparks-baird/CrabNet ``` -- [PyPi](https://pypi.org/project/crabnet) (📥 390 / month · 📦 2 · ⏱️ 10.01.2023): +- [PyPi](https://pypi.org/project/crabnet) (📥 660 / month · 📦 2 · ⏱️ 10.01.2023): ``` pip install crabnet ```
hippynn (🥈12 · ⭐ 67) - python library for atomistic machine learning. Custom workflows -- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 14 · 🔀 23 · 📦 1 · 📋 18 - 33% open · ⏱️ 20.09.2024): +- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 14 · 🔀 23 · 📦 1 · 📋 18 - 33% open · ⏱️ 27.09.2024): ``` git clone https://github.com/lanl/hippynn @@ -2273,7 +2238,7 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/idocx/Atom2Vec ``` -- [PyPi](https://pypi.org/project/atom2vec) (📥 93 / month · ⏱️ 23.02.2024): +- [PyPi](https://pypi.org/project/atom2vec) (📥 96 / month · ⏱️ 23.02.2024): ``` pip install atom2vec ``` @@ -2285,33 +2250,33 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/vict0rsch/faenet ``` -- [PyPi](https://pypi.org/project/faenet) (📥 95 / month · ⏱️ 14.09.2023): +- [PyPi](https://pypi.org/project/faenet) (📥 120 / month · ⏱️ 14.09.2023): ``` pip install faenet ```
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Equiformer (🥉9 · ⭐ 200) - [ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT transformer +
ai4material_design (🥉9 · ⭐ 6 · 💤) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pretrained material-defect -- [GitHub](https://github.com/atomicarchitects/equiformer) (👨‍💻 2 · 🔀 38 · 📋 14 - 42% open · ⏱️ 18.07.2024): +- [GitHub](https://github.com/HSE-LAMBDA/ai4material_design) (👨‍💻 11 · 🔀 3 · ⏱️ 21.11.2023): ``` - git clone https://github.com/atomicarchitects/equiformer + git clone https://github.com/HSE-LAMBDA/ai4material_design ```
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EquiformerV2 (🥉9 · ⭐ 200) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT +
Equiformer (🥉8 · ⭐ 200) - [ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT transformer -- [GitHub](https://github.com/atomicarchitects/equiformer_v2) (👨‍💻 2 · 🔀 26 · 📋 18 - 83% open · ⏱️ 16.07.2024): +- [GitHub](https://github.com/atomicarchitects/equiformer) (👨‍💻 2 · 🔀 37 · 📋 14 - 42% open · ⏱️ 18.07.2024): ``` - git clone https://github.com/atomicarchitects/equiformer_v2 + git clone https://github.com/atomicarchitects/equiformer ```
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ai4material_design (🥉9 · ⭐ 6 · 💤) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pretrained material-defect +
EquiformerV2 (🥉8 · ⭐ 200) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT -- [GitHub](https://github.com/HSE-LAMBDA/ai4material_design) (👨‍💻 11 · 🔀 3 · ⏱️ 21.11.2023): +- [GitHub](https://github.com/atomicarchitects/equiformer_v2) (👨‍💻 2 · 🔀 26 · 📋 18 - 83% open · ⏱️ 16.07.2024): ``` - git clone https://github.com/HSE-LAMBDA/ai4material_design + git clone https://github.com/atomicarchitects/equiformer_v2 ```
graphite (🥉8 · ⭐ 58) - A repository for implementing graph network models based on atomic structures. MIT @@ -2330,24 +2295,31 @@ _General models that learn a representations aka embeddings of atomistic systems git clone https://github.com/usccolumbia/deeperGATGNN ```
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Show 34 hidden projects... +
T-e3nn (🥉8 · ⭐ 8) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism + +- [GitHub](https://github.com/Hongyu-yu/T-e3nn) (👨‍💻 26 · ⏱️ 29.09.2024): -- dgl-lifesci (🥇23 · ⭐ 710 · 💀) - Python package for graph neural networks in chemistry and biology. Apache-2 + ``` + git clone https://github.com/Hongyu-yu/T-e3nn + ``` +
+
Show 33 hidden projects... + +- dgl-lifesci (🥇23 · ⭐ 720 · 💀) - Python package for graph neural networks in chemistry and biology. Apache-2 - benchmarking-gnns (🥈14 · ⭐ 2.5K · 💀) - Repository for benchmarking graph neural networks. MIT single-paper benchmarking - Crystal Graph Convolutional Neural Networks (CGCNN) (🥈12 · ⭐ 640 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT - Neural fingerprint (nfp) (🥈12 · ⭐ 57 · 💀) - Keras layers for end-to-end learning with rdkit and pymatgen. Custom - pretrained-gnns (🥈10 · ⭐ 960 · 💀) - Strategies for Pre-training Graph Neural Networks. MIT pretrained -- GDC (🥈10 · ⭐ 260 · 💀) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative -- SE(3)-Transformers (🥉9 · ⭐ 480 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer +- GDC (🥈10 · ⭐ 270 · 💀) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative +- SE(3)-Transformers (🥉9 · ⭐ 490 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer - GATGNN: Global Attention Graph Neural Network (🥉9 · ⭐ 69 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT - molecularGNN_smiles (🥉8 · ⭐ 290 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2 - CGAT (🥉8 · ⭐ 25 · 💀) - Crystal graph attention neural networks for materials prediction. MIT - UVVisML (🥉8 · ⭐ 22 · 💀) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic -- T-e3nn (🥉8 · ⭐ 8 · 💀) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism - tensorfieldnetworks (🥉7 · ⭐ 150 · 💀) - Rotation- and translation-equivariant neural networks for 3D point clouds. MIT - DTNN (🥉7 · ⭐ 76 · 💀) - Deep Tensor Neural Network. MIT - Cormorant (🥉7 · ⭐ 59 · 💀) - Codebase for Cormorant Neural Networks. Custom -- AdsorbML (🥉7 · ⭐ 35 · 💀) - MIT surface-science single-paper +- AdsorbML (🥉7 · ⭐ 36 · 💀) - MIT surface-science single-paper - escnn_jax (🥉7 · ⭐ 26 · 💀) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom - ML4pXRDs (🥉7 · 💀) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. MIT XRD single-paper - MACE-Layer (🥉6 · ⭐ 33 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. MIT @@ -2361,7 +2333,7 @@ _General models that learn a representations aka embeddings of atomistic systems - Per-Site CGCNN (🥉5 · ⭐ 1 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT pretrained single-paper - Per-site PAiNN (🥉5 · ⭐ 1 · 💀) - Fork of PaiNN for PerovskiteOrderingGCNNs. MIT probabilistic pretrained single-paper - Graph Transport Network (🥉4 · ⭐ 16 · 💀) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. Custom transport-phenomena -- gkx: Green-Kubo Method in JAX (🥉4 · ⭐ 4) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena +- gkx: Green-Kubo Method in JAX (🥉4 · ⭐ 4 · 💤) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena - atom_by_atom (🥉3 · ⭐ 7 · 💤) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. Unlicensed surface-science single-paper - Element encoder (🥉3 · ⭐ 6 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper - Point Edge Transformer (🥉2) - Smooth, exact rotational symmetrization for deep learning on point clouds. CC-BY-4.0 @@ -2381,9 +2353,9 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large 🔗 MatterSim - A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967. ML-IAP active-learning proprietary -
DPA-2 (🥇26 · ⭐ 1.5K) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets +
DPA-2 (🥇24 · ⭐ 1.5K · 📉) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets -- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 500 · 📥 40K · 📦 16 · 📋 780 - 12% open · ⏱️ 17.09.2024): +- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 500 · 📥 40K · 📦 16 · 📋 790 - 12% open · ⏱️ 17.09.2024): ``` git clone https://github.com/deepmodeling/deepmd-kit @@ -2396,46 +2368,46 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large ``` git clone https://github.com/CederGroupHub/chgnet ``` -- [PyPi](https://pypi.org/project/chgnet) (📥 29K / month · 📦 21 · ⏱️ 16.09.2024): +- [PyPi](https://pypi.org/project/chgnet) (📥 30K / month · 📦 21 · ⏱️ 16.09.2024): ``` pip install chgnet ```
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MACE-MP (🥈19 · ⭐ 460) - Pretrained foundation models for materials chemistry. MIT ML-IAP pretrained rep-learn MD +
MACE-MP (🥉18 · ⭐ 460) - Pretrained foundation models for materials chemistry. MIT ML-IAP pretrained rep-learn MD -- [GitHub](https://github.com/ACEsuit/mace-mp) (👨‍💻 2 · 🔀 170 · 📥 26K · 📋 9 - 22% open · ⏱️ 24.04.2024): +- [GitHub](https://github.com/ACEsuit/mace-mp) (👨‍💻 2 · 🔀 170 · 📥 28K · 📋 9 - 22% open · ⏱️ 24.04.2024): ``` git clone https://github.com/ACEsuit/mace-mp ``` -- [PyPi](https://pypi.org/project/mace-torch) (📥 9.3K / month · 📦 14 · ⏱️ 16.07.2024): +- [PyPi](https://pypi.org/project/mace-torch) (📥 11K / month · 📦 14 · ⏱️ 16.07.2024): ``` pip install mace-torch ```
SevenNet (🥉15 · ⭐ 110) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained -- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 11 · 🔀 13 · 📦 3 · 📋 21 - 52% open · ⏱️ 18.09.2024): +- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 11 · 🔀 14 · 📦 3 · 📋 22 - 50% open · ⏱️ 18.09.2024): ``` git clone https://github.com/MDIL-SNU/SevenNet ```
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Orb Models (🥉14 · ⭐ 150 · 🐣) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained +
Orb Models (🥉14 · ⭐ 160 · 🐣) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained -- [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 6 · 🔀 18 · 📦 1 · 📋 10 - 20% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 6 · 🔀 19 · 📦 1 · 📋 11 - 18% open · ⏱️ 02.10.2024): ``` git clone https://github.com/orbital-materials/orb-models ``` -- [PyPi](https://pypi.org/project/orb-models) (📥 1.3K / month · ⏱️ 13.09.2024): +- [PyPi](https://pypi.org/project/orb-models) (📥 1.2K / month · ⏱️ 13.09.2024): ``` pip install orb-models ```
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Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 38 · 🐣) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. CC-BY-NC-4.0 pretrained ML-IAP general-tool +
Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 38) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. CC-BY-NC-4.0 pretrained ML-IAP general-tool -- [GitHub](https://github.com/facebookresearch/JMP) (👨‍💻 1 · 🔀 5 · ⏱️ 07.05.2024): +- [GitHub](https://github.com/facebookresearch/JMP) (👨‍💻 1 · 🔀 6 · ⏱️ 07.05.2024): ``` git clone https://github.com/facebookresearch/JMP @@ -2475,7 +2447,7 @@ _Projects that focus on unsupervised learning (USL) for atomistic ML, such as di
Show 5 hidden projects... -- Sketchmap (🥈8 · ⭐ 44 · 💀) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. GPL-3.0 C++ +- Sketchmap (🥈9 · ⭐ 44 · 💀) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. GPL-3.0 C++ - Coarse-Graining-Auto-encoders (🥉5 · ⭐ 21 · 💀) - Implementation of coarse-graining Autoencoders. Unlicensed single-paper - paper-ml-robustness-material-property (🥉5 · ⭐ 4 · 💀) - A critical examination of robustness and generalizability of machine learning prediction of materials properties. BSD-3 datasets single-paper - KmdPlus (🥉4 · ⭐ 3) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. MIT @@ -2489,62 +2461,62 @@ _Projects that focus on unsupervised learning (USL) for atomistic ML, such as di _Projects that focus on visualization (viz.) for atomistic ML._ -
Crystal Toolkit (🥇23 · ⭐ 150) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT +
Crystal Toolkit (🥇24 · ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT - [GitHub](https://github.com/materialsproject/crystaltoolkit) (👨‍💻 28 · 🔀 57 · 📦 38 · 📋 110 - 47% open · ⏱️ 20.09.2024): ``` git clone https://github.com/materialsproject/crystaltoolkit ``` -- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 1.6K / month · 📦 8 · ⏱️ 04.09.2024): +- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 2K / month · 📦 8 · ⏱️ 04.09.2024): ``` pip install crystal-toolkit ```
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pymatviz (🥈21 · ⭐ 160 · 📉) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic +
pymatviz (🥈21 · ⭐ 160) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic -- [GitHub](https://github.com/janosh/pymatviz) (👨‍💻 7 · 🔀 13 · 📦 8 · 📋 45 - 24% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/janosh/pymatviz) (👨‍💻 8 · 🔀 14 · 📦 8 · 📋 47 - 25% open · ⏱️ 03.10.2024): ``` git clone https://github.com/janosh/pymatviz ``` -- [PyPi](https://pypi.org/project/pymatviz) (📥 3.7K / month · 📦 2 · ⏱️ 01.09.2024): +- [PyPi](https://pypi.org/project/pymatviz) (📥 3.5K / month · 📦 2 · ⏱️ 01.09.2024): ``` pip install pymatviz ```
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Chemiscope (🥈19 · ⭐ 130) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript +
ZnDraw (🥈20 · ⭐ 30 · 📈) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript -- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 22 · 🔀 32 · 📥 310 · 📦 6 · 📋 120 - 29% open · ⏱️ 26.09.2024): +- [GitHub](https://github.com/zincware/ZnDraw) (👨‍💻 7 · 🔀 3 · 📦 4 · 📋 310 - 29% open · ⏱️ 17.09.2024): ``` - git clone https://github.com/lab-cosmo/chemiscope + git clone https://github.com/zincware/ZnDraw ``` -- [npm](https://www.npmjs.com/package/chemiscope) (📥 23 / month · 📦 3 · ⏱️ 15.03.2023): +- [PyPi](https://pypi.org/project/zndraw) (📥 1.2K / month · 📦 2 · ⏱️ 26.08.2024): ``` - npm install chemiscope + pip install zndraw ```
-
ZnDraw (🥈19 · ⭐ 30) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript +
Chemiscope (🥉18 · ⭐ 130) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript -- [GitHub](https://github.com/zincware/ZnDraw) (👨‍💻 7 · 🔀 3 · 📦 4 · 📋 320 - 31% open · ⏱️ 17.09.2024): +- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 22 · 🔀 32 · 📥 310 · 📦 6 · 📋 120 - 29% open · ⏱️ 26.09.2024): ``` - git clone https://github.com/zincware/ZnDraw + git clone https://github.com/lab-cosmo/chemiscope ``` -- [PyPi](https://pypi.org/project/zndraw) (📥 880 / month · 📦 2 · ⏱️ 26.08.2024): +- [npm](https://www.npmjs.com/package/chemiscope) (📥 14 / month · 📦 3 · ⏱️ 15.03.2023): ``` - pip install zndraw + npm install chemiscope ```
-
Elementari (🥉12 · ⭐ 130) - Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,.. MIT JavaScript +
Elementari (🥉11 · ⭐ 140) - Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,.. MIT JavaScript - [GitHub](https://github.com/janosh/elementari) (👨‍💻 2 · 🔀 12 · 📦 3 · 📋 7 - 28% open · ⏱️ 19.07.2024): ``` git clone https://github.com/janosh/elementari ``` -- [npm](https://www.npmjs.com/package/elementari) (📥 140 / month · 📦 1 · ⏱️ 15.01.2024): +- [npm](https://www.npmjs.com/package/elementari) (📥 120 / month · 📦 1 · ⏱️ 15.01.2024): ``` npm install elementari ``` @@ -2561,34 +2533,34 @@ _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._ -
DeepQMC (🥇20 · ⭐ 340 · 📈) - Deep learning quantum Monte Carlo for electrons in real space. MIT +
DeepQMC (🥇20 · ⭐ 350) - Deep learning quantum Monte Carlo for electrons in real space. MIT -- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 58 · 📦 2 · 📋 46 - 8% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 59 · 📦 2 · 📋 46 - 8% open · ⏱️ 24.09.2024): ``` git clone https://github.com/deepqmc/deepqmc ``` -- [PyPi](https://pypi.org/project/deepqmc) (📥 210 / month · ⏱️ 24.09.2024): +- [PyPi](https://pypi.org/project/deepqmc) (📥 280 / month · ⏱️ 24.09.2024): ``` pip install deepqmc ```
FermiNet (🥈15 · ⭐ 720) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2 transformer -- [GitHub](https://github.com/google-deepmind/ferminet) (👨‍💻 18 · 🔀 120 · 📋 50 - 2% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/google-deepmind/ferminet) (👨‍💻 18 · 🔀 120 · 📋 50 - 2% open · ⏱️ 03.10.2024): ``` git clone https://github.com/google-deepmind/ferminet ```
-
DeepErwin (🥉8 · ⭐ 47) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom +
DeepErwin (🥉8 · ⭐ 48) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom - [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 7 · 🔀 6 · 📥 10 · ⏱️ 07.06.2024): ``` git clone https://github.com/mdsunivie/deeperwin ``` -- [PyPi](https://pypi.org/project/deeperwin) (📥 73 / month · ⏱️ 14.12.2021): +- [PyPi](https://pypi.org/project/deeperwin) (📥 110 / month · ⏱️ 14.12.2021): ``` pip install deeperwin ``` diff --git a/history/2024-10-03_changes.md b/history/2024-10-03_changes.md new file mode 100644 index 0000000..4266c47 --- /dev/null +++ b/history/2024-10-03_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._ + +- QUIP (🥈27 · ⭐ 350 · 📈) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran +- Crystal Toolkit (🥇24 · ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT +- MAST-ML (🥈20 · ⭐ 100 · 📈) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT +- ZnDraw (🥈20 · ⭐ 30 · 📈) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript +- mlcolvar (🥈19 · ⭐ 91 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT sampling + +## 📉 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._ + +- DeepChem (🥇34 · ⭐ 5.4K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT +- DeePMD-kit (🥇25 · ⭐ 1.5K · 📉) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++ +- DPA-2 (🥇24 · ⭐ 1.5K · 📉) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets +- NequIP (🥇23 · ⭐ 610 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +- SchNetPack G-SchNet (🥈12 · ⭐ 46 · 📉) - G-SchNet extension for SchNetPack. MIT + diff --git a/history/2024-10-03_projects.csv b/history/2024-10-03_projects.csv new file mode 100644 index 0000000..2f7277a --- /dev/null +++ b/history/2024-10-03_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,github_release_downloads,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,trending,updated_github_id,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,ignore,docs_url +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/,Explore State-of-the-Art 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 16:48:33,2024-09-17 16:52:27.000000,2024-09-17 16:42:28,831.0,15.0,43.0,5.0,321.0,18.0,2.0,58.0,2024-05-16 16:20:41.000,2024.4.26,28.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +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 14:49:09,2024-09-25 02:40:08.000000,2024-09-25 02:40:07,4404.0,239.0,2997.0,173.0,5039.0,524.0,2355.0,13403.0,2024-09-03 04:16:25.000,2.4.0,453.0,295.0,dgl,dglteam/dgl,449.0,301.0,https://pypi.org/project/dgl,2024-05-13 01:10:39.000,148.0,112261.0,117633.0,https://anaconda.org/dglteam/dgl,2024-09-03 05:08:29.197,376083.0,1.0,,,,,,,,,,,,,,,,,,,,, +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 16:03:03,2024-10-03 16:23:12.000000,2024-09-24 15:27:12,7624.0,52.0,3629.0,252.0,3128.0,1029.0,2658.0,21080.0,2024-09-26 07:09:50.000,2.6.1,42.0,519.0,,,6555.0,6555.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +34,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 06:19:15,2024-10-03 14:05:43.000000,2024-10-03 14:05:43,7875.0,97.0,860.0,81.0,3271.0,997.0,2379.0,2608.0,2024-09-27 11:53:59.000,Release_2024_09_1,100.0,231.0,rdkit,rdkit/rdkit,731.0,3.0,https://pypi.org/project/rdkit,2024-08-07 12:34:25.000,728.0,2402772.0,2423529.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2572752.0,1.0,1095.0,,,,,,,,,,,,,,,,,,,, +35,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",34,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2024-09-20 21:42:55.872537,2024-09-20 16:37:57,10535.0,40.0,1662.0,143.0,2432.0,633.0,1237.0,5427.0,2024-04-03 16:21:23.000,2.8.0,923.0,247.0,deepchem,conda-forge/deepchem,447.0,434.0,https://pypi.org/project/deepchem,2024-09-20 21:09:41.000,13.0,43672.0,45856.0,https://anaconda.org/conda-forge/deepchem,2024-04-05 16:46:45.105,109971.0,1.0,,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2024-09-20 21:42:55.872537,5.0,7652.0,-2.0,,,,,,,,,,,,,, +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.,29,True,['ai-agent'],whitead/paper-qa,https://github.com/Future-House/paper-qa,2023-02-05 01:07:25,2024-10-03 15:55:24.000000,2024-10-02 23:50:57,386.0,140.0,551.0,54.0,292.0,66.0,143.0,6004.0,2024-09-27 23:51:31.000,5.0.10,115.0,25.0,paper-qa,,79.0,71.0,https://pypi.org/project/paper-qa,2024-09-27 23:51:31.000,8.0,15962.0,15962.0,,,,1.0,,,,,,,,Future-House/paper-qa,,,,,,,,,,,,, +37,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,28,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2024-08-25 22:32:06.000000,2024-08-25 22:32:06,2174.0,11.0,138.0,20.0,226.0,23.0,133.0,948.0,2024-07-27 03:01:58.000,0.5.2,29.0,31.0,e3nn,conda-forge/e3nn,313.0,309.0,https://pypi.org/project/e3nn,2022-04-13 19:24:30.000,4.0,88733.0,89504.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,22374.0,1.0,,,,,,,,,,,,,,,,,,,,, +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 14:00:57,1698.0,34.0,213.0,32.0,419.0,6.0,245.0,774.0,2024-09-05 11:35:29.000,2.1.1,12.0,36.0,schnetpack,,94.0,90.0,https://pypi.org/project/schnetpack,2024-09-05 11:35:29.000,4.0,1366.0,1366.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +39,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,28,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2024-10-02 18:13:34.000000,2024-10-02 18:13:34,4170.0,14.0,189.0,29.0,726.0,29.0,198.0,471.0,2024-03-27 14:48:51.000,0.9.2,71.0,55.0,matminer,conda-forge/matminer,377.0,319.0,https://pypi.org/project/matminer,2024-03-27 14:48:51.000,58.0,14281.0,15757.0,https://anaconda.org/conda-forge/matminer,2024-03-28 11:24:38.014,70854.0,1.0,,,,,,,,,,,,,,,,,,,,, +40,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,27,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 15:18:25,10866.0,10.0,122.0,26.0,177.0,104.0,362.0,351.0,2023-06-15 19:54:01.129,0.9.14,15.0,85.0,quippy-ase,,46.0,42.0,https://pypi.org/project/quippy-ase,2023-01-15 16:54:03.041,4.0,9482.0,9565.0,,,,2.0,674.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9957.0,1.0,,,,,,,,,,,,,, +41,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 21:00:07,2024-09-30 11:34:49.000000,2024-09-30 11:31:02,1668.0,43.0,42.0,7.0,1713.0,105.0,349.0,68.0,2024-09-16 14:36:02.000,1.1.3,109.0,28.0,optimade,conda-forge/optimade,64.0,60.0,https://pypi.org/project/optimade,2024-09-16 14:36:02.000,4.0,8757.0,10709.0,https://anaconda.org/conda-forge/optimade,2024-09-16 18:49:37.804,91766.0,1.0,,,,,,,,,,,,,,,,,,,,, +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 09:24:47,922.0,22.0,187.0,47.0,171.0,71.0,81.0,1163.0,2023-08-09 23:18:24.000,0.2.8,38.0,34.0,jax-md,,59.0,56.0,https://pypi.org/project/jax-md,2023-08-09 23:18:24.000,3.0,3366.0,3366.0,,,,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 08:30:07,2024-09-19 17:14:23.000000,2024-09-19 17:14:23,17776.0,91.0,156.0,40.0,820.0,32.0,260.0,493.0,2023-08-21 19:50:47.000,cdk-2.9,20.0,165.0,,,16.0,,,,,,181.0,,,,1.0,21942.0,,,,,,,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,2023-08-21 08:05:58,16.0,,,,,,,,, +44,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 18:42:48,2024-10-03 14:09:39.000000,2024-10-03 14:09:32,4673.0,113.0,7847.0,752.0,872.0,1474.0,330.0,33968.0,,,,803.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +45,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.,25,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-10-02 14:45:46.000000,2024-09-17 18:00:40,2534.0,2.0,502.0,47.0,2048.0,96.0,698.0,1463.0,2024-07-03 19:29:34.000,2.2.11,54.0,69.0,deepmd-kit,deepmodeling/deepmd-kit,20.0,16.0,https://pypi.org/project/deepmd-kit,2024-09-25 16:10:59.000,4.0,3162.0,3903.0,https://anaconda.org/deepmodeling/deepmd-kit,2024-04-06 21:22:08.456,1316.0,1.0,40438.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2024-07-27 08:24:51.741318,1.0,2871.0,-2.0,,,,,,,,,,,,,, +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,,87.0,19.0,27.0,12.0,92.0,398.0,2024-05-28 18:22:25.000,2.1.1,32.0,18.0,dscribe,conda-forge/dscribe,237.0,202.0,https://pypi.org/project/dscribe,2024-05-28 18:22:25.000,35.0,21786.0,24508.0,https://anaconda.org/conda-forge/dscribe,2024-05-28 23:16:49.298,138830.0,1.0,,,,,,,,,,,,,,,,,,,,, +47,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 15:23:44,2024-10-02 14:45:46.000000,2024-09-17 18:00:40,2534.0,2.0,502.0,47.0,2048.0,96.0,698.0,1462.0,2024-07-03 19:22:15.000,2.2.11,50.0,69.0,,,16.0,16.0,,,,,685.0,,,,1.0,40438.0,,,,,,-2.0,,,,,,,,,,,,,, +48,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,,126.0,30.0,484.0,24.0,150.0,461.0,2023-11-14 16:41:14.000,2.2.4,24.0,19.0,torchani,conda-forge/torchani,46.0,42.0,https://pypi.org/project/torchani,2023-11-14 16:41:14.000,4.0,2983.0,12843.0,https://anaconda.org/conda-forge/torchani,2024-09-11 21:03:45.224,493034.0,1.0,,,,,,,,,,,,,,,,,,,,, +49,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 15:04:21,2024-10-02 13:14:28.000000,2024-09-18 16:37:21,1768.0,21.0,78.0,21.0,594.0,9.0,62.0,362.0,2024-06-13 15:29:41.000,2024.6.13,16.0,33.0,maml,,12.0,10.0,https://pypi.org/project/maml,2024-06-13 15:29:41.000,2.0,525.0,525.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +50,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 18:36:05,2024-10-03 13:22:18.000000,2024-10-03 05:17:16,1071.0,83.0,59.0,12.0,267.0,7.0,90.0,256.0,2024-08-07 12:24:58.000,1.1.3,31.0,17.0,m3gnet,,51.0,46.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,1838.0,1838.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +51,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 13:24:23,2024-09-30 22:18:02.000000,2024-09-20 18:23:06,761.0,32.0,130.0,9.0,506.0,33.0,82.0,195.0,2024-09-20 18:25:00.000,0.2.21,48.0,61.0,dpdata,deepmodeling/dpdata,163.0,123.0,https://pypi.org/project/dpdata,2024-09-20 18:25:00.000,40.0,43081.0,43087.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,231.0,1.0,,,,,,,,,,,,,,,,,,,,, +52,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 21:06:36,2024-09-30 08:05:22.000000,2024-09-20 00:46:12,3277.0,17.0,57.0,10.0,312.0,53.0,59.0,149.0,2024-09-04 19:26:59.000,2024.9.4rc0,60.0,28.0,crystal-toolkit,,46.0,38.0,https://pypi.org/project/crystal-toolkit,2024-09-04 19:29:25.000,8.0,1951.0,1951.0,,,,1.0,,,,,,,1.0,,,,,,,,,,,,,, +53,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,,144.0,17.0,141.0,26.0,57.0,716.0,2023-02-13 08:45:17.000,0.3.2,17.0,22.0,dgllife,,249.0,245.0,https://pypi.org/project/dgllife,2022-12-21 13:18:00.570,4.0,13752.0,13752.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +54,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 23:44:39,2024-09-17 18:24:57.000000,2024-07-09 15:58:45,1873.0,4.0,131.0,22.0,162.0,26.0,66.0,611.0,2024-07-09 16:05:06.000,0.6.1,16.0,11.0,nequip,conda-forge/nequip,26.0,25.0,https://pypi.org/project/nequip,2024-07-09 16:05:26.000,1.0,3032.0,3238.0,https://anaconda.org/conda-forge/nequip,2024-07-10 05:13:00.157,5990.0,1.0,,,,,,,-1.0,,,,,,,,,,,,,, +55,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,,152.0,25.0,314.0,21.0,57.0,499.0,2022-11-16 21:25:01.818,1.3.2,37.0,14.0,megnet,,86.0,82.0,https://pypi.org/project/megnet,2022-11-16 21:25:01.818,4.0,2081.0,2081.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +56,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 19:34:02,2024-09-27 21:54:30.000000,2024-09-07 20:21:38,2108.0,2.0,123.0,26.0,237.0,45.0,45.0,302.0,2024-09-07 20:22:34.000,2024.8.30,110.0,15.0,jarvis-tools,conda-forge/jarvis-tools,130.0,99.0,https://pypi.org/project/jarvis-tools,2024-09-07 20:21:10.000,31.0,20179.0,21807.0,https://anaconda.org/conda-forge/jarvis-tools,2024-09-07 20:30:56.460,78147.0,2.0,,,,,,,,,,,,,,,,,,,,, +57,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 23:44:24,2024-09-16 22:18:58.000000,2024-09-16 21:57:28,426.0,18.0,62.0,6.0,97.0,3.0,56.0,232.0,2024-09-16 22:18:58.000,0.4.0,17.0,9.0,chgnet,,53.0,32.0,https://pypi.org/project/chgnet,2024-09-16 22:18:58.000,21.0,30174.0,30174.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +58,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,23,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2024-09-30 17:10:54.000000,2024-09-30 17:10:52,5636.0,74.0,20.0,10.0,1739.0,21.0,78.0,35.0,2024-06-20 22:37:55.000,5.8.4,162.0,25.0,mpcontribs-client,,42.0,39.0,https://pypi.org/project/mpcontribs-client,2024-06-20 22:37:55.000,3.0,2654.0,2654.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +59,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.,22,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2024-10-03 16:37:25.000000,2024-09-26 17:12:20,506.0,11.0,2336.0,405.0,273.0,25.0,34.0,16304.0,2024-09-26 15:53:57.000,2024.09.26,100.0,47.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +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 18:44:34,2024-10-02 18:02:28.000000,2024-10-02 17:45:12,903.0,152.0,176.0,24.0,238.0,70.0,207.0,493.0,2024-10-02 18:02:28.000,0.3.7,8.0,41.0,,,,,,,,,,,,,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 14:56:04,1287.0,38.0,71.0,10.0,230.0,34.0,84.0,325.0,2024-08-29 06:54:59.000,2.4.1,27.0,16.0,,conda-forge/torchmd-net,,,,,,,14945.0,https://anaconda.org/conda-forge/torchmd-net,2024-09-12 06:52:04.422,179349.0,1.0,,,,,,,,,,,,,,,,,,,,, +62,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 23:40:56,2024-09-30 22:44:48.000000,2024-09-30 15:46:43,4556.0,61.0,67.0,21.0,196.0,36.0,179.0,287.0,2024-03-25 15:48:12.000,1.3.0,6.0,42.0,,,11.0,11.0,,,,,0.0,,,,1.0,8.0,,,,,,,,,,,,,,,,,,,, +63,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 21:53:25,1004.0,18.0,18.0,12.0,48.0,1.0,21.0,177.0,2024-08-14 05:14:56.000,0.20.7,43.0,7.0,e3nn-jax,,51.0,38.0,https://pypi.org/project/e3nn-jax,2024-08-14 05:15:15.000,13.0,3572.0,3572.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +64,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,,3183.0,295.0,540.0,337.0,570.0,13325.0,,,,115.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +65,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 04:47:27,2024-10-02 23:03:32.000000,2024-10-01 21:05:18,860.0,73.0,233.0,24.0,666.0,13.0,193.0,773.0,2024-09-14 00:47:10.000,fairchem_core-1.2.0,11.0,42.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +66,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 15:32:56,2024-10-03 17:48:33.000000,2024-10-01 08:11:41,4008.0,137.0,115.0,27.0,545.0,17.0,164.0,453.0,2024-08-18 13:16:02.000,3.9.5,42.0,34.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +67,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.,21,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2024-10-02 10:15:04.000000,2024-04-10 06:31:36,2083.0,,172.0,13.0,847.0,35.0,264.0,298.0,2024-04-10 06:37:54.000,0.12.1,18.0,64.0,dpgen,deepmodeling/dpgen,7.0,6.0,https://pypi.org/project/dpgen,2024-04-10 06:37:54.000,1.0,764.0,799.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,206.0,1.0,1794.0,,,,,,,,,,,,,,,,,,,, +68,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.,21,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2024-09-09 21:38:54.000000,2024-09-09 21:38:06,717.0,12.0,79.0,11.0,109.0,40.0,25.0,219.0,2024-09-09 21:38:54.000,2024.8.30,48.0,7.0,alignn,,20.0,14.0,https://pypi.org/project/alignn,2024-09-09 21:37:57.000,6.0,3309.0,3309.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +69,pymatviz,,visualization,MIT,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,21,True,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2024-10-03 18:01:27.000000,2024-10-03 15:29:34,345.0,42.0,14.0,7.0,171.0,12.0,35.0,159.0,2024-09-01 05:39:02.000,0.11.0,28.0,8.0,pymatviz,,10.0,8.0,https://pypi.org/project/pymatviz,2024-09-01 05:39:02.000,2.0,3533.0,3533.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +70,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 15:58:28,2024-10-02 08:03:05.000000,2024-10-02 08:03:02,808.0,59.0,15.0,18.0,535.0,72.0,135.0,52.0,2024-09-03 09:35:24.000,metatensor-torch-v0.5.5,43.0,22.0,,,11.0,11.0,,,,,2295.0,,,,2.0,27545.0,,,,,,,metatensor/metatensor,,,,,,,,,,,,, +71,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,,2576.0,325.0,237.0,266.0,139.0,13111.0,,,,92.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +72,DIG: Dive into Graphs,,rep-learn,GPL-3.0,https://github.com/divelab/DIG,A library for graph deep learning research.,20,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,,281.0,31.0,41.0,34.0,176.0,1853.0,2023-04-07 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,,582.0,582.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +73,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,20,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2024-09-24 11:12:20.000000,2024-09-24 11:10:06,1465.0,5.0,59.0,22.0,161.0,4.0,42.0,347.0,2024-09-24 11:12:20.000,1.2.0,12.0,13.0,deepqmc,,2.0,2.0,https://pypi.org/project/deepqmc,2024-09-24 11:12:20.000,,285.0,285.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +74,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 17:03:57,2024-09-27 21:45:56.000000,2024-09-27 21:45:56,3308.0,12.0,58.0,14.0,37.0,32.0,191.0,104.0,2024-09-27 21:44:25.000,3.2.1,8.0,19.0,,,43.0,43.0,,,,,2.0,,,,2.0,117.0,,,,,,1.0,,,,,,,,,,,,,, +75,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 15:01:21,2024-09-30 21:58:34.000000,2024-09-17 06:02:31,395.0,73.0,3.0,1.0,356.0,90.0,218.0,30.0,2024-08-26 14:32:18.000,0.4.7,56.0,7.0,zndraw,,6.0,4.0,https://pypi.org/project/zndraw,2024-08-26 14:33:31.000,2.0,1222.0,1222.0,,,,2.0,,,,,,,1.0,,,,,,,,,,,,,, +76,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 14:48:35,2024-09-09 04:31:04.000000,2024-09-09 04:30:59,7724.0,44.0,774.0,250.0,21.0,,14.0,4749.0,,,,12.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +77,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 04:47:27,2024-10-02 23:03:32.000000,2024-10-01 21:05:18,860.0,73.0,233.0,24.0,666.0,13.0,193.0,773.0,2024-09-14 00:47:10.000,fairchem_core-1.2.0,11.0,42.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +78,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,,35.0,17.0,6.0,21.0,40.0,300.0,2022-07-20 00:58:03.115,0.2.6,15.0,10.0,atom3d,,41.0,41.0,https://pypi.org/project/atom3d,2022-07-20 00:58:03.115,,1166.0,1166.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +79,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..,19,False,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2024-09-19 17:01:30.000000,2023-06-06 23:56:03,261.0,,60.0,11.0,36.0,15.0,20.0,233.0,2022-11-17 23:25:35.000,0.2.4,16.0,15.0,m3gnet,,30.0,25.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,1838.0,1838.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +80,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,19,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 13:39:30,873.0,63.0,18.0,7.0,112.0,9.0,27.0,103.0,2024-07-02 15:52:45.000,0.3.1,6.0,20.0,dadapy,,8.0,8.0,https://pypi.org/project/dadapy,2024-07-02 15:49:35.000,,203.0,203.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +81,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,19,True,['sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2024-10-02 15:17:35.000000,2024-10-02 15:17:32,1130.0,41.0,24.0,7.0,83.0,15.0,58.0,91.0,2024-06-12 17:08:54.000,1.1.1,10.0,8.0,mlcolvar,,2.0,2.0,https://pypi.org/project/mlcolvar,2024-06-12 17:08:54.000,,244.0,244.0,,,,2.0,,,,,,,2.0,,,,,,,,,,,,,, +82,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 13:26:41,2024-09-26 03:45:39.000000,2024-09-26 03:45:38,138.0,14.0,119.0,16.0,116.0,66.0,94.0,676.0,2024-07-06 07:05:10.000,0.2.1,3.0,17.0,,,,,,,,,632.0,,,,2.0,15189.0,,,,,,,,,,,,,,,,,,,, +83,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,,174.0,11.0,1.0,2.0,7.0,457.0,2024-07-16 11:55:19.000,0.3.6,4.0,2.0,mace-torch,,14.0,,https://pypi.org/project/mace-torch,2024-07-16 11:55:19.000,14.0,11460.0,14538.0,,,,3.0,27705.0,,,,,,,,,,,,,,,,,,,, +84,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 13:43:18,298.0,2.0,68.0,17.0,150.0,14.0,98.0,336.0,2024-06-13 15:18:45.000,1.4.1,86.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,2024-09-12 13:44:36.000,,2115.0,2115.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +85,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,18,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,,40.0,8.0,77.0,11.0,58.0,283.0,2023-06-19 20:58:01.262,3.0.3,27.0,7.0,exmol,,21.0,20.0,https://pypi.org/project/exmol,2022-06-03 18:52:10.000,1.0,1049.0,1049.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +86,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,18,True,,google-deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2024-10-03 02:29:37.000000,2024-09-27 14:11:12,236.0,24.0,18.0,11.0,256.0,9.0,10.0,234.0,2024-04-04 10:59:13.000,0.0.6,5.0,15.0,kfac-jax,,11.0,10.0,https://pypi.org/project/kfac-jax,2024-04-04 10:59:13.000,1.0,1086.0,1086.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +87,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating machine-learning interatomic potentials for LAMMPS.,18,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2024-09-19 15:26:23.000000,2024-09-19 15:26:23,1399.0,16.0,50.0,7.0,184.0,16.0,57.0,148.0,2023-06-28 16:00:48.000,3.1.0,7.0,24.0,,conda-forge/fitsnap3,2.0,2.0,,,,,181.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,8539.0,2.0,11.0,,,,,,,,,,,,,,,,,,,, +88,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 09:59:42,2024-09-26 13:45:27.000000,2024-09-26 13:38:28,737.0,12.0,32.0,19.0,244.0,36.0,88.0,130.0,2024-08-30 11:47:55.000,0.7.3,18.0,22.0,,,9.0,6.0,,,,,19.0,,,,3.0,314.0,,,,,,,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,2023-03-15 15:39:26.701,3.0,14.0,,,, +89,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,,46.0,8.0,299.0,39.0,26.0,112.0,2022-07-27 04:40:26.000,0.6,5.0,25.0,matbench,,18.0,16.0,https://pypi.org/project/matbench,2022-07-27 04:44:21.961,2.0,406.0,406.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +90,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",18,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,,30.0,7.0,30.0,12.0,74.0,107.0,2024-02-27 12:33:28.000,4.0.1,28.0,7.0,kgcnn,,21.0,18.0,https://pypi.org/project/kgcnn,2024-02-27 12:33:28.000,3.0,661.0,661.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +91,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,18,True,"['datasets', 'benchmarking', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2024-10-02 20:31:45.000000,2024-10-02 20:31:44,381.0,39.0,12.0,8.0,92.0,4.0,35.0,92.0,2024-09-11 19:00:12.000,1.3.1,10.0,8.0,matbench-discovery,,2.0,2.0,https://pypi.org/project/matbench-discovery,2024-09-11 19:00:12.000,,1684.0,1684.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +92,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,,,,,,,,,,,,,,,,,,,,, +93,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 19:23:26,2024-08-12 16:25:31.000000,2024-08-06 07:51:07,386.0,4.0,20.0,17.0,162.0,14.0,56.0,76.0,2023-08-24 17:18:49.000,0.2.0,7.0,15.0,skmatter,conda-forge/skmatter,10.0,10.0,https://pypi.org/project/skmatter,2023-08-24 17:18:19.000,,2158.0,2274.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,2207.0,2.0,,,,,,,,,,,,,,,,,,,,, +94,apax,,ml-iap,MIT,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,18,True,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2024-10-03 18:20:03.000000,2024-10-01 09:57:43,1823.0,273.0,2.0,4.0,230.0,13.0,110.0,15.0,2024-09-17 10:55:44.000,0.7.0,7.0,7.0,apax,,2.0,2.0,https://pypi.org/project/apax,2024-09-17 10:55:44.000,,284.0,284.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +95,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 11:27:40,2024-09-08 12:35:49.000000,2024-09-08 12:35:46,2320.0,3.0,90.0,48.0,203.0,367.0,560.0,663.0,,,,35.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +96,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,17,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 20:39:32,3124.0,27.0,48.0,7.0,6.0,2.0,18.0,234.0,2024-05-29 21:15:00.000,1.0.0,1.0,41.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +97,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,17,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,,24.0,7.0,69.0,8.0,32.0,202.0,2024-03-20 06:39:54.000,0.1.8,16.0,6.0,gpax,,2.0,2.0,https://pypi.org/project/gpax,2024-03-20 06:39:54.000,,385.0,385.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +98,OpenBioML ChemNLP,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,17,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2024-09-30 17:30:54.000000,2024-08-19 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 12:49:57.000,1.0,98.0,98.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +99,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 20:27:28,2024-10-01 22:42:43.000000,2024-10-01 22:42:43,2500.0,242.0,19.0,5.0,237.0,20.0,40.0,143.0,2023-08-31 23:59:40.000,1.0.0,2.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +100,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.,17,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2024-08-21 12:43:15.000000,2024-07-04 09:53:01,2307.0,,23.0,9.0,304.0,42.0,231.0,81.0,2024-02-01 08:57:56.000,1.2.1,9.0,44.0,,,1.0,1.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +101,SpheriCart,,math,MIT,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,17,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2024-10-02 14:16:07.000000,2024-09-07 05:19:57,380.0,21.0,11.0,5.0,116.0,23.0,18.0,70.0,2024-09-04 06:56:55.000,0.5.0,11.0,10.0,sphericart,,3.0,3.0,https://pypi.org/project/sphericart,2024-09-04 06:56:55.000,,978.0,982.0,,,,1.0,86.0,,,,,,,,,,,,,,,,,,,, +102,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 22:44:02,2024-09-28 00:16:14.000000,2024-02-23 02:53:46,287.0,,7.0,9.0,116.0,1.0,3.0,36.0,2024-02-23 02:55:26.000,0.4.4,28.0,8.0,mp-pyrho,,27.0,24.0,https://pypi.org/project/mp-pyrho,2024-02-23 02:55:26.000,3.0,18047.0,18047.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +103,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 16:34:45,2024-09-30 21:58:36.000000,2024-09-19 19:38:38,447.0,20.0,10.0,3.0,206.0,68.0,65.0,18.0,2024-08-08 20:37:20.000,0.1.3,7.0,8.0,ipsuite,,6.0,6.0,https://pypi.org/project/ipsuite,2024-08-08 20:37:48.000,,151.0,151.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +104,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/.,16,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 09:29:54,246.0,2.0,44.0,19.0,33.0,38.0,37.0,350.0,2023-07-17 22:58:13.120,1.0.11,16.0,10.0,escnn,,6.0,,https://pypi.org/project/escnn,2022-04-01 11:46:00.000,6.0,1008.0,1008.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +105,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,,108.0,18.0,16.0,21.0,9.0,305.0,2017-02-03 00:28:29.000,1.3.0,8.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,123.0,115.0,https://pypi.org/project/chemdataextractor,2017-02-03 00:12:36.000,8.0,871.0,934.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3165.0,1.0,3045.0,,,,,,,,,,,,,,,,,,,, +106,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,16,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,,31.0,31.0,https://pypi.org/project/qml,2018-08-13 10:37:42.000,,628.0,628.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +107,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 21:20:06,74.0,4.0,23.0,11.0,63.0,26.0,66.0,180.0,2023-10-09 08:49:10.000,1.4,16.0,8.0,,conda-forge/openmm-torch,,,,,,,10510.0,https://anaconda.org/conda-forge/openmm-torch,2024-09-30 23:48:17.883,472971.0,2.0,,,,,,,,,,,,,,,,,,,,, +108,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,16,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,,10.0,9.0,https://pypi.org/project/sgdml,2023-08-31 12:59:32.000,1.0,594.0,594.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +109,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,16,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,,6.0,5.0,https://pypi.org/project/catlearn,2020-03-27 09:27:26.000,1.0,524.0,524.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +110,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,16,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 09:57:16,282.0,5.0,32.0,7.0,176.0,26.0,27.0,77.0,2024-05-07 14:09:13.000,0.4.4,21.0,10.0,,,9.0,9.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +111,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 13:47:48,2024-09-23 01:49:19.000000,2024-09-23 01:49:19,62.0,17.0,9.0,3.0,22.0,1.0,3.0,48.0,2024-08-22 00:40:08.000,3.10.1,18.0,3.0,mlatom,,,,https://pypi.org/project/mlatom,2024-09-23 01:35:47.000,,1637.0,1637.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +112,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,,331.0,31.0,46.0,92.0,66.0,2084.0,2024-04-03 08:23:10.000,dig-v1.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +113,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 19:37:03,76.0,1.0,130.0,48.0,78.0,29.0,42.0,894.0,2024-09-09 15:40:21.000,1.0,10.0,24.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +114,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 13:11:21,243.0,14.0,122.0,34.0,30.0,1.0,49.0,725.0,,,,18.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +115,ChemCrow,,language-models,MIT,https://github.com/ur-whitelab/chemcrow-public,Open source package for the accurate solution of reasoning-intensive chemical tasks.,15,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,,87.0,18.0,23.0,8.0,14.0,596.0,2024-03-27 04:30:13.000,0.3.24,27.0,3.0,chemcrow,,5.0,5.0,https://pypi.org/project/chemcrow,2024-03-27 04:30:13.000,,634.0,634.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +116,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 13:43:18,298.0,2.0,68.0,17.0,150.0,14.0,98.0,336.0,2024-06-13 15:18:45.000,1.4.1,57.0,20.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +117,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,,42.0,11.0,37.0,9.0,30.0,264.0,2024-01-05 14:31:05.000,0.4.1,5.0,5.0,molecule-generation,,1.0,,https://pypi.org/project/molecule-generation,2024-01-05 14:31:05.000,1.0,315.0,315.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +118,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,15,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,,8.0,8.0,https://pypi.org/project/automatminer,2020-07-28 02:23:45.000,,333.0,333.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +119,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,15,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,,57.0,11.0,184.0,21.0,66.0,135.0,2023-05-21 15:54:32.000,0.6.8,54.0,10.0,xenonpy,,1.0,,https://pypi.org/project/xenonpy,2022-10-31 15:40:18.355,1.0,673.0,691.0,,,,3.0,1428.0,,,,,,,,,,,,,,,,,,,, +120,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..,15,True,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2024-10-03 15:11:12.000000,2024-09-18 03:51:31,471.0,142.0,14.0,4.0,74.0,11.0,11.0,112.0,2024-07-26 01:40:27.000,0.9.3,6.0,11.0,,,3.0,3.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +121,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,,232.0,232.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +122,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,,37.0,85.0,2024-06-20 07:02:24.000,2.2.0,17.0,2.0,moftransformer,,7.0,6.0,https://pypi.org/project/moftransformer,2024-06-20 07:01:44.000,1.0,443.0,443.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +123,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.,15,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2024-10-02 17:04:39.000000,2024-10-02 17:04:39,723.0,12.0,20.0,6.0,83.0,19.0,31.0,60.0,2023-10-27 16:37:16.000,0.4.0,4.0,10.0,uf3,,1.0,1.0,https://pypi.org/project/uf3,2023-10-27 16:37:16.000,,51.0,51.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +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 12:29:05,2024-09-10 19:56:02.000000,2024-09-10 19:56:00,641.0,26.0,11.0,3.0,62.0,4.0,25.0,47.0,2024-07-22 19:03:03.000,1.0.0,5.0,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +125,load-atoms,,datasets,MIT,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,15,True,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-09-16 14:12:32.000000,2024-09-16 14:11:05,277.0,6.0,2.0,1.0,35.0,1.0,30.0,38.0,2024-09-16 14:12:32.000,0.3.1,38.0,3.0,load-atoms,,3.0,3.0,https://pypi.org/project/load-atoms,2024-09-16 14:12:32.000,,1023.0,1023.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +126,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 17:03:34,2024-10-03 18:18:02.000000,2024-09-03 17:28:08,1186.0,90.0,18.0,9.0,182.0,67.0,92.0,31.0,2024-04-25 15:07:11.000,0.2.4,4.0,19.0,,,1.0,1.0,,,,,,,,,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,,448.0,58.0,17.0,5.0,63.0,2497.0,,,,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 15:27:25,2024-10-02 20:00:23.000000,2024-10-02 20:00:23,15.0,15.0,19.0,4.0,12.0,2.0,9.0,157.0,2024-09-13 10:55:05.000,0.3.2,4.0,6.0,orb-models,,1.0,1.0,https://pypi.org/project/orb-models,2024-09-13 10:55:05.000,,1225.0,1225.0,,,,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 09:41:03,150.0,17.0,15.0,7.0,21.0,3.0,6.0,79.0,2024-06-24 11:09:20.000,0.3.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +132,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 03:32:03,2024-09-27 18:38:54.000000,2024-09-27 18:38:54,691.0,22.0,26.0,11.0,244.0,17.0,32.0,61.0,2023-11-10 15:25:43.000,3.0,2.0,14.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +133,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,14,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2024-10-01 20:44:08.000000,2024-07-06 01:33:46,1073.0,1.0,20.0,4.0,150.0,22.0,19.0,34.0,2023-12-17 02:12:19.000,0.4.3,18.0,9.0,kliff,conda-forge/kliff,3.0,3.0,https://pypi.org/project/kliff,2023-12-17 02:12:19.000,,453.0,2666.0,https://anaconda.org/conda-forge/kliff,2024-09-10 06:39:09.645,106270.0,2.0,,,,,,,,,,,,,,,,,,,,, +134,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,14,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-09-05 19:43:43.000000,2024-01-21 13:59:55,334.0,,7.0,6.0,14.0,7.0,6.0,26.0,2024-09-05 19:43:43.000,0.5.3,15.0,1.0,glasspy,,6.0,6.0,https://pypi.org/project/glasspy,2024-09-05 19:43:43.000,,599.0,599.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +135,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 06:44:00,439.0,17.0,58.0,18.0,5.0,1.0,14.0,496.0,,,,29.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +136,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 06:44:00,439.0,17.0,58.0,18.0,5.0,1.0,14.0,496.0,,,,29.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +137,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 06:44:00,439.0,17.0,58.0,18.0,5.0,1.0,14.0,496.0,,,,29.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +138,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 06:44:00,439.0,17.0,58.0,18.0,5.0,1.0,14.0,496.0,,,,29.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +139,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 01:35:50,2024-08-20 02:36:26.000000,2024-01-12 00:58:20,431.0,,42.0,9.0,161.0,10.0,16.0,151.0,2023-11-09 14:32:37.000,1.0.0,4.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +140,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,13,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 15:29:02,95.0,1.0,17.0,8.0,63.0,21.0,34.0,81.0,2023-07-26 11:21:58.000,0.6,7.0,9.0,,conda-forge/nnpops,,,,,,,7614.0,https://anaconda.org/conda-forge/nnpops,2024-09-11 06:01:38.318,236045.0,2.0,,,,,,,,,,,,,,,,,,,,, +141,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,,,,,,,,,,,,,,,,,,,,, +142,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,,,,,,,203.0,https://anaconda.org/conda-forge/openmm-ml,2024-06-07 16:52:07.157,5291.0,3.0,,,,,,,,,,,,,,,,,,,,, +143,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,13,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 05:02:12.000,2.0,629.0,629.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +144,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,,20.0,19.0,https://pypi.org/project/synspace,2023-01-16 17:29:00.461,1.0,1245.0,1245.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +145,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 15:31:59,716.0,18.0,4.0,3.0,46.0,1.0,5.0,30.0,2024-09-26 12:33:31.000,3.0.1,3.0,17.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +146,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 02:20:53,2024-09-22 05:47:21.000000,2024-09-22 05:31:17,107.0,12.0,3.0,3.0,8.0,2.0,,22.0,2024-09-22 05:47:21.000,2024.9.18,3.0,2.0,atomgpt,,1.0,1.0,https://pypi.org/project/atomgpt,2024-09-22 05:47:21.000,,257.0,257.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +147,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,,,,,,,,,,,,,,,,,,,,, +148,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,13,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,,15.0,13.0,https://pypi.org/project/crabnet,2023-01-10 04:27:02.444,2.0,659.0,659.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +149,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,,276.0,23.0,7.0,18.0,20.0,639.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +150,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,,,,,,,,,,,,,,,,,,,,, +151,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,,41.0,10.0,5.0,3.0,6.0,464.0,2023-06-18 23:20:44.000,0.2.0,2.0,3.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +152,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,,,,,,,,,,,,,,,,,,,,, +153,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,,,,,,,,,,,,,,,,,,,,, +154,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,12,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,,119.0,119.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +155,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,,,,,,,,,,,,,,,,,,,,, +156,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 02:51:32,2024-05-22 10:50:01.000000,2024-05-22 10:50:01,66.0,,44.0,7.0,18.0,13.0,38.0,219.0,2023-07-11 08:13:06.000,0.2.2,2.0,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +157,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,,32.0,6.0,15.0,1.0,5.0,104.0,2019-10-09 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,,,,,,,,,,,,,,, +158,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.,12,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-09-12 07:53:45.000000,2024-09-06 12:34:16,164.0,5.0,16.0,5.0,24.0,16.0,37.0,69.0,2022-10-24 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,,17.0,17.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +159,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 00:45:13,2024-09-27 22:29:54.000000,2024-09-27 22:28:33,161.0,23.0,23.0,9.0,89.0,6.0,12.0,67.0,2024-01-29 22:04:53.000,hippynn-0.0.3,3.0,14.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +160,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/.,12,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 02:50:35,753.0,51.0,26.0,4.0,47.0,,,62.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +161,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,,,13.0,13.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +162,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,,15.0,46.0,2024-07-03 16:43:48.000,1.1.0,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,-2.0,,,,,,,,,,,,,, +163,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 14:28:34,2024-10-02 08:36:02.000000,2024-10-02 08:30:07,574.0,15.0,13.0,7.0,264.0,32.0,37.0,44.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +164,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,,1.0,1.0,https://pypi.org/project/nlcc,2022-12-07 05:07:49.878,,222.0,222.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +165,Atomvision,,visualization,https://github.com/usnistgov/atomvision/blob/master/LICENSE.md,https://github.com/usnistgov/atomvision,Deep learning framework for atomistic image data.,12,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,,2.0,2.0,https://pypi.org/project/atomvision,2023-05-08 03:15:44.402,,218.0,218.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +166,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,,13.0,13.0,https://pypi.org/project/CBFV,2021-10-24 17:22:06.000,,536.0,536.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +167,BOSS,,materials-discovery,Apache-2.0,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,12,True,['probabilistic'],,,2020-02-12 08:48:33,2024-07-20 17:27:04.000000,,,,11.0,,,1.0,29.0,20.0,2024-05-03 13:49:43.000,1.10.1,49.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,2024-07-20 17:27:04.000,,5193.0,5193.0,,,,1.0,,,,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,, +168,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,,5.0,5.0,9.0,3.0,10.0,15.0,2022-07-14 08:49:29.365,0.3.4,3.0,9.0,benchml,,,,https://pypi.org/project/benchml,2022-07-14 08:49:29.365,,240.0,240.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +169,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,,1359.0,1387.0,,,,2.0,646.0,,,,,,,,,,,,,,,,,,,, +170,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 11:17:37,266.0,45.0,6.0,4.0,31.0,22.0,35.0,7.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +171,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,,114.0,16.0,92.0,28.0,130.0,608.0,,,,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +172,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,,131.0,19.0,9.0,27.0,8.0,348.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +173,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,,28.0,7.0,37.0,6.0,19.0,145.0,2023-08-30 13:54:23.000,1,1.0,6.0,,,6.0,6.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +174,Elementari,,visualization,MIT,https://github.com/janosh/elementari,"Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,..",11,True,['lang-js'],janosh/elementari,https://github.com/janosh/elementari,2022-06-01 15:29:36,2024-07-27 19:03:30.000000,2024-07-19 21:52:55,180.0,1.0,12.0,5.0,43.0,2.0,5.0,135.0,2024-01-15 14:26:00.710,0.2.3,11.0,2.0,,,4.0,3.0,,,,,125.0,,,,3.0,,,,,,,,,,,,,elementari,https://www.npmjs.com/package/elementari,2024-01-15 14:26:00.710,1.0,125.0,,,, +175,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,,,,,,,,,,,,,,,,,,,,, +176,Neural-Network-Models-for-Chemistry,,community,,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,A collection of Nerual Network Models for chemistry.,11,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 01:25:39,216.0,12.0,10.0,3.0,22.0,1.0,1.0,78.0,2024-07-17 02:01:45.000,0.0.5,5.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +177,NIST ChemNLP,,language-models,MIT,https://github.com/usnistgov/chemnlp,ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data.,11,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,,70.0,2023-08-07 12:49:57.000,2023.7.1,6.0,2.0,chemnlp,,4.0,3.0,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,98.0,98.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +178,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,,,,,,,,,,,,,,,,,,,,, +179,ChatMOF,,language-models,MIT,https://github.com/Yeonghun1675/ChatMOF,Predict and Inverse design for metal-organic framework with large-language models (llms).,11,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,,5.0,58.0,2024-06-14 09:56:27.000,0.2.1,17.0,1.0,chatmof,,2.0,2.0,https://pypi.org/project/chatmof,2024-07-01 05:01:35.000,,337.0,337.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +180,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,,,,,,,,,,,,,,,,,,,,, +181,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,11,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,,6.0,5.0,https://pypi.org/project/cmlkit,2022-03-25 22:27:16.000,1.0,621.0,621.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +182,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 08:18:27,2024-09-26 20:12:39.000000,2024-09-26 19:50:25,55.0,18.0,1.0,3.0,1.0,,3.0,7.0,2024-09-26 20:12:39.000,3.1.2,3.0,,,,,,,,,,,,,,3.0,,,,,,,,Rutgers-ZRG/libfp,,,,,,,,,,,,, +183,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,,160.0,17.0,8.0,34.0,29.0,957.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +184,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,,112.0,36.0,12.0,15.0,2.0,671.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +185,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,,,,,,,,,,,,,,,,,,,,, +186,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,10,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 16:56:05,43.0,1.0,9.0,16.0,47.0,17.0,47.0,147.0,2024-04-15 20:17:14.000,2.0.1,8.0,1.0,,,,,,,,,9.0,,,,2.0,261.0,,,,,,,,,,,,,,,,,,,, +187,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,,31.0,4.0,9.0,1.0,3.0,133.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +188,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,,,,,,,,,,,,,,,,,,,,, +189,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,,6.0,5.0,43.0,11.0,43.0,74.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +190,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..,10,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 08:15:34,2024-09-10 19:39:55.000000,2024-09-10 19:39:52,374.0,5.0,7.0,,30.0,8.0,17.0,61.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +191,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,,11.0,3.0,40.0,5.0,15.0,42.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +192,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,,12.0,9.0,8.0,10.0,20.0,41.0,2022-05-20 00:39:04.000,0.5.2,4.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +193,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,,2.0,2.0,https://pypi.org/project/atom2vec,2024-02-23 21:43:41.000,,96.0,96.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +194,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,,,,,,,,,,,,,,,,,,,,, +195,FAENet,,rep-learn,MIT,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,10,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,,2.0,2.0,https://pypi.org/project/faenet,2023-09-14 21:06:36.000,,120.0,120.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +196,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,,,,,,,,,,,,,,,,,,,,, +197,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,,,,,,,,,,,,,,,,,,,,, +198,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,,264.0,264.0,,,,2.0,,,,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,, +199,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 09:35:09.000,2.0,2180.0,2180.0,,,,3.0,,,,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,, +200,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.",10,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,,1.0,1.0,https://pypi.org/project/pyNNsMD,2022-04-12 15:15:00.183,,84.0,84.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +201,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,..",9,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 13:12:36,126.0,4.0,57.0,29.0,13.0,,1.0,913.0,,,,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +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 18:51:58,10.0,2.0,137.0,45.0,7.0,18.0,4.0,871.0,,,,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 18:51:58,10.0,2.0,137.0,45.0,7.0,18.0,4.0,871.0,,,,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,,66.0,17.0,5.0,11.0,17.0,488.0,,,,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,,110.0,8.0,,10.0,36.0,432.0,,,,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 13:34:19,139.0,2.0,81.0,17.0,54.0,,8.0,372.0,2023-03-02 19:56:59.000,2023.03.02,1.0,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +207,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..,9,False,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2024-07-01 20:43:10.000000,2023-05-08 21:16:45,38.0,,45.0,19.0,5.0,20.0,15.0,328.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +208,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,,1.0,30.0,288.0,,,,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +209,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,,41.0,10.0,3.0,9.0,10.0,254.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +210,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,,41.0,10.0,3.0,9.0,10.0,254.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +211,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,,1.0,2.0,217.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +212,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,,14.0,177.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +213,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 17:27:43.196,310.0,3.0,,,,,,,,,,,,,,,,,,,,, +214,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,,3.0,3.0,69.0,2021-04-05 06:49:29.000,0.2,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +215,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,,,,,,,,,,,,,,,,,,,,, +216,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,,,,,,,,,,,,,,,,,,,,, +217,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.,9,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 16:58:10,79.0,61.0,6.0,2.0,10.0,,,57.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +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,,10.0,31.0,1.0,4.0,5.0,44.0,,,,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 21:27:41,2024-06-26 12:42:53.000000,2024-06-26 12:42:45,242.0,,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,1.0,1.0,https://pypi.org/project/skipatom,2022-05-04 13:20:18.000,,256.0,314.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1577.0,3.0,,,,,,,,,,,,,,,,,,,,, +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 18:36:10,2024-08-22 09:01:16.000000,2024-07-02 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,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-..",9,False,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2024-06-05 15:25:30.000000,2023-04-12 15:04:14,33.0,,7.0,5.0,42.0,2.0,3.0,12.0,2024-02-07 16:35:47.000,0.1.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +227,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,,2.0,2.0,23.0,1.0,3.0,12.0,2024-02-14 10:35:02.000,0.1.0,2.0,4.0,simgen,,1.0,1.0,https://pypi.org/project/simgen,2024-02-14 11:08:25.000,,47.0,47.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +228,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,,,,,,,,,,,,,,,,,,,,, +229,2DMD 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,,,,,,,,,,,,,,,,,,,,, +230,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,,,,,,,,,,,,,,,,,,,,, +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,,6.0,1.0,294.0,,,,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,,72.0,17.0,7.0,5.0,1.0,258.0,,,,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,,1.0,3.0,202.0,,,,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 10:32:17,6.0,3.0,37.0,5.0,2.0,6.0,8.0,198.0,,,,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 05:51:23,16.0,4.0,26.0,5.0,1.0,15.0,3.0,198.0,,,,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,,70.0,8.0,8.0,5.0,2.0,174.0,,,,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,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,,,,,,,,,,,,,,,,,,,,, +239,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,,24.0,10.0,2.0,4.0,,95.0,,,,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,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,,5.0,11.0,1.0,3.0,4.0,60.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +242,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 04:10:44,30.0,2.0,9.0,5.0,4.0,3.0,1.0,58.0,,,,2.0,,,11.0,11.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +243,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 08:22:48,70.0,22.0,15.0,5.0,1.0,23.0,6.0,55.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +244,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..,8,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,,6.0,2.0,5.0,,11.0,48.0,2024-03-25 13:47:47.000,transferable_atomic_orbitals,6.0,7.0,deeperwin,,,,https://pypi.org/project/deeperwin,2021-12-14 11:03:19.657,,114.0,114.0,,,,3.0,10.0,,,,,,,,,,,,,,,,,,,, +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,,8.0,3.0,1.0,4.0,8.0,46.0,2022-03-08 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 08:35:27,206.0,2.0,20.0,10.0,69.0,,,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,,17.0,11.0,1.0,1.0,3.0,36.0,,,,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 23:13:24,2024-10-01 23:18:28.000000,2024-08-08 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,,6.0,4.0,1.0,,1.0,22.0,2022-02-06 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 10:33:47,2024-10-03 16:35:20.000000,2024-10-03 16:28:13,88.0,22.0,1.0,16.0,71.0,2.0,10.0,16.0,,,,10.0,,,,,,,,,,,,,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 09:19:05,2024-10-02 06:18:26.000000,2024-09-30 12:20:28,315.0,8.0,9.0,8.0,7.0,8.0,3.0,16.0,,,,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,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,,,,,,,,,,,,,,,,,,,,, +259,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,,,,8.0,,,,26.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +260,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.,8,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 21:32:12,53.0,13.0,2.0,5.0,,,,6.0,,,,7.0,,,,,,,,,,,,,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 08:17:42,2023-08-21 17:11:34.000000,2023-08-21 17:11:25,19.0,,1.0,2.0,,,,1.0,2023-08-21 17:11:01.000,0.0.10,9.0,2.0,MEGNetSparse,,1.0,1.0,https://pypi.org/project/MEGNetSparse,2023-08-21 17:11:01.000,,284.0,284.0,,,,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 05:43:46,2024-06-24 01:56:37.000000,2024-03-17 06:07:46,84.0,,17.0,7.0,2.0,,,274.0,,,,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,,24.0,10.0,,2.0,11.0,196.0,,,,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,,32.0,9.0,2.0,1.0,2.0,152.0,,,,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,,6.0,6.0,2.0,1.0,1.0,85.0,,,,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,,,,60.0,,,,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,,16.0,2.0,15.0,4.0,7.0,46.0,,,,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,,,39.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +275,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,,,,,,,,,,,,,,,,,,,,, +276,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,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +277,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,,,,,,,,,,,,,,,,,,,,, +278,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 14:35:27,12.0,4.0,8.0,5.0,1.0,2.0,3.0,29.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +279,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,,,,,,,,,,,,,,,,,,,,, +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,,5.0,2.0,,1.0,1.0,27.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +281,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,,,,,,,,,,,,,,,,,,,,, +282,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,MIT,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,7,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 07:36:11,24.0,17.0,9.0,2.0,,2.0,1.0,20.0,,,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +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 08:13:30,422.0,2.0,5.0,2.0,46.0,9.0,20.0,14.0,,,1.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +285,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,,,,,,,,,,,,,,,,,,,,, +286,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,,8.0,8.0,210.0,5.0,1.0,10.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +287,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.",7,False,"['workflows', 'sampling', 'md']",MMunibas/Asparagus,https://github.com/MMunibas/Asparagus,2024-07-08 13:44:56,2024-10-01 15:15:56.000000,2024-10-01 15:15:23,38.0,38.0,3.0,1.0,4.0,,,4.0,,,,6.0,,,,,,,,,,,,,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 07:35:07,217.0,4.0,5.0,4.0,17.0,1.0,14.0,1.0,,,,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,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,,,,,,,,,,,,,,,,,,,, +291,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,,42.0,12.0,2.0,1.0,1.0,255.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +292,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,,17.0,6.0,,10.0,6.0,70.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +293,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,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +294,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,,,5.0,57.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +295,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,,4.0,3.0,55.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +296,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,,,,,,,,,,,,,,,,,,,,, +297,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,6,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 12:45:42,7.0,2.0,10.0,3.0,,1.0,,38.0,,,,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,,33.0,,,,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,,,,29.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +300,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,, +301,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,,,,,,,,,,,,,,,,,,,,, +302,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,,,,,,,,,,,,,,,,,,,,, +303,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,,,,,,,,,,,,,,,,,,,,, +304,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,,,,,,,,,,,,,,,,,,,,, +305,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,,,,,,,,,,,,,,,,,,,,, +306,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,,,,,9.0,,,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,, +307,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,,,,,,,,,,,,,,,,,,,,, +308,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,,,,,,,,,,,,,,,,,,,,, +309,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,,,,,,,,,,,,,,,,,,,,, +310,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,,,,,,,,,,,,,,,,,,,,, +311,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,,,,,,,,,,,,,,,,,,,,, +312,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,,5.0,3.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +313,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, +314,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 03:21:10,381.0,10.0,4.0,2.0,7.0,1.0,1.0,4.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/ +315,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,,,,,,,,,,,,,,,,,,,,, +316,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,,,,,,,,,,,,,,,,,,,,, +317,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,,,,,,,,,,,,,,,,,,,,, +318,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,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +319,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,6,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,,104.0,104.0,,,,3.0,,,,,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/ +320,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,,5.0,2.0,6.0,1.0,2.0,98.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +321,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,,13.0,9.0,29.0,2.0,1.0,83.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +322,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,,12.0,4.0,3.0,7.0,2.0,68.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +323,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,,,,,,,,,,,,,,,,,,,,, +324,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,,18.0,3.0,7.0,1.0,,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +325,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,,,,,,,,,,,,,,,,,,,,, +326,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,,,,,,,,,,,,,,,,,,,,, +327,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,,3.0,3.0,,1.0,,32.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +328,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,5,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,,,2.0,2.0,,,,,,,,,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,,,1.0,1.0,,,,,,,,,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,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,,,,,,,,,,,,,,,,,,,,, +334,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,,3.0,1.0,2.0,1.0,,18.0,2023-06-22 22:36:36.000,1.1.0,3.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +335,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,,,,,,,,,,,,,,,,,,,,, +336,3DSC 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,,,,,,,,,,,,,,,,,,,,, +337,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,,,,,,,,,,,,,,,,,,,,, +338,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,,,,13.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +339,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,,,,,,,,,,,,,,,,,,,,, +340,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,,,,,,,,,,,,,,,,,,,,, +341,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,, +342,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,,,,,,,,,,,,,,,,,,,,, +343,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,,,,,,,,,,,,,,,,,,,, +344,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,,,,,,,,,,,,,,,,,,,,, +345,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,, +346,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,,,,,,,,,,,,,,,,,,,,, +347,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,,,,,,,,,,,,,,,,,,,,, +348,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,,5.0,6.0,13.0,2.0,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +349,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,,,,,,,,,,,,,,,,,,,,, +350,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,5,True,['rep-learn'],aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-08-19 08:43:44.000000,2024-08-19 08:40:19,92.0,6.0,1.0,3.0,1.0,1.0,2.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +351,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,,,,,,,,,,,,,,,,,,,,, +352,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,, +353,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,,,,,,,,,,,,,,,,,,,,, +354,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,,,,,,,,,,,,, +355,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,, +356,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,,,,,,,,,,,,,,,,,,,,, +357,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,,,,,,,,,,,,,,,,,,,,, +358,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,,,,,,,,,,,,,,,,,,,,, +359,"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,,,,,,,,,,,,,,,,,,,,, +360,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 +361,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,,,,,,,,,,,,,,,,,,,,, +362,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,,,,,,,,,,,,,,,,,,,,, +363,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,,1.0,92.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +364,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,,,,,,,,,,,,,,,,,,,,, +365,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,,,,,60.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,, +366,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,,,,,,,,,,,,,,,,,,,,, +367,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,,,,,,,,,,,,,,,,,,,,, +368,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,,,,,,,,,,,,,,,,,,,,, +369,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,,,,,,,,,,,,,,,,,,,,, +370,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,,,,,,,,,,,,,,,,,,,,, +371,SOMD,,md,AGPL-3.0,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,4,False,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2024-08-17 16:10:44.000000,2024-08-17 16:07:39,303.0,2.0,2.0,1.0,11.0,,1.0,12.0,,,,,,,,,,,,,,,,,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,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,,,,,,,,,,,,,,,,,,,,, +374,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,,,,6.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +375,Cephalo,,language-models,Apache-2.0,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,4,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 09:27:57,24.0,7.0,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +376,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,,,,,,,,,,,,,,,,,,,,, +377,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,,,,,,,,,,,,,,,,,,,,, +378,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,,,,,,,,,,,,,,,,,,,,, +379,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,,,,,,,,,,,,,,,,,,,,, +380,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,,,,,,,,,,,,,,,,,,,,, +381,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,,,,,,,,,,,,,,,,,,,,, +382,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 16:15:07,2024-10-03 06:38:10.000000,2024-10-01 19:09:06,140.0,2.0,3.0,19.0,142.0,10.0,18.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +383,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,,,,,,,,,,,,,,,,,,,,, +384,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,,,,,,,,,,,,,,,,,,,,, +385,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,,,,,,,,,,,,,,,,,,,,, +386,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,, +387,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,,,,,,,,,,,,,,,,,,,,, +388,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,,,,,,,,,,,,,,,,,,,,, +389,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,,,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +390,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,,,,,,,,,,,,,,,,,,,,, +391,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,,,,,,,,,,,,,,,,,,,,, +392,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,,,,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +393,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,,,,,,,,,,,,,,,,,,,,, +394,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,,,,,,,,,,,,,,,,,,,,, +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,,,,,,,1.0,1.0,,,,,,,,,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,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,,,,,,,,,,,,,,,,,,,,, +404,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +405,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,, +406,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,, +407,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,,,,,,,,,,,,,,,,,,,,, +408,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,,,,,,,,,,,,,,,,,,,,, +409,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,,,,,,,,,,,,,,,,,,,,, +410,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,,,,,,,,,,,,,,,,,,,,, +411,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,,,,,,,,,,,,,,,,,,,,, +412,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,,,,,,,,,,,,,,,,,,,,, +413,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,,,,,,,,,,,,,,,,,,,,, +414,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,,,,,,,,,,,,,,,,,,,,, +415,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,2,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2024-07-26 14:19:51.000000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +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,,2.0,2.0,1.0,1.0,1.0,17.0,,,,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 bb8adfa..4266c47 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._ -- FLARE (🥇22 · ⭐ 290 · 📈) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP -- DeepQMC (🥇20 · ⭐ 340 · 📈) - Deep learning quantum Monte Carlo for electrons in real space. MIT -- M3GNet (🥈19 · ⭐ 230 · 💀) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3 ML-IAP pretrained -- fplib (🥉10 · ⭐ 7 · 📈) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper -- Asparagus (🥉6 · ⭐ 4 · 🐣) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. MIT workflows sampling MD +- QUIP (🥈27 · ⭐ 350 · 📈) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran +- Crystal Toolkit (🥇24 · ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT +- MAST-ML (🥈20 · ⭐ 100 · 📈) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT +- ZnDraw (🥈20 · ⭐ 30 · 📈) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript +- mlcolvar (🥈19 · ⭐ 91 · 📈) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT sampling ## 📉 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._ -- JARVIS-Tools (🥈23 · ⭐ 300 · 📉) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom -- TorchMD-NET (🥇22 · ⭐ 320 · 📉) - Training neural network potentials. MIT MD rep-learn transformer pretrained -- GPUMD (🥈21 · ⭐ 450 · 📉) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics -- pymatviz (🥈21 · ⭐ 160 · 📉) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic -- MALA (🥇17 · ⭐ 81 · 📉) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3 +- DeepChem (🥇34 · ⭐ 5.4K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT +- DeePMD-kit (🥇25 · ⭐ 1.5K · 📉) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++ +- DPA-2 (🥇24 · ⭐ 1.5K · 📉) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets +- NequIP (🥇23 · ⭐ 610 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +- SchNetPack G-SchNet (🥈12 · ⭐ 46 · 📉) - G-SchNet extension for SchNetPack. MIT