πΒ A ranked list of awesome atomistic machine learning (AML) projects. Updated regularly.
This curated list contains 420 awesome open-source projects with a total of 180K stars grouped into 22 categories. All projects are ranked by a project-quality score, 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.
π§ββοΈ Discover other best-of lists or create your own.
- Active learning 6 projects
- Community resources 30 projects
- Datasets 45 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) 69 projects
- Language Models 20 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 9 projects
- Unsupervised Learning 7 projects
- Visualization 3 projects
- Wavefunction methods (ML-WFT) 5 projects
- Others 1 projects
- π₯π₯π₯Β Combined project-quality score
- βοΈΒ Star count from GitHub
- π£Β New project (less than 6 months old)
- π€Β Inactive project (6 months no activity)
- πΒ Dead project (12 months no activity)
- ππΒ Project is trending up or down
- βΒ Project was recently added
- π¨βπ»Β Contributors count from GitHub
- πΒ Fork count from GitHub
- πΒ Issue count from GitHub
- β±οΈΒ Last update timestamp on package manager
- π₯Β Download count from package manager
- π¦Β Number of dependent projects
Projects that focus on enabling active learning, iterative learning schemes for atomistic ML.
FLARE (π₯21 Β· β 280) - An open-source Python package for creating fast and accurate interatomic potentials. MIT
C++
ML-IAP
-
GitHub (π¨βπ» 36 Β· π 65 Β· π₯ 7 Β· π¦ 11 Β· π 210 - 17% open Β· β±οΈ 15.08.2024):
git clone https://github.com/mir-group/flare
IPSuite (π₯17 Β· β 18 Β· π) - A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials. EPL-2.0
ML-IAP
MD
workflows
HTC
FAIR
Finetuna (π₯10 Β· β 42) - Active Learning for Machine Learning Potentials. MIT
-
GitHub (π¨βπ» 11 Β· π 11 Β· π 20 - 25% open Β· β±οΈ 15.05.2024):
git clone https://github.com/ulissigroup/finetuna
ACEHAL (π₯5 Β· β 11 Β· π€) - Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials. Unlicensed
Julia
-
GitHub (π¨βπ» 3 Β· π 7 Β· π 10 - 40% open Β· β±οΈ 21.09.2023):
git clone https://github.com/ACEsuit/ACEHAL
Show 2 hidden projects...
Projects that collect atomistic ML resources or foster communication within community.
πΒ AI for Science Map - Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..
πΒ Atomic Cluster Expansion - Atomic Cluster Expansion (ACE) community homepage.
πΒ CrystaLLM - Generate a crystal structure from a composition. language-models
generative
pretrained
transformer
πΒ GAP-ML.org community homepage ML-IAP
πΒ matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..
πΒ Matter Modeling Stack Exchange - Machine Learning - Forum StackExchange, site Matter Modeling, ML-tagged questions.
Best-of Machine Learning with Python (π₯21 Β· β 16K) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0
general-ml
Python
-
GitHub (π¨βπ» 46 Β· π 2.3K Β· π 59 - 42% open Β· β±οΈ 22.07.2024):
git clone https://github.com/ml-tooling/best-of-ml-python
Graph-based Deep Learning Literature (π₯18 Β· β 4.7K) - links to conference publications in graph-based deep learning. MIT
general-ml
rep-learn
-
GitHub (π¨βπ» 12 Β· π 740 Β· β±οΈ 17.08.2024):
git clone https://github.com/naganandy/graph-based-deep-learning-literature
MatBench (π₯18 Β· β 100 Β· π€) - Matbench: Benchmarks for materials science property prediction. MIT
datasets
benchmarking
model-repository
MatBench Discovery (π₯17 Β· β 83 Β· π) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT
datasets
benchmarking
model-repository
OpenML (π₯16 Β· β 660 Β· π€) - Open Machine Learning. BSD-3
datasets
-
GitHub (π¨βπ» 35 Β· π 90 Β· π 930 - 39% open Β· β±οΈ 12.01.2024):
git clone https://github.com/openml/OpenML
GT4SD - Generative Toolkit for Scientific Discovery (π₯16 Β· β 330) - Gradio apps of generative models in GT4SD. MIT
generative
pretrained
drug-discovery
model-repository
-
GitHub (π¨βπ» 20 Β· π 66 Β· π 110 - 12% open Β· β±οΈ 04.07.2024):
git clone https://github.com/GT4SD/gt4sd-core
AI for Science Resources (π₯12 Β· β 480) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
-
GitHub (π¨βπ» 28 Β· π 57 Β· π 14 - 14% open Β· β±οΈ 12.07.2024):
git clone https://github.com/divelab/AIRS
Neural-Network-Models-for-Chemistry (π₯11 Β· β 67) - A collection of Nerual Network Models for chemistry. Unlicensed
rep-learn
-
GitHub (π¨βπ» 3 Β· π 8 Β· π 2 - 50% open Β· β±οΈ 08.08.2024):
git clone https://github.com/Eipgen/Neural-Network-Models-for-Chemistry
GNoME Explorer (π₯10 Β· β 860 Β· π€) - Graph Networks for Materials Exploration Database. Apache-2
datasets
materials-discovery
-
GitHub (π¨βπ» 2 Β· π 130 Β· π 22 - 81% open Β· β±οΈ 02.12.2023):
git clone https://github.com/google-deepmind/materials_discovery
Awesome Materials Informatics (π₯10 Β· β 360) - Curated list of known efforts in materials informatics, i.e. in modern materials science. Custom
-
GitHub (π¨βπ» 19 Β· π 80 Β· β±οΈ 12.07.2024):
git clone https://github.com/tilde-lab/awesome-materials-informatics
MoLFormers UI (π₯9 Β· β 230 Β· π€) - A family of foundation models trained on chemicals. Apache-2
transformer
language-models
pretrained
drug-discovery
-
GitHub (π¨βπ» 5 Β· π 40 Β· π 19 - 47% open Β· β±οΈ 16.10.2023):
git clone https://github.com/IBM/molformer
AI for Science paper collection (π₯9 Β· β 47 Β· π£) - List the AI for Science papers accepted by top conferences. Apache-2
-
GitHub (π¨βπ» 5 Β· π 5 Β· β±οΈ 19.08.2024):
git clone https://github.com/sherrylixuecheng/AI_for_Science_paper_collection
optimade.science (π₯9 Β· β 8) - A sky-scanner Optimade browser-only GUI. MIT
datasets
-
GitHub (π¨βπ» 8 Β· π 2 Β· π 26 - 26% open Β· β±οΈ 10.06.2024):
git clone https://github.com/tilde-lab/optimade.science
Awesome Neural Geometry (π₯8 Β· β 890) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. Unlicensed
educational
rep-learn
-
GitHub (π¨βπ» 11 Β· π 57 Β· β±οΈ 17.07.2024):
git clone https://github.com/neurreps/awesome-neural-geometry
Awesome-Graph-Generation (π₯8 Β· β 260) - A curated list of up-to-date graph generation papers and resources. Unlicensed
rep-learn
-
GitHub (π¨βπ» 4 Β· π 17 Β· β±οΈ 17.03.2024):
git clone https://github.com/yuanqidu/awesome-graph-generation
Awesome Neural SBI (π₯8 Β· β 82) - Community-sourced list of papers and resources on neural simulation-based inference. MIT
active-learning
-
GitHub (π¨βπ» 3 Β· π 4 Β· π 2 - 50% open Β· β±οΈ 17.06.2024):
git clone https://github.com/smsharma/awesome-neural-sbi
Awesome-Crystal-GNNs (π₯7 Β· β 54) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. MIT
-
GitHub (π¨βπ» 2 Β· π 7 Β· β±οΈ 16.06.2024):
git clone https://github.com/kdmsit/Awesome-Crystal-GNNs
The Collection of Database and Dataset Resources in Materials Science (π₯6 Β· β 250) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed
datasets
-
GitHub (π¨βπ» 2 Β· π 41 Β· π 2 - 50% open Β· β±οΈ 07.06.2024):
git clone https://github.com/sedaoturak/data-resources-for-materials-science
Does this material exist? (π₯5 Β· β 15) - Vote on whether you think predicted crystal structures could be synthesised. MIT
for-fun
materials-discovery
-
GitHub (π¨βπ» 2 Β· π 3 Β· β±οΈ 10.04.2024):
git clone https://github.com/ml-evs/this-material-does-not-exist
Show 5 hidden projects...
- MADICES Awesome Interoperability (π₯9 Β· β 1) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences..
MIT
datasets
- A Highly Opinionated List of Open-Source Materials Informatics Resources (π₯7 Β· β 120 Β· π) - A Highly Opinionated List of Open Source Materials Informatics Resources.
MIT
- Geometric-GNNs (π₯4 Β· β 89) - List of Geometric GNNs for 3D atomic systems.
Unlicensed
datasets
educational
rep-learn
- GitHub topic materials-informatics (π₯1) - GitHub topic materials-informatics.
Unlicensed
- MateriApps (π₯1) - A Portal Site of Materials Science Simulation.
Unlicensed
Datasets, databases and trained models for atomistic ML.
πΒ Alexandria Materials Database - A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..
πΒ Catalysis Hub - A web-platform for sharing data and software for computational catalysis research!.
πΒ Citrination Datasets - AI-Powered Materials Data Platform. Open Citrination has been decommissioned.
πΒ crystals.ai - Curated datasets for reproducible AI in materials science.
πΒ DeepChem Models - DeepChem models on HuggingFace. model-repository
pretrained
language-models
πΒ Graphs of Materials Project 20190401 - The dataset used to train the MEGNet interatomic potential. ML-IAP
πΒ HME21 Dataset - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. UIP
πΒ JARVIS-Leaderboard ( β 56) - Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository
benchmarking
community-resource
educational
πΒ Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API.
πΒ Materials Project Trajectory (MPtrj) Dataset - The dataset used to train the CHGNet universal potential. UIP
πΒ matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.
πΒ MPF.2021.2.8 - The dataset used to train the M3GNet universal potential. UIP
πΒ NRELMatDB - Computational materials database with the specific focus on materials for renewable energy applications including, but..
πΒ Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.
πΒ sGDML Datasets - MD17, MD22, DFT datasets.
πΒ MoleculeNet - A Benchmark for Molecular Machine Learning. benchmarking
πΒ ZINC15 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph
biomolecules
πΒ ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph
biomolecules
OPTIMADE Python tools (π₯26 Β· β 64) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT
-
GitHub (π¨βπ» 28 Β· π 42 Β· π¦ 48 Β· π 450 - 22% open Β· β±οΈ 19.08.2024):
git clone https://github.com/Materials-Consortia/optimade-python-tools
-
PyPi (π₯ 5.7K / month Β· π¦ 4 Β· β±οΈ 20.07.2024):
pip install optimade
-
Conda (π₯ 85K Β· β±οΈ 20.07.2024):
conda install -c conda-forge optimade
MPContribs (π₯22 Β· β 34) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT
FAIR Chemistry datasets (π₯21 Β· β 750) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT
catalysis
-
GitHub (π¨βπ» 41 Β· π 230 Β· π 200 - 6% open Β· β±οΈ 22.08.2024):
git clone https://github.com/FAIR-Chem/fairchem
Open Databases Integration for Materials Design (OPTIMADE) (π₯17 Β· β 76) - Specification of a common REST API for access to materials databases. CC-BY-4.0
-
GitHub (π¨βπ» 21 Β· π 35 Β· π 240 - 28% open Β· β±οΈ 12.06.2024):
git clone https://github.com/Materials-Consortia/OPTIMADE
load-atoms (π₯14 Β· β 37) - download and manipulate atomistic datasets. MIT
data-structures
QH9 (π₯12 Β· β 480) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0
ML-DFT
-
GitHub (π¨βπ» 28 Β· π 57 Β· π 14 - 14% open Β· β±οΈ 12.07.2024):
git clone https://github.com/divelab/AIRS
SPICE (π₯11 Β· β 140 Β· π) - A collection of QM data for training potential functions. MIT
ML-IAP
MD
-
GitHub (π¨βπ» 1 Β· π 8 Β· π₯ 240 Β· π 61 - 24% open Β· β±οΈ 19.08.2024):
git clone https://github.com/openmm/spice-dataset
Materials Data Facility (MDF) (π₯9 Β· β 10) - A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,.. Apache-2
-
GitHub (π¨βπ» 7 Β· π 1 Β· π 7 - 14% open Β· β±οΈ 05.02.2024):
git clone https://github.com/materials-data-facility/connect_client
2DMD dataset (π₯9 Β· β 6 Β· π€) - 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 (π¨βπ» 11 Β· π 3 Β· β±οΈ 21.11.2023):
git clone https://github.com/HSE-LAMBDA/ai4material_design
AIS Square (π₯7 Β· β 10 Β· π€) - 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 (π¨βπ» 8 Β· π 8 Β· π 6 - 83% open Β· β±οΈ 06.12.2023):
git clone https://github.com/deepmodeling/AIS-Square
The Perovskite Database Project (π₯5 Β· β 58) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed
community-resource
-
GitHub (π¨βπ» 2 Β· π 18 Β· β±οΈ 07.03.2024):
git clone https://github.com/Jesperkemist/perovskitedatabase
3DSC Database (π₯5 Β· β 15 Β· π€) - Repo for the paper publishing the superconductor database with 3D crystal structures. Custom
superconductors
materials-discovery
-
GitHub (π 4 Β· β±οΈ 08.01.2024):
git clone https://github.com/aimat-lab/3DSC
SciGlass (π₯5 Β· β 10 Β· π€) - The database contains a vast set of data on the properties of glass materials. MIT
-
GitHub (π¨βπ» 2 Β· π 3 Β· π₯ 16 Β· β±οΈ 27.08.2023):
git clone https://github.com/drcassar/SciGlass
GDB-9-Ex9 and ORNL_AISD-Ex (π₯5 Β· β 6 Β· π€) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed
-
GitHub (π¨βπ» 7 Β· π 5 Β· β±οΈ 11.08.2023):
git clone https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python
Show 13 hidden projects...
- ATOM3D (π₯18 Β· β 300 Β· π) - ATOM3D: tasks on molecules in three dimensions.
MIT
biomolecules
benchmarking
- OpenKIM (π₯10 Β· β 31 Β· π) - 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 (π₯8 Β· β 96 Β· π) - A data set of 20 million calculated off-equilibrium conformations for organic molecules.
MIT
- MoleculeNet Leaderboard (π₯8 Β· β 88 Β· π) -
MIT
benchmarking
- GEOM (π₯7 Β· β 190 Β· π) - GEOM: Energy-annotated molecular conformations.
Unlicensed
drug-discovery
- ANI-1x Datasets (π₯6 Β· β 53 Β· π) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.
MIT
- COMP6 Benchmark dataset (π₯6 Β· β 39 Β· π) - COMP6 Benchmark dataset for ML potentials.
MIT
- OPTIMADE providers dashboard (π₯6 Β· β 1) - A dashboard of known providers.
Unlicensed
- Visual Graph Datasets (π₯5 Β· β 1) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..
MIT
XAI
rep-learn
- linear-regression-benchmarks (π₯5 Β· β 1 Β· π) - Data sets used for linear regression benchmarks.
MIT
benchmarking
single-paper
- paper-data-redundancy (π₯4 Β· β 7) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data.
BSD-3
small-data
single-paper
- nep-data (π₯2 Β· β 12 Β· π) - Data related to the NEP machine-learned potential of GPUMD.
Unlicensed
ML-IAP
MD
transport-phenomena
- tmQM_wB97MV Dataset (π₯2 Β· β 6) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV..
Unlicensed
catalysis
rep-learn
Projects that focus on providing data structures used in atomistic machine learning.
dpdata (π₯23 Β· β 200) - Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc. LGPL-3.0
Metatensor (π₯21 Β· β 47) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3
Rust
C-lang
C++
Python
-
GitHub (π¨βπ» 21 Β· π 15 Β· π₯ 24K Β· π¦ 11 Β· π 210 - 37% open Β· β±οΈ 15.08.2024):
git clone https://github.com/lab-cosmo/metatensor
mp-pyrho (π₯16 Β· β 36) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom
ML-DFT
dlpack (π₯14 Β· β 880) - common in-memory tensor structure. Apache-2
C++
-
GitHub (π¨βπ» 23 Β· π 130 Β· π 69 - 39% open Β· β±οΈ 26.03.2024):
git clone https://github.com/dmlc/dlpack
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.
πΒ 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 (π₯25 Β· β 34K) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2
-
GitHub (π¨βπ» 800 Β· π 7.8K Β· π 1.8K - 81% open Β· β±οΈ 22.08.2024):
git clone https://github.com/google-research/google-research
MALA (π₯19 Β· β 80) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3
-
GitHub (π¨βπ» 44 Β· π 23 Β· π¦ 1 Β· π 270 - 14% open Β· β±οΈ 04.07.2024):
git clone https://github.com/mala-project/mala
SALTED (π₯14 Β· β 30) - Symmetry-Adapted Learning of Three-dimensional Electron Densities. GPL-3.0
-
GitHub (π¨βπ» 16 Β· π 4 Β· π 6 - 16% open Β· β±οΈ 08.07.2024):
git clone https://github.com/andreagrisafi/SALTED
QHNet (π₯12 Β· β 480) - Artificial Intelligence Research for Science (AIRS). GPL-3.0
rep-learn
-
GitHub (π¨βπ» 28 Β· π 57 Β· π 14 - 14% open Β· β±οΈ 12.07.2024):
git clone https://github.com/divelab/AIRS
DeepH-pack (π₯12 Β· β 200) - Deep neural networks for density functional theory Hamiltonian. LGPL-3.0
Julia
-
GitHub (π¨βπ» 8 Β· π 36 Β· π 50 - 24% open Β· β±οΈ 22.05.2024):
git clone https://github.com/mzjb/DeepH-pack
DeePKS-kit (π₯10 Β· β 96) - a package for developing machine learning-based chemically accurate energy and density functional models. LGPL-3.0
-
GitHub (π¨βπ» 7 Β· π 35 Β· π 19 - 26% open Β· β±οΈ 13.04.2024):
git clone https://github.com/deepmodeling/deepks-kit
Grad DFT (π₯10 Β· β 71) - GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2
-
GitHub (π¨βπ» 4 Β· π 5 Β· π 54 - 20% open Β· β±οΈ 13.02.2024):
git clone https://github.com/XanaduAI/GradDFT
Q-stack (π₯9 Β· β 14) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT
excited-states
general-tool
-
GitHub (π¨βπ» 7 Β· π 5 Β· π 29 - 31% open Β· β±οΈ 19.07.2024):
git clone https://github.com/lcmd-epfl/Q-stack
HamGNN (π₯8 Β· β 50) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0
rep-learn
magnetism
C-lang
-
GitHub (π¨βπ» 1 Β· π 12 Β· π 24 - 83% open Β· β±οΈ 06.08.2024):
git clone https://github.com/QuantumLab-ZY/HamGNN
ChargE3Net (π₯7 Β· β 29) - Higher-order equivariant neural networks for charge density prediction in materials. MIT
rep-learn
-
GitHub (π¨βπ» 2 Β· π 7 Β· π 4 - 25% open Β· β±οΈ 15.08.2024):
git clone https://github.com/AIforGreatGood/charge3net
InfGCN for Electron Density Estimation (π₯5 Β· β 10 Β· π€) - Official implementation of the NeurIPS 23 spotlight paper of InfGCN. MIT
rep-learn
-
GitHub (π 3 Β· β±οΈ 05.12.2023):
git clone https://github.com/ccr-cheng/infgcn-pytorch
Show 21 hidden projects...
- DM21 (π₯20 Β· β 13K Β· π) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..
Apache-2
- NeuralXC (π₯11 Β· β 33 Β· π) - Implementation of a machine learned density functional.
BSD-3
- ACEhamiltonians (π₯10 Β· β 12 Β· π) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..
MIT
Julia
- PROPhet (π₯9 Β· β 62 Β· π) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.
GPL-3.0
ML-IAP
MD
single-paper
C++
- Mat2Spec (π₯7 Β· β 27 Β· π) - Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings.
MIT
spectroscopy
- Libnxc (π₯7 Β· β 16 Β· π) - A library for using machine-learned exchange-correlation functionals for density-functional theory.
MPL-2.0
C++
Fortran
- rho_learn (π₯7 Β· β 3) - A proof-of-concept framework for torch-based learning of the electron density and related scalar fields.
MIT
- DeepH-E3 (π₯6 Β· β 62 Β· π) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.
MIT
magnetism
- DeepDFT (π₯6 Β· β 55 Β· π) - Official implementation of DeepDFT model.
MIT
- KSR-DFT (π₯6 Β· β 4 Β· π) - Kohn-Sham regularizer for machine-learned DFT functionals.
Apache-2
- xDeepH (π₯5 Β· β 30 Β· π) - Extended DeepH (xDeepH) method for magnetic materials.
LGPL-3.0
magnetism
Julia
- ML-DFT (π₯5 Β· β 23 Β· π) - A package for density functional approximation using machine learning.
MIT
- charge-density-models (π₯4 Β· β 10 Β· π€) - Tools to build charge density models using fairchem.
MIT
rep-learn
- gprep (π₯4 Β· π) - Fitting DFTB repulsive potentials with GPR.
MIT
single-paper
- DeepCDP (π₯3 Β· β 6 Β· π) - DeepCDP: Deep learning Charge Density Prediction.
Unlicensed
- APET (π₯3 Β· β 4 Β· π€) - Atomic Positional Embedding-based Transformer.
GPL-3.0
density-of-states
transformer
- CSNN (π₯3 Β· β 2 Β· π) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.
BSD-3
- MALADA (π₯3 Β· β 1 Β· π) - MALA Data Acquisition: Helpful tools to build data for MALA.
BSD-3
- A3MD (π₯2 Β· β 8 Β· π) - MPNN-like + Analytic Density Model = Accurate electron densities.
Unlicensed
rep-learn
single-paper
- kdft (π₯1 Β· β 2 Β· π) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.
Unlicensed
- MLDensity ( β 2 Β· π) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..
Unlicensed
Tutorials, guides, cookbooks, recipes, etc.
πΒ AI for Science 101 community-resource
rep-learn
πΒ AL4MS 2023 workshop tutorials active-learning
πΒ Quantum Chemistry in the Age of Machine Learning - Book, 2022.
jarvis-tools-notebooks (π₯12 Β· β 59) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST
-
GitHub (π¨βπ» 5 Β· π 26 Β· β±οΈ 14.08.2024):
git clone https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks
AI4Chemistry course (π₯10 Β· β 130) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT
chemistry
-
GitHub (π¨βπ» 6 Β· π 30 Β· π 4 - 25% open Β· β±οΈ 02.05.2024):
git clone https://github.com/schwallergroup/ai4chem_course
DSECOP (π₯10 Β· β 43) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0
-
GitHub (π¨βπ» 13 Β· π 25 Β· π 8 - 12% open Β· β±οΈ 26.06.2024):
git clone https://github.com/GDS-Education-Community-of-Practice/DSECOP
iam-notebooks (π₯9 Β· β 24) - Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling. Apache-2
-
GitHub (π¨βπ» 6 Β· π 5 Β· β±οΈ 26.06.2024):
git clone https://github.com/ceriottm/iam-notebooks
OPTIMADE Tutorial Exercises (π₯9 Β· β 14 Β· π€) - Tutorial exercises for the OPTIMADE API. MIT
datasets
-
GitHub (π¨βπ» 6 Β· π 7 Β· β±οΈ 27.09.2023):
git clone https://github.com/Materials-Consortia/optimade-tutorial-exercises
BestPractices (π₯8 Β· β 170 Β· π€) - Things that you should (and should not) do in your Materials Informatics research. MIT
-
GitHub (π¨βπ» 3 Β· π 68 Β· π 7 - 71% open Β· β±οΈ 17.11.2023):
git clone https://github.com/anthony-wang/BestPractices
COSMO Software Cookbook (π₯7 Β· β 12) - The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules. BSD-3
-
GitHub (π¨βπ» 9 Β· π 1 Β· π 12 - 16% open Β· β±οΈ 14.08.2024):
git clone https://github.com/lab-cosmo/software-cookbook
MACE-tutorials (π₯6 Β· β 32) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT
ML-IAP
rep-learn
MD
-
GitHub (π¨βπ» 2 Β· π 9 Β· β±οΈ 16.07.2024):
git clone https://github.com/ilyes319/mace-tutorials
Show 17 hidden projects...
- Geometric GNN Dojo (π₯12 Β· β 450 Β· π) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge.
MIT
rep-learn
- DeepLearningLifeSciences (π₯12 Β· β 340 Β· π) - Example code from the book Deep Learning for the Life Sciences.
MIT
- Deep Learning for Molecules and Materials Book (π₯11 Β· β 600 Β· π) - Deep learning for molecules and materials book.
Custom
- RDKit Tutorials (π₯8 Β· β 250 Β· π) - Tutorials to learn how to work with the RDKit.
Custom
- MAChINE (π₯7 Β· β 1 Β· π€) - Client-Server Web App to introduce usage of ML in materials science to beginners.
MIT
- Applied AI for Materials (π₯6 Β· β 58 Β· π) - Course materials for Applied AI for Materials Science and Engineering.
Unlicensed
- ML for catalysis tutorials (π₯6 Β· β 8 Β· π) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis.
MIT
- AI4Science101 (π₯5 Β· β 83 Β· π) - AI for Science.
Unlicensed
- Machine Learning for Materials Hard and Soft (π₯5 Β· β 34 Β· π) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.
Unlicensed
- Data Handling, DoE and Statistical Analysis for Material Chemists (π₯5 Β· β 1 Β· π) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.
GPL-3.0
- ML-in-chemistry-101 (π₯4 Β· β 67 Β· π) - The course materials for Machine Learning in Chemistry 101.
Unlicensed
- chemrev-gpr (π₯4 Β· β 6 Β· π) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.
Unlicensed
- AI4ChemMat Hands-On Series (π₯4 Β· β 1) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.
MPL-2.0
- PiNN Lab (π₯3 Β· β 2 Β· π) - Material for running a lab session on atomic neural networks.
GPL-3.0
- MLDensity_tutorial (π₯2 Β· β 9 Β· π) - Tutorial files to work with ML for the charge density in molecules and solids.
Unlicensed
- LAMMPS-style pair potentials with GAP (π₯2 Β· β 4 Β· π) - 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 (π₯2 Β· β 2 Β· π€) - A full MALA hands-on tutorial.
Unlicensed
Projects that focus on explainability and model interpretability in atomistic ML.
exmol (π₯19 Β· β 280 Β· π€) - Explainer for black box models that predict molecule properties. MIT
MEGAN: Multi Explanation Graph Attention Student (π₯6 Β· β 5) - Minimal implementation of graph attention student model architecture. MIT
rep-learn
-
GitHub (π¨βπ» 2 Β· π 1 Β· β±οΈ 19.08.2024):
git clone https://github.com/aimat-lab/graph_attention_student
Show 1 hidden projects...
- Linear vs blackbox (π₯3 Β· β 2 Β· π) - Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.
MIT
XAI
single-paper
rep-eng
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 (π₯8 Β· β 200 Β· π) - Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..
MIT
- QMLearn (π₯5 Β· β 11 Β· π) - Quantum Machine Learning by learning one-body reduced density matrices in the AO basis...
MIT
- q-pac (π₯5 Β· β 4 Β· π) - Kernel charge equilibration method.
MIT
electrostatics
- halex (π₯4 Β· β 2) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.
Unlicensed
excited-states
- e3psi (π₯3 Β· β 3 Β· π€) - Equivariant machine learning library for learning from electronic structures.
LGPL-3.0
General tools for atomistic machine learning.
DeepChem (π₯36 Β· β 5.4K) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT
-
GitHub (π¨βπ» 250 Β· π 1.6K Β· π¦ 420 Β· π 1.9K - 33% open Β· β±οΈ 21.08.2024):
git clone https://github.com/deepchem/deepchem
-
PyPi (π₯ 47K / month Β· π¦ 13 Β· β±οΈ 21.08.2024):
pip install deepchem
-
Conda (π₯ 110K Β· β±οΈ 05.04.2024):
conda install -c conda-forge deepchem
-
Docker Hub (π₯ 7.5K Β· β 5 Β· β±οΈ 21.08.2024):
docker pull deepchemio/deepchem
Matminer (π₯28 Β· β 460 Β· π) - Data mining for materials science. Custom
-
GitHub (π¨βπ» 54 Β· π 180 Β· π¦ 310 Β· π 230 - 12% open Β· β±οΈ 21.08.2024):
git clone https://github.com/hackingmaterials/matminer
-
PyPi (π₯ 11K / month Β· π¦ 58 Β· β±οΈ 27.03.2024):
pip install matminer
-
Conda (π₯ 67K Β· β±οΈ 28.03.2024):
conda install -c conda-forge matminer
QUIP (π₯26 Β· β 350) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0
MD
ML-IAP
rep-eng
Fortran
-
GitHub (π¨βπ» 85 Β· π 120 Β· π₯ 530 Β· π¦ 41 Β· π 460 - 22% open Β· β±οΈ 15.08.2024):
git clone https://github.com/libAtoms/QUIP
-
PyPi (π₯ 4.2K / month Β· π¦ 4 Β· β±οΈ 15.01.2023):
pip install quippy-ase
-
Docker Hub (π₯ 9.9K Β· β 4 Β· β±οΈ 24.04.2023):
docker pull libatomsquip/quip
MAML (π₯25 Β· β 350) - Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc. BSD-3
JARVIS-Tools (π₯23 Β· β 300) - JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:.. Custom
-
GitHub (π¨βπ» 15 Β· π 120 Β· π¦ 93 Β· π 91 - 50% open Β· β±οΈ 28.06.2024):
git clone https://github.com/usnistgov/jarvis
-
PyPi (π₯ 22K / month Β· π¦ 31 Β· β±οΈ 28.06.2024):
pip install jarvis-tools
-
Conda (π₯ 74K Β· β±οΈ 28.06.2024):
conda install -c conda-forge jarvis-tools
MAST-ML (π₯19 Β· β 100) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT
-
GitHub (π¨βπ» 18 Β· π 58 Β· π₯ 95 Β· π¦ 43 Β· π 220 - 14% open Β· β±οΈ 17.04.2024):
git clone https://github.com/uw-cmg/MAST-ML
Scikit-Matter (π₯18 Β· β 73 Β· π) - A collection of scikit-learn compatible utilities that implement methods born out of the materials science and.. BSD-3
scikit-learn
-
GitHub (π¨βπ» 15 Β· π 20 Β· π¦ 10 Β· π 70 - 20% open Β· β±οΈ 06.08.2024):
git clone https://github.com/scikit-learn-contrib/scikit-matter
-
PyPi (π₯ 970 / month Β· β±οΈ 24.08.2023):
pip install skmatter
-
Conda (π₯ 2K Β· β±οΈ 24.08.2023):
conda install -c conda-forge skmatter
MLatom (π₯13 Β· β 37) - AI-enhanced computational chemistry. MIT
UIP
ML-IAP
MD
ML-DFT
ML-ESM
transfer-learning
active-learning
spectroscopy
structure-optimization
Artificial Intelligence for Science (AIRS) (π₯12 Β· β 480) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license
rep-learn
generative
ML-IAP
MD
ML-DFT
ML-WFT
biomolecules
-
GitHub (π¨βπ» 28 Β· π 57 Β· π 14 - 14% open Β· β±οΈ 12.07.2024):
git clone https://github.com/divelab/AIRS
Equisolve (π₯6 Β· β 5 Β· π€) - A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties.. BSD-3
ML-IAP
-
GitHub (π¨βπ» 6 Β· π 1 Β· π 23 - 82% open Β· β±οΈ 27.10.2023):
git clone https://github.com/lab-cosmo/equisolve
Show 10 hidden projects...
- QML (π₯16 Β· β 200 Β· π) - QML: Quantum Machine Learning.
MIT
- Automatminer (π₯15 Β· β 140 Β· π) - An automatic engine for predicting materials properties.
Custom
autoML
- AMPtorch (π₯11 Β· β 59 Β· π) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.
GPL-3.0
- OpenChem (π₯10 Β· β 670 Β· π) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.
MIT
- JAXChem (π₯7 Β· β 76 Β· π) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.
MIT
- uncertainty_benchmarking (π₯7 Β· β 38 Β· π) - Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.
Unlicensed
benchmarking
probabilistic
- torchchem (π₯7 Β· β 34 Β· π) - An experimental repo for experimenting with PyTorch models.
MIT
- ACEatoms (π₯4 Β· β 2 Β· π) - Generic code for modelling atomic properties using ACE.
Custom
Julia
- Magpie (π₯3) - Materials Agnostic Platform for Informatics and Exploration (Magpie).
MIT
Java
- quantum-structure-ml (π₯2 Β· β 2 Β· π) - Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..
Unlicensed
magnetism
benchmarking
Projects that implement generative models for atomistic ML.
GT4SD (π₯18 Β· β 330) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT
pretrained
drug-discovery
rep-learn
PMTransformer (π₯16 Β· β 82) - Universal Transfer Learning in Porous Materials, including MOFs. MIT
transfer-learning
pretrained
transformer
MoLeR (π₯14 Β· β 250 Β· π€) - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation. MIT
SchNetPack G-SchNet (π₯14 Β· β 43) - G-SchNet extension for SchNetPack. MIT
-
GitHub (π¨βπ» 3 Β· π 8 Β· β±οΈ 05.07.2024):
git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet
COATI (π₯5 Β· β 89) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2
drug-discovery
multimodal
pretrained
rep-learn
-
GitHub (π¨βπ» 5 Β· π 5 Β· π 3 - 33% open Β· β±οΈ 23.03.2024):
git clone https://github.com/terraytherapeutics/COATI
Show 8 hidden projects...
- synspace (π₯12 Β· β 35 Β· π) - Synthesis generative model.
MIT
- EDM (π₯9 Β· β 420 Β· π) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D.
MIT
- G-SchNet (π₯8 Β· β 130 Β· π) - G-SchNet - a generative model for 3d molecular structures.
MIT
- bVAE-IM (π₯8 Β· β 11 Β· π) - Implementation of Chemical Design with GPU-based Ising Machine.
MIT
QML
single-paper
- cG-SchNet (π₯7 Β· β 51 Β· π) - cG-SchNet - a conditional generative neural network for 3d molecular structures.
MIT
- rxngenerator (π₯7 Β· β 11 Β· π) - A generative model for molecular generation via multi-step chemical reactions.
MIT
- MolSLEPA (π₯5 Β· β 5 Β· π) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.
MIT
XAI
- Mapping out phase diagrams with generative classifiers (π₯4 Β· β 7 Β· π) - Repository for our ``Mapping out phase diagrams with generative models paper.
MIT
phase-transition
Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and force fields (ML-FF) for molecular dynamics.
DeePMD-kit (π₯27 Β· β 1.4K) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0
C++
-
GitHub (π¨βπ» 69 Β· π 490 Β· π₯ 39K Β· π¦ 16 Β· π 760 - 11% open Β· β±οΈ 06.07.2024):
git clone https://github.com/deepmodeling/deepmd-kit
-
PyPi (π₯ 1.5K / month Β· π¦ 4 Β· β±οΈ 27.07.2024):
pip install deepmd-kit
-
Conda (π₯ 1.2K Β· β±οΈ 06.04.2024):
conda install -c deepmodeling deepmd-kit
-
Docker Hub (π₯ 2.7K Β· β 1 Β· β±οΈ 27.07.2024):
docker pull deepmodeling/deepmd-kit
NequIP (π₯24 Β· β 580) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT
TorchMD-NET (π₯23 Β· β 310) - Training neural network potentials. MIT
MD
rep-learn
transformer
pretrained
MACE (π₯22 Β· β 450) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT
-
GitHub (π¨βπ» 35 Β· π 170 Β· π 240 - 29% open Β· β±οΈ 12.08.2024):
git clone https://github.com/ACEsuit/mace
GPUMD (π₯22 Β· β 430) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0
MD
C++
electrostatics
-
GitHub (π¨βπ» 33 Β· π 110 Β· π 180 - 12% open Β· β±οΈ 22.08.2024):
git clone https://github.com/brucefan1983/GPUMD
DP-GEN (π₯22 Β· β 290) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. LGPL-3.0
workflows
-
GitHub (π¨βπ» 64 Β· π 170 Β· π₯ 1.7K Β· π¦ 6 Β· π 300 - 11% open Β· β±οΈ 10.04.2024):
git clone https://github.com/deepmodeling/dpgen
-
PyPi (π₯ 390 / month Β· π¦ 1 Β· β±οΈ 10.04.2024):
pip install dpgen
-
Conda (π₯ 200 Β· β±οΈ 16.06.2023):
conda install -c deepmodeling dpgen
fairchem (π₯19 Β· β 750) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp). Unlicensed
pretrained
rep-learn
catalysis
-
GitHub (π¨βπ» 41 Β· π 230 Β· π 200 - 6% open Β· β±οΈ 22.08.2024):
git clone https://github.com/FAIR-Chem/fairchem
Neural Force Field (π₯18 Β· β 230) - Neural Network Force Field based on PyTorch. MIT
pretrained
-
GitHub (π¨βπ» 40 Β· π 47 Β· π 19 - 5% open Β· β±οΈ 24.07.2024):
git clone https://github.com/learningmatter-mit/NeuralForceField
apax (π₯18 Β· β 14) - A flexible and performant framework for training machine learning potentials. MIT
sGDML (π₯16 Β· β 140 Β· π€) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT
wfl (π₯16 Β· β 28) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0
workflows
HTC
-
GitHub (π¨βπ» 18 Β· π 18 Β· π¦ 1 Β· π 160 - 42% open Β· β±οΈ 16.08.2024):
git clone https://github.com/libAtoms/workflow
Ultra-Fast Force Fields (UF3) (π₯15 Β· β 57) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2
KLIFF (π₯15 Β· β 34) - KIM-based Learning-Integrated Fitting Framework for interatomic potentials. LGPL-2.1
probabilistic
workflows
So3krates (MLFF) (π₯13 Β· β 72) - Build neural networks for machine learning force fields with JAX. MIT
-
GitHub (π¨βπ» 4 Β· π 15 Β· π 9 - 33% open Β· β±οΈ 22.08.2024):
git clone https://github.com/thorben-frank/mlff
ANI-1 (π₯12 Β· β 220) - ANI-1 neural net potential with python interface (ASE). MIT
-
GitHub (π¨βπ» 6 Β· π 55 Β· π 37 - 43% open Β· β±οΈ 11.03.2024):
git clone https://github.com/isayev/ASE_ANI
DMFF (π₯12 Β· β 150 Β· π€) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. LGPL-3.0
-
GitHub (π¨βπ» 14 Β· π 40 Β· π 26 - 38% open Β· β±οΈ 12.01.2024):
git clone https://github.com/deepmodeling/DMFF
PiNN (π₯11 Β· β 100) - A Python library for building atomic neural networks. BSD-3
-
GitHub (π¨βπ» 5 Β· π 32 Β· π 6 - 16% open Β· β±οΈ 27.06.2024):
git clone https://github.com/Teoroo-CMC/PiNN
-
Docker Hub (π₯ 240 Β· β±οΈ 27.06.2024):
docker pull teoroo/pinn
tinker-hp (π₯11 Β· β 78) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom
-
GitHub (π¨βπ» 12 Β· π 22 Β· π 20 - 15% open Β· β±οΈ 05.08.2024):
git clone https://github.com/TinkerTools/tinker-hp
Pacemaker (π₯11 Β· β 62) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom
calorine (π₯11 Β· β 12) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom
Point Edge Transformer (PET) (π₯10 Β· β 18) - Point Edge Transformer. MIT
rep-learn
transformer
-
GitHub (π¨βπ» 7 Β· π 5 Β· β±οΈ 02.07.2024):
git clone https://github.com/spozdn/pet
ACEfit (π₯10 Β· β 7) - MIT
Julia
-
GitHub (π¨βπ» 7 Β· π 5 Β· π 55 - 40% open Β· β±οΈ 13.08.2024):
git clone https://github.com/ACEsuit/ACEfit.jl
DimeNet (π₯9 Β· β 280 Β· π€) - DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and.. Custom
-
GitHub (π¨βπ» 2 Β· π 59 Β· π¦ 1 Β· π 31 - 3% open Β· β±οΈ 03.10.2023):
git clone https://github.com/gasteigerjo/dimenet
GAP (π₯9 Β· β 39) - Gaussian Approximation Potential (GAP). Custom
-
GitHub (π¨βπ» 13 Β· π 20 Β· β±οΈ 17.08.2024):
git clone https://github.com/libAtoms/GAP
ALF (π₯9 Β· β 29) - A framework for performing active learning for training machine-learned interatomic potentials. Custom
active-learning
-
GitHub (π¨βπ» 5 Β· π 11 Β· β±οΈ 08.08.2024):
git clone https://github.com/lanl/alf
TurboGAP (π₯9 Β· β 16) - The TurboGAP code. Custom
Fortran
-
GitHub (π¨βπ» 8 Β· π 8 Β· π 9 - 66% open Β· β±οΈ 09.07.2024):
git clone https://github.com/mcaroba/turbogap
MACE-Jax (π₯8 Β· β 58 Β· π€) - Equivariant machine learning interatomic potentials in JAX. MIT
-
GitHub (π¨βπ» 2 Β· π 5 Β· π 6 - 50% open Β· β±οΈ 04.10.2023):
git clone https://github.com/ACEsuit/mace-jax
SIMPLE-NN v2 (π₯8 Β· β 40 Β· π€) - SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab.. GPL-3.0
-
GitHub (π¨βπ» 13 Β· π 17 Β· π 13 - 30% open Β· β±οΈ 29.12.2023):
git clone https://github.com/MDIL-SNU/SIMPLE-NN_v2
ACE1.jl (π₯8 Β· β 20) - Atomic Cluster Expansion for Modelling Invariant Atomic Properties. Custom
Julia
-
GitHub (π¨βπ» 9 Β· π 7 Β· π 46 - 47% open Β· β±οΈ 02.07.2024):
git clone https://github.com/ACEsuit/ACE1.jl
MLXDM (π₯8 Β· β 6) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. MIT
long-range
-
GitHub (π¨βπ» 7 Β· π 2 Β· β±οΈ 15.08.2024):
git clone https://github.com/RowleyGroup/MLXDM
PyNEP (π₯7 Β· β 44) - A python interface of the machine learning potential NEP used in GPUMD. MIT
-
GitHub (π¨βπ» 7 Β· π 17 Β· π 11 - 36% open Β· β±οΈ 01.06.2024):
git clone https://github.com/bigd4/PyNEP
ACE1Pack.jl (π₯6 Β· π€) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. MIT
Julia
-
GitHub (π¨βπ» 11 Β· β±οΈ 21.08.2023):
git clone https://github.com/ACEsuit/ACE1pack.jl
NequIP-JAX (π₯5 Β· β 17 Β· π€) - JAX implementation of the NequIP interatomic potential. Unlicensed
-
GitHub (π¨βπ» 2 Β· π 3 Β· β±οΈ 01.11.2023):
git clone https://github.com/mariogeiger/nequip-jax
Allegro-JAX ( β 17) - JAX implementation of the Allegro interatomic potential. Unlicensed
-
GitHub (π¨βπ» 2 Β· π 2 Β· π 2 - 50% open Β· β±οΈ 09.04.2024):
git clone https://github.com/mariogeiger/allegro-jax
Show 31 hidden projects...
- MEGNet (π₯22 Β· β 490 Β· π) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.
BSD-3
multifidelity
- n2p2 (π₯13 Β· β 220 Β· π) - n2p2 - A Neural Network Potential Package.
GPL-3.0
C++
- TensorMol (π₯12 Β· β 270 Β· π) - Tensorflow + Molecules = TensorMol.
GPL-3.0
single-paper
- SIMPLE-NN (π₯11 Β· β 47 Β· π) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).
GPL-3.0
- Allegro (π₯10 Β· β 310 Β· π) - Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..
MIT
- NNsforMD (π₯10 Β· β 10 Β· π) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.
MIT
- SchNet (π₯9 Β· β 210 Β· π) - SchNet - a deep learning architecture for quantum chemistry.
MIT
- GemNet (π₯9 Β· β 180 Β· π) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..
Custom
- ACE.jl (π₯9 Β· β 65 Β· π) - Parameterisation of Equivariant Properties of Particle Systems.
Custom
Julia
- AIMNet (π₯8 Β· β 94 Β· π) - Atoms In Molecules Neural Network Potential.
MIT
single-paper
- SNAP (π₯8 Β· β 36 Β· π) - Repository for spectral neighbor analysis potential (SNAP) model development.
BSD-3
- Atomistic Adversarial Attacks (π₯8 Β· β 30 Β· π) - Code for performing adversarial attacks on atomistic systems using NN potentials.
MIT
probabilistic
- MEGNetSparse (π₯8 Β· β 1 Β· π€) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,..
MIT
material-defect
- PhysNet (π₯7 Β· β 88 Β· π) - Code for training PhysNet models.
MIT
electrostatics
- MLIP-3 (π₯6 Β· β 25 Β· π) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).
BSD-2
C++
- testing-framework (π₯6 Β· β 11 Β· π) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..
Unlicensed
benchmarking
- PANNA (π₯6 Β· β 9 Β· π) - 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 (π₯5 Β· β 10 Β· π) - 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 (π₯5 Β· β 2 Β· π) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article.
BSD-3
- Allegro-Legato (π₯4 Β· β 19 Β· π€) - An extension of Allegro with enhanced robustness and time-to-failure.
MIT
MD
- glp (π₯4 Β· β 17) - tools for graph-based machine-learning potentials in jax.
MIT
- TensorPotential (π₯4 Β· β 7 Β· π) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..
Custom
- ACE Workflows (π₯4 Β· π€) - Workflow Examples for ACE Models.
Unlicensed
Julia
workflows
- PeriodicPotentials (π₯4 Β· π) - A Periodic table app that displays potentials based on the selected elements.
MIT
community-resource
viz
JavaScript
- PyFLAME (π₯3 Β· π) - An automated approach for developing neural network interatomic potentials with FLAME..
Unlicensed
active-learning
structure-prediction
structure-optimization
rep-eng
Fortran
- SingleNN (π₯2 Β· β 8 Β· π) - An efficient package for training and executing neural-network interatomic potentials.
Unlicensed
C++
- AisNet (π₯2 Β· β 3 Β· π) - A Universal Interatomic Potential Neural Network with Encoded Local Environment Features..
MIT
- RuNNer (π₯2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..
GPL-3.0
Fortran
- nnp-pre-training (π₯1 Β· β 6 Β· π€) - Synthetic pre-training for neural-network interatomic potentials.
Unlicensed
pretrained
MD
- mag-ace (π₯1 Β· β 2 Β· π€) - Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.
Unlicensed
magnetism
MD
Fortran
- mlp (π₯1 Β· β 1 Β· π) - Proper orthogonal descriptors for efficient and accurate interatomic potentials...
Unlicensed
Julia
Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML.
paper-qa (π₯27 Β· β 3.8K) - LLM Chain for answering questions from documents with citations. Apache-2
ai-agent
ChemNLP project (π₯17 Β· β 150) - ChemNLP project. MIT
datasets
ChemCrow (π₯16 Β· β 560) - Open source package for the accurate solution of reasoning-intensive chemical tasks. MIT
ai-agent
ChatMOF (π₯12 Β· β 54) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT
generative
LLaMP (π₯10 Β· β 54) - 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 (π¨βπ» 5 Β· π 7 Β· π 25 - 32% open Β· β±οΈ 01.08.2024):
git clone https://github.com/chiang-yuan/llamp
MoLFormer (π₯9 Β· β 230 Β· π€) - Repository for MolFormer. Apache-2
transformer
pretrained
drug-discovery
-
GitHub (π¨βπ» 5 Β· π 40 Β· π 19 - 47% open Β· β±οΈ 16.10.2023):
git clone https://github.com/IBM/molformer
MolSkill (π₯9 Β· β 100 Β· π€) - Extracting medicinal chemistry intuition via preference machine learning. MIT
drug-discovery
recommender
chemlift (π₯7 Β· β 31 Β· π€) - Language-interfaced fine-tuning for chemistry. MIT
-
GitHub (π¨βπ» 2 Β· π 3 Β· π 18 - 61% open Β· β±οΈ 14.10.2023):
git clone https://github.com/lamalab-org/chemlift
LLM-Prop (π₯7 Β· β 26) - A repository for the LLM-Prop implementation. MIT
-
GitHub (π¨βπ» 6 Β· π 5 Β· π 2 - 50% open Β· β±οΈ 26.04.2024):
git clone https://github.com/vertaix/LLM-Prop
Cephalo (π₯6 Β· β 5 Β· π£) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2
generative
multimodal
pretrained
-
GitHub (π 1 Β· β±οΈ 23.07.2024):
git clone https://github.com/lamm-mit/Cephalo
crystal-text-llm (π₯5 Β· β 63 Β· π£) - Large language models to generate stable crystals. CC-BY-NC-4.0
materials-discovery
-
GitHub (π¨βπ» 3 Β· π 10 Β· π 9 - 77% open Β· β±οΈ 18.06.2024):
git clone https://github.com/facebookresearch/crystal-text-llm
SciBot (π₯5 Β· β 28) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed
ai-agent
-
GitHub (π¨βπ» 1 Β· π 8 Β· π¦ 1 Β· β±οΈ 19.04.2024):
git clone https://github.com/CFN-softbio/SciBot
BERT-PSIE-TC (π₯5 Β· β 11 Β· π€) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT
magnetism
-
GitHub (π¨βπ» 2 Β· π 3 Β· β±οΈ 18.08.2023):
git clone https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC
MAPI_LLM (π₯5 Β· β 9) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT
ai-agent
dataset
-
GitHub (π¨βπ» 2 Β· π 2 Β· β±οΈ 11.04.2024):
git clone https://github.com/maykcaldas/MAPI_LLM
Show 5 hidden projects...
- ChemDataExtractor (π₯16 Β· β 300 Β· π) - Automatically extract chemical information from scientific documents.
MIT
literature-data
- mat2vec (π₯12 Β· β 620 Β· π) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..
MIT
rep-learn
- nlcc (π₯11 Β· β 44 Β· π) - Natural language computational chemistry command line interface.
MIT
single-paper
- ChemDataWriter (π₯4 Β· β 14 Β· π€) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.
MIT
literature-data
- CatBERTa (π₯3 Β· β 19) - Large Language Model for Catalyst Property Prediction.
Unlicensed
transformer
catalysis
Projects that implement materials discovery methods using atomistic ML.
πΒ MatterGen - A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687. generative
proprietary
aviary (π₯15 Β· β 46) - The Wren sits on its Roost in the Aviary. MIT
-
GitHub (π¨βπ» 4 Β· π 11 Β· π 28 - 14% open Β· β±οΈ 22.08.2024):
git clone https://github.com/CompRhys/aviary
Materials Discovery: GNoME (π₯10 Β· β 860 Β· π€) - Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials. Apache-2
UIP
datasets
rep-learn
proprietary
-
GitHub (π¨βπ» 2 Β· π 130 Β· π 22 - 81% open Β· β±οΈ 02.12.2023):
git clone https://github.com/google-deepmind/materials_discovery
AGOX (π₯9 Β· β 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
CSPML (crystal structure prediction with machine learning-based element substitution) (π₯5 Β· β 18) - Original implementation of CSPML. Unlicensed
structure-prediction
-
GitHub (π 8 Β· π 3 - 66% open Β· β±οΈ 09.07.2024):
git clone https://github.com/minoru938/cspml
Show 6 hidden projects...
- Computational Autonomy for Materials Discovery (CAMD) (π₯6 Β· β 1 Β· π) - Agent-based sequential learning software for materials discovery.
Apache-2
- MAGUS (π₯4 Β· β 56 Β· π) - Machine learning And Graph theory assisted Universal structure Searcher.
Unlicensed
structure-prediction
active-learning
- SPINNER (π₯4 Β· β 12 Β· π) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..
GPL-3.0
C++
structure-prediction
- ML-atomate (π₯4 Β· β 4 Β· π€) - Machine learning-assisted Atomate code for autonomous computational materials screening.
GPL-3.0
active-learning
workflows
- closed-loop-acceleration-benchmarks (π₯4 Β· π) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..
MIT
materials-discovery
active-learning
single-paper
- sl_discovery (π₯3 Β· β 5 Β· π) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery.
Apache-2
materials-discovery
single-paper
Projects that implement mathematical objects used in atomistic machine learning.
KFAC-JAX (π₯19 Β· β 220) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2
gpax (π₯18 Β· β 200) - Gaussian Processes for Experimental Sciences. MIT
probabilistic
active-learning
SpheriCart (π₯16 Β· β 64) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT
Polynomials4ML.jl (π₯15 Β· β 12) - Polynomials for ML: fast evaluation, batching, differentiation. MIT
Julia
-
GitHub (π¨βπ» 10 Β· π 5 Β· π 51 - 33% open Β· β±οΈ 22.06.2024):
git clone https://github.com/ACEsuit/Polynomials4ML.jl
GElib (π₯8 Β· β 19) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0
C++
-
GitHub (π¨βπ» 4 Β· π 3 Β· π 8 - 50% open Β· β±οΈ 27.07.2024):
git clone https://github.com/risi-kondor/GElib
EquivariantOperators.jl (π₯6 Β· β 18 Β· π€) - This package is deprecated. Functionalities are migrating to Porcupine.jl. MIT
Julia
-
GitHub (β±οΈ 27.09.2023):
git clone https://github.com/aced-differentiate/EquivariantOperators.jl
COSMO Toolbox (π₯6 Β· β 7) - Assorted libraries and utilities for atomistic simulation analysis. Unlicensed
C++
-
GitHub (π¨βπ» 9 Β· π 5 Β· β±οΈ 19.03.2024):
git clone https://github.com/lab-cosmo/toolbox
Show 4 hidden projects...
- lie-nn (π₯9 Β· β 26 Β· π) - Tools for building equivariant polynomials on reductive Lie groups.
MIT
rep-learn
- cnine (π₯6 Β· β 4) - Cnine tensor library.
Unlicensed
C++
- torch_spex (π₯3 Β· β 3) - Spherical expansions in PyTorch.
Unlicensed
- Wigner Kernels (π₯2 Β· β 2 Β· π) - Collection of programs to benchmark Wigner kernels.
Unlicensed
benchmarking
Projects that simplify the integration of molecular dynamics and atomistic machine learning.
mlcolvar (π₯19 Β· β 91) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT
enhanced-sampling
FitSNAP (π₯17 Β· β 140) - Software for generating SNAP machine-learning interatomic potentials. GPL-2.0
openmm-torch (π₯16 Β· β 180) - OpenMM plugin to define forces with neural networks. Custom
ML-IAP
C++
OpenMM-ML (π₯13 Β· β 79) - High level API for using machine learning models in OpenMM simulations. MIT
ML-IAP
pair_nequip (π₯11 Β· β 41) - LAMMPS pair style for NequIP. MIT
ML-IAP
rep-learn
-
GitHub (π¨βπ» 3 Β· π 12 Β· π 29 - 31% open Β· β±οΈ 05.06.2024):
git clone https://github.com/mir-group/pair_nequip
pair_allegro (π₯9 Β· β 34) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT
ML-IAP
rep-learn
-
GitHub (π¨βπ» 2 Β· π 8 Β· π 29 - 37% open Β· β±οΈ 05.06.2024):
git clone https://github.com/mir-group/pair_allegro
PACE (π₯9 Β· β 23 Β· π€) - The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,.. Custom
-
GitHub (π¨βπ» 6 Β· π 10 Β· π 8 - 25% open Β· β±οΈ 27.11.2023):
git clone https://github.com/ICAMS/lammps-user-pace
SOMD (π₯5 Β· β 11) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0
ML-IAP
active-learning
-
GitHub (π 2 Β· β±οΈ 17.08.2024):
git clone https://github.com/initqp/somd
Show 1 hidden projects...
- interface-lammps-mlip-3 (π₯3 Β· β 5 Β· π) - An interface between LAMMPS and MLIP (version 3).
GPL-2.0
Projects that focus on reinforcement learning for atomistic ML.
Show 2 hidden projects...
- ReLeaSE (π₯11 Β· β 350 Β· π) - Deep Reinforcement Learning for de-novo Drug Design.
MIT
drug-discovery
- CatGym (π₯6 Β· β 11 Β· π) - Surface segregation using Deep Reinforcement Learning.
GPL
Projects that offer implementations of representations aka descriptors, fingerprints of atomistic systems, and models built with them, aka feature engineering.
cdk (π₯26 Β· β 490) - The Chemistry Development Kit. LGPL-2.1
cheminformatics
Java
-
GitHub (π¨βπ» 160 Β· π 160 Β· π₯ 21K Β· π 290 - 11% open Β· β±οΈ 20.08.2024):
git clone https://github.com/cdk/cdk
-
Maven (π¦ 16 Β· β±οΈ 21.08.2023):
<dependency> <groupId>org.openscience.cdk</groupId> <artifactId>cdk-bundle</artifactId> <version>[VERSION]</version> </dependency>
DScribe (π₯24 Β· β 390 Β· π) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2
MODNet (π₯16 Β· β 75 Β· π) - MODNet: a framework for machine learning materials properties. MIT
pretrained
small-data
transfer-learning
-
GitHub (π¨βπ» 9 Β· π 32 Β· π¦ 8 Β· π 53 - 49% open Β· β±οΈ 22.08.2024):
git clone https://github.com/ppdebreuck/modnet
SISSO (π₯13 Β· β 230 Β· π€) - A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models. Apache-2
Fortran
-
GitHub (π¨βπ» 3 Β· π 76 Β· π 75 - 24% open Β· β±οΈ 12.09.2023):
git clone https://github.com/rouyang2017/SISSO
Librascal (π₯13 Β· β 80 Β· π€) - A scalable and versatile library to generate representations for atomic-scale learning. LGPL-2.1
-
GitHub (π¨βπ» 30 Β· π 20 Β· π 250 - 46% open Β· β±οΈ 30.11.2023):
git clone https://github.com/lab-cosmo/librascal
GlassPy (π₯13 Β· β 26 Β· π€) - Python module for scientists working with glass materials. GPL-3.0
Rascaline (π₯12 Β· β 44) - Computing representations for atomistic machine learning. BSD-3
Rust
C++
-
GitHub (π¨βπ» 14 Β· π 13 Β· π 67 - 47% open Β· β±οΈ 14.08.2024):
git clone https://github.com/Luthaf/rascaline
NICE (π₯7 Β· β 12) - NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and.. MIT
-
GitHub (π¨βπ» 4 Β· π 3 Β· π 3 - 66% open Β· β±οΈ 15.04.2024):
git clone https://github.com/lab-cosmo/nice
SA-GPR (π₯6 Β· β 19) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0
C-lang
-
GitHub (π¨βπ» 5 Β· π 13 Β· π 7 - 28% open Β· β±οΈ 23.07.2024):
git clone https://github.com/dilkins/TENSOAP
milad (π₯5 Β· β 29) - Moment Invariants Local Atomic Descriptor. GPL-3.0
generative
-
GitHub (π¨βπ» 1 Β· π 1 Β· π¦ 2 Β· β±οΈ 20.08.2024):
git clone https://github.com/muhrin/milad
Show 15 hidden projects...
- CatLearn (π₯16 Β· β 100 Β· π) -
GPL-3.0
surface-science
- CBFV (π₯12 Β· β 23 Β· π) - Tool to quickly create a composition-based feature vector.
Unlicensed
- cmlkit (π₯11 Β· β 34 Β· π) - tools for machine learning in condensed matter physics and quantum chemistry.
MIT
benchmarking
- BenchML (π₯11 Β· β 15 Β· π) - ML benchmarking and pipeling framework.
Apache-2
benchmarking
- SkipAtom (π₯9 Β· β 24 Β· π) - Distributed representations of atoms, inspired by the Skip-gram model.
MIT
- fplib (π₯6 Β· β 7 Β· π) - a fingerprint library.
MIT
C-lang
single-paper
- SOAPxx (π₯6 Β· β 7 Β· π) - A SOAP implementation.
GPL-2.0
C++
- soap_turbo (π₯6 Β· β 4 Β· π) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP.
Custom
Fortran
- pyLODE (π₯6 Β· β 3 Β· π) - Pythonic implementation of LOng Distance Equivariants.
Apache-2
electrostatics
- AMP (π₯6 Β· π) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.
Unlicensed
- MXenes4HER (π₯5 Β· β 5 Β· π) - 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++ (π₯5 Β· β 3 Β· π) - C++ Implementation of SISSO with python bindings.
Apache-2
C++
- automl-materials (π₯4 Β· β 5 Β· π) - AutoML for Regression Tasks on Small Tabular Data in Materials Design.
MIT
autoML
benchmarking
single-paper
- magnetism-prediction (π₯4 Β· β 1 Β· π) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.
Apache-2
magnetism
single-paper
- ML-for-CurieTemp-Predictions (π₯3 Β· β 1 Β· π) - Machine Learning Predictions of High-Curie-Temperature Materials.
MIT
single-paper
magnetism
General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN).
Deep Graph Library (DGL) (π₯39 Β· β 13K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2
PyG Models (π₯35 Β· β 21K) - Representation learning models implemented in PyTorch Geometric. MIT
general-ml
-
GitHub (π¨βπ» 510 Β· π 3.6K Β· π¦ 6.3K Β· π 3.7K - 27% open Β· β±οΈ 22.08.2024):
git clone https://github.com/pyg-team/pytorch_geometric
SchNetPack (π₯26 Β· β 760) - SchNetPack - Deep Neural Networks for Atomistic Systems. MIT
MatGL (Materials Graph Library) (π₯23 Β· β 250) - Graph deep learning library for materials. BSD-3
multifidelity
NVIDIA Deep Learning Examples for Tensor Cores (π₯21 Β· β 13K) - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and.. Custom
educational
drug-discovery
-
GitHub (π¨βπ» 120 Β· π 3.1K Β· π 880 - 35% open Β· β±οΈ 04.04.2024):
git clone https://github.com/NVIDIA/DeepLearningExamples
DIG: Dive into Graphs (π₯21 Β· β 1.8K) - A library for graph deep learning research. GPL-3.0
ALIGNN (π₯20 Β· β 210) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom
Uni-Mol (π₯18 Β· β 640) - Official Repository for the Uni-Mol Series Methods. MIT
pretrained
-
GitHub (π¨βπ» 16 Β· π 120 Β· π₯ 15K Β· π 150 - 40% open Β· β±οΈ 14.08.2024):
git clone https://github.com/deepmodeling/Uni-Mol
escnn (π₯17 Β· β 340 Β· π€) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
matsciml (π₯17 Β· β 140) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT
workflows
benchmarking
-
GitHub (π¨βπ» 12 Β· π 18 Β· π 56 - 35% open Β· β±οΈ 19.08.2024):
git clone https://github.com/IntelLabs/matsciml
Graphormer (π₯16 Β· β 2K) - Graphormer is a general-purpose deep learning backbone for molecular modeling. MIT
transformer
pretrained
-
GitHub (π¨βπ» 14 Β· π 330 Β· π 160 - 58% open Β· β±οΈ 28.05.2024):
git clone https://github.com/microsoft/Graphormer
HydraGNN (π₯14 Β· β 57) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3
-
GitHub (π¨βπ» 13 Β· π 23 Β· π¦ 1 Β· π 49 - 34% open Β· β±οΈ 06.08.2024):
git clone https://github.com/ORNL/HydraGNN
hippynn (π₯11 Β· β 65) - python library for atomistic machine learning. Custom
workflows
-
GitHub (π¨βπ» 14 Β· π 23 Β· π¦ 1 Β· π 15 - 40% open Β· β±οΈ 21.08.2024):
git clone https://github.com/lanl/hippynn
Equiformer (π₯9 Β· β 190) - [ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. MIT
transformer
-
GitHub (π¨βπ» 2 Β· π 36 Β· π 13 - 38% open Β· β±οΈ 18.07.2024):
git clone https://github.com/atomicarchitects/equiformer
ai4material_design (π₯9 Β· β 6 Β· π€) - 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 (π¨βπ» 11 Β· π 3 Β· β±οΈ 21.11.2023):
git clone https://github.com/HSE-LAMBDA/ai4material_design
EquiformerV2 (π₯8 Β· β 180) - [ICLR24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT
-
GitHub (π¨βπ» 2 Β· π 25 Β· π 17 - 88% open Β· β±οΈ 16.07.2024):
git clone https://github.com/atomicarchitects/equiformer_v2
graphite (π₯8 Β· β 53) - A repository for implementing graph network models based on atomic structures. MIT
-
GitHub (π¨βπ» 2 Β· π 9 Β· π¦ 11 Β· π 3 - 66% open Β· β±οΈ 08.08.2024):
git clone https://github.com/llnl/graphite
DeeperGATGNN (π₯8 Β· β 46 Β· π€) - Scalable graph neural networks for materials property prediction. MIT
-
GitHub (π¨βπ» 3 Β· π 8 Β· π 12 - 33% open Β· β±οΈ 19.01.2024):
git clone https://github.com/usccolumbia/deeperGATGNN
Show 35 hidden projects...
- dgl-lifesci (π₯23 Β· β 700 Β· π) - Python package for graph neural networks in chemistry and biology.
Apache-2
- benchmarking-gnns (π₯14 Β· β 2.5K Β· π) - Repository for benchmarking graph neural networks.
MIT
single-paper
benchmarking
- Crystal Graph Convolutional Neural Networks (CGCNN) (π₯12 Β· β 620 Β· π) - Crystal graph convolutional neural networks for predicting material properties.
MIT
- Neural fingerprint (nfp) (π₯12 Β· β 57 Β· π) - Keras layers for end-to-end learning with rdkit and pymatgen.
Custom
- Compositionally-Restricted Attention-Based Network (CrabNet) (π₯12 Β· β 12 Β· π) - Predict materials properties using only the composition information!.
MIT
- pretrained-gnns (π₯10 Β· β 950 Β· π) - Strategies for Pre-training Graph Neural Networks.
MIT
pretrained
- GDC (π₯10 Β· β 260 Β· π) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).
MIT
generative
- SE(3)-Transformers (π₯9 Β· β 480 Β· π) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.
MIT
single-paper
transformer
- GATGNN: Global Attention Graph Neural Network (π₯9 Β· β 68 Β· π) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..
MIT
- UVVisML (π₯9 Β· β 21 Β· π) - Predict optical properties of molecules with machine learning.
MIT
optical-properties
single-paper
probabilistic
- molecularGNN_smiles (π₯8 Β· β 290 Β· π) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..
Apache-2
- CGAT (π₯8 Β· β 25 Β· π) - Crystal graph attention neural networks for materials prediction.
MIT
- T-e3nn (π₯8 Β· β 8 Β· π) - Time-reversal Euclidean neural networks based on e3nn.
MIT
magnetism
- tensorfieldnetworks (π₯7 Β· β 150 Β· π) - Rotation- and translation-equivariant neural networks for 3D point clouds.
MIT
- DTNN (π₯7 Β· β 76 Β· π) - Deep Tensor Neural Network.
MIT
- Cormorant (π₯7 Β· β 59 Β· π) - Codebase for Cormorant Neural Networks.
Custom
- AdsorbML (π₯7 Β· β 35 Β· π) -
MIT
surface-science
single-paper
- escnn_jax (π₯7 Β· β 26 Β· π) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.
Custom
- ML4pXRDs (π₯7 Β· π) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.
MIT
XRD
single-paper
- MACE-Layer (π₯6 Β· β 33 Β· π) - Higher order equivariant graph neural networks for 3D point clouds.
MIT
- charge_transfer_nnp (π₯6 Β· β 28 Β· π) - Graph neural network potential with charge transfer.
MIT
electrostatics
- GLAMOUR (π₯6 Β· β 20 Β· π) - Graph Learning over Macromolecule Representations.
MIT
single-paper
- Autobahn (π₯5 Β· β 30 Β· π) - Repository for Autobahn: Automorphism Based Graph Neural Networks.
MIT
- FieldSchNet (π₯5 Β· β 16 Β· π) - Deep neural network for molecules in external fields.
MIT
- SCFNN (π₯5 Β· β 15 Β· π) - Self-consistent determination of long-range electrostatics in neural network potentials.
MIT
C++
electrostatics
single-paper
- CraTENet (π₯5 Β· β 13 Β· π) - An attention-based deep neural network for thermoelectric transport properties.
MIT
transport-phenomena
- EGraFFBench (π₯5 Β· β 8 Β· π€) -
Unlicensed
single-paper
benchmarking
ML-IAP
- Per-Site CGCNN (π₯5 Β· β 1 Β· π) - Crystal graph convolutional neural networks for predicting material properties.
MIT
pretrained
single-paper
- Per-site PAiNN (π₯5 Β· β 1 Β· π) - Fork of PaiNN for PerovskiteOrderingGCNNs.
MIT
probabilistic
pretrained
single-paper
- Graph Transport Network (π₯4 Β· β 16 Β· π) - 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 (π₯4 Β· β 3) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.
MIT
transport-phenomena
- atom_by_atom (π₯3 Β· β 6 Β· π€) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.
Unlicensed
surface-science
single-paper
- Element encoder (π₯3 Β· β 6 Β· π) - Autoencoder neural network to compress properties of atomic species into a vector representation.
GPL-3.0
single-paper
- Point Edge Transformer (π₯2) - Smooth, exact rotational symmetrization for deep learning on point clouds.
CC-BY-4.0
- SphericalNet (π₯1 Β· β 3 Β· π) - Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..
Unlicensed
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.
πΒ TeaNet - Universal neural network interatomic potential inspired by iterative electronic relaxations.. ML-IAP
πΒ PreFerred Potential (PFP) - Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9. ML-IAP
proprietary
πΒ 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 (π₯26 Β· β 1.4K) - 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 (π¨βπ» 69 Β· π 490 Β· π₯ 39K Β· π¦ 16 Β· π 760 - 11% open Β· β±οΈ 06.07.2024):
git clone https://github.com/deepmodeling/deepmd-kit
CHGNet (π₯23 Β· β 220) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom
ML-IAP
MD
pretrained
electrostatics
magnetism
structure-relaxation
MACE-MP (π₯19 Β· β 450) - Pretrained foundation models for materials chemistry. MIT
ML-IAP
pretrained
rep-learn
MD
SevenNet (π₯15 Β· β 91) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0
ML-IAP
MD
pretrained
-
GitHub (π¨βπ» 9 Β· π 11 Β· π¦ 3 Β· π 12 - 41% open Β· β±οΈ 20.08.2024):
git clone https://github.com/MDIL-SNU/SevenNet
Joint Multidomain Pre-Training (JMP) (π₯5 Β· β 32 Β· π£) - 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 (π 4 Β· β±οΈ 07.05.2024):
git clone https://github.com/facebookresearch/JMP
Show 1 hidden projects...
Projects that focus on unsupervised learning (USL) for atomistic ML, such as dimensionality reduction, clustering and visualization.
ASAP (π₯12 Β· β 140) - ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures. MIT
-
GitHub (π¨βπ» 6 Β· π 28 Β· π¦ 6 Β· π 25 - 24% open Β· β±οΈ 27.06.2024):
git clone https://github.com/BingqingCheng/ASAP
Show 5 hidden projects...
- Sketchmap (π₯8 Β· β 44 Β· π) - Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.
GPL-3.0
C++
- Coarse-Graining-Auto-encoders (π₯5 Β· β 21 Β· π) - Implementation of coarse-graining Autoencoders.
Unlicensed
single-paper
- paper-ml-robustness-material-property (π₯5 Β· β 4 Β· π) - A critical examination of robustness and generalizability of machine learning prediction of materials properties.
BSD-3
datasets
single-paper
- KmdPlus (π₯2 Β· β 3 Β· π€) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..
Unlicensed
- Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF) ( β 2) - Provides a workflow to obtain clustering of local environments in dataset of structures.
Unlicensed
Projects that focus on visualization (viz.) for atomistic ML.
pymatviz (π₯22 Β· β 150) - A toolkit for visualizations in materials informatics. MIT
general-tool
probabilistic
Chemiscope (π₯19 Β· β 120) - An interactive structure/property explorer for materials and molecules. BSD-3
JavaScript
ZnDraw (π₯19 Β· β 30) - A powerful tool for visualizing, modifying, and analysing atomistic systems. EPL-2.0
MD
generative
JavaScript
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.
FermiNet (π₯15 Β· β 660 Β· π) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2
transformer
-
GitHub (π¨βπ» 18 Β· π 110 Β· π 45 - 2% open Β· β±οΈ 22.08.2024):
git clone https://github.com/google-deepmind/ferminet
DeepErwin (π₯11 Β· β 46) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom
Show 2 hidden projects...
Show 1 hidden projects...
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.