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Update best-of list for version 2024.08.19-13.22
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<a href="https://doi.org/10.5281/zenodo.10430261"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.10430261.svg" alt="DOI"></a>
</p>

This curated list contains 420 awesome open-source projects with a total of 190K stars grouped into 23 categories. All projects are ranked by a [project-quality score](https://github.com/best-of-lists/best-of-generator#project-quality-score), which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an [issue](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/new/choose), submit a [pull request](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/pulls), or directly edit the [projects.yaml](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/edit/main/projects.yaml).
This curated list contains 420 awesome open-source projects with a total of 180K stars grouped into 22 categories. All projects are ranked by a [project-quality score](https://github.com/best-of-lists/best-of-generator#project-quality-score), which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an [issue](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/issues/new/choose), submit a [pull request](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/pulls), or directly edit the [projects.yaml](https://github.com/JuDFTteam/best-of-atomistic-machine-learning/edit/main/projects.yaml).

The current focus of this list is more on simulation data rather than experimental data, and more on materials rather than drug design. Nevertheless, contributions from other fields are warmly welcome!

Expand All @@ -37,7 +37,6 @@ The current focus of this list is more on simulation data rather than experiment
## Contents

- [Active learning](#active-learning) _6 projects_
- [Biomolecules](#biomolecules) _2 projects_
- [Community resources](#community-resources) _30 projects_
- [Datasets](#datasets) _45 projects_
- [Data Structures](#data-structures) _4 projects_
Expand Down Expand Up @@ -127,30 +126,6 @@ _Projects that focus on enabling active learning, iterative learning schemes for
</details>
<br>

## Biomolecules

<a href="#contents"><img align="right" width="15" height="15" src="https://git.io/JtehR" alt="Back to top"></a>

_Projects that focus on biomolecules, protein structure, protein folding, etc. using atomistic ML._

<details><summary><b><a href="https://github.com/google-deepmind/alphafold">AlphaFold</a></b> (🥇23 · ⭐ 12K) - Open source code for AlphaFold. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>

- [GitHub](https://github.com/google-deepmind/alphafold) (👨‍💻 20 · 🔀 2.1K · 📦 15 · 📋 850 - 29% open · ⏱️ 08.05.2024):

```
git clone https://github.com/google-deepmind/alphafold
```
</details>
<details><summary><b><a href="https://github.com/dptech-corp/Uni-Fold">Uni-Fold</a></b> (🥉16 · ⭐ 370 · 💤) - An open-source platform for developing protein models beyond AlphaFold. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>

- [GitHub](https://github.com/dptech-corp/Uni-Fold) (👨‍💻 7 · 🔀 67 · 📥 3.6K · 📋 70 - 28% open · ⏱️ 08.01.2024):

```
git clone https://github.com/dptech-corp/Uni-Fold
```
</details>
<br>

## Community resources

<a href="#contents"><img align="right" width="15" height="15" src="https://git.io/JtehR" alt="Back to top"></a>
Expand Down Expand Up @@ -213,9 +188,9 @@ _Projects that collect atomistic ML resources or foster communication within com
git clone https://github.com/GT4SD/gt4sd-core
```
</details>
<details><summary><b><a href="https://github.com/janosh/matbench-discovery">MatBench Discovery</a></b> (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code></summary>
<details><summary><b><a href="https://github.com/janosh/matbench-discovery">MatBench Discovery</a></b> (🥈16 · ⭐ 82) - An evaluation framework for machine learning models simulating high-throughput materials discovery. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code></summary>

- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 7 · 🔀 11 · 📦 2 · 📋 35 - 8% open · ⏱️ 18.08.2024):
- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 7 · 🔀 11 · 📦 2 · 📋 35 - 5% open · ⏱️ 19.08.2024):

```
git clone https://github.com/janosh/matbench-discovery
Expand All @@ -233,7 +208,7 @@ _Projects that collect atomistic ML resources or foster communication within com
git clone https://github.com/divelab/AIRS
```
</details>
<details><summary><b><a href="https://github.com/Eipgen/Neural-Network-Models-for-Chemistry">Neural-Network-Models-for-Chemistry</a></b> (🥈11 · ⭐ 64) - A collection of Nerual Network Models for chemistry. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
<details><summary><b><a href="https://github.com/Eipgen/Neural-Network-Models-for-Chemistry">Neural-Network-Models-for-Chemistry</a></b> (🥈11 · ⭐ 65) - A collection of Nerual Network Models for chemistry. <code>Unlicensed</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>

- [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 8 · 📋 2 - 50% open · ⏱️ 08.08.2024):

Expand Down Expand Up @@ -381,7 +356,7 @@ _Datasets, databases and trained models for atomistic ML._

🔗&nbsp;<b><a href="https://zinc.docking.org/">ZINC20</a></b> - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. <code>graph</code> <a href="https://en.wikipedia.org/wiki/Biomolecule"><code>biomolecules</code></a>

<details><summary><b><a href="https://github.com/Materials-Consortia/optimade-python-tools">OPTIMADE Python tools</a></b> (🥇26 · ⭐ 64 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>
<details><summary><b><a href="https://github.com/Materials-Consortia/optimade-python-tools">OPTIMADE Python tools</a></b> (🥇26 · ⭐ 64) - Tools for implementing and consuming OPTIMADE APIs in Python. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>

- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 48 · 📋 450 - 22% open · ⏱️ 19.08.2024):

Expand Down Expand Up @@ -817,7 +792,7 @@ _Projects that focus on explainability and model interpretability in atomistic M
pip install exmol
```
</details>
<details><summary><b><a href="https://github.com/aimat-lab/graph_attention_student">MEGAN: Multi Explanation Graph Attention Student</a></b> (🥉6 · ⭐ 5 · 📈) - Minimal implementation of graph attention student model architecture. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>
<details><summary><b><a href="https://github.com/aimat-lab/graph_attention_student">MEGAN: Multi Explanation Graph Attention Student</a></b> (🥉6 · ⭐ 5) - Minimal implementation of graph attention student model architecture. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>

- [GitHub](https://github.com/aimat-lab/graph_attention_student) (👨‍💻 2 · 🔀 1 · ⏱️ 19.08.2024):

Expand Down Expand Up @@ -923,7 +898,7 @@ _General tools for atomistic machine learning._
</details>
<details><summary><b><a href="https://github.com/materialsvirtuallab/maml">MAML</a></b> (🥈25 · ⭐ 350) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code></summary>

- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 32 · 🔀 74 · 📦 10 · 📋 70 - 12% open · ⏱️ 03.07.2024):
- [GitHub](https://github.com/materialsvirtuallab/maml) (👨‍💻 32 · 🔀 75 · 📦 10 · 📋 70 - 12% open · ⏱️ 03.07.2024):

```
git clone https://github.com/materialsvirtuallab/maml
Expand Down Expand Up @@ -957,7 +932,7 @@ _General tools for atomistic machine learning._
git clone https://github.com/uw-cmg/MAST-ML
```
</details>
<details><summary><b><a href="https://github.com/scikit-learn-contrib/scikit-matter">Scikit-Matter</a></b> (🥈19 · ⭐ 73 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>scikit-learn</code></summary>
<details><summary><b><a href="https://github.com/scikit-learn-contrib/scikit-matter">Scikit-Matter</a></b> (🥈19 · ⭐ 73) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>scikit-learn</code></summary>

- [GitHub](https://github.com/scikit-learn-contrib/scikit-matter) (👨‍💻 15 · 🔀 20 · 📦 10 · 📋 70 - 20% open · ⏱️ 06.08.2024):

Expand Down Expand Up @@ -985,7 +960,7 @@ _General tools for atomistic machine learning._
pip install xenonpy
```
</details>
<details><summary><b><a href="https://github.com/dralgroup/mlatom">MLatom</a></b> (🥉13 · ⭐ 36 · 📈) - AI-enhanced computational chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-DFT</code> <code>ML-ESM</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Spectroscopy"><code>spectroscopy</code></a> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>structure-optimization</code></a></summary>
<details><summary><b><a href="https://github.com/dralgroup/mlatom">MLatom</a></b> (🥉13 · ⭐ 36) - AI-enhanced computational chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-DFT</code> <code>ML-ESM</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Spectroscopy"><code>spectroscopy</code></a> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>structure-optimization</code></a></summary>

- [GitHub](https://github.com/dralgroup/mlatom) (👨‍💻 3 · 🔀 6 · ⏱️ 24.07.2024):

Expand Down Expand Up @@ -1573,7 +1548,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
</details>
<details><summary><b><a href="https://github.com/ur-whitelab/chemcrow-public">ChemCrow</a></b> (🥈16 · ⭐ 560) - Open source package for the accurate solution of reasoning-intensive chemical tasks. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Large_language_model#Agency"><code>ai-agent</code></a></summary>

- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 77 · 📦 4 · 📋 19 - 26% open · ⏱️ 27.03.2024):
- [GitHub](https://github.com/ur-whitelab/chemcrow-public) (👨‍💻 3 · 🔀 80 · 📦 4 · 📋 20 - 30% open · ⏱️ 27.03.2024):

```
git clone https://github.com/ur-whitelab/chemcrow-public
Expand Down Expand Up @@ -1607,7 +1582,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce
pip install chatmof
```
</details>
<details><summary><b><a href="https://github.com/chiang-yuan/llamp">LLaMP</a></b> (🥉10 · ⭐ 51) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Cheminformatics"><code>cheminformatics</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <a href="https://en.wikipedia.org/wiki/Language_model"><code>language-models</code></a> <code>Python</code> <code>general-tool</code></summary>
<details><summary><b><a href="https://github.com/chiang-yuan/llamp">LLaMP</a></b> (🥉10 · ⭐ 52) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>materials-discovery</code></a> <a href="https://en.wikipedia.org/wiki/Cheminformatics"><code>cheminformatics</code></a> <a href="https://en.wikipedia.org/wiki/Generative_model"><code>generative</code></a> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <a href="https://en.wikipedia.org/wiki/Multimodal_learning"><code>multimodal</code></a> <a href="https://en.wikipedia.org/wiki/Language_model"><code>language-models</code></a> <code>Python</code> <code>general-tool</code></summary>

- [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 5 · 🔀 7 · 📋 25 - 32% open · ⏱️ 01.08.2024):

Expand Down Expand Up @@ -1776,7 +1751,7 @@ _Projects that implement mathematical objects used in atomistic machine learning

<details><summary><b><a href="https://github.com/google-deepmind/kfac-jax">KFAC-JAX</a></b> (🥇19 · ⭐ 220) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. <code><a href="http://bit.ly/3nYMfla">Apache-2</a></code></summary>

- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 13 · 🔀 16 · 📦 10 · 📋 18 - 50% open · ⏱️ 16.08.2024):
- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 13 · 🔀 16 · 📦 10 · 📋 18 - 50% open · ⏱️ 19.08.2024):

```
git clone https://github.com/google-deepmind/kfac-jax
Expand All @@ -1786,7 +1761,7 @@ _Projects that implement mathematical objects used in atomistic machine learning
pip install kfac-jax
```
</details>
<details><summary><b><a href="https://github.com/ziatdinovmax/gpax">gpax</a></b> (🥇18 · ⭐ 200 · 📈) - Gaussian Processes for Experimental Sciences. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>
<details><summary><b><a href="https://github.com/ziatdinovmax/gpax">gpax</a></b> (🥇18 · ⭐ 200) - Gaussian Processes for Experimental Sciences. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a></summary>

- [GitHub](https://github.com/ziatdinovmax/gpax) (👨‍💻 6 · 🔀 22 · 📦 1 · 📋 40 - 20% open · ⏱️ 21.05.2024):

Expand All @@ -1800,7 +1775,7 @@ _Projects that implement mathematical objects used in atomistic machine learning
</details>
<details><summary><b><a href="https://github.com/lab-cosmo/sphericart">SpheriCart</a></b> (🥈16 · ⭐ 63) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. <code><a href="http://bit.ly/34MBwT8">MIT</a></code></summary>

- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 10 · 📥 60 · 📦 3 · 📋 32 - 59% open · ⏱️ 18.08.2024):
- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 10 · 📥 60 · 📦 3 · 📋 32 - 59% open · ⏱️ 19.08.2024):

```
git clone https://github.com/lab-cosmo/sphericart
Expand Down Expand Up @@ -1927,7 +1902,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
</details>
<details><summary><b><a href="https://github.com/mir-group/pair_allegro">pair_allegro</a></b> (🥉9 · ⭐ 34) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a></summary>

- [GitHub](https://github.com/mir-group/pair_allegro) (👨‍💻 2 · 🔀 8 · 📋 28 - 35% open · ⏱️ 05.06.2024):
- [GitHub](https://github.com/mir-group/pair_allegro) (👨‍💻 2 · 🔀 8 · 📋 29 - 37% open · ⏱️ 05.06.2024):

```
git clone https://github.com/mir-group/pair_allegro
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## 📈 Trending Up

_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._

- <b><a href="https://github.com/Materials-Consortia/optimade-python-tools">OPTIMADE Python tools</a></b> (🥇26 · ⭐ 64 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. <code><a href="http://bit.ly/34MBwT8">MIT</a></code>
- <b><a href="https://github.com/scikit-learn-contrib/scikit-matter">Scikit-Matter</a></b> (🥈19 · ⭐ 73 · 📈) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. <code><a href="http://bit.ly/3aKzpTv">BSD-3</a></code> <code>scikit-learn</code>
- <b><a href="https://github.com/ziatdinovmax/gpax">gpax</a></b> (🥇18 · ⭐ 200 · 📈) - Gaussian Processes for Experimental Sciences. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>probabilistic</code> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a>
- <b><a href="https://github.com/dralgroup/mlatom">MLatom</a></b> (🥉13 · ⭐ 36 · 📈) - AI-enhanced computational chemistry. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://www.google.com/search?q=universal+interatomic+potential"><code>UIP</code></a> <code>ML-IAP</code> <a href="https://en.wikipedia.org/wiki/Molecular_dynamics"><code>MD</code></a> <code>ML-DFT</code> <code>ML-ESM</code> <a href="https://en.wikipedia.org/wiki/Transfer_learning"><code>transfer-learning</code></a> <a href="https://en.wikipedia.org/wiki/Active_learning_(machine_learning)"><code>active-learning</code></a> <a href="https://en.wikipedia.org/wiki/Spectroscopy"><code>spectroscopy</code></a> <a href="https://www.psik2022.net/program/symposia#h.p_hM6hJbQD9dex"><code>structure-optimization</code></a>
- <b><a href="https://github.com/aimat-lab/graph_attention_student">MEGAN: Multi Explanation Graph Attention Student</a></b> (🥉6 · ⭐ 5 · 📈) - Minimal implementation of graph attention student model architecture. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <a href="https://en.wikipedia.org/wiki/Feature_learning"><code>rep-learn</code></a>

## 📉 Trending Down

_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._

- <b><a href="https://github.com/janosh/matbench-discovery">MatBench Discovery</a></b> (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. <code><a href="http://bit.ly/34MBwT8">MIT</a></code> <code>datasets</code> <code>benchmarking</code> <code>model-repository</code>

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