Best of Atomistic Machine Learning ⚛️🧬💎

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

This curated list contains 430 awesome open-source projects with a total of 190K stars grouped into 22 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 drug design. Nevertheless, contributions from other fields are warmly welcome!

How to cite. See the button "Cite this repository" on the right side-bar.

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

Active learning 6 projects
Community resources 31 projects
Datasets 46 projects
Data Structures 4 projects
Density functional theory (ML-DFT) 33 projects
Educational Resources 28 projects
Explainable Artificial intelligence (XAI) 3 projects
Electronic structure methods (ML-ESM) 5 projects
General Tools 22 projects
Generative Models 14 projects
Interatomic Potentials (ML-IAP) 70 projects
Language Models 22 projects
Materials Discovery 12 projects
Mathematical tools 11 projects
Molecular Dynamics 10 projects
Reinforcement Learning 2 projects
Representation Engineering 25 projects
Representation Learning 58 projects
Universal Potentials 12 projects
Unsupervised Learning 7 projects
Visualization 6 projects
Wavefunction methods (ML-WFT) 5 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 (🥇21 · ⭐ 290) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP

GitHub (👨‍💻 43 · 🔀 70 · 📥 8 · 📦 12 · 📋 220 - 16% open · ⏱️ 01.11.2024):
```
 git clone https://github.com/mir-group/flare
```

IPSuite (🥈16 · ⭐ 19) - A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials. EPL-2.0 ML-IAP MD workflows HTC FAIR

GitHub (👨‍💻 8 · 🔀 11 · 📦 7 · 📋 130 - 51% open · ⏱️ 19.09.2024):
```
 git clone https://github.com/zincware/IPSuite
```
PyPi (📥 280 / month · ⏱️ 08.08.2024):
```
 pip install ipsuite
```

Finetuna (🥉10 · ⭐ 45) - Active Learning for Machine Learning Potentials. MIT

GitHub (👨‍💻 11 · 🔀 11 · 📦 1 · 📋 20 - 25% open · ⏱️ 15.05.2024):
```
 git clone https://github.com/ulissigroup/finetuna
```

Show 3 hidden projects...

flare++ (🥈13 · ⭐ 35 · 💀) - A many-body extension of the FLARE code. MIT C++ ML-IAP
ACEHAL (🥉5 · ⭐ 11 · 💀) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed Julia
ALEBREW (🥉3 · ⭐ 13) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD

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. language-models generative pretrained transformer

🔗 GAP-ML.org community homepage ML-IAP

🔗 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.

🔗 ACE / GRACE support - Support forum for the Atomic Cluster Expansion (ACE) and extensions.

Best-of Machine Learning with Python (🥇23 · ⭐ 18K) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python

GitHub (👨‍💻 49 · 🔀 2.4K · 📋 61 - 44% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/ml-tooling/best-of-ml-python
```

OpenML (🥇20 · ⭐ 670 · 📈) - Open Machine Learning. BSD-3 datasets

GitHub (👨‍💻 35 · 🔀 90 · 📋 930 - 39% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/openml/OpenML
```

MatBench Discovery (🥇19 · ⭐ 110) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository

GitHub (👨‍💻 9 · 🔀 17 · 📦 4 · 📋 40 - 10% open · ⏱️ 16.11.2024):
```
 git clone https://github.com/janosh/matbench-discovery
```
PyPi (📥 1.6K / month · ⏱️ 11.09.2024):
```
 pip install matbench-discovery
```

Graph-based Deep Learning Literature (🥈18 · ⭐ 4.8K) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn

GitHub (👨‍💻 12 · 🔀 770 · ⏱️ 16.11.2024):

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

MatBench (🥈17 · ⭐ 120 · 💤) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking model-repository

GitHub (👨‍💻 25 · 🔀 45 · 📦 18 · 📋 65 - 60% open · ⏱️ 20.01.2024):
```
 git clone https://github.com/materialsproject/matbench
```
PyPi (📥 430 / month · 📦 2 · ⏱️ 27.07.2022):
```
 pip install matbench
```

GT4SD - Generative Toolkit for Scientific Discovery (🥈15 · ⭐ 340) - Gradio apps of generative models in GT4SD. MIT generative pretrained drug-discovery model-repository

GitHub (👨‍💻 20 · 🔀 72 · 📋 110 - 12% open · ⏱️ 12.09.2024):
```
 git clone https://github.com/GT4SD/gt4sd-core
```

AI for Science Resources (🥈13 · ⭐ 520) - List of resources for AI4Science research, including learning resources. GPL-3.0 license

GitHub (👨‍💻 30 · 🔀 61 · 📋 18 - 11% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/divelab/AIRS
```

Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 91) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn

GitHub (👨‍💻 3 · 🔀 14 · 📋 2 - 50% open · ⏱️ 12.11.2024):

 git clone https://github.com/Eipgen/Neural-Network-Models-for-Chemistry

GNoME Explorer (🥈10 · ⭐ 890) - Graph Networks for Materials Exploration Database. Apache-2 datasets materials-discovery

GitHub (👨‍💻 2 · 🔀 140 · 📋 22 - 81% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/google-deepmind/materials_discovery
```

Awesome Materials Informatics (🥈9 · ⭐ 390) - Curated list of known efforts in materials informatics, i.e. in modern materials science. Custom

GitHub (👨‍💻 19 · 🔀 84 · ⏱️ 18.09.2024):

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

Awesome Neural Geometry (🥉8 · ⭐ 920) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. Unlicensed educational rep-learn

GitHub (👨‍💻 12 · 🔀 57 · ⏱️ 25.09.2024):

 git clone https://github.com/neurreps/awesome-neural-geometry

optimade.science (🥉8 · ⭐ 8) - A sky-scanner Optimade browser-only GUI. MIT datasets

GitHub (👨‍💻 8 · 🔀 2 · 📋 26 - 26% open · ⏱️ 10.06.2024):
```
 git clone https://github.com/tilde-lab/optimade.science
```

Awesome-Graph-Generation (🥉7 · ⭐ 290) - A curated list of up-to-date graph generation papers and resources. Unlicensed rep-learn

GitHub (👨‍💻 4 · 🔀 18 · ⏱️ 14.10.2024):

 git clone https://github.com/yuanqidu/awesome-graph-generation

Awesome Neural SBI (🥉7 · ⭐ 95) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning

GitHub (👨‍💻 3 · 🔀 6 · 📋 2 - 50% open · ⏱️ 17.06.2024):
```
 git clone https://github.com/smsharma/awesome-neural-sbi
```

Awesome-Crystal-GNNs (🥉7 · ⭐ 70) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. MIT

GitHub (👨‍💻 2 · 🔀 8 · ⏱️ 19.10.2024):

 git clone https://github.com/kdmsit/Awesome-Crystal-GNNs

AI for Science paper collection (🥉7 · ⭐ 66 · 🐣) - List the AI for Science papers accepted by top conferences. Apache-2

GitHub (👨‍💻 5 · 🔀 7 · ⏱️ 14.09.2024):

 git clone https://github.com/sherrylixuecheng/AI_for_Science_paper_collection

The Collection of Database and Dataset Resources in Materials Science (🥉6 · ⭐ 270) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed datasets

GitHub (👨‍💻 2 · 🔀 45 · 📋 2 - 50% open · ⏱️ 07.06.2024):

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

Show 7 hidden projects...

MoLFormers UI (🥈9 · ⭐ 270 · 💀) - A family of foundation models trained on chemicals. Apache-2 transformer language-models pretrained drug-discovery
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 · ⭐ 93 · 💤) - 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

Datasets

Datasets, databases and trained models for atomistic ML.

🔗 Alexandria Materials Database - A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..

🔗 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. model-repository pretrained language-models

🔗 Graphs of Materials Project 20190401 - The dataset used to train the MEGNet interatomic potential. ML-IAP

🔗 HME21 Dataset - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. UIP

🔗 JARVIS-Leaderboard ( ⭐ 61) - A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository benchmarking community-resource educational

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

🔗 Materials Project Trajectory (MPtrj) Dataset - The dataset used to train the CHGNet universal potential. UIP

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

🔗 MPF.2021.2.8 - The dataset used to train the M3GNet universal potential. UIP

🔗 NRELMatDB - Computational materials database with the specific focus on materials for renewable energy applications including, but..

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

🔗 sGDML Datasets - MD17, MD22, DFT datasets.

🔗 MoleculeNet - A Benchmark for Molecular Machine Learning. benchmarking

🔗 ZINC15 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules

🔗 ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules

OPTIMADE Python tools (🥇27 · ⭐ 71 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT

GitHub (👨‍💻 28 · 🔀 44 · 📦 61 · 📋 460 - 23% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/Materials-Consortia/optimade-python-tools
```
PyPi (📥 15K / month · 📦 4 · ⏱️ 15.10.2024):
```
 pip install optimade
```
Conda (📥 99K · ⏱️ 16.10.2024):
```
 conda install -c conda-forge optimade
```

MPContribs (🥇25 · ⭐ 36) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT

GitHub (👨‍💻 25 · 🔀 20 · 📦 40 · 📋 100 - 21% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/materialsproject/MPContribs
```
PyPi (📥 9.5K / month · 📦 3 · ⏱️ 17.10.2024):
```
 pip install mpcontribs-client
```

FAIR Chemistry datasets (🥇24 · ⭐ 880) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis

GitHub (👨‍💻 42 · 🔀 250 · 📋 240 - 12% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/FAIR-Chem/fairchem
```
PyPi (📥 3.5K / month · 📦 1 · ⏱️ 14.09.2024):
```
 pip install fairchem-core
```

Open Databases Integration for Materials Design (OPTIMADE) (🥈18 · ⭐ 83) - Specification of a common REST API for access to materials databases. CC-BY-4.0

GitHub (👨‍💻 21 · 🔀 35 · 📋 240 - 28% open · ⏱️ 12.06.2024):
```
 git clone https://github.com/Materials-Consortia/OPTIMADE
```

load-atoms (🥈16 · ⭐ 38) - download and manipulate atomistic datasets. MIT data-structures

GitHub (👨‍💻 3 · 🔀 2 · 📦 4 · 📋 31 - 3% open · ⏱️ 16.10.2024):
```
 git clone https://github.com/jla-gardner/load-atoms
```
PyPi (📥 2.3K / month · ⏱️ 04.10.2024):
```
 pip install load-atoms
```

QH9 (🥈13 · ⭐ 520) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT

GitHub (👨‍💻 30 · 🔀 61 · 📋 18 - 11% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/divelab/AIRS
```

SPICE (🥈11 · ⭐ 160) - A collection of QM data for training potential functions. MIT ML-IAP MD

GitHub (👨‍💻 1 · 🔀 9 · 📥 270 · 📋 68 - 25% open · ⏱️ 19.08.2024):
```
 git clone https://github.com/openmm/spice-dataset
```

AIS Square (🥈9 · ⭐ 12 · 📈) - A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the.. LGPL-3.0 community-resource model-repository

GitHub (👨‍💻 8 · 🔀 8 · 📋 6 - 83% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/deepmodeling/AIS-Square
```

Materials Data Facility (MDF) (🥈9 · ⭐ 10 · 💤) - A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,.. Apache-2

GitHub (👨‍💻 7 · 🔀 1 · 📋 7 - 14% open · ⏱️ 05.02.2024):
```
 git clone https://github.com/materials-data-facility/connect_client
```

2DMD dataset (🥈9 · ⭐ 6 · 💤) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 material-defect

GitHub (👨‍💻 11 · 🔀 3 · ⏱️ 21.11.2023):

 git clone https://github.com/HSE-LAMBDA/ai4material_design

Meta Open Materials 2024 (OMat24) Dataset (🥈9) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0

GitHub:

 git clone https://github.com/https://github.com/FAIR-Chem/fairchem

PyPi (📥 3.5K / month · 📦 1 · ⏱️ 14.09.2024):
```
 pip install fairchem-core
```

The Perovskite Database Project (🥉5 · ⭐ 60 · 💤) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource

GitHub (👨‍💻 2 · 🔀 20 · ⏱️ 07.03.2024):

 git clone https://github.com/Jesperkemist/perovskitedatabase

3DSC Database (🥉5 · ⭐ 15) - Repo for the paper publishing the superconductor database with 3D crystal structures. Custom superconductors materials-discovery

GitHub (🔀 5 · 📋 2 - 50% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/aimat-lab/3DSC
```

Show 15 hidden projects...

ATOM3D (🥈20 · ⭐ 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 · ⭐ 90 · 💀) - MIT benchmarking
GEOM (🥉7 · ⭐ 200 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
ANI-1x Datasets (🥉6 · ⭐ 60 · 💀) - 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
GDB-9-Ex9 and ORNL_AISD-Ex (🥉6 · ⭐ 6 · 💀) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed
SciGlass (🥉5 · ⭐ 11 · 💀) - The database contains a vast set of data on the properties of glass materials. MIT
linear-regression-benchmarks (🥉5 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper
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 · ⭐ 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

Data Structures

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

dpdata (🥇25 · ⭐ 200) - A Python package for manipulating atomistic data of software in computational science. LGPL-3.0

GitHub (👨‍💻 61 · 🔀 130 · 📦 130 · 📋 120 - 28% open · ⏱️ 20.09.2024):
```
 git clone https://github.com/deepmodeling/dpdata
```
PyPi (📥 72K / month · 📦 40 · ⏱️ 20.09.2024):
```
 pip install dpdata
```
Conda (📥 240 · ⏱️ 27.09.2023):
```
 conda install -c deepmodeling dpdata
```

Metatensor (🥈22 · ⭐ 54) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3 Rust C-lang C++ Python

GitHub (👨‍💻 25 · 🔀 18 · 📥 32K · 📦 13 · 📋 220 - 30% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/lab-cosmo/metatensor
```

mp-pyrho (🥉18 · ⭐ 37) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom ML-DFT

GitHub (👨‍💻 10 · 🔀 7 · 📦 25 · 📋 5 - 40% open · ⏱️ 22.10.2024):
```
 git clone https://github.com/materialsproject/pyrho
```
PyPi (📥 11K / month · 📦 5 · ⏱️ 22.10.2024):
```
 pip install mp-pyrho
```

dlpack (🥉15 · ⭐ 910) - common in-memory tensor structure. Apache-2 C++

GitHub (👨‍💻 24 · 🔀 130 · 📋 72 - 41% open · ⏱️ 28.09.2024):
```
 git clone https://github.com/dmlc/dlpack
```

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.

🔗 IKS-PIML - Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning.. neural-operator pinn datasets single-paper

JAX-DFT (🥇25 · ⭐ 34K) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2

GitHub (👨‍💻 810 · 🔀 7.9K · 📋 1.8K - 81% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/google-research/google-research
```

MALA (🥇19 · ⭐ 82) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3

GitHub (👨‍💻 44 · 🔀 24 · 📦 2 · 📋 280 - 11% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/mala-project/mala
```

QHNet (🥇13 · ⭐ 520) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn

GitHub (👨‍💻 30 · 🔀 61 · 📋 18 - 11% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/divelab/AIRS
```

SALTED (🥇13 · ⭐ 31) - Symmetry-Adapted Learning of Three-dimensional Electron Densities. GPL-3.0

GitHub (👨‍💻 17 · 🔀 4 · 📋 6 - 16% open · ⏱️ 27.09.2024):
```
 git clone https://github.com/andreagrisafi/SALTED
```

DeepH-pack (🥈12 · ⭐ 240) - Deep neural networks for density functional theory Hamiltonian. LGPL-3.0 Julia

GitHub (👨‍💻 8 · 🔀 44 · 📋 53 - 26% open · ⏱️ 07.10.2024):
```
 git clone https://github.com/mzjb/DeepH-pack
```

DeePKS-kit (🥈10 · ⭐ 100 · 💤) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0

GitHub (👨‍💻 7 · 🔀 36 · 📋 21 - 33% open · ⏱️ 13.04.2024):
```
 git clone https://github.com/deepmodeling/deepks-kit
```

Grad DFT (🥈10 · ⭐ 77 · 💤) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2

GitHub (👨‍💻 4 · 🔀 7 · 📋 54 - 20% open · ⏱️ 13.02.2024):
```
 git clone https://github.com/XanaduAI/GradDFT
```

HamGNN (🥈7 · ⭐ 62) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0 rep-learn magnetism C-lang

GitHub (👨‍💻 2 · 🔀 15 · 📋 32 - 81% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/QuantumLab-ZY/HamGNN
```

Q-stack (🥈7 · ⭐ 15) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT excited-states general-tool

GitHub (👨‍💻 7 · 🔀 5 · 📋 29 - 31% open · ⏱️ 26.09.2024):
```
 git clone https://github.com/lcmd-epfl/Q-stack
```

ChargE3Net (🥉6 · ⭐ 36) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn

GitHub (👨‍💻 2 · 🔀 12 · 📋 7 - 42% open · ⏱️ 30.10.2024):
```
 git clone https://github.com/AIforGreatGood/charge3net
```

charge-density-models (🥉6 · ⭐ 10 · 💤) - Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem). MIT rep-learn

GitHub (🔀 3 · 📋 4 - 25% open · ⏱️ 29.11.2023):

 git clone https://github.com/ulissigroup/charge-density-models

InfGCN for Electron Density Estimation (🥉5 · ⭐ 11 · 💤) - Official implementation of the NeurIPS 23 spotlight paper of InfGCN. MIT rep-learn neural-operator

GitHub (🔀 3 · ⏱️ 05.12.2023):

 git clone https://github.com/ccr-cheng/infgcn-pytorch

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
ACEhamiltonians (🥈10 · ⭐ 13 · 💀) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. MIT Julia
PROPhet (🥈9 · ⭐ 64 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
DeepH-E3 (🥈7 · ⭐ 75 · 💀) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. MIT magnetism
Mat2Spec (🥈7 · ⭐ 28 · 💀) - 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
DeepDFT (🥉6 · ⭐ 61 · 💀) - Official implementation of DeepDFT model. MIT
KSR-DFT (🥉6 · ⭐ 4 · 💀) - Kohn-Sham regularizer for machine-learned DFT functionals. Apache-2
xDeepH (🥉5 · ⭐ 33 · 💀) - 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
rho_learn (🥉5 · ⭐ 4 · 💀) - A proof-of-concept workflow for torch-based electron density learning. MIT
DeepCDP (🥉4 · ⭐ 6 · 💀) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
MALADA (🥉4 · ⭐ 1) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3
gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper
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
A3MD (🥉2 · ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed rep-learn single-paper
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

Educational Resources

Tutorials, guides, cookbooks, recipes, etc.

🔗 AI for Science 101 community-resource rep-learn

🔗 AL4MS 2023 workshop tutorials active-learning

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

AI4Chemistry course (🥈10 · ⭐ 140) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT chemistry

GitHub (👨‍💻 6 · 🔀 35 · 📋 4 - 25% open · ⏱️ 02.05.2024):
```
 git clone https://github.com/schwallergroup/ai4chem_course
```

jarvis-tools-notebooks (🥈9 · ⭐ 65) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST

GitHub (👨‍💻 5 · 🔀 27 · ⏱️ 14.08.2024):

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

DSECOP (🥈9 · ⭐ 44) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0

GitHub (👨‍💻 14 · 🔀 26 · 📋 8 - 12% open · ⏱️ 26.06.2024):
```
 git clone https://github.com/GDS-Education-Community-of-Practice/DSECOP
```

iam-notebooks (🥈9 · ⭐ 26) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2

GitHub (👨‍💻 6 · 🔀 5 · ⏱️ 09.10.2024):

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

COSMO Software Cookbook (🥈9 · ⭐ 16) - A cookbook wtih recipes for atomic-scale modeling of materials and molecules. BSD-3

GitHub (👨‍💻 11 · 🔀 1 · 📋 12 - 8% open · ⏱️ 16.11.2024):
```
 git clone https://github.com/lab-cosmo/software-cookbook
```

BestPractices (🥈8 · ⭐ 180 · 💤) - Things that you should (and should not) do in your Materials Informatics research. MIT

GitHub (👨‍💻 3 · 🔀 71 · 📋 7 - 71% open · ⏱️ 17.11.2023):
```
 git clone https://github.com/anthony-wang/BestPractices
```

MACE-tutorials (🥉7 · ⭐ 41) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD

GitHub (👨‍💻 2 · 🔀 11 · ⏱️ 16.07.2024):

 git clone https://github.com/ilyes319/mace-tutorials

Show 18 hidden projects...

Geometric GNN Dojo (🥇12 · ⭐ 470 · 💀) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn
DeepLearningLifeSciences (🥇12 · ⭐ 360 · 💀) - Example code from the book Deep Learning for the Life Sciences. MIT
Deep Learning for Molecules and Materials Book (🥇11 · ⭐ 620 · 💀) - 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
Applied AI for Materials (🥉6 · ⭐ 59 · 💀) - 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 · ⭐ 84 · 💀) - AI for Science. Unlicensed
Machine Learning for Materials Hard and Soft (🥉5 · ⭐ 34 · 💀) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. Unlicensed
Data Handling, DoE and Statistical Analysis for Material Chemists (🥉5 · ⭐ 1 · 💀) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University. GPL-3.0
ML-in-chemistry-101 (🥉4 · ⭐ 71 · 💀) - The course materials for Machine Learning in Chemistry 101. Unlicensed
chemrev-gpr (🥉4 · ⭐ 7 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. Unlicensed
AI4ChemMat Hands-On Series (🥉4 · ⭐ 1 · 💤) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab. MPL-2.0
PiNN Lab (🥉3 · ⭐ 2 · 💀) - Material for running a lab session on atomic neural networks. GPL-3.0
MLDensity_tutorial (🥉2 · ⭐ 9 · 💀) - Tutorial files to work with ML for the charge density in molecules and solids. Unlicensed
LAMMPS-style pair potentials with GAP (🥉2 · ⭐ 4 · 💀) - A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD.. Unlicensed ML-IAP MD rep-eng
MALA Tutorial (🥉2 · ⭐ 2 · 💤) - A full MALA hands-on tutorial. Unlicensed

Explainable Artificial intelligence (XAI)

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

exmol (🥇19 · ⭐ 290 · 💤) - Explainer for black box models that predict molecule properties. MIT

GitHub (👨‍💻 7 · 🔀 41 · 📦 21 · 📋 69 - 15% open · ⏱️ 04.12.2023):
```
 git clone https://github.com/ur-whitelab/exmol
```
PyPi (📥 2.4K / 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

GitHub (👨‍💻 2 · 🔀 1 · 📋 3 - 33% open · ⏱️ 07.10.2024):
```
 git clone https://github.com/aimat-lab/graph_attention_student
```

Show 1 hidden projects...

Linear vs blackbox (🥉3 · ⭐ 2 · 💀) - 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 5 hidden projects...

QDF for molecule (🥇8 · ⭐ 210 · 💀) - Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation.. MIT
QMLearn (🥈5 · ⭐ 11 · 💀) - Quantum Machine Learning by learning one-body reduced density matrices in the AO basis... MIT
q-pac (🥈5 · ⭐ 4 · 💀) - Kernel charge equilibration method. MIT electrostatics
halex (🥈5 · ⭐ 3 · 💤) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed excited-states
e3psi (🥉3 · ⭐ 3 · 💤) - Equivariant machine learning library for learning from electronic structures. LGPL-3.0

General Tools

General tools for atomistic machine learning.

RDKit (🥇36 · ⭐ 2.7K) - BSD-3 C++

GitHub (👨‍💻 240 · 🔀 870 · 📥 920 · 📦 3 · 📋 3.5K - 27% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/rdkit/rdkit
```
PyPi (📥 2.4M / month · 📦 790 · ⏱️ 07.11.2024):
```
 pip install rdkit
```
Conda (📥 2.6M · ⏱️ 16.06.2023):
```
 conda install -c rdkit rdkit
```

DeepChem (🥇35 · ⭐ 5.5K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT

GitHub (👨‍💻 250 · 🔀 1.7K · 📦 460 · 📋 1.9K - 34% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/deepchem/deepchem
```
PyPi (📥 90K / month · 📦 13 · ⏱️ 18.11.2024):
```
 pip install deepchem
```
Conda (📥 110K · ⏱️ 05.04.2024):
```
 conda install -c conda-forge deepchem
```
Docker Hub (📥 7.8K · ⭐ 5 · ⏱️ 06.11.2024):
```
 docker pull deepchemio/deepchem
```

Matminer (🥇29 · ⭐ 480) - Data mining for materials science. Custom

GitHub (👨‍💻 56 · 🔀 190 · 📦 330 · 📋 230 - 13% open · ⏱️ 11.10.2024):
```
 git clone https://github.com/hackingmaterials/matminer
```
PyPi (📥 15K / month · 📦 60 · ⏱️ 06.10.2024):
```
 pip install matminer
```
Conda (📥 75K · ⏱️ 06.10.2024):
```
 conda install -c conda-forge matminer
```

MAML (🥈24 · ⭐ 370) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3

GitHub (👨‍💻 33 · 🔀 78 · 📦 11 · 📋 71 - 12% open · ⏱️ 06.11.2024):
```
 git clone https://github.com/materialsvirtuallab/maml
```
PyPi (📥 650 / month · 📦 2 · ⏱️ 13.06.2024):
```
 pip install maml
```

QUIP (🥈24 · ⭐ 350) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran

GitHub (👨‍💻 85 · 🔀 120 · 📥 700 · 📦 45 · 📋 470 - 22% open · ⏱️ 27.09.2024):
```
 git clone https://github.com/libAtoms/QUIP
```
PyPi (📥 7.2K / month · 📦 4 · ⏱️ 15.01.2023):
```
 pip install quippy-ase
```
Docker Hub (📥 10K · ⭐ 4 · ⏱️ 24.04.2023):
```
 docker pull libatomsquip/quip
```

JARVIS-Tools (🥈24 · ⭐ 310) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom

GitHub (👨‍💻 15 · 🔀 120 · 📦 100 · 📋 91 - 50% open · ⏱️ 18.10.2024):
```
 git clone https://github.com/usnistgov/jarvis
```
PyPi (📥 38K / month · 📦 31 · ⏱️ 18.10.2024):
```
 pip install jarvis-tools
```

Conda (📥 83K · ⏱️ 07.09.2024):

 conda install -c conda-forge jarvis-tools

MAST-ML (🥈20 · ⭐ 100) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT

GitHub (👨‍💻 19 · 🔀 61 · 📥 130 · 📦 45 · 📋 220 - 14% open · ⏱️ 09.10.2024):
```
 git clone https://github.com/uw-cmg/MAST-ML
```

Scikit-Matter (🥈17 · ⭐ 77) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3 scikit-learn

GitHub (👨‍💻 15 · 🔀 20 · 📦 11 · 📋 70 - 20% open · ⏱️ 09.10.2024):
```
 git clone https://github.com/scikit-learn-contrib/scikit-matter
```
PyPi (📥 2.3K / month · ⏱️ 24.08.2023):
```
 pip install skmatter
```
Conda (📥 2.4K · ⏱️ 24.08.2023):
```
 conda install -c conda-forge skmatter
```

XenonPy (🥈16 · ⭐ 140 · 💤) - XenonPy is a Python Software for Materials Informatics. BSD-3

GitHub (👨‍💻 10 · 🔀 61 · 📥 1.4K · 📋 87 - 24% open · ⏱️ 21.04.2024):
```
 git clone https://github.com/yoshida-lab/XenonPy
```
PyPi (📥 1.6K / month · 📦 1 · ⏱️ 31.10.2022):
```
 pip install xenonpy
```

MLatom (🥈16 · ⭐ 65) - AI-enhanced computational chemistry. MIT UIP ML-IAP MD ML-DFT ML-ESM transfer-learning active-learning spectroscopy structure-optimization

GitHub (👨‍💻 3 · 🔀 11 · 📋 5 - 20% open · ⏱️ 06.11.2024):
```
 git clone https://github.com/dralgroup/mlatom
```
PyPi (📥 2.3K / month · ⏱️ 06.11.2024):
```
 pip install mlatom
```

Artificial Intelligence for Science (AIRS) (🥉13 · ⭐ 520) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules

GitHub (👨‍💻 30 · 🔀 61 · 📋 18 - 11% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/divelab/AIRS
```

Show 11 hidden projects...

QML (🥈16 · ⭐ 200 · 💀) - QML: Quantum Machine Learning. MIT
Automatminer (🥈16 · ⭐ 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 · ⭐ 680 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT
JAXChem (🥉7 · ⭐ 79 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT
uncertainty_benchmarking (🥉7 · ⭐ 40 · 💀) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions. Unlicensed benchmarking probabilistic
torchchem (🥉7 · ⭐ 35 · 💀) - An experimental repo for experimenting with PyTorch models. MIT
Equisolve (🥉6 · ⭐ 5 · 💀) - A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties.. 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
quantum-structure-ml (🥉2 · ⭐ 2 · 💀) - 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 · ⭐ 340) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pretrained drug-discovery rep-learn

GitHub (👨‍💻 20 · 🔀 72 · 📋 110 - 12% open · ⏱️ 12.09.2024):
```
 git clone https://github.com/GT4SD/gt4sd-core
```
PyPi (📥 3.3K / month · ⏱️ 12.09.2024):
```
 pip install gt4sd
```

MoLeR (🥇15 · ⭐ 280 · 💤) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT

GitHub (👨‍💻 5 · 🔀 42 · 📋 39 - 20% open · ⏱️ 03.01.2024):
```
 git clone https://github.com/microsoft/molecule-generation
```
PyPi (📥 280 / month · 📦 1 · ⏱️ 05.01.2024):
```
 pip install molecule-generation
```

PMTransformer (🥇15 · ⭐ 86) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer

GitHub (👨‍💻 2 · 🔀 13 · 📦 8 · ⏱️ 20.06.2024):
```
 git clone https://github.com/hspark1212/MOFTransformer
```
PyPi (📥 820 / month · 📦 1 · ⏱️ 20.06.2024):
```
 pip install moftransformer
```

SchNetPack G-SchNet (🥈13 · ⭐ 49) - G-SchNet extension for SchNetPack. MIT

GitHub (👨‍💻 3 · 🔀 8 · ⏱️ 07.11.2024):

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

SiMGen (🥈9 · ⭐ 15 · 💤) - Zero Shot Molecular Generation via Similarity Kernels. MIT viz

GitHub (👨‍💻 4 · 🔀 2 · 📦 2 · 📋 4 - 25% open · ⏱️ 15.02.2024):
```
 git clone https://github.com/RokasEl/simgen
```
PyPi (📥 46 / month · ⏱️ 14.02.2024):
```
 pip install simgen
```

COATI (🥉5 · ⭐ 100 · 💤) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery multimodal pretrained rep-learn

GitHub (👨‍💻 5 · 🔀 6 · 📋 3 - 33% open · ⏱️ 23.03.2024):
```
 git clone https://github.com/terraytherapeutics/COATI
```

Show 8 hidden projects...

synspace (🥈13 · ⭐ 36 · 💀) - Synthesis generative model. MIT
EDM (🥈9 · ⭐ 440 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
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 · ⭐ 53 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT
rxngenerator (🥉6 · ⭐ 12 · 💀) - 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

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 · ⭐ 1.5K · 📈) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++

GitHub (👨‍💻 70 · 🔀 510 · 📥 43K · 📦 20 · 📋 830 - 9% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/deepmodeling/deepmd-kit
```
PyPi (📥 8.2K / month · 📦 4 · ⏱️ 14.11.2024):
```
 pip install deepmd-kit
```

Conda (📥 1.5K · ⏱️ 06.04.2024):

 conda install -c deepmodeling deepmd-kit

Docker Hub (📥 2.9K · ⭐ 1 · ⏱️ 15.11.2024):
```
 docker pull deepmodeling/deepmd-kit
```

fairchem (🥇24 · ⭐ 880) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis

GitHub (👨‍💻 42 · 🔀 250 · 📋 240 - 12% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/FAIR-Chem/fairchem
```
PyPi (📥 3.5K / month · 📦 1 · ⏱️ 14.09.2024):
```
 pip install fairchem-core
```

TorchANI (🥇24 · ⭐ 460 · 💤) - Accurate Neural Network Potential on PyTorch. MIT

GitHub (👨‍💻 19 · 🔀 130 · 📦 45 · 📋 170 - 13% open · ⏱️ 14.11.2023):
```
 git clone https://github.com/aiqm/torchani
```
PyPi (📥 3.9K / month · 📦 4 · ⏱️ 14.11.2023):
```
 pip install torchani
```
Conda (📥 560K · ⏱️ 13.11.2024):
```
 conda install -c conda-forge torchani
```

NequIP (🥇23 · ⭐ 630) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT

GitHub (👨‍💻 12 · 🔀 140 · 📦 32 · 📋 96 - 26% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/mir-group/nequip
```
PyPi (📥 4.5K / month · 📦 1 · ⏱️ 09.07.2024):
```
 pip install nequip
```
Conda (📥 6.6K · ⏱️ 10.07.2024):
```
 conda install -c conda-forge nequip
```

MACE (🥇22 · ⭐ 550) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT

GitHub (👨‍💻 46 · 🔀 200 · 📋 300 - 22% open · ⏱️ 12.11.2024):
```
 git clone https://github.com/ACEsuit/mace
```

GPUMD (🥇22 · ⭐ 470) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics

GitHub (👨‍💻 38 · 🔀 120 · 📋 190 - 10% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/brucefan1983/GPUMD
```

DP-GEN (🥈21 · ⭐ 310) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0 workflows

GitHub (👨‍💻 65 · 🔀 170 · 📥 1.8K · 📦 7 · 📋 300 - 11% open · ⏱️ 04.11.2024):
```
 git clone https://github.com/deepmodeling/dpgen
```
PyPi (📥 1.3K / month · 📦 1 · ⏱️ 10.04.2024):
```
 pip install dpgen
```
Conda (📥 210 · ⏱️ 16.06.2023):
```
 conda install -c deepmodeling dpgen
```

TorchMD-NET (🥈20 · ⭐ 330) - Training neural network potentials. MIT MD rep-learn transformer pretrained

GitHub (👨‍💻 16 · 🔀 72 · 📋 120 - 28% open · ⏱️ 28.08.2024):
```
 git clone https://github.com/torchmd/torchmd-net
```

Conda (📥 220K · ⏱️ 15.11.2024):

 conda install -c conda-forge torchmd-net

apax (🥈20 · ⭐ 18) - A flexible and performant framework for training machine learning potentials. MIT

GitHub (👨‍💻 8 · 🔀 3 · 📦 3 · 📋 130 - 11% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/apax-hub/apax
```
PyPi (📥 1.2K / month · ⏱️ 17.09.2024):
```
 pip install apax
```

KLIFF (🥈16 · ⭐ 34) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1 probabilistic workflows

GitHub (👨‍💻 9 · 🔀 19 · 📦 4 · 📋 42 - 54% open · ⏱️ 08.10.2024):
```
 git clone https://github.com/openkim/kliff
```
PyPi (📥 430 / month · ⏱️ 17.12.2023):
```
 pip install kliff
```
Conda (📥 120K · ⏱️ 10.09.2024):
```
 conda install -c conda-forge kliff
```

Neural Force Field (🥈15 · ⭐ 240) - Neural Network Force Field based on PyTorch. MIT pretrained

GitHub (👨‍💻 41 · 🔀 48 · 📋 20 - 10% open · ⏱️ 24.09.2024):
```
 git clone https://github.com/learningmatter-mit/NeuralForceField
```

PyXtalFF (🥈15 · ⭐ 86 · 💤) - Machine Learning Interatomic Potential Predictions. MIT

GitHub (👨‍💻 9 · 🔀 23 · 📋 63 - 19% open · ⏱️ 07.01.2024):
```
 git clone https://github.com/MaterSim/PyXtal_FF
```
PyPi (📥 430 / month · ⏱️ 21.12.2022):
```
 pip install pyxtal_ff
```

NNPOps (🥈15 · ⭐ 83) - High-performance operations for neural network potentials. MIT MD C++

GitHub (👨‍💻 9 · 🔀 18 · 📋 57 - 38% open · ⏱️ 10.07.2024):
```
 git clone https://github.com/openmm/NNPOps
```
Conda (📥 270K · ⏱️ 14.11.2024):
```
 conda install -c conda-forge nnpops
```

So3krates (MLFF) (🥈14 · ⭐ 91) - Build neural networks for machine learning force fields with JAX. MIT

GitHub (👨‍💻 4 · 🔀 19 · 📋 10 - 40% open · ⏱️ 23.08.2024):
```
 git clone https://github.com/thorben-frank/mlff
```

Pacemaker (🥈14 · ⭐ 72) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom

GitHub (👨‍💻 6 · 🔀 19 · 📋 57 - 35% open · ⏱️ 06.11.2024):
```
 git clone https://github.com/ICAMS/python-ace
```
PyPi (📥 24 / month · ⏱️ 24.10.2022):
```
 pip install python-ace
```

Ultra-Fast Force Fields (UF3) (🥈14 · ⭐ 61) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2

GitHub (👨‍💻 10 · 🔀 20 · 📦 2 · 📋 50 - 38% open · ⏱️ 04.10.2024):
```
 git clone https://github.com/uf3/uf3
```
PyPi (📥 80 / month · ⏱️ 27.10.2023):
```
 pip install uf3
```

wfl (🥈14 · ⭐ 35 · 📉) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0 workflows HTC

GitHub (👨‍💻 19 · 🔀 18 · 📦 2 · 📋 160 - 41% open · ⏱️ 01.11.2024):
```
 git clone https://github.com/libAtoms/workflow
```

DMFF (🥈13 · ⭐ 150 · 💤) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0

GitHub (👨‍💻 14 · 🔀 42 · 📋 26 - 38% open · ⏱️ 12.01.2024):
```
 git clone https://github.com/deepmodeling/DMFF
```

calorine (🥈13 · ⭐ 13) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom

PyPi (📥 2.8K / month · 📦 4 · ⏱️ 25.10.2024):
```
 pip install calorine
```

GitLab (🔀 4 · 📋 87 - 3% open · ⏱️ 25.10.2024):

 git clone https://gitlab.com/materials-modeling/calorine

ANI-1 (🥈12 · ⭐ 220 · 💤) - ANI-1 neural net potential with python interface (ASE). MIT

GitHub (👨‍💻 6 · 🔀 55 · 📋 37 - 43% open · ⏱️ 11.03.2024):
```
 git clone https://github.com/isayev/ASE_ANI
```

PiNN (🥈12 · ⭐ 110) - A Python library for building atomic neural networks. BSD-3

GitHub (👨‍💻 5 · 🔀 33 · 📋 7 - 14% open · ⏱️ 27.06.2024):
```
 git clone https://github.com/Teoroo-CMC/PiNN
```
Docker Hub (📥 250 · ⏱️ 27.06.2024):
```
 docker pull teoroo/pinn
```

CCS_fit (🥈12 · ⭐ 8 · 💤) - Curvature Constrained Splines. GPL-3.0

GitHub (👨‍💻 8 · 🔀 11 · 📥 660 · 📋 14 - 57% open · ⏱️ 16.02.2024):
```
 git clone https://github.com/Teoroo-CMC/CCS
```
PyPi (📥 6.7K / month · ⏱️ 16.02.2024):
```
 pip install ccs_fit
```

tinker-hp (🥈11 · ⭐ 81) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom

GitHub (👨‍💻 12 · 🔀 22 · 📋 20 - 15% open · ⏱️ 26.10.2024):
```
 git clone https://github.com/TinkerTools/tinker-hp
```

ACEfit (🥈11 · ⭐ 7) - MIT Julia

GitHub (👨‍💻 8 · 🔀 7 · 📋 57 - 38% open · ⏱️ 14.09.2024):
```
 git clone https://github.com/ACEsuit/ACEfit.jl
```

PyNEP (🥉10 · ⭐ 49) - A python interface of the machine learning potential NEP used in GPUMD. MIT

GitHub (👨‍💻 7 · 🔀 16 · 📋 11 - 36% open · ⏱️ 01.06.2024):
```
 git clone https://github.com/bigd4/PyNEP
```

Allegro (🥉9 · ⭐ 350) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic.. MIT

GitHub (👨‍💻 2 · 🔀 44 · 📋 39 - 61% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/mir-group/allegro
```

ACE1.jl (🥉9 · ⭐ 20) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom Julia

GitHub (👨‍💻 9 · 🔀 7 · 📋 46 - 47% open · ⏱️ 11.09.2024):
```
 git clone https://github.com/ACEsuit/ACE1.jl
```

Point Edge Transformer (PET) (🥉9 · ⭐ 19) - Point Edge Transformer. MIT rep-learn transformer

GitHub (👨‍💻 7 · 🔀 5 · ⏱️ 02.07.2024):
```
 git clone https://github.com/spozdn/pet
```

ACE.jl (🥉8 · ⭐ 65) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia

GitHub (👨‍💻 12 · 🔀 15 · 📋 82 - 29% open · ⏱️ 31.08.2024):
```
 git clone https://github.com/ACEsuit/ACE.jl
```

SIMPLE-NN v2 (🥉8 · ⭐ 41 · 💤) - SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab.. GPL-3.0

GitHub (👨‍💻 13 · 🔀 18 · 📋 13 - 30% open · ⏱️ 29.12.2023):
```
 git clone https://github.com/MDIL-SNU/SIMPLE-NN_v2
```

GAP (🥉8 · ⭐ 40) - Gaussian Approximation Potential (GAP). Custom

GitHub (👨‍💻 13 · 🔀 20 · ⏱️ 17.08.2024):
```
 git clone https://github.com/libAtoms/GAP
```

ALF (🥉8 · ⭐ 31) - A framework for performing active learning for training machine-learned interatomic potentials. Custom active-learning

GitHub (👨‍💻 5 · 🔀 12 · ⏱️ 04.11.2024):
```
 git clone https://github.com/lanl/alf
```

TurboGAP (🥉8 · ⭐ 16) - The TurboGAP code. Custom Fortran

GitHub (👨‍💻 8 · 🔀 9 · 📋 11 - 72% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/mcaroba/turbogap
```

TensorPotential (🥉6 · ⭐ 10) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom

GitHub (👨‍💻 4 · 🔀 4 · ⏱️ 12.09.2024):

 git clone https://github.com/ICAMS/TensorPotential

MLXDM (🥉6 · ⭐ 6) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. MIT long-range

GitHub (👨‍💻 7 · 🔀 2 · ⏱️ 15.08.2024):
```
 git clone https://github.com/RowleyGroup/MLXDM
```

Show 35 hidden projects...

MEGNet (🥇23 · ⭐ 510 · 💀) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3 multifidelity
sGDML (🥈16 · ⭐ 140 · 💀) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT
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
NNsforMD (🥈11 · ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT
DimeNet (🥉9 · ⭐ 300 · 💀) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom
SchNet (🥉9 · ⭐ 230 · 💀) - 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
Asparagus (🥉9 · ⭐ 4 · 🐣) - Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175. MIT workflows sampling MD
AIMNet (🥉8 · ⭐ 98 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper
MACE-Jax (🥉8 · ⭐ 62 · 💀) - Equivariant machine learning interatomic potentials in JAX. MIT
SNAP (🥉8 · ⭐ 37 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3
Atomistic Adversarial Attacks (🥉8 · ⭐ 32 · 💀) - 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 · ⭐ 91 · 💀) - Code for training PhysNet models. MIT electrostatics
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 · ⭐ 10 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking
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 · ⭐ 18 · 💤) - tools for graph-based machine-learning potentials in jax. MIT
NequIP-JAX (🥉4 · ⭐ 18 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed
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
SingleNN (🥉2 · ⭐ 9 · 💀) - An efficient package for training and executing neural-network interatomic potentials. Unlicensed C++
AisNet (🥉2 · ⭐ 3 · 💀) - A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.. MIT
RuNNer (🥉2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-.. GPL-3.0 Fortran
Allegro-JAX (🥉1 · ⭐ 21 · 💤) - JAX implementation of the Allegro interatomic potential. Unlicensed
nnp-pre-training (🥉1 · ⭐ 6 · 💤) - Synthetic pre-training for neural-network interatomic potentials. Unlicensed pretrained MD
mag-ace (🥉1 · ⭐ 2 · 💤) - Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package. Unlicensed magnetism MD Fortran
mlp (🥉1 · ⭐ 1 · 💀) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia

Language Models

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

paper-qa (🥇31 · ⭐ 6.4K) - High accuracy RAG for answering questions from scientific documents with citations. Apache-2 ai-agent

GitHub (👨‍💻 27 · 🔀 610 · 📦 81 · 📋 270 - 42% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/whitead/paper-qa
```
PyPi (📥 22K / month · 📦 10 · ⏱️ 18.11.2024):
```
 pip install paper-qa
```

OpenBioML ChemNLP (🥇17 · ⭐ 150) - ChemNLP project. MIT datasets

GitHub (👨‍💻 27 · 🔀 46 · 📋 250 - 44% open · ⏱️ 19.08.2024):
```
 git clone https://github.com/OpenBioML/chemnlp
```
PyPi (📥 400 / month · 📦 1 · ⏱️ 07.08.2023):
```
 pip install chemnlp
```

ChemCrow (🥇16 · ⭐ 620 · 💤) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT ai-agent

GitHub (👨‍💻 3 · 🔀 91 · 📦 6 · 📋 22 - 36% open · ⏱️ 27.03.2024):
```
 git clone https://github.com/ur-whitelab/chemcrow-public
```
PyPi (📥 1.8K / month · ⏱️ 27.03.2024):
```
 pip install chemcrow
```

ChatMOF (🥈13 · ⭐ 64) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative

GitHub (👨‍💻 1 · 🔀 10 · 📦 3 · ⏱️ 01.07.2024):
```
 git clone https://github.com/Yeonghun1675/ChatMOF
```
PyPi (📥 1.3K / month · ⏱️ 01.07.2024):
```
 pip install chatmof
```

AtomGPT (🥈13 · ⭐ 31) - AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design.. Custom generative pretrained transformer

GitHub (👨‍💻 2 · 🔀 5 · 📦 2 · ⏱️ 04.10.2024):
```
 git clone https://github.com/usnistgov/atomgpt
```
PyPi (📥 530 / month · ⏱️ 22.09.2024):
```
 pip install atomgpt
```

NIST ChemNLP (🥈12 · ⭐ 73) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. MIT literature-data

GitHub (👨‍💻 2 · 🔀 16 · 📦 4 · ⏱️ 19.08.2024):
```
 git clone https://github.com/usnistgov/chemnlp
```
PyPi (📥 400 / month · 📦 1 · ⏱️ 07.08.2023):
```
 pip install chemnlp
```

LLaMP (🥉8 · ⭐ 64) - 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 (👨‍💻 6 · 🔀 10 · 📋 25 - 32% open · ⏱️ 14.10.2024):
```
 git clone https://github.com/chiang-yuan/llamp
```

LLM-Prop (🥉7 · ⭐ 29 · 💤) - A repository for the LLM-Prop implementation. MIT

GitHub (👨‍💻 6 · 🔀 6 · 📋 2 - 50% open · ⏱️ 26.04.2024):
```
 git clone https://github.com/vertaix/LLM-Prop
```

crystal-text-llm (🥉5 · ⭐ 81) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery

GitHub (👨‍💻 3 · 🔀 14 · 📋 11 - 81% open · ⏱️ 18.06.2024):
```
 git clone https://github.com/facebookresearch/crystal-text-llm
```

SciBot (🥉5 · ⭐ 28) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed ai-agent

GitHub (👨‍💻 1 · 🔀 9 · 📦 2 · ⏱️ 03.09.2024):
```
 git clone https://github.com/CFN-softbio/SciBot
```

MAPI_LLM (🥉5 · ⭐ 9 · 💤) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT ai-agent dataset

GitHub (👨‍💻 2 · 🔀 2 · ⏱️ 11.04.2024):
```
 git clone https://github.com/maykcaldas/MAPI_LLM
```

Cephalo (🥉5 · ⭐ 6 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2 generative multimodal pretrained

GitHub (🔀 1 · ⏱️ 23.07.2024):

 git clone https://github.com/lamm-mit/Cephalo

Show 10 hidden projects...

ChemDataExtractor (🥇16 · ⭐ 310 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data
gptchem (🥈13 · ⭐ 230 · 💀) - Use GPT-3 to solve chemistry problems. MIT
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
MoLFormer (🥉9 · ⭐ 270 · 💀) - Repository for MolFormer. Apache-2 transformer pretrained drug-discovery
MolSkill (🥉9 · ⭐ 100 · 💀) - Extracting medicinal chemistry intuition via preference machine learning. MIT drug-discovery recommender
chemlift (🥉7 · ⭐ 32 · 💀) - Language-interfaced fine-tuning for chemistry. MIT
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 · ⭐ 21 · 💤) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis

Materials Discovery

Projects that implement materials discovery methods using atomistic ML.

🔗 MatterGen - A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687. generative proprietary

aviary (🥇14 · ⭐ 48) - The Wren sits on its Roost in the Aviary. MIT

GitHub (👨‍💻 4 · 🔀 12 · 📋 29 - 13% open · ⏱️ 09.11.2024):
```
 git clone https://github.com/CompRhys/aviary
```

BOSS (🥇14 · ⭐ 20) - Bayesian Optimization Structure Search (BOSS). Apache-2 probabilistic

PyPi (📥 16K / month · ⏱️ 13.11.2024):
```
 pip install aalto-boss
```
GitLab (🔀 11 · 📋 31 - 6% open · ⏱️ 13.11.2024):
```
 git clone https://gitlab.com/cest-group/boss
```

AGOX (🥈11 · ⭐ 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 (📥 1.3K / month · ⏱️ 23.10.2024):
```
 pip install agox
```
GitLab (🔀 5 · 📋 26 - 42% open · ⏱️ 23.10.2024):
```
 git clone https://gitlab.com/agox/agox
```

Materials Discovery: GNoME (🥈10 · ⭐ 890) - Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials. Apache-2 UIP datasets rep-learn proprietary

GitHub (👨‍💻 2 · 🔀 140 · 📋 22 - 81% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/google-deepmind/materials_discovery
```

CSPML (crystal structure prediction with machine learning-based element substitution) (🥉5 · ⭐ 21) - Original implementation of CSPML. MIT structure-prediction

GitHub (👨‍💻 1 · 🔀 8 · 📋 3 - 66% open · ⏱️ 25.09.2024):
```
 git clone https://github.com/minoru938/cspml
```

Show 6 hidden projects...

Computational Autonomy for Materials Discovery (CAMD) (🥈6 · ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. Apache-2
MAGUS (🥉4 · ⭐ 62 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning
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

Mathematical tools

Projects that implement mathematical objects used in atomistic machine learning.

KFAC-JAX (🥇19 · ⭐ 250 · 📉) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2

GitHub (👨‍💻 17 · 🔀 22 · 📦 11 · 📋 19 - 47% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/google-deepmind/kfac-jax
```
PyPi (📥 1K / month · 📦 1 · ⏱️ 04.04.2024):
```
 pip install kfac-jax
```

gpax (🥇18 · ⭐ 210) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning

GitHub (👨‍💻 6 · 🔀 26 · 📦 3 · 📋 40 - 20% open · ⏱️ 21.05.2024):
```
 git clone https://github.com/ziatdinovmax/gpax
```
PyPi (📥 810 / month · ⏱️ 20.03.2024):
```
 pip install gpax
```

SpheriCart (🥈17 · ⭐ 73) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT

GitHub (👨‍💻 11 · 🔀 12 · 📥 97 · 📦 5 · 📋 41 - 56% open · ⏱️ 07.11.2024):
```
 git clone https://github.com/lab-cosmo/sphericart
```
PyPi (📥 1.1K / month · ⏱️ 04.09.2024):
```
 pip install sphericart
```

Polynomials4ML.jl (🥈13 · ⭐ 12) - Polynomials for ML: fast evaluation, batching, differentiation. MIT Julia

GitHub (👨‍💻 10 · 🔀 5 · 📋 51 - 33% open · ⏱️ 22.06.2024):
```
 git clone https://github.com/ACEsuit/Polynomials4ML.jl
```

GElib (🥈9 · ⭐ 19) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0 C++

GitHub (👨‍💻 4 · 🔀 3 · 📋 8 - 50% open · ⏱️ 27.07.2024):
```
 git clone https://github.com/risi-kondor/GElib
```

COSMO Toolbox (🥉6 · ⭐ 7 · 💤) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed C++

GitHub (👨‍💻 9 · 🔀 7 · ⏱️ 19.03.2024):
```
 git clone https://github.com/lab-cosmo/toolbox
```

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 (🥉5 · ⭐ 4) - Cnine tensor library. Unlicensed C++
torch_spex (🥉3 · ⭐ 3 · 💤) - Spherical expansions in PyTorch. Unlicensed
Wigner Kernels (🥉1 · ⭐ 2 · 💀) - 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 (🥇25 · ⭐ 1.2K) - Differentiable, Hardware Accelerated, Molecular Dynamics. Apache-2

GitHub (👨‍💻 35 · 🔀 200 · 📦 62 · 📋 160 - 49% open · ⏱️ 31.10.2024):
```
 git clone https://github.com/jax-md/jax-md
```
PyPi (📥 5.2K / month · 📦 3 · ⏱️ 09.08.2023):
```
 pip install jax-md
```

mlcolvar (🥈21 · ⭐ 92) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT sampling

GitHub (👨‍💻 8 · 🔀 26 · 📦 3 · 📋 74 - 17% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/luigibonati/mlcolvar
```
PyPi (📥 330 / month · ⏱️ 12.06.2024):
```
 pip install mlcolvar
```

FitSNAP (🥈19 · ⭐ 150) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0

GitHub (👨‍💻 24 · 🔀 53 · 📥 13 · 📋 73 - 21% open · ⏱️ 19.09.2024):
```
 git clone https://github.com/FitSNAP/FitSNAP
```
Conda (📥 9.3K · ⏱️ 16.06.2023):
```
 conda install -c conda-forge fitsnap3
```

openmm-torch (🥈17 · ⭐ 180) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++

GitHub (👨‍💻 8 · 🔀 24 · 📋 96 - 29% open · ⏱️ 11.11.2024):
```
 git clone https://github.com/openmm/openmm-torch
```

Conda (📥 530K · ⏱️ 12.11.2024):

 conda install -c conda-forge openmm-torch

OpenMM-ML (🥉13 · ⭐ 82) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP

GitHub (👨‍💻 5 · 🔀 19 · 📋 55 - 36% open · ⏱️ 06.08.2024):
```
 git clone https://github.com/openmm/openmm-ml
```

Conda (📥 6K · ⏱️ 07.06.2024):

 conda install -c conda-forge openmm-ml

pair_nequip (🥉10 · ⭐ 41) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn

GitHub (👨‍💻 3 · 🔀 12 · 📋 31 - 35% open · ⏱️ 05.06.2024):
```
 git clone https://github.com/mir-group/pair_nequip
```

PACE (🥉9 · ⭐ 26) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom

GitHub (👨‍💻 7 · 🔀 11 · 📋 8 - 25% open · ⏱️ 09.11.2024):
```
 git clone https://github.com/ICAMS/lammps-user-pace
```

pair_allegro (🥉8 · ⭐ 36) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT ML-IAP rep-learn

GitHub (👨‍💻 2 · 🔀 8 · 📋 33 - 45% open · ⏱️ 05.06.2024):
```
 git clone https://github.com/mir-group/pair_allegro
```

SOMD (🥉6 · ⭐ 12) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0 ML-IAP active-learning

GitHub (🔀 2 · ⏱️ 04.11.2024):

 git clone https://github.com/initqp/somd

Show 1 hidden projects...

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

Reinforcement Learning

Projects that focus on reinforcement learning for atomistic ML.

Show 2 hidden projects...

ReLeaSE (🥇11 · ⭐ 350 · 💀) - Deep Reinforcement Learning for de-novo Drug Design. MIT drug-discovery
CatGym (🥉6 · ⭐ 11 · 💀) - 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 (🥇26 · ⭐ 500) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java

GitHub (👨‍💻 170 · 🔀 160 · 📥 23K · 📋 290 - 10% open · ⏱️ 13.11.2024):
```
 git clone https://github.com/cdk/cdk
```

Maven (📦 16 · ⏱️ 21.08.2023):

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

DScribe (🥇24 · ⭐ 400) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2

GitHub (👨‍💻 18 · 🔀 89 · 📦 210 · 📋 100 - 11% open · ⏱️ 28.05.2024):
```
 git clone https://github.com/SINGROUP/dscribe
```
PyPi (📥 20K / month · 📦 35 · ⏱️ 28.05.2024):
```
 pip install dscribe
```
Conda (📥 150K · ⏱️ 28.05.2024):
```
 conda install -c conda-forge dscribe
```

MODNet (🥈16 · ⭐ 80) - MODNet: a framework for machine learning materials properties. MIT pretrained small-data transfer-learning

GitHub (👨‍💻 11 · 🔀 33 · 📦 10 · 📋 55 - 47% open · ⏱️ 13.11.2024):
```
 git clone https://github.com/ppdebreuck/modnet
```

GlassPy (🥈15 · ⭐ 28) - Python module for scientists working with glass materials. GPL-3.0

GitHub (👨‍💻 2 · 🔀 7 · 📦 7 · 📋 15 - 46% open · ⏱️ 13.10.2024):
```
 git clone https://github.com/drcassar/glasspy
```
PyPi (📥 820 / month · ⏱️ 05.09.2024):
```
 pip install glasspy
```

SISSO (🥈14 · ⭐ 250) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2 Fortran

GitHub (👨‍💻 3 · 🔀 83 · 📋 77 - 23% open · ⏱️ 20.09.2024):
```
 git clone https://github.com/rouyang2017/SISSO
```

Librascal (🥈13 · ⭐ 80 · 💤) - A scalable and versatile library to generate representations for atomic-scale learning. LGPL-2.1

GitHub (👨‍💻 30 · 🔀 20 · 📋 250 - 46% open · ⏱️ 30.11.2023):
```
 git clone https://github.com/lab-cosmo/librascal
```

Rascaline (🥈12 · ⭐ 44) - Computing representations for atomistic machine learning. BSD-3 Rust C++

GitHub (👨‍💻 14 · 🔀 13 · 📋 70 - 45% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/Luthaf/rascaline
```

fplib (🥉11 · ⭐ 7) - libfp is a library for calculating crystalline fingerprints and measuring similarities of materials. MIT C-lang single-paper

GitHub (🔀 1 · 📦 1 · ⏱️ 15.10.2024):

 git clone https://github.com/zhuligs/fplib

NICE (🥉7 · ⭐ 12 · 💤) - NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and.. MIT

GitHub (👨‍💻 4 · 🔀 3 · 📋 3 - 66% open · ⏱️ 15.04.2024):
```
 git clone https://github.com/lab-cosmo/nice
```

milad (🥉6 · ⭐ 30) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative

GitHub (👨‍💻 1 · 🔀 2 · 📦 3 · ⏱️ 20.08.2024):
```
 git clone https://github.com/muhrin/milad
```

SA-GPR (🥉6 · ⭐ 19) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang

GitHub (👨‍💻 5 · 🔀 14 · 📋 7 - 28% open · ⏱️ 23.07.2024):
```
 git clone https://github.com/dilkins/TENSOAP
```

Show 14 hidden projects...

CatLearn (🥇17 · ⭐ 100 · 💀) - GPL-3.0 surface-science
cmlkit (🥈12 · ⭐ 34 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking
CBFV (🥈12 · ⭐ 25 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
BenchML (🥈12 · ⭐ 15 · 💀) - ML benchmarking and pipeling framework. Apache-2 benchmarking
SkipAtom (🥉9 · ⭐ 24 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
AMP (🥉7 · 💀) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed
SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++
soap_turbo (🥉6 · ⭐ 5 · 💀) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
pyLODE (🥉6 · ⭐ 3 · 💀) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
MXenes4HER (🥉5 · ⭐ 6 · 💀) - Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B. GPL-3.0 materials-discovery catalysis scikit-learn single-paper
SISSO++ (🥉5 · ⭐ 3 · 💀) - C++ Implementation of SISSO with python bindings. Apache-2 C++
automl-materials (🥉4 · ⭐ 5 · 💀) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. MIT autoML benchmarking single-paper
magnetism-prediction (🥉4 · ⭐ 1 · 💀) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper
ML-for-CurieTemp-Predictions (🥉3 · ⭐ 1 · 💀) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism

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 · ⭐ 14K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2

GitHub (👨‍💻 300 · 🔀 3K · 📦 320 · 📋 2.9K - 18% open · ⏱️ 18.10.2024):
```
 git clone https://github.com/dmlc/dgl
```
PyPi (📥 310K / month · 📦 150 · ⏱️ 13.05.2024):
```
 pip install dgl
```
Conda (📥 390K · ⏱️ 03.09.2024):
```
 conda install -c dglteam dgl
```

PyG Models (🥇35 · ⭐ 21K) - Representation learning models implemented in PyTorch Geometric. MIT general-ml

GitHub (👨‍💻 530 · 🔀 3.7K · 📦 7K · 📋 3.7K - 28% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/pyg-team/pytorch_geometric
```

e3nn (🥇28 · ⭐ 960) - A modular framework for neural networks with Euclidean symmetry. MIT

GitHub (👨‍💻 33 · 🔀 140 · 📦 340 · 📋 160 - 14% open · ⏱️ 06.11.2024):
```
 git clone https://github.com/e3nn/e3nn
```
PyPi (📥 79K / month · 📦 34 · ⏱️ 06.11.2024):
```
 pip install e3nn
```
Conda (📥 25K · ⏱️ 06.11.2024):
```
 conda install -c conda-forge e3nn
```

SchNetPack (🥇27 · ⭐ 790 · 📉) - SchNetPack - Deep Neural Networks for Atomistic Systems. MIT

GitHub (👨‍💻 36 · 🔀 210 · 📦 93 · 📋 260 - 2% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/atomistic-machine-learning/schnetpack
```
PyPi (📥 1.1K / month · 📦 4 · ⏱️ 05.09.2024):
```
 pip install schnetpack
```

MatGL (Materials Graph Library) (🥇25 · ⭐ 280) - Graph deep learning library for materials. BSD-3 multifidelity

GitHub (👨‍💻 17 · 🔀 64 · 📦 55 · 📋 99 - 7% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/materialsvirtuallab/matgl
```
PyPi (📥 1.5K / month · 📦 5 · ⏱️ 17.11.2022):
```
 pip install m3gnet
```

DIG: Dive into Graphs (🥈21 · ⭐ 1.9K · 💤) - A library for graph deep learning research. GPL-3.0

GitHub (👨‍💻 50 · 🔀 280 · 📋 210 - 16% open · ⏱️ 04.02.2024):
```
 git clone https://github.com/divelab/DIG
```
PyPi (📥 1.2K / month · ⏱️ 27.06.2022):
```
 pip install dive-into-graphs
```

ALIGNN (🥈21 · ⭐ 240) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en.. Custom

GitHub (👨‍💻 7 · 🔀 81 · 📦 16 · 📋 65 - 61% open · ⏱️ 09.09.2024):
```
 git clone https://github.com/usnistgov/alignn
```
PyPi (📥 14K / month · 📦 6 · ⏱️ 09.09.2024):
```
 pip install alignn
```

e3nn-jax (🥈21 · ⭐ 180) - jax library for E3 Equivariant Neural Networks. Apache-2

GitHub (👨‍💻 7 · 🔀 18 · 📦 42 · 📋 23 - 8% open · ⏱️ 28.09.2024):
```
 git clone https://github.com/e3nn/e3nn-jax
```
PyPi (📥 4.5K / month · 📦 13 · ⏱️ 14.08.2024):
```
 pip install e3nn-jax
```

NVIDIA Deep Learning Examples for Tensor Cores (🥈20 · ⭐ 14K · 💤) - 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 · 🔀 3.2K · 📋 910 - 37% open · ⏱️ 04.04.2024):
```
 git clone https://github.com/NVIDIA/DeepLearningExamples
```

matsciml (🥈19 · ⭐ 150) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT workflows benchmarking

GitHub (👨‍💻 12 · 🔀 23 · 📋 65 - 33% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/IntelLabs/matsciml
```

kgcnn (🥈19 · ⭐ 110) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT

GitHub (👨‍💻 7 · 🔀 30 · 📦 19 · 📋 86 - 13% open · ⏱️ 06.05.2024):
```
 git clone https://github.com/aimat-lab/gcnn_keras
```
PyPi (📥 1.5K / month · 📦 3 · ⏱️ 27.02.2024):
```
 pip install kgcnn
```

Uni-Mol (🥈18 · ⭐ 720) - Official Repository for the Uni-Mol Series Methods. MIT pretrained

GitHub (👨‍💻 17 · 🔀 130 · 📥 16K · 📋 170 - 43% open · ⏱️ 24.10.2024):
```
 git clone https://github.com/deepmodeling/Uni-Mol
```

escnn (🥈18 · ⭐ 360) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom

GitHub (👨‍💻 10 · 🔀 45 · 📋 75 - 50% open · ⏱️ 31.10.2024):
```
 git clone https://github.com/QUVA-Lab/escnn
```
PyPi (📥 1.4K / 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 (👨‍💻 14 · 🔀 330 · 📋 160 - 58% open · ⏱️ 28.05.2024):
```
 git clone https://github.com/microsoft/Graphormer
```

HydraGNN (🥈14 · ⭐ 68) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3

GitHub (👨‍💻 14 · 🔀 28 · 📦 2 · 📋 49 - 34% open · ⏱️ 09.11.2024):
```
 git clone https://github.com/ORNL/HydraGNN
```

Compositionally-Restricted Attention-Based Network (CrabNet) (🥈14 · ⭐ 12) - Predict materials properties using only the composition information!. MIT

GitHub (👨‍💻 6 · 🔀 5 · 📦 14 · 📋 19 - 84% open · ⏱️ 09.09.2024):
```
 git clone https://github.com/sparks-baird/CrabNet
```
PyPi (📥 2.2K / month · 📦 2 · ⏱️ 10.01.2023):
```
 pip install crabnet
```

hippynn (🥈12 · ⭐ 71) - python library for atomistic machine learning. Custom workflows

GitHub (👨‍💻 14 · 🔀 23 · 📦 2 · 📋 22 - 45% open · ⏱️ 31.10.2024):
```
 git clone https://github.com/lanl/hippynn
```

Atom2Vec (🥈10 · ⭐ 35 · 💤) - Atom2Vec: a simple way to describe atoms for machine learning. MIT

GitHub (👨‍💻 1 · 🔀 9 · 📦 3 · 📋 4 - 75% open · ⏱️ 23.02.2024):
```
 git clone https://github.com/idocx/Atom2Vec
```
PyPi (📥 330 / month · ⏱️ 23.02.2024):
```
 pip install atom2vec
```

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 (👨‍💻 11 · 🔀 3 · ⏱️ 21.11.2023):

 git clone https://github.com/HSE-LAMBDA/ai4material_design

EquiformerV2 (🥉8 · ⭐ 220) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT

GitHub (👨‍💻 2 · 🔀 26 · 📋 19 - 68% open · ⏱️ 16.07.2024):
```
 git clone https://github.com/atomicarchitects/equiformer_v2
```

Equiformer (🥉8 · ⭐ 210) - [ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT transformer

GitHub (👨‍💻 2 · 🔀 37 · 📋 16 - 43% open · ⏱️ 18.07.2024):
```
 git clone https://github.com/atomicarchitects/equiformer
```

graphite (🥉8 · ⭐ 63) - A repository for implementing graph network models based on atomic structures. MIT

GitHub (👨‍💻 2 · 🔀 9 · 📦 13 · 📋 4 - 75% open · ⏱️ 08.08.2024):
```
 git clone https://github.com/llnl/graphite
```

DeeperGATGNN (🥉8 · ⭐ 48 · 💤) - Scalable graph neural networks for materials property prediction. MIT

GitHub (👨‍💻 3 · 🔀 7 · 📋 12 - 33% open · ⏱️ 19.01.2024):
```
 git clone https://github.com/usccolumbia/deeperGATGNN
```

T-e3nn (🥉8 · ⭐ 11) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism

GitHub (👨‍💻 26 · ⏱️ 29.09.2024):

 git clone https://github.com/Hongyu-yu/T-e3nn

Show 34 hidden projects...

dgl-lifesci (🥇23 · ⭐ 730 · 💀) - 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) (🥈13 · ⭐ 660 · 💀) - 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
FAENet (🥈12 · ⭐ 33 · 💀) - Frame Averaging Equivariant GNN for materials modeling. MIT
pretrained-gnns (🥈10 · ⭐ 970 · 💀) - Strategies for Pre-training Graph Neural Networks. MIT pretrained
GDC (🥈10 · ⭐ 270 · 💀) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative
SE(3)-Transformers (🥉9 · ⭐ 500 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer
GATGNN: Global Attention Graph Neural Network (🥉9 · ⭐ 71 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT
molecularGNN_smiles (🥉8 · ⭐ 300 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
CGAT (🥉8 · ⭐ 26 · 💀) - Crystal graph attention neural networks for materials prediction. MIT
UVVisML (🥉8 · ⭐ 23 · 💀) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
tensorfieldnetworks (🥉7 · ⭐ 150 · 💀) - Rotation- and translation-equivariant neural networks for 3D point clouds. MIT
DTNN (🥉7 · ⭐ 77 · 💀) - Deep Tensor Neural Network. MIT
Cormorant (🥉7 · ⭐ 60 · 💀) - Codebase for Cormorant Neural Networks. Custom
AdsorbML (🥉7 · ⭐ 37 · 💀) - MIT surface-science single-paper
escnn_jax (🥉7 · ⭐ 29 · 💀) - 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
charge_transfer_nnp (🥉6 · ⭐ 32 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
GLAMOUR (🥉6 · ⭐ 21 · 💀) - Graph Learning over Macromolecule Representations. MIT single-paper
Autobahn (🥉5 · ⭐ 29 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
FieldSchNet (🥉5 · ⭐ 19 · 💀) - Deep neural network for molecules in external fields. MIT
SCFNN (🥉5 · ⭐ 14 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
CraTENet (🥉5 · ⭐ 14 · 💀) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena
EGraFFBench (🥉5 · ⭐ 8 · 💤) - Unlicensed single-paper benchmarking ML-IAP
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 · ⭐ 5 · 💤) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena
atom_by_atom (🥉3 · ⭐ 9 · 💀) - 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
SphericalNet (🥉1 · ⭐ 3 · 💀) - Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in.. Unlicensed

Universal Potentials

Machine-learned interatomic potentials (ML-IAP) that have been trained on large, chemically and structural diverse datasets. For materials, this means e.g. datasets that include a majority of the periodic table.

🔗 TeaNet - Universal neural network interatomic potential inspired by iterative electronic relaxations.. ML-IAP

🔗 PreFerred Potential (PFP) - Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9. ML-IAP proprietary

🔗 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

GitHub (👨‍💻 70 · 🔀 510 · 📥 43K · 📦 20 · 📋 830 - 9% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/deepmodeling/deepmd-kit
```

CHGNet (🥈23 · ⭐ 250) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom ML-IAP MD pretrained electrostatics magnetism structure-relaxation

GitHub (👨‍💻 10 · 🔀 66 · 📦 40 · 📋 62 - 4% open · ⏱️ 16.11.2024):
```
 git clone https://github.com/CederGroupHub/chgnet
```
PyPi (📥 47K / month · 📦 21 · ⏱️ 16.09.2024):
```
 pip install chgnet
```

MACE-MP (🥈19 · ⭐ 540) - Pretrained foundation models for materials chemistry. MIT ML-IAP pretrained rep-learn MD

GitHub (👨‍💻 2 · 🔀 200 · 📥 37K · 📋 10 - 10% open · ⏱️ 15.11.2024):
```
 git clone https://github.com/ACEsuit/mace-mp
```
PyPi (📥 13K / month · 📦 20 · ⏱️ 12.11.2024):
```
 pip install mace-torch
```

M3GNet (🥈18 · ⭐ 240) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3 ML-IAP pretrained

GitHub (👨‍💻 16 · 🔀 61 · 📦 28 · 📋 35 - 42% open · ⏱️ 04.10.2024):
```
 git clone https://github.com/materialsvirtuallab/m3gnet
```
PyPi (📥 1.5K / month · 📦 5 · ⏱️ 17.11.2022):
```
 pip install m3gnet
```

Orb Models (🥉17 · ⭐ 200 · 🐣) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained

GitHub (👨‍💻 6 · 🔀 23 · 📦 3 · ⏱️ 17.10.2024):
```
 git clone https://github.com/orbital-materials/orb-models
```
PyPi (📥 1.3K / month · ⏱️ 15.10.2024):
```
 pip install orb-models
```

SevenNet (🥉16 · ⭐ 130) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained

GitHub (👨‍💻 12 · 🔀 16 · 📦 6 · 📋 26 - 30% open · ⏱️ 07.11.2024):
```
 git clone https://github.com/MDIL-SNU/SevenNet
```

MLIP Arena Leaderboard (🥉13 · ⭐ 44) - Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics. Apache-2 ML-IAP community-resource

GitHub (👨‍💻 3 · 🔀 1 · 📦 2 · 📋 9 - 66% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/atomind-ai/mlip-arena
```

GRACE (🥉8 · ⭐ 10 · 🐣) - GRACE models and gracemaker (as implemented in TensorPotential package). Custom ML-IAP pretrained MD rep-learn rep-eng

GitHub (👨‍💻 3 · 🔀 2 · 📦 1 · ⏱️ 19.11.2024):
```
 git clone https://github.com/ICAMS/grace-tensorpotential
```

Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 42) - 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 (👨‍💻 2 · 🔀 6 · 📋 5 - 40% open · ⏱️ 22.10.2024):
```
 git clone https://github.com/facebookresearch/JMP
```

Unsupervised Learning

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

DADApy (🥇20 · ⭐ 110) - Distance-based Analysis of DAta-manifolds in python. Apache-2

GitHub (👨‍💻 20 · 🔀 18 · 📦 10 · 📋 36 - 25% open · ⏱️ 13.11.2024):
```
 git clone https://github.com/sissa-data-science/DADApy
```
PyPi (📥 470 / month · ⏱️ 02.07.2024):
```
 pip install dadapy
```

ASAP (🥈11 · ⭐ 140) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT

GitHub (👨‍💻 6 · 🔀 28 · 📦 7 · 📋 25 - 24% open · ⏱️ 27.06.2024):
```
 git clone https://github.com/BingqingCheng/ASAP
```

Show 5 hidden projects...

Sketchmap (🥈9 · ⭐ 46 · 💀) - 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 · ⭐ 4) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. MIT
Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF) ( ⭐ 2) - Provides a workflow to obtain clustering of local environments in dataset of structures. Unlicensed

Visualization

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

Crystal Toolkit (🥇25 · ⭐ 160) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT

GitHub (👨‍💻 30 · 🔀 57 · 📦 40 · 📋 110 - 46% open · ⏱️ 22.10.2024):
```
 git clone https://github.com/materialsproject/crystaltoolkit
```
PyPi (📥 4.8K / month · 📦 10 · ⏱️ 22.10.2024):
```
 pip install crystal-toolkit
```

pymatviz (🥈22 · ⭐ 170) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic

GitHub (👨‍💻 9 · 🔀 16 · 📦 14 · 📋 54 - 24% open · ⏱️ 19.11.2024):
```
 git clone https://github.com/janosh/pymatviz
```
PyPi (📥 5.3K / month · 📦 2 · ⏱️ 03.11.2024):
```
 pip install pymatviz
```

ZnDraw (🥈20 · ⭐ 31) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0 MD generative JavaScript

GitHub (👨‍💻 7 · 🔀 4 · 📦 6 · 📋 350 - 27% open · ⏱️ 18.11.2024):
```
 git clone https://github.com/zincware/ZnDraw
```
PyPi (📥 2.8K / month · 📦 2 · ⏱️ 18.11.2024):
```
 pip install zndraw
```

Chemiscope (🥉18 · ⭐ 130) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript

GitHub (👨‍💻 24 · 🔀 34 · 📥 350 · 📦 6 · 📋 140 - 28% open · ⏱️ 14.11.2024):
```
 git clone https://github.com/lab-cosmo/chemiscope
```
npm (📥 11 / month · 📦 3 · ⏱️ 15.03.2023):
```
 npm install chemiscope
```

Elementari (🥉13 · ⭐ 140) - Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,.. MIT JavaScript

GitHub (👨‍💻 2 · 🔀 12 · 📦 3 · 📋 7 - 28% open · ⏱️ 07.10.2024):
```
 git clone https://github.com/janosh/elementari
```
npm (📥 380 / month · 📦 1 · ⏱️ 15.01.2024):
```
 npm install elementari
```

Show 1 hidden projects...

Atomvision (🥉12 · ⭐ 29 · 💀) - Deep learning framework for atomistic image data. Custom computer-vision experimental-data rep-learn

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 (🥇22 · ⭐ 360) - Deep learning quantum Monte Carlo for electrons in real space. MIT

GitHub (👨‍💻 13 · 🔀 61 · 📦 3 · 📋 47 - 6% open · ⏱️ 23.10.2024):
```
 git clone https://github.com/deepqmc/deepqmc
```
PyPi (📥 620 / month · ⏱️ 24.09.2024):
```
 pip install deepqmc
```

FermiNet (🥈15 · ⭐ 740) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2 transformer

GitHub (👨‍💻 18 · 🔀 130 · 📋 53 - 1% open · ⏱️ 03.10.2024):
```
 git clone https://github.com/google-deepmind/ferminet
```

DeepErwin (🥉9 · ⭐ 51) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom

GitHub (👨‍💻 7 · 🔀 7 · 📥 12 · ⏱️ 07.06.2024):
```
 git clone https://github.com/mdsunivie/deeperwin
```
PyPi (📥 290 / month · ⏱️ 14.12.2021):
```
 pip install deeperwin
```

Show 2 hidden projects...

ACEpsi.jl (🥉6 · ⭐ 2 · 💀) - ACE wave function parameterizations. MIT rep-eng Julia
SchNOrb (🥉5 · ⭐ 60 · 💀) - 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
Latest commit History 399 Commits
.github		.github
config		config
history		history
.gitignore		.gitignore
CITATION.cff		CITATION.cff
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
latest-changes.md		latest-changes.md
projects.yaml		projects.yaml

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Best of Atomistic Machine Learning ⚛️🧬💎

Contents

Explanation

Active learning

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

Reinforcement Learning

Representation Engineering

Representation Learning

Universal Potentials

Unsupervised Learning

Visualization

Wavefunction methods (ML-WFT)

Others

Contribution

License

About

Releases 10

Packages

Contributors 6

License

JuDFTteam/best-of-atomistic-machine-learning

Folders and files

Latest commit

History

Repository files navigation

Best of Atomistic Machine Learning ⚛️🧬💎

Contents

Explanation

Active learning

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

Reinforcement Learning

Representation Engineering

Representation Learning

Universal Potentials

Unsupervised Learning

Visualization

Wavefunction methods (ML-WFT)

Others

Contribution

License

About

Topics

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases 10

Packages 0

Contributors 6

Packages