JuDFTteam / best-of-atomistic-machine-learning
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
☆363Updated this week
Related projects: ⓘ
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆481Updated this week
- Graph deep learning library for materials☆253Updated this week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆229Updated this week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆396Updated last month
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆322Updated 2 months ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆359Updated last week
- An open-source Python package for creating fast and accurate interatomic potentials.☆286Updated this week
- NequIP is a code for building E(3)-equivariant interatomic potentials☆597Updated last month
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en☆214Updated last week
- A code to generate atomic structure with symmetry☆246Updated this week
- n2p2 - A Neural Network Potential Package☆219Updated last year
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆549Updated 6 months ago
- Neural Network Force Field based on PyTorch☆229Updated this week
- MatDeepLearn, package for graph neural networks in materials chemistry☆167Updated last year
- SchNet - a deep learning architecture for quantum chemistry☆214Updated 6 years ago
- Matbench: Benchmarks for materials science property prediction☆111Updated last month
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆144Updated 2 months ago
- Training neural network potentials☆320Updated 3 weeks ago
- i-PI: a universal force engine☆220Updated last week
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.☆289Updated 3 years ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆227Updated last month
- A toolkit for visualizations in materials informatics.☆156Updated last week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆231Updated last year
- molSimplify code☆168Updated this week
- quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.☆167Updated last week
- New API client for the Materials Project☆107Updated last week
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆105Updated this week
- ANI-1 neural net potential with python interface (ASE)☆220Updated 6 months ago
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆223Updated 2 months ago
- Workflow for creating and analyzing the Open Catalyst Dataset☆89Updated 3 months ago