JuDFTteam / best-of-atomistic-machine-learningLinks
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
☆555Updated 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
Sorting:
- Graph deep learning library for materials☆432Updated this week
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆330Updated 2 weeks ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆887Updated 2 weeks ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆432Updated last month
- NequIP is a code for building E(3)-equivariant interatomic potentials☆792Updated 2 weeks ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆429Updated 2 weeks ago
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆444Updated last week
- An open-source Python package for creating fast and accurate interatomic potentials.☆335Updated last month
- Torch-native, batchable, atomistic simulations.☆291Updated this week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆296Updated 6 months ago
- A toolkit for visualizations in materials informatics.☆276Updated 2 weeks ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆193Updated 3 weeks ago
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆193Updated this week
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆282Updated last month
- A code to generate atomic structure with symmetry☆336Updated 2 weeks ago
- SchNetPack - Deep Neural Networks for Atomistic Systems☆876Updated this week
- MACE foundation models (MP, OMAT, Matpes)☆148Updated 3 weeks ago
- Matbench: Benchmarks for materials science property prediction☆167Updated last year
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆328Updated last year
- Neural Network Force Field based on PyTorch☆281Updated last month
- ☆234Updated this week
- Training neural network potentials☆439Updated 3 weeks ago
- A collection of Nerual Network Models for chemistry☆162Updated 3 weeks ago
- atomate2 is a library of computational materials science workflows☆244Updated this week
- n2p2 - A Neural Network Potential Package☆239Updated 6 months ago
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆361Updated last week
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆661Updated 8 months ago
- SchNet - a deep learning architecture for quantum chemistry☆265Updated 7 years ago
- MSE5540/6640 Materials Informatics course at the University of Utah. Learn how data science tools are revolutionizing materials science!☆173Updated 2 months ago
- MatDeepLearn, package for graph neural networks in materials chemistry☆197Updated 2 years ago