Best of Atomistic Machine Learning ⚛️🧬💎

🏆 A ranked list of awesome atomistic machine learning (AML) projects. Updated quarterly.

This curated list contains 280 awesome open-source projects with a total of 120K stars grouped into 23 categories. All projects are ranked by a 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, submit a pull request, or directly edit the projects.yaml.

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

🧙‍♂️ Discover other best-of lists or create your own.

Active learning 4 projects
Biomolecules 2 projects
Community resources 14 projects
Datasets 23 projects
Data Structures 3 projects
Density functional theory (ML-DFT) 19 projects
Educational Resources 18 projects
Explainable Artificial intelligence (XAI) 4 projects
Electronic structure methods (ML-ESM) 2 projects
General Tools 22 projects
Generative Models 10 projects
Interatomic Potentials (ML-IAP) 51 projects
Language Models 6 projects
Materials Discovery 8 projects
Mathematical tools 8 projects
Molecular Dynamics 6 projects
Probabilistic ML 0 projects
Reinforcement Learning 2 projects
Representation Engineering 21 projects
Representation Learning 49 projects
Unsupervised Learning 6 projects
Visualization 1 projects
Wavefunction methods (ML-WFT) 4 projects
Others 1 projects

Explanation

🥇🥈🥉 Combined project-quality score
⭐️ Star count from GitHub
🐣 New project (less than 6 months old)
💤 Inactive project (6 months no activity)
💀 Dead project (12 months no activity)
📈📉 Project is trending up or down
➕ Project was recently added
👨‍💻 Contributors count from GitHub
🔀 Fork count from GitHub
📋 Issue count from GitHub
⏱️ Last update timestamp on package manager
📥 Download count from package manager
📦 Number of dependent projects

Active learning

Projects that focus on enabling active learning, iterative learning schemes for atomistic ML.

FLARE (🥇19 · ⭐ 240) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP

GitHub (👨‍💻 36 · 🔀 55 · 📥 1 · 📦 10 · 📋 180 - 10% open · ⏱️ 26.05.2023):
```
 git clone https://github.com/mir-group/flare
```

Finetuna (🥈10 · ⭐ 25) - Active Learning for Machine Learning Potentials. MIT

GitHub (👨‍💻 11 · 🔀 6 · 📋 17 - 17% open · ⏱️ 13.02.2023):
```
 git clone https://github.com/ulissigroup/finetuna
```

Show 2 hidden projects...

flare++ (🥈10 · ⭐ 34 · 💀) - A many-body extension of the FLARE code. MIT C++ ML-IAP
ACEHAL (🥉4 · ⭐ 7 · 🐣) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed Julia

Biomolecules

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

AlphaFold (🥇23 · ⭐ 11K) - Open source code for AlphaFold. Apache-2

GitHub (🔀 1.8K · 📦 6 · 📋 730 - 22% open · ⏱️ 10.08.2023):
```
 git clone https://github.com/deepmind/alphafold
```

Uni-Fold (🥉15 · ⭐ 280) - An open-source platform for developing protein models beyond AlphaFold. Apache-2

GitHub (👨‍💻 7 · 🔀 47 · 📥 2.1K · 📋 56 - 16% open · ⏱️ 19.06.2023):
```
 git clone https://github.com/dptech-corp/Uni-Fold
```

Community resources

Projects that collect atomistic ML resources or foster communication within community.

🔗 AI for Science Map - Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..

🔗 Atomic Cluster Expansion - Atomic Cluster Expansion (ACE) community homepage.

🔗 CrystaLLM - Generate a crystal structure from a composition. LM generative pre-trained

🔗 matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..

🔗 Matter Modeling Stack Exchange - Machine Learning - Forum StackExchange, site Matter Modeling, ML-tagged questions.

Best-of Machine Learning with Python (🥇22 · ⭐ 14K) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python

GitHub (👨‍💻 41 · 🔀 2K · 📋 48 - 33% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/ml-tooling/best-of-ml-python
```

Graph-based Deep Learning Literature (🥈18 · ⭐ 4.3K) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn

GitHub (👨‍💻 11 · 🔀 700 · ⏱️ 13.08.2023):

 git clone https://github.com/naganandy/graph-based-deep-learning-literature

MatBench (🥈16 · ⭐ 77) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking

GitHub (👨‍💻 22 · 🔀 30 · 📦 9 · 📋 57 - 54% open · ⏱️ 07.08.2023):
```
 git clone https://github.com/materialsproject/matbench
```
PyPi (📥 150 / month):
```
 pip install matbench
```

AI for Science Resources (🥉14 · ⭐ 220 · 🐣) - List of resources for AI4Science research, including learning resources. GPL-3.0 license

GitHub (👨‍💻 23 · 🔀 25 · ⏱️ 24.08.2023):

 git clone https://github.com/divelab/AIRS

Awesome Materials Informatics (🥉11 · ⭐ 290) - Curated list of known efforts in materials informatics = modern materials science. Custom

GitHub (👨‍💻 18 · 🔀 72 · ⏱️ 21.08.2023):

 git clone https://github.com/tilde-lab/awesome-materials-informatics

The Collection of Database and Dataset Resources in Materials Science (🥉8 · ⭐ 160) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed datasets

GitHub (👨‍💻 2 · 🔀 25 · ⏱️ 27.06.2023):

 git clone https://github.com/sedaoturak/data-resources-for-materials-science

Show 3 hidden projects...

A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉7 · ⭐ 93 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
GitHub topic materials-informatics - Unlicensed
MateriApps - Unlicensed

Datasets

Datasets, databases and trained models for atomistic ML.

🔗 Catalysis Hub - A web-platform for sharing data and software for computational catalysis research!.

🔗 Citrination Datasets - AI-Powered Materials Data Platform. Open Citrination has been decommissioned.

🔗 crystals.ai - Curated datasets for reproducible AI in materials science.

🔗 DeepChem Models - DeepChem models on HuggingFace. pre-trained LM

🔗 JARVIS-Leaderboard ( ⭐ 40) - This project provides benchmark-performances for materials science applications including Artificial Intelligence.. benchmarking

🔗 Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API.

🔗 matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.

🔗 Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.

🔗 sGDML Datasets - MD17, MD22, DFT datasets.

MPContribs (🥇23 · ⭐ 32) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT

GitHub (👨‍💻 25 · 🔀 20 · 📦 21 · 📋 98 - 20% open · ⏱️ 21.08.2023):
```
 git clone https://github.com/materialsproject/MPContribs
```
PyPi (📥 3.1K / month):
```
 pip install mpcontribs-client
```

Open Catalyst datasets (🥇18 · ⭐ 450) - The datasets of the Open Catalyst project, OC20, OC22. CC-BY-4.0

GitHub (👨‍💻 31 · 🔀 170 · 📋 120 - 9% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/Open-Catalyst-Project/ocp
```

SPICE (🥈14 · ⭐ 89) - A collection of QM data for training potential functions. MIT ML-IAP MD

GitHub (🔀 4 · 📥 200 · 📋 45 - 24% open · ⏱️ 18.08.2023):
```
 git clone https://github.com/openmm/spice-dataset
```

ANI-1 Dataset (🥈8 · ⭐ 87 · 💤) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT

GitHub (👨‍💻 3 · 🔀 19 · 📋 9 - 66% open · ⏱️ 08.08.2022):
```
 git clone https://github.com/isayev/ANI1_dataset
```

3DSC Database (🥉5 · ⭐ 6) - Repo for the paper publishing the superconductor database with 3D crystal structures. Custom superconductors materials-discovery

GitHub (🔀 2 · ⏱️ 21.07.2023):

 git clone https://github.com/aimat-lab/3DSC

SciGlass (🥉5 · ⭐ 6) - The database contains a vast set of data on the properties of glass materials. MIT

GitHub (👨‍💻 2 · 🔀 3 · ⏱️ 10.03.2023):

 git clone https://github.com/drcassar/SciGlass

Visual Graph Datasets (🥉5 · 🐣) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT

GitHub (🔀 1 · ⏱️ 12.06.2023):

 git clone https://github.com/aimat-lab/visual_graph_datasets

Show 7 hidden projects...

OpenKIM (🥈11 · ⭐ 29 · 💀) - The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-.. LGPL-2.1 knowledge-base pre-trained
2DMD dataset (🥈10 · ⭐ 1) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 material-defect
MoleculeNet Leaderboard (🥈8 · ⭐ 73 · 💀) - MIT benchmarking
ANI-1x Datasets (🥉6 · ⭐ 45 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
COMP6 Benchmark dataset (🥉6 · ⭐ 35 · 💀) - COMP6 Benchmark dataset for ML potentials. MIT
GEOM (🥉5 · ⭐ 130 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
linear-regression-benchmarks (🥉5 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper

Data Structures

Projects that focus on providing data structures used in atomistic machine learning.

dpdata (🥇21 · ⭐ 140) - Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc. LGPL-3.0

GitHub (👨‍💻 45 · 🔀 100 · 📦 100 · 📋 68 - 32% open · ⏱️ 04.05.2023):
```
 git clone https://github.com/deepmodeling/dpdata
```
PyPi (📥 2.2K / month):
```
 pip install dpdata
```
Conda (📥 490 · ⏱️ 16.06.2023):
```
 conda install -c deepmodeling dpdata
```

mp-pyrho (🥉15 · ⭐ 27 · 📈) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom ML-DFT

GitHub (👨‍💻 8 · 🔀 6 · 📦 18 · 📋 4 - 25% open · ⏱️ 21.03.2023):
```
 git clone https://github.com/materialsproject/pyrho
```
PyPi (📥 270 / month):
```
 pip install mp-pyrho
```

Equistore (🥉13 · ⭐ 24) - Storage format for equivariant atomistic machine learning. BSD-3

GitHub (👨‍💻 16 · 🔀 12 · 📋 100 - 40% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/lab-cosmo/equistore
```

Density functional theory (ML-DFT)

Projects and models that focus on quantities of DFT, such as density functional approximations (ML-DFA), the charge density, density of states, the Hamiltonian, etc.

DM21 (🥇20 · ⭐ 12K · 📉) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described.. Apache-2

GitHub (🔀 2.4K · 📋 290 - 53% open · ⏱️ 02.06.2023):

 git clone https://github.com/deepmind/deepmind-research

MALA (🥇18 · ⭐ 51) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3

GitHub (👨‍💻 41 · 🔀 19 · 📋 230 - 9% open · ⏱️ 04.08.2023):
```
 git clone https://github.com/mala-project/mala
```

DeepH-pack (🥈13 · ⭐ 130) - Deep neural networks for density functional theory Hamiltonian. LGPL-3.0 Julia

GitHub (🔀 25 · 📋 34 - 14% open · ⏱️ 11.07.2023):

 git clone https://github.com/mzjb/DeepH-pack

DeePKS-kit (🥈10 · ⭐ 93) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0

GitHub (👨‍💻 6 · 🔀 29 · 📋 10 - 10% open · ⏱️ 01.04.2023):
```
 git clone https://github.com/deepmodeling/deepks-kit
```

SALTED (🥈9 · ⭐ 12) - Symmetry-Adapted Learning of Three-dimensional Electron Densities. GPL-3.0

GitHub (👨‍💻 7 · 🔀 2 · ⏱️ 22.08.2023):

 git clone https://github.com/andreagrisafi/SALTED

ACEhamiltonians (🥈7 · ⭐ 7) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. MIT Julia

GitHub (👨‍💻 4 · 🔀 4 · 📋 4 - 25% open · ⏱️ 12.04.2023):
```
 git clone https://github.com/ACEsuit/ACEhamiltonians.jl
```

DeepDFT (🥉6 · ⭐ 35) - Official implementation of DeepDFT model. MIT

GitHub (👨‍💻 2 · 🔀 6 · ⏱️ 28.02.2023):

 git clone https://github.com/peterbjorgensen/DeepDFT

DeepH-E3 (🥉6 · ⭐ 29 · 🐣) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. MIT magnetism

GitHub (🔀 8 · 📋 7 - 28% open · ⏱️ 04.04.2023):

 git clone https://github.com/Xiaoxun-Gong/DeepH-E3

Show 11 hidden projects...

NeuralXC (🥈10 · ⭐ 28 · 💀) - Implementation of a machine learned density functional. BSD-3
PROPhet (🥈9 · ⭐ 59 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
Libnxc (🥈7 · ⭐ 13 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
charge-density-models (🥉4 · ⭐ 2) - Tools to build charge density models using ocpmodels. MIT
gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper
CSNN (🥉4 · 💤) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3
ML-DFT (🥉3 · ⭐ 18 · 💀) - A package for density functional approximation using machine learning. MIT
xDeepH (🥉3 · ⭐ 16 · 🐣) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia
DeepCDP (🥉3 · ⭐ 2) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
MALADA (🥉3) - 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

Educational Resources

Tutorials, guides, cookbooks, recipes, etc.

🔗 Quantum Chemistry in the Age of Machine Learning - Book, 2022.

Deep Learning for Molecules and Materials Book (🥇12 · ⭐ 520) - Deep learning for molecules and materials book. Custom

GitHub (👨‍💻 19 · 🔀 96 · 📋 150 - 16% open · ⏱️ 02.07.2023):
```
 git clone https://github.com/whitead/dmol-book
```

jarvis-tools-notebooks (🥇12 · ⭐ 37) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST

GitHub (👨‍💻 5 · 🔀 21 · ⏱️ 19.08.2023):

 git clone https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks

RDKit Tutorials (🥈8 · ⭐ 200) - Tutorials to learn how to work with the RDKit. Custom

GitHub (👨‍💻 5 · 🔀 65 · 📋 4 - 75% open · ⏱️ 19.03.2023):
```
 git clone https://github.com/rdkit/rdkit-tutorials
```

iam-notebooks (🥈8 · ⭐ 16) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2

GitHub (👨‍💻 6 · 🔀 4 · ⏱️ 07.08.2023):

 git clone https://github.com/ceriottm/iam-notebooks

Data Handling, DoE and Statistical Analysis for Material Chemists (🥈8 · 🐣) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University. GPL-3.0

GitHub (👨‍💻 3 · 🔀 12 · ⏱️ 26.06.2023):

 git clone https://github.com/Teoroo-CMC/DoE_Course_Material

AI4Science101 (🥉5 · ⭐ 71 · 💤) - AI for Science. Unlicensed

GitHub (👨‍💻 5 · 🔀 11 · ⏱️ 04.09.2022):

 git clone https://github.com/deepmodeling/AI4Science101

Show 11 hidden projects...

DeepLearningLifeSciences (🥈11 · ⭐ 300 · 💀) - Example code from the book Deep Learning for the Life Sciences. MIT
MAChINE (🥈8 · ⭐ 1 · 🐣) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT
BestPractices (🥉6 · ⭐ 130 · 💀) - Things that you should (and should not) do in your Materials Informatics research. MIT
Applied AI for Materials (🥉6 · ⭐ 46 · 💀) - Course materials for Applied AI for Materials Science and Engineering. Unlicensed
COSMO Software Cookbook (🥉6 · ⭐ 2 · 🐣) - The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules. BSD-3
Machine Learning for Materials Hard and Soft (🥉5 · ⭐ 32 · 💀) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. Unlicensed
ML-in-chemistry-101 (🥉4 · ⭐ 54 · 💀) - The course materials for Machine Learning in Chemistry 101. Unlicensed
chemrev-gpr (🥉4 · ⭐ 5 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. Unlicensed
MLDensity_tutorial (🥉2 · ⭐ 5) - Tutorial files to work with ML for the charge density in molecules and solids. Unlicensed
PiNN Lab (🥉1 · ⭐ 2) - GPL-3.0
MALA Tutorial (🥉1 · ⭐ 1 · 🐣) - A full MALA hands-on tutorial. Unlicensed

Explainable Artificial intelligence (XAI)

Projects that focus on explainability and model interpretability in atomistic ML.

exmol (🥇19 · ⭐ 250) - Explainer for black box models that predict molecule properties. MIT

GitHub (🔀 38 · 📦 12 · 📋 67 - 14% open · ⏱️ 19.06.2023):
```
 git clone https://github.com/ur-whitelab/exmol
```
PyPi (📥 420 / month):
```
 pip install exmol
```

Show 3 hidden projects...

MEGAN: Multi Explanation Graph Attention Student (🥈6 · ⭐ 1) - Minimal implementation of graph attention student model architecture. MIT
MEGAN (🥈6 · ⭐ 1) - Minimal implementation of graph attention student model architecture. MIT XAI rep-learn
Linear vs blackbox (🥉4 · 💤) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning. MIT XAI single-paper rep-eng

Electronic structure methods (ML-ESM)

Projects and models that focus on quantities of electronic structure methods, which do not fit into either of the categories ML-WFT or ML-DFT.

Show 2 hidden projects...

QDF for molecule (🥇9 · ⭐ 170 · 💀) - Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation.. MIT
e3psi (🥉3 · ⭐ 2) - Equivariant machine learning library for learning from electronic structures. LGPL-3.0

General Tools

General tools for atomistic machine learning.

DeepChem (🥇36 · ⭐ 4.6K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT

GitHub (👨‍💻 220 · 🔀 1.5K · 📦 250 · 📋 1.6K - 25% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/deepchem/deepchem
```
PyPi (📥 11K / month):
```
 pip install deepchem
```
Conda (📥 100K · ⏱️ 16.06.2023):
```
 conda install -c conda-forge deepchem
```
Docker Hub (📥 6.9K · ⭐ 4 · ⏱️ 11.03.2022):
```
 docker pull deepchemio/deepchem
```

RDKit (🥇30 · ⭐ 2.2K) - BSD-3 C++

GitHub (👨‍💻 200 · 🔀 750 · 📥 1.4K · 📦 2 · 📋 2.9K - 29% open · ⏱️ 26.08.2023):
```
 git clone https://github.com/rdkit/rdkit
```
PyPi (📥 190K / month):
```
 pip install rdkit
```
Conda (📥 2.5M · ⏱️ 16.06.2023):
```
 conda install -c rdkit rdkit
```

Matminer (🥇28 · ⭐ 400) - Data mining for materials science. Custom

GitHub (👨‍💻 49 · 🔀 170 · 📦 220 · 📋 210 - 11% open · ⏱️ 27.06.2023):
```
 git clone https://github.com/hackingmaterials/matminer
```
PyPi (📥 17K / month):
```
 pip install matminer
```
Conda (📥 48K · ⏱️ 27.06.2023):
```
 conda install -c conda-forge matminer
```

QUIP (🥈27 · ⭐ 300) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0

GitHub (👨‍💻 79 · 🔀 120 · 📥 350 · 📦 22 · 📋 420 - 20% open · ⏱️ 18.07.2023):
```
 git clone https://github.com/libAtoms/QUIP
```
PyPi (📥 1.4K / month):
```
 pip install quippy-ase
```
Docker Hub (📥 9.8K · ⭐ 4 · ⏱️ 24.04.2023):
```
 docker pull libatomsquip/quip
```

MAML (🥈23 · ⭐ 260) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3

GitHub (👨‍💻 27 · 🔀 58 · 📦 4 · 📋 62 - 6% open · ⏱️ 16.08.2023):
```
 git clone https://github.com/materialsvirtuallab/maml
```
PyPi (📥 150 / month):
```
 pip install maml
```

JARVIS-Tools (🥈23 · ⭐ 240) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom

GitHub (👨‍💻 15 · 🔀 100 · 📦 60 · 📋 81 - 46% open · ⏱️ 20.08.2023):
```
 git clone https://github.com/usnistgov/jarvis
```
PyPi (📥 16K / month):
```
 pip install jarvis-tools
```

Conda (📥 55K · ⏱️ 16.06.2023):

 conda install -c conda-forge jarvis-tools

Scikit-Matter (🥈18 · ⭐ 60) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn

GitHub (👨‍💻 12 · 🔀 15 · 📦 5 · 📋 68 - 17% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/scikit-learn-contrib/scikit-matter
```
PyPi (📥 330 / month):
```
 pip install skmatter
```
Conda (📥 430 · ⏱️ 24.08.2023):
```
 conda install -c conda-forge skmatter
```

MAST-ML (🥈17 · ⭐ 86) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT

GitHub (👨‍💻 19 · 🔀 50 · 📥 81 · 📦 6 · 📋 210 - 10% open · ⏱️ 28.07.2023):
```
 git clone https://github.com/uw-cmg/MAST-ML
```

XenonPy (🥈16 · ⭐ 110) - XenonPy is a Python Software for Materials Informatics. BSD-3

GitHub (👨‍💻 10 · 🔀 57 · 📥 1.2K · 📋 82 - 19% open · ⏱️ 21.05.2023):
```
 git clone https://github.com/yoshida-lab/XenonPy
```
PyPi (📥 160 / month):
```
 pip install xenonpy
```

Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 220 · 🐣) - Artificial Intelligence for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules

GitHub (👨‍💻 23 · 🔀 25 · ⏱️ 24.08.2023):

 git clone https://github.com/divelab/AIRS

AMPtorch (🥉13 · ⭐ 53) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. GPL-3.0

GitHub (👨‍💻 14 · 🔀 31 · 📋 29 - 13% open · ⏱️ 16.07.2023):
```
 git clone https://github.com/ulissigroup/amptorch
```

Show 11 hidden projects...

QML (🥈16 · ⭐ 180 · 💀) - QML: Quantum Machine Learning. MIT
Automatminer (🥉14 · ⭐ 120 · 💀) - An automatic engine for predicting materials properties. Custom
OpenChem (🥉11 · ⭐ 610 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT
JAXChem (🥉7 · ⭐ 74 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT
uncertainty_benchmarking (🥉7 · ⭐ 34 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. Unlicensed benchmarking probabilistic
torchchem (🥉7 · ⭐ 33 · 💀) - An experimental repo for experimenting with PyTorch models. MIT
Equisolve (🥉7 · ⭐ 4) - A package tasked with taking equistore objects and computing machine learning models using them. BSD-3 ML-IAP
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
MLatom (🥉3) - Machine learning for atomistic simulations. Custom
quantum-structure-ml (🥉2 · ⭐ 1 · 💤) - Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification.. Unlicensed magnetism benchmarking

Generative Models

Projects that implement generative models for atomistic ML.

GT4SD (🥇18 · ⭐ 240) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pre-trained drug-discovery rep-learn

GitHub (👨‍💻 19 · 🔀 53 · 📋 89 - 2% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/GT4SD/gt4sd-core
```
PyPi (📥 620 / month):
```
 pip install gt4sd
```

MoLeR (🥈17 · ⭐ 200) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT

GitHub (👨‍💻 5 · 🔀 31 · 📋 30 - 20% open · ⏱️ 09.08.2023):
```
 git clone https://github.com/microsoft/molecule-generation
```
PyPi (📥 170 / month):
```
 pip install molecule-generation
```

synspace (🥈12 · ⭐ 29) - Synthesis generative model. MIT

GitHub (👨‍💻 2 · 🔀 3 · 📦 4 · 📋 3 - 66% open · ⏱️ 15.04.2023):
```
 git clone https://github.com/whitead/synspace
```
PyPi (📥 380 / month):
```
 pip install synspace
```

SchNetPack G-SchNet (🥈11 · ⭐ 26) - G-SchNet extension for SchNetPack. MIT

GitHub (👨‍💻 3 · 🔀 3 · ⏱️ 01.06.2023):

 git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet

G-SchNet (🥉8 · ⭐ 120) - G-SchNet - a generative model for 3d molecular structures. MIT

GitHub (👨‍💻 2 · 🔀 22 · ⏱️ 24.03.2023):

 git clone https://github.com/atomistic-machine-learning/G-SchNet

cG-SchNet (🥉8 · ⭐ 43) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT

GitHub (🔀 13 · ⏱️ 24.03.2023):

 git clone https://github.com/atomistic-machine-learning/cG-SchNet

bVAE-IM (🥉8 · ⭐ 8 · 🐣) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper

GitHub (🔀 2 · ⏱️ 11.07.2023):

 git clone https://github.com/tsudalab/bVAE-IM

rxngenerator (🥉5 · ⭐ 11 · 💤) - A generative model for molecular generation via multi-step chemical reactions. MIT

GitHub (🔀 2 · ⏱️ 09.08.2022):

 git clone https://github.com/tsudalab/rxngenerator

Show 2 hidden projects...

EDM (🥉9 · ⭐ 290 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
MolSLEPA (🥉4 · ⭐ 3 · 🐣) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing. MIT XAI

Interatomic Potentials (ML-IAP)

Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics.

DeePMD-kit (🥇28 · ⭐ 1.2K) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++

GitHub (👨‍💻 60 · 🔀 430 · 📥 25K · 📦 11 · 📋 440 - 11% open · ⏱️ 08.08.2023):
```
 git clone https://github.com/deepmodeling/deepmd-kit
```
PyPi (📥 760 / month):
```
 pip install deepmd-kit
```

Conda (📥 830 · ⏱️ 11.08.2023):

 conda install -c deepmodeling deepmd-kit

Docker Hub (📥 2K · ⭐ 1 · ⏱️ 13.08.2023):
```
 docker pull deepmodeling/deepmd-kit
```

TorchANI (🥇22 · ⭐ 390) - Accurate Neural Network Potential on PyTorch. MIT

GitHub (👨‍💻 16 · 🔀 110 · 📦 25 · 📋 150 - 11% open · ⏱️ 21.03.2023):
```
 git clone https://github.com/aiqm/torchani
```
PyPi (📥 1.8K / month):
```
 pip install torchani
```
Conda (📥 210K · ⏱️ 25.06.2023):
```
 conda install -c conda-forge torchani
```

DP-GEN (🥇22 · ⭐ 230) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0 workflows

GitHub (👨‍💻 59 · 🔀 150 · 📥 1.4K · 📦 4 · 📋 240 - 17% open · ⏱️ 21.03.2023):
```
 git clone https://github.com/deepmodeling/dpgen
```
PyPi (📥 190 / month):
```
 pip install dpgen
```
Conda (📥 160 · ⏱️ 16.06.2023):
```
 conda install -c deepmodeling dpgen
```

MEGNet (🥇21 · ⭐ 460) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3

GitHub (👨‍💻 13 · 🔀 140 · 📦 69 · 📋 74 - 22% open · ⏱️ 27.04.2023):
```
 git clone https://github.com/materialsvirtuallab/megnet
```
PyPi (📥 750 / month):
```
 pip install megnet
```

NequIP (🥇20 · ⭐ 420) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT

GitHub (👨‍💻 8 · 🔀 91 · 📦 14 · 📋 60 - 20% open · ⏱️ 26.03.2023):
```
 git clone https://github.com/mir-group/nequip
```
PyPi (📥 510 / month):
```
 pip install nequip
```
Conda (📥 3.3K · ⏱️ 18.06.2023):
```
 conda install -c conda-forge nequip
```

Pre-trained OCP models (🥇19 · ⭐ 450) - Pre-trained models released as part of the Open Catalyst Project. MIT pre-trained

GitHub (👨‍💻 31 · 🔀 170 · 📋 120 - 9% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/Open-Catalyst-Project/ocp
```

M3GNet (🥈18 · ⭐ 170) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3

GitHub (👨‍💻 14 · 🔀 50 · 📦 16 · 📋 35 - 42% open · ⏱️ 06.06.2023):
```
 git clone https://github.com/materialsvirtuallab/m3gnet
```
PyPi (📥 790 / month):
```
 pip install m3gnet
```

CHGNet (🥈18 · ⭐ 80 · 🐣) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom MD pre-trained electrostatics magnetism structure-relaxation

GitHub (👨‍💻 5 · 🔀 16 · 📦 1 · 📋 15 - 6% open · ⏱️ 26.08.2023):
```
 git clone https://github.com/CederGroupHub/chgnet
```
PyPi (📥 10K / month):
```
 pip install chgnet
```

sGDML (🥈15 · ⭐ 120) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT

GitHub (👨‍💻 7 · 🔀 34 · 📦 8 · 📋 16 - 31% open · ⏱️ 08.06.2023):
```
 git clone https://github.com/stefanch/sGDML
```
PyPi (📥 120 / month):
```
 pip install sgdml
```

PyXtalFF (🥈15 · ⭐ 71) - Machine Learning Interatomic Potential Predictions. MIT

GitHub (👨‍💻 8 · 🔀 19 · 📋 60 - 15% open · ⏱️ 17.08.2023):
```
 git clone https://github.com/MaterSim/PyXtal_FF
```
PyPi (📥 47 / month):
```
 pip install pyxtal_ff
```

NNPOps (🥈15 · ⭐ 61) - High-performance operations for neural network potentials. MIT MD C++

GitHub (👨‍💻 7 · 🔀 12 · 📋 52 - 38% open · ⏱️ 25.07.2023):
```
 git clone https://github.com/openmm/NNPOps
```
Conda (📥 64K · ⏱️ 17.08.2023):
```
 conda install -c conda-forge nnpops
```

MACE (🥈14 · ⭐ 180) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT

GitHub (👨‍💻 11 · 🔀 61 · 📋 54 - 29% open · ⏱️ 17.08.2023):
```
 git clone https://github.com/ACEsuit/mace
```

Ultra-Fast Force Fields (UF3) (🥈14 · ⭐ 30) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2

GitHub (🔀 14 · 📋 27 - 37% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/uf3/uf3
```
PyPi (📥 26 / month):
```
 pip install uf3
```

KLIFF (🥈14 · ⭐ 27) - KIM-based Learning-Integrated Fitting Framework (KLIFF). LGPL-2.1 probabilistic workflows

GitHub (👨‍💻 10 · 🔀 17 · 📋 33 - 51% open · ⏱️ 09.08.2023):
```
 git clone https://github.com/openkim/kliff
```
PyPi (📥 90 / month):
```
 pip install kliff
```
Conda (📥 63K · ⏱️ 16.06.2023):
```
 conda install -c conda-forge kliff
```

n2p2 (🥈13 · ⭐ 180 · 💤) - n2p2 - A Neural Network Potential Package. GPL-3.0 C++

GitHub (👨‍💻 9 · 🔀 66 · 📋 140 - 38% open · ⏱️ 05.09.2022):
```
 git clone https://github.com/CompPhysVienna/n2p2
```

So3krates (MLFF) (🥈13 · ⭐ 35) - Build neural networks for machine learning force fields with JAX. MIT

GitHub (👨‍💻 3 · 🔀 6 · 📋 5 - 20% open · ⏱️ 18.08.2023):
```
 git clone https://github.com/thorben-frank/mlff
```

CCS_fit (🥈13 · ⭐ 5) - Curvature Constrained Splines. GPL-3.0

GitHub (👨‍💻 8 · 🔀 8 · 📥 370 · 📋 14 - 57% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/Teoroo-CMC/CCS
```
PyPi (📥 340 / month):
```
 pip install ccs_fit
```

DMFF (🥈12 · ⭐ 110) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0

GitHub (👨‍💻 7 · 🔀 27 · 📋 14 - 57% open · ⏱️ 14.02.2023):
```
 git clone https://github.com/deepmodeling/DMFF
```

Pacemaker (🥈12 · ⭐ 42) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom

GitHub (👨‍💻 5 · 🔀 9 · 📋 31 - 19% open · ⏱️ 21.07.2023):
```
 git clone https://github.com/ICAMS/python-ace
```
PyPi (📥 6 / month):
```
 pip install python-ace
```

Neural Force Field (🥈11 · ⭐ 180) - Neural Network Force Field based on PyTorch. MIT pre-trained

GitHub (👨‍💻 10 · 🔀 41 · ⏱️ 25.07.2023):

 git clone https://github.com/learningmatter-mit/NeuralForceField

PiNN (🥈11 · ⭐ 94) - A Python library for building atomic neural networks. BSD-3

GitHub (👨‍💻 2 · 🔀 25 · 📋 6 - 33% open · ⏱️ 18.08.2023):
```
 git clone https://github.com/Teoroo-CMC/PiNN
```
Docker Hub (📥 220 · ⏱️ 23.08.2023):
```
 docker pull teoroo/pinn
```

ACE1.jl (🥉10 · ⭐ 18) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia

GitHub (👨‍💻 7 · 🔀 4 · 📋 45 - 46% open · ⏱️ 22.08.2023):
```
 git clone https://github.com/ACEsuit/ACE1.jl
```

NNsforMD (🥉10 · ⭐ 9 · 💤) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT

GitHub (👨‍💻 2 · 🔀 3 · ⏱️ 10.11.2022):

 git clone https://github.com/aimat-lab/NNsForMD

PyPi (📥 34 / month):
```
 pip install pyNNsMD
```

DimeNet (🥉9 · ⭐ 240) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom

GitHub (👨‍💻 2 · 🔀 52 · 📋 30 - 3% open · ⏱️ 01.08.2023):
```
 git clone https://github.com/gasteigerjo/dimenet
```

Allegro (🥉9 · ⭐ 220) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT

GitHub (👨‍💻 2 · 🔀 31 · 📋 18 - 33% open · ⏱️ 08.05.2023):
```
 git clone https://github.com/mir-group/allegro
```

ACE.jl (🥉9 · ⭐ 61) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia

GitHub (👨‍💻 12 · 🔀 15 · 📋 81 - 28% open · ⏱️ 09.06.2023):
```
 git clone https://github.com/ACEsuit/ACE.jl
```

GAP (🥉9 · ⭐ 30) - Gaussian Approximation Potential (GAP). Custom

GitHub (👨‍💻 12 · 🔀 20 · ⏱️ 08.06.2023):

 git clone https://github.com/libAtoms/GAP

ALF (🥉9 · ⭐ 19) - A framework for performing active learning for training machine-learned interatomic potentials. Custom active-learning

GitHub (🔀 7 · ⏱️ 04.08.2023):

 git clone https://github.com/lanl/alf

ACE1Pack.jl (🥉9) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. MIT Julia

GitHub (👨‍💻 11 · ⏱️ 21.08.2023):

 git clone https://github.com/ACEsuit/ACE1pack.jl

GemNet (🥉8 · ⭐ 150) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. Custom

GitHub (👨‍💻 5 · 🔀 25 · ⏱️ 26.04.2023):

 git clone https://github.com/TUM-DAML/gemnet_pytorch

Atomistic Adversarial Attacks (🥉8 · ⭐ 24 · 💤) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic

GitHub (👨‍💻 6 · 🔀 6 · ⏱️ 03.10.2022):

 git clone https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks

wfl (🥉8 · ⭐ 13) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. Unlicensed workflows HTC

GitHub (👨‍💻 12 · 🔀 13 · 📋 120 - 46% open · ⏱️ 17.08.2023):
```
 git clone https://github.com/libAtoms/workflow
```

MACE-Jax (🥉7 · ⭐ 33 · 🐣) - Equivariant machine learning interatomic potentials in JAX. MIT

GitHub (👨‍💻 2 · 🔀 1 · ⏱️ 20.07.2023):

 git clone https://github.com/ACEsuit/mace-jax

Show 18 hidden projects...

TensorMol (🥈12 · ⭐ 260 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper
ANI-1 (🥈11 · ⭐ 200 · 💀) - ANI-1 neural net potential with python interface (ASE). MIT
SIMPLE-NN (🥈11 · ⭐ 41 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0
ACEfit (🥈11 · ⭐ 4) - MIT Julia
SchNet (🥉9 · ⭐ 180 · 💀) - SchNet - a deep learning architecture for quantum chemistry. MIT
SNAP (🥉8 · ⭐ 32 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3
SIMPLE-NN v2 (🥉8 · ⭐ 24) - GPL-3.0
AIMNet (🥉7 · ⭐ 75 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper
PhysNet (🥉7 · ⭐ 74 · 💀) - Code for training PhysNet models. MIT electrostatics
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 · ⭐ 6 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking
Alchemical learning (🥉5 · ⭐ 2) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3
TensorPotential (🥉4 · ⭐ 5) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom
glp (🥉3 · ⭐ 10 · 🐣) - tools for graph-based machine-learning potentials in jax. MIT
MLIP-3 (🥉2 · ⭐ 11 · 🐣) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++
SingleNN (🥉2 · ⭐ 6 · 💀) - An efficient package for training and executing neural-network interatomic potentials. Unlicensed C++
ACE Workflows (🥉2 · 🐣) - Workflow Examples for ACE Models. Unlicensed Julia workflows
RuNNer (🥉2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-.. GPL-3.0 Fortran

Language Models

Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML.

paper-qa (🥇25 · ⭐ 3K · 🐣) - LLM Chain for answering questions from documents with citations. Apache-2

GitHub (👨‍💻 12 · 🔀 270 · 📦 25 · 📋 98 - 39% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/whitead/paper-qa
```
PyPi (📥 3.7K / month):
```
 pip install paper-qa
```

mat2vec (🥈12 · ⭐ 590) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials.. MIT rep-learn

GitHub (👨‍💻 5 · 🔀 170 · 📋 23 - 26% open · ⏱️ 06.05.2023):
```
 git clone https://github.com/materialsintelligence/mat2vec
```

gptchem (🥈12 · ⭐ 160 · 📈) - Use GPT-3 to solve chemistry problems. MIT

GitHub (🔀 27 · 📋 5 - 60% open · ⏱️ 22.06.2023):

 git clone https://github.com/kjappelbaum/gptchem

PyPi (📥 130 / month):
```
 pip install gptchem
```

MolSkill (🥉11 · ⭐ 77) - Learning chemical intuition from humans in the loop. Supporting code. MIT drug-discovery recommender

GitHub (👨‍💻 4 · 🔀 5 · 📋 5 - 40% open · ⏱️ 13.06.2023):
```
 git clone https://github.com/microsoft/molskill
```

Conda (📥 93 · ⏱️ 18.06.2023):

 conda install -c msr-ai4science molskill

nlcc (🥉11 · ⭐ 41) - Natural language computational chemistry command line interface. MIT single-paper

GitHub (👨‍💻 3 · 🔀 6 · ⏱️ 04.02.2023):

 git clone https://github.com/whitead/nlcc

PyPi (📥 48 / month):
```
 pip install nlcc
```

Show 1 hidden projects...

BERT-PSIE-TC (🥉6 · ⭐ 2) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT magnetism

Materials Discovery

Projects that implement materials discovery methods using atomistic ML.

aviary (🥇13 · ⭐ 27) - The Wren sits on its Roost in the Aviary. MIT

GitHub (👨‍💻 4 · 🔀 7 · 📋 26 - 15% open · ⏱️ 10.08.2023):
```
 git clone https://github.com/CompRhys/aviary
```

closed-loop-acceleration-benchmarks (🥉5) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper

GitHub (👨‍💻 2 · 🔀 1 · ⏱️ 02.05.2023):

 git clone https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks

Show 6 hidden projects...

BOSS (🥈7 · ⭐ 18 · 💀) - Bayesian Optimization Structure Search (BOSS). Unlicensed probabilistic
Computational Autonomy for Materials Discovery (CAMD) (🥈6 · ⭐ 1 · 💤) - Agent-based sequential learning software for materials discovery. Apache-2
AGOX (🥉5 · ⭐ 10 · 💀) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization
CSPML (crystal structure prediction with machine learning-based element substitution) (🥉3 · ⭐ 12 · 💀) - Original implementation of CSPML. Unlicensed structure-prediction
SPINNER (🥉3 · ⭐ 9 · 💀) - 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

Mathematical tools

Projects that implement mathematical objects used in atomistic machine learning.

gpax (🥇16 · ⭐ 100) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning

GitHub (🔀 15 · 📋 7 - 28% open · ⏱️ 07.08.2023):

 git clone https://github.com/ziatdinovmax/gpax

PyPi (📥 130 / month):
```
 pip install gpax
```

SpheriCart (🥈14 · ⭐ 40 · 🐣) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. Apache-2

GitHub (👨‍💻 6 · 🔀 3 · 📦 1 · 📋 14 - 57% open · ⏱️ 18.08.2023):
```
 git clone https://github.com/lab-cosmo/sphericart
```
PyPi (📥 24 / month):
```
 pip install sphericart
```

Polynomials4ML.jl (🥈14 · ⭐ 10) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia

GitHub (👨‍💻 8 · 🔀 2 · 📋 38 - 42% open · ⏱️ 09.08.2023):
```
 git clone https://github.com/ACEsuit/Polynomials4ML.jl
```

GElib (🥈7 · ⭐ 16) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0 C++

GitHub (👨‍💻 3 · 🔀 2 · 📋 4 - 75% open · ⏱️ 23.07.2023):
```
 git clone https://github.com/risi-kondor/GElib
```

COSMO Toolbox (🥉6 · ⭐ 6) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++

GitHub (👨‍💻 9 · 🔀 5 · ⏱️ 23.06.2023):

 git clone https://github.com/lab-cosmo/toolbox

Show 3 hidden projects...

EquivariantOperators.jl (🥉5 · ⭐ 17 · 💤) - MIT Julia
cnine (🥉4 · ⭐ 1) - Cnine tensor library. Unlicensed C++
Wigner Kernels (🥉4) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking

Molecular Dynamics

Projects that simplify the integration of molecular dynamics and atomistic machine learning.

JAX-MD (🥇23 · ⭐ 980) - Differentiable, Hardware Accelerated, Molecular Dynamics. Apache-2

GitHub (👨‍💻 28 · 🔀 160 · 📦 33 · 📋 130 - 45% open · ⏱️ 22.08.2023):
```
 git clone https://github.com/jax-md/jax-md
```
PyPi (📥 1.9K / month):
```
 pip install jax-md
```

FitSNAP (🥈19 · ⭐ 120) - Software for generating SNAP machine-learning interatomic potentials. GPL-2.0

GitHub (👨‍💻 24 · 🔀 42 · 📥 5 · 📋 62 - 12% open · ⏱️ 22.08.2023):
```
 git clone https://github.com/FitSNAP/FitSNAP
```
Conda (📥 4.4K · ⏱️ 16.06.2023):
```
 conda install -c conda-forge fitsnap3
```

openmm-torch (🥈14 · ⭐ 130) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++

GitHub (👨‍💻 6 · 🔀 23 · 📋 64 - 25% open · ⏱️ 27.07.2023):
```
 git clone https://github.com/openmm/openmm-torch
```

Conda (📥 210K · ⏱️ 27.07.2023):

 conda install -c conda-forge openmm-torch

OpenMM-ML (🥉10 · ⭐ 50) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP

GitHub (👨‍💻 2 · 🔀 13 · 📋 39 - 53% open · ⏱️ 21.08.2023):
```
 git clone https://github.com/openmm/openmm-ml
```

Conda (📥 1.6K · ⏱️ 21.08.2023):

 conda install -c conda-forge openmm-ml

PACE (🥉6 · ⭐ 20 · 💤) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom

GitHub (👨‍💻 6 · 🔀 10 · 📋 6 - 16% open · ⏱️ 31.01.2023):
```
 git clone https://github.com/ICAMS/lammps-user-pace
```

Show 1 hidden projects...

interface-lammps-mlip-3 (🥉2 · ⭐ 5 · 🐣) - An interface between LAMMPS and MLIP (version 3). GPL-2.0

Probabilistic ML

Projects that focus on probabilistic, Bayesian, Gaussian process and adversarial methods for atomistic ML, for optimization, uncertainty quantification (UQ), etc.

Reinforcement Learning

Projects that focus on reinforcement learning for atomistic ML.

Show 2 hidden projects...

ReLeaSE (🥇11 · ⭐ 310 · 💀) - Deep Reinforcement Learning for de-novo Drug Design. MIT drug-discovery
CatGym (🥉6 · ⭐ 10 · 💀) - Surface segregation using Deep Reinforcement Learning. GPL

Representation Engineering

Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering.

cdk (🥇25 · ⭐ 430) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java

GitHub (👨‍💻 160 · 🔀 140 · 📥 15K · 📦 18 · 📋 250 - 8% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/cdk/cdk
```

Maven:

 <dependency>
 	<groupId>org.openscience.cdk</groupId>
 	<artifactId>cdk-bundle</artifactId>
 	<version>[VERSION]</version>
 </dependency>

DScribe (🥇22 · ⭐ 340) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2

GitHub (👨‍💻 18 · 🔀 78 · 📦 140 · 📋 85 - 9% open · ⏱️ 19.07.2023):
```
 git clone https://github.com/SINGROUP/dscribe
```
PyPi (📥 5.7K / month):
```
 pip install dscribe
```
Conda (📥 76K · ⏱️ 19.07.2023):
```
 conda install -c conda-forge dscribe
```

CatLearn (🥇16 · ⭐ 92) - GPL-3.0 surface-science

GitHub (👨‍💻 22 · 🔀 50 · 📦 4 · 📋 25 - 36% open · ⏱️ 07.02.2023):
```
 git clone https://github.com/SUNCAT-Center/CatLearn
```
PyPi (📥 88 / month):
```
 pip install catlearn
```

SISSO (🥈14 · ⭐ 180) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2 Fortran

GitHub (👨‍💻 3 · 🔀 60 · 📋 51 - 1% open · ⏱️ 26.07.2023):
```
 git clone https://github.com/rouyang2017/SISSO
```

GlassPy (🥈13 · ⭐ 14) - Python module for scientists working with glass materials. GPL-3.0

GitHub (🔀 6 · 📦 2 · 📋 3 - 66% open · ⏱️ 21.08.2023):
```
 git clone https://github.com/drcassar/glasspy
```
PyPi (📥 200 / month):
```
 pip install glasspy
```

Librascal (🥈12 · ⭐ 70) - A scalable and versatile library to generate representations for atomic-scale learning. LGPL-2.1

GitHub (👨‍💻 29 · 🔀 18 · 📋 230 - 43% open · ⏱️ 06.06.2023):
```
 git clone https://github.com/lab-cosmo/librascal
```

Rascaline (🥈11 · ⭐ 13) - Computing representations for atomistic machine learning. BSD-3 Rust C++

GitHub (👨‍💻 10 · 🔀 11 · 📋 36 - 50% open · ⏱️ 22.08.2023):
```
 git clone https://github.com/Luthaf/rascaline
```

BenchML (🥉7 · ⭐ 13) - ML benchmarking and pipeling framework. Apache-2 benchmarking

GitHub (👨‍💻 9 · 🔀 2 · 📋 13 - 23% open · ⏱️ 24.05.2023):
```
 git clone https://github.com/capoe/benchml
```
PyPi (📥 12 / month):
```
 pip install benchml
```

milad (🥉6 · ⭐ 27 · 💤) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative

GitHub (🔀 1 · 📦 1 · ⏱️ 03.12.2022):

 git clone https://github.com/muhrin/milad

SA-GPR (🥉6 · ⭐ 14 · 💤) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang

GitHub (👨‍💻 5 · 🔀 9 · 📋 7 - 28% open · ⏱️ 29.09.2022):
```
 git clone https://github.com/dilkins/TENSOAP
```

NICE (🥉6 · ⭐ 10) - NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and.. MIT

GitHub (👨‍💻 4 · 🔀 2 · 📋 3 - 66% open · ⏱️ 01.05.2023):
```
 git clone https://github.com/lab-cosmo/nice
```

ML-for-CurieTemp-Predictions (🥉5 · 🐣) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism

GitHub (👨‍💻 2 · ⏱️ 14.06.2023):

 git clone https://github.com/msg-byu/ML-for-CurieTemp-Predictions

Show 9 hidden projects...

cmlkit (🥈10 · ⭐ 29 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking
SkipAtom (🥈8 · ⭐ 21 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
CBFV (🥉7 · ⭐ 13 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
pyLODE (🥉7 · ⭐ 2) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
fplib (🥉6 · ⭐ 7 · 💀) - a fingerprint library. MIT C-lang single-paper
SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++
magnetism-prediction (🥉4 · ⭐ 1) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper
SISSO++ (🥉3 · ⭐ 1 · 💀) - C++ Implementation of SISSO with python bindings. Apache-2 C++
AMP (🥉2) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed

Representation Learning

General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN).

Deep Graph Library (DGL) (🥇38 · ⭐ 12K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2

GitHub (👨‍💻 270 · 🔀 2.8K · 📦 140 · 📋 2.3K - 12% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/dmlc/dgl
```
PyPi (📥 120K / month):
```
 pip install dgl
```
Conda (📥 280K · ⏱️ 17.08.2023):
```
 conda install -c dglteam dgl
```

PyG Models (🥇30 · ⭐ 18K) - Representation learning models implemented in PyTorch Geometric. MIT general-ml

GitHub (👨‍💻 430 · 🔀 3.3K · 📋 3.2K - 22% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/pyg-team/pytorch_geometric
```

SchNetPack (🥇27 · ⭐ 630) - SchNetPack - Deep Neural Networks for Atomistic Systems. MIT

GitHub (👨‍💻 31 · 🔀 180 · 📦 56 · 📋 210 - 1% open · ⏱️ 17.08.2023):
```
 git clone https://github.com/atomistic-machine-learning/schnetpack
```
PyPi (📥 480 / month):
```
 pip install schnetpack
```

e3nn (🥇24 · ⭐ 730) - A modular framework for neural networks with Euclidean symmetry. MIT

GitHub (👨‍💻 28 · 🔀 110 · 📦 100 · 📋 150 - 10% open · ⏱️ 15.08.2023):
```
 git clone https://github.com/e3nn/e3nn
```
PyPi (📥 14K / month):
```
 pip install e3nn
```
Conda (📥 8.6K · ⏱️ 18.06.2023):
```
 conda install -c conda-forge e3nn
```

dgl-lifesci (🥇23 · ⭐ 610) - Python package for graph neural networks in chemistry and biology. Apache-2

GitHub (👨‍💻 22 · 🔀 130 · 📦 110 · 📋 80 - 30% open · ⏱️ 16.04.2023):
```
 git clone https://github.com/awslabs/dgl-lifesci
```
PyPi (📥 13K / month):
```
 pip install dgllife
```

NVIDIA Deep Learning Examples for Tensor Cores (🥈22 · ⭐ 11K) - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and.. Custom educational drug-discovery

GitHub (👨‍💻 120 · 🔀 2.8K · 📋 770 - 28% open · ⏱️ 23.08.2023):
```
 git clone https://github.com/NVIDIA/DeepLearningExamples
```

kgcnn (🥈22 · ⭐ 80) - Graph convolution with tf.keras. MIT

GitHub (👨‍💻 7 · 🔀 24 · 📦 15 · 📋 77 - 6% open · ⏱️ 17.08.2023):
```
 git clone https://github.com/aimat-lab/gcnn_keras
```
PyPi (📥 400 / month):
```
 pip install kgcnn
```

MatGL (Materials Graph Library) (🥈20 · ⭐ 110) - Graph deep learning library for materials. BSD-3

GitHub (👨‍💻 11 · 🔀 24 · 📦 2 · 📋 34 - 2% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/materialsvirtuallab/matgl
```
PyPi (📥 790 / month):
```
 pip install m3gnet
```

ocp (🥈19 · ⭐ 450) - ocp is the Open Catalyst Projects library of state-of-the-art machine learning algorithms for catalysis. MIT

GitHub (👨‍💻 31 · 🔀 170 · 📋 120 - 9% open · ⏱️ 24.08.2023):
```
 git clone https://github.com/Open-Catalyst-Project/ocp
```

ALIGNN (🥈18 · ⭐ 140) - Atomistic Line Graph Neural Network. Custom

GitHub (👨‍💻 7 · 🔀 64 · 📦 4 · 📋 46 - 50% open · ⏱️ 11.08.2023):
```
 git clone https://github.com/usnistgov/alignn
```
PyPi (📥 530 / month):
```
 pip install alignn
```

e3nn-jax (🥈18 · ⭐ 120) - jax library for E3 Equivariant Neural Networks. Apache-2

GitHub (👨‍💻 4 · 🔀 13 · 📋 8 - 12% open · ⏱️ 11.08.2023):
```
 git clone https://github.com/e3nn/e3nn-jax
```
PyPi (📥 2.2K / month):
```
 pip install e3nn-jax
```

Uni-Mol (🥈17 · ⭐ 400) - Official Repository for the Uni-Mol Series Methods. MIT pre-trained

GitHub (👨‍💻 8 · 🔀 72 · 📥 5K · 📋 94 - 28% open · ⏱️ 25.08.2023):
```
 git clone https://github.com/dptech-corp/Uni-Mol
```

escnn (🥈17 · ⭐ 200) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom

GitHub (👨‍💻 9 · 🔀 33 · 📋 52 - 30% open · ⏱️ 09.08.2023):
```
 git clone https://github.com/QUVA-Lab/escnn
```
PyPi (📥 1.1K / month):
```
 pip install escnn
```

Compositionally-Restricted Attention-Based Network (CrabNet) (🥈12 · ⭐ 10) - Predict materials properties using only the composition information!. MIT

GitHub (👨‍💻 5 · 🔀 3 · 📦 10 · 📋 16 - 87% open · ⏱️ 19.06.2023):
```
 git clone https://github.com/sparks-baird/CrabNet
```
PyPi (📥 110 / month):
```
 pip install crabnet
```

graphite (🥈11 · ⭐ 21 · 📈) - A repository for implementing graph network models based on atomic structures. MIT

GitHub (🔀 4 · 📦 6 · ⏱️ 16.08.2023):

 git clone https://github.com/llnl/graphite

GDC (🥈10 · ⭐ 220) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative

GitHub (👨‍💻 3 · 🔀 38 · ⏱️ 26.04.2023):

 git clone https://github.com/gasteigerjo/gdc

ML4pXRDs (🥈10) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. MIT XRD single-paper

GitHub (🔀 1 · 📥 2 · ⏱️ 14.07.2023):

 git clone https://github.com/aimat-lab/ML4pXRDs

GATGNN: Global Attention Graph Neural Network (🥉9 · ⭐ 60 · 💤) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT

GitHub (👨‍💻 3 · 🔀 18 · 📋 6 - 50% open · ⏱️ 03.10.2022):
```
 git clone https://github.com/superlouis/GATGNN
```

CGAT (🥉9 · ⭐ 13 · 💤) - Crystal graph attention neural networks for materials prediction. MIT

GitHub (👨‍💻 4 · 🔀 7 · ⏱️ 10.01.2023):

 git clone https://github.com/hyllios/CGAT

Equiformer (🥉8 · ⭐ 120 · 🐣) - [ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT

GitHub (🔀 22 · 📋 12 - 41% open · ⏱️ 21.06.2023):

 git clone https://github.com/atomicarchitects/equiformer

matsciml (🥉8 · ⭐ 49) - Open MatSci ML Toolkit is a single framework for prototyping and scaling out deep learning models for materials.. MIT workflows

GitHub (👨‍💻 5 · 🔀 8 · 📋 9 - 44% open · ⏱️ 11.07.2023):
```
 git clone https://github.com/IntelLabs/matsciml
```

hippynn (🥉8 · ⭐ 41) - python library for atomistic machine learning. Custom workflows

GitHub (🔀 18 · 📋 4 - 75% open · ⏱️ 05.08.2023):
```
 git clone https://github.com/lanl/hippynn
```

DeeperGATGNN (🥉8 · ⭐ 32) - Scalable graph neural networks for materials property prediction. MIT

GitHub (👨‍💻 3 · 🔀 7 · ⏱️ 19.04.2023):

 git clone https://github.com/usccolumbia/deeperGATGNN

escnn_jax (🥉8 · ⭐ 21 · 🐣) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom

GitHub (👨‍💻 8 · 🔀 2 · ⏱️ 28.06.2023):

 git clone https://github.com/emilemathieu/escnn_jax

PyPi:
```
 pip install escnn_jax
```

AdsorbML (🥉8 · ⭐ 17) - MIT surface-science single-paper

GitHub (👨‍💻 5 · 🔀 4 · ⏱️ 31.07.2023):

 git clone https://github.com/Open-Catalyst-Project/AdsorbML

UVVisML (🥉8 · ⭐ 11) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic

GitHub (🔀 4 · ⏱️ 26.05.2023):

 git clone https://github.com/learningmatter-mit/uvvisml

EquiformerV2 (🥉6 · ⭐ 62 · 🐣) - [arXiv23] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT

GitHub (👨‍💻 2 · 🔀 5 · ⏱️ 28.07.2023):

 git clone https://github.com/atomicarchitects/equiformer_v2

MACE-Layer (🥉6 · ⭐ 25) - Higher order equivariant graph neural networks for 3D point clouds. MIT

GitHub (👨‍💻 2 · 🔀 3 · ⏱️ 06.06.2023):

 git clone https://github.com/ACEsuit/mace-layer

GLAMOUR (🥉6 · ⭐ 18 · 💤) - Graph Learning over Macromolecule Representations. MIT single-paper

GitHub (🔀 5 · ⏱️ 31.12.2022):

 git clone https://github.com/learningmatter-mit/GLAMOUR

CraTENet (🥉5 · ⭐ 6) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena

GitHub (🔀 1 · ⏱️ 05.04.2023):

 git clone https://github.com/lantunes/CraTENet

Per-site PAiNN (🥉5 · 🐣) - Fork of PaiNN for PerovskiteOrderingGCNNs. MIT probabilistic pre-trained single-paper

GitHub (👨‍💻 10 · ⏱️ 05.06.2023):

 git clone https://github.com/learningmatter-mit/per-site_painn

Show 18 hidden projects...

benchmarking-gnns (🥈15 · ⭐ 2.2K · 💀) - Repository for benchmarking graph neural networks. MIT single-paper benchmarking
Crystal Graph Convolutional Neural Networks (CGCNN) (🥈12 · ⭐ 500 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT
Neural fingerprint (nfp) (🥈12 · ⭐ 53 · 💀) - Keras layers for end-to-end learning with rdkit and pymatgen. Custom
SE(3)-Transformers (🥈10 · ⭐ 410 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper
ai4material_design (🥈10 · ⭐ 1) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pre-trained material-defect
molecularGNN_smiles (🥉9 · ⭐ 250 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
DTNN (🥉7 · ⭐ 77 · 💀) - Deep Tensor Neural Network. MIT
tensorfieldnetworks (🥉5 · ⭐ 140 · 💀) - MIT
charge_transfer_nnp (🥉5 · ⭐ 20 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
SCFNN (🥉5 · ⭐ 14 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
FieldSchNet (🥉5 · ⭐ 9 · 💀) - MIT
Per-Site CGCNN (🥉5 · ⭐ 1 · 🐣) - Crystal graph convolutional neural networks for predicting material properties. MIT pre-trained single-paper
Graph Transport Network (🥉4 · ⭐ 14) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. Custom transport-phenomena
Atom2Vec (🥉3 · ⭐ 24 · 💀) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
Element encoder (🥉3 · ⭐ 5 · 💀) - Autoencoder neural network to compress properties of atomic species into a vector representation. GPL-3.0 single-paper
atom_by_atom (🥉3 · ⭐ 2 · 🐣) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. Unlicensed surface-science single-paper
Point Edge Transformer (🥉2) - Smooth, exact rotational symmetrization for deep learning on point clouds. CC-BY-4.0
gkx: Green-Kubo Method in JAX (🥉1 · ⭐ 2 · 🐣) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena

Unsupervised Learning

Projects that focus on unsupervised learning (USL) for atomistic ML, such as dimensionality reduction, clustering and visualization.

DADApy (🥇15 · ⭐ 69) - Distance-based Analysis of DAta-manifolds in python. Apache-2

GitHub (👨‍💻 15 · 🔀 8 · 📦 2 · 📋 24 - 37% open · ⏱️ 15.08.2023):
```
 git clone https://github.com/sissa-data-science/DADApy
```
PyPi (📥 37 / month):
```
 pip install dadapy
```

ASAP (🥈12 · ⭐ 110) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT

GitHub (👨‍💻 6 · 🔀 25 · 📦 4 · 📋 24 - 25% open · ⏱️ 25.07.2023):
```
 git clone https://github.com/BingqingCheng/ASAP
```

Sketchmap (🥉8 · ⭐ 39) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular. GPL-3.0 C++

GitHub (👨‍💻 8 · 🔀 10 · 📋 8 - 37% open · ⏱️ 24.05.2023):
```
 git clone https://github.com/lab-cosmo/sketchmap
```

Show 3 hidden projects...

Coarse-Graining-Auto-encoders (🥉3 · ⭐ 19 · 💀) - Unlicensed single-paper
Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF) ( ⭐ 2) - Provides a workflow to obtain clustering of local environments in dataset of structures. Unlicensed
KmdPlus ( ⭐ 1 · 🐣) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. Unlicensed

Visualization

Projects that focus on visualization (viz.) for atomistic ML.

Chemiscope (🥇17 · ⭐ 86) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript

GitHub (👨‍💻 17 · 🔀 26 · 📥 140 · 📦 5 · 📋 110 - 37% open · ⏱️ 25.07.2023):
```
 git clone https://github.com/lab-cosmo/chemiscope
```
npm (📥 36 / month):
```
 npm install chemiscope
```

Wavefunction methods (ML-WFT)

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 · ⭐ 300) - Deep learning quantum Monte Carlo for electrons in real space. MIT

GitHub (🔀 56 · 📦 1 · 📋 36 - 16% open · ⏱️ 08.08.2023):
```
 git clone https://github.com/deepqmc/deepqmc
```
PyPi (📥 49 / month):
```
 pip install deepqmc
```

FermiNet (🥈15 · ⭐ 580) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2

GitHub (🔀 99 · ⏱️ 14.08.2023):

 git clone https://github.com/deepmind/ferminet

DeepErwin (🥉9 · ⭐ 33) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom

GitHub (👨‍💻 6 · 🔀 5 · 📋 8 - 12% open · ⏱️ 17.07.2023):
```
 git clone https://github.com/mdsunivie/deeperwin
```
PyPi (📥 8 / month):
```
 pip install deeperwin
```

Show 1 hidden projects...

SchNOrb (🥉5 · ⭐ 54 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT

Others

Show 1 hidden projects...

Contribution

Contributions are encouraged and always welcome! If you like to add or update projects, choose one of the following ways:

Open an issue by selecting one of the provided categories from the issue page and fill in the requested information.
Modify the projects.yaml with your additions or changes, and submit a pull request. This can also be done directly via the Github UI.

If you like to contribute to or share suggestions regarding the project metadata collection or markdown generation, please refer to the best-of-generator repository. If you like to create your own best-of list, we recommend to follow this guide.

For more information on how to add or update projects, please read the contribution guidelines. By participating in this project, you agree to abide by its Code of Conduct.

Name		Name	Last commit message	Last commit date
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.github		.github
config		config
history		history
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
latest-changes.md		latest-changes.md
projects.yaml		projects.yaml

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Best of Atomistic Machine Learning ⚛️🧬💎

Contents

Explanation

Active learning

Biomolecules

Community resources

Datasets

Data Structures

Density functional theory (ML-DFT)

Educational Resources

Explainable Artificial intelligence (XAI)

Electronic structure methods (ML-ESM)

General Tools

Generative Models

Interatomic Potentials (ML-IAP)

Language Models

Materials Discovery

Mathematical tools

Molecular Dynamics

Probabilistic ML

Reinforcement Learning

Representation Engineering

Representation Learning

Unsupervised Learning

Visualization

Wavefunction methods (ML-WFT)

Others

Contribution

License

About

Releases

Packages

License

ml-evs/best-of-atomistic-machine-learning

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Latest commit

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Repository files navigation

Best of Atomistic Machine Learning ⚛️🧬💎

Contents

Explanation

Active learning

Biomolecules

Community resources

Datasets

Data Structures

Density functional theory (ML-DFT)

Educational Resources

Explainable Artificial intelligence (XAI)

Electronic structure methods (ML-ESM)

General Tools

Generative Models

Interatomic Potentials (ML-IAP)

Language Models

Materials Discovery

Mathematical tools

Molecular Dynamics

Probabilistic ML

Reinforcement Learning

Representation Engineering

Representation Learning

Unsupervised Learning

Visualization

Wavefunction methods (ML-WFT)

Others

Contribution

License

About

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases

Packages 0

Packages