JuDFTteam / best-of-atomistic-machine-learning
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
☆443Updated this week
Alternatives and similar repositories for best-of-atomistic-machine-learning:
Users that are interested in best-of-atomistic-machine-learning are comparing it to the libraries listed below
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆624Updated this week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆273Updated last month
- Graph deep learning library for materials☆308Updated this week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆412Updated 2 months ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆388Updated 2 weeks ago
- NequIP is a code for building E(3)-equivariant interatomic potentials☆686Updated this week
- An open-source Python package for creating fast and accurate interatomic potentials.☆309Updated 2 weeks ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆371Updated 2 months ago
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePj…☆251Updated 3 weeks ago
- Matbench: Benchmarks for materials science property prediction☆142Updated 6 months ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆268Updated 6 months ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆590Updated last month
- n2p2 - A Neural Network Potential Package☆231Updated this week
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆148Updated 2 weeks ago
- A collection of Nerual Network Models for chemistry☆112Updated this week
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆324Updated last week
- A code to generate atomic structure with symmetry☆294Updated this week
- Neural Network Force Field based on PyTorch☆262Updated 2 weeks ago
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆130Updated this week
- Training neural network potentials☆363Updated last week
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆145Updated 7 months ago
- A Python package for manipulating atomistic data of software in computational science☆205Updated last week
- Deep neural networks for density functional theory Hamiltonian.☆259Updated 4 months ago
- i-PI: a universal force engine☆247Updated last month
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.☆308Updated 3 years ago
- Workflow for creating and analyzing the Open Catalyst Dataset☆104Updated 2 weeks ago
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆268Updated 2 weeks ago
- SchNetPack - Deep Neural Networks for Atomistic Systems☆820Updated this week
- molSimplify code☆181Updated this week
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆237Updated 7 months ago