materialsvirtuallab / matglLinks
Graph deep learning library for materials
☆407Updated this week
Alternatives and similar repositories for matgl
Users that are interested in matgl are comparing it to the libraries listed below
Sorting:
- Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov☆323Updated 4 months ago
- 🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.☆535Updated this week
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆275Updated this week
- Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.☆294Updated 4 months ago
- Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.☆426Updated 3 weeks ago
- A toolkit for visualizations in materials informatics.☆261Updated 2 weeks ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆191Updated 2 weeks ago
- An open-source Python package for creating fast and accurate interatomic potentials.☆333Updated 2 months ago
- Matbench: Benchmarks for materials science property prediction☆164Updated last year
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆853Updated last week
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆180Updated this week
- DScribe is a python package for creating machine learning descriptors for atomistic systems.☆443Updated last month
- MatDeepLearn, package for graph neural networks in materials chemistry☆197Updated 2 years ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆319Updated last year
- NequIP is a code for building E(3)-equivariant interatomic potentials☆770Updated this week
- The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field☆356Updated this week
- Torch-native, batchable, atomistic simulations.☆275Updated last week
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆425Updated 3 weeks ago
- atomate2 is a library of computational materials science workflows☆238Updated this week
- MACE foundation models (MP, OMAT, Matpes)☆139Updated last month
- A code to generate atomic structure with symmetry☆331Updated 3 weeks ago
- n2p2 - A Neural Network Potential Package☆238Updated 5 months ago
- ☆208Updated 3 weeks ago
- Neural Network Force Field based on PyTorch☆279Updated 3 weeks ago
- Deep neural networks for density functional theory Hamiltonian.☆287Updated 10 months ago
- MSE5540/6640 Materials Informatics course at the University of Utah. Learn how data science tools are revolutionizing materials science!☆163Updated 3 weeks ago
- Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.☆257Updated 3 weeks ago
- This add-on to pymatgen provides tools for analyzing diffusion in materials.☆124Updated 2 weeks ago
- An automatic engine for predicting materials properties.☆162Updated last year
- molSimplify code☆194Updated last week