IntelLabs / matsciml
Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery supporting widely used materials science datasets, and built on top of PyTorch Lightning, the Deep Graph Library, and PyTorch Geometric.
☆152Updated this week
Related projects ⓘ
Alternatives and complementary repositories for matsciml
- Workflow for creating and analyzing the Open Catalyst Dataset☆93Updated 5 months ago
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆107Updated this week
- Matbench: Benchmarks for materials science property prediction☆124Updated 3 months ago
- Corresponding dataset and tools for the AdsorbML manuscript.☆37Updated 6 months ago
- ORB forcefield models from Orbital Materials☆203Updated last month
- Neural Network Force Field based on PyTorch☆238Updated last week
- GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆184Updated last year
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆66Updated 2 years ago
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en https://www.youtube.com/watch?v=WYePj…☆236Updated this week
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆145Updated 4 months ago
- a package for developing machine learning-based chemically accurate energy and density functional models☆102Updated last month
- AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch☆59Updated last year
- MatDeepLearn, package for graph neural networks in materials chemistry☆175Updated last year
- Build neural networks for machine learning force fields with JAX☆91Updated this week
- FTCP code☆31Updated last year
- Pytorch differentiable molecular dynamics☆169Updated 2 years ago
- Higher-order equivariant neural networks for charge density prediction in materials☆38Updated 3 weeks ago
- Training neural network potentials☆335Updated 2 months ago
- Differentiable Quantum Chemistry (only Differentiable Density Functional Theory and Hartree Fock at the moment)☆106Updated 2 years ago
- sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model☆142Updated last year
- [TMLR 2023] Training and simulating MD with ML force fields☆103Updated 3 weeks ago
- Graph deep learning library for materials☆279Updated this week
- SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT☆76Updated this week
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆246Updated 3 months ago
- [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations☆218Updated 3 months ago
- A repository for implementing graph network models based on atomic structures.☆64Updated 3 months ago
- PySCF with auto-differentiation☆69Updated last week
- LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support☆35Updated last month
- python library for atomistic machine learning☆71Updated 2 weeks ago
- Official code for Periodic Graph Transformers for Crystal Material Property Prediction (NeurIPS 2022)☆81Updated 11 months ago