CHGNet

github.com/cedergrouphub/chgnet
Active412updated 3 weeks ago
Python
NOASSERTION

Universal pretrained neural network potential with charge and magnetic moment awareness, trained on 1.5M+ Materials Project inorganic structures for charge-informed molecular dynamics and phase diagram prediction (Berkeley, Nature Machine Intelligence 2023 Cover)

Sourced from

  • Awesome Python Chemistry — github.com/cedergrouphub/chgnet
  • GitHub — github.com/cedergrouphub/chgnet
  • Awesome AI for Science — github.com/cedergrouphub/chgnet

Related resources

E(3)-equivariant neural network interatomic potentials achieving DFT accuracy with up to 1000× less training data than invariant models, foundational architecture behind MACE and Allegro (Harvard, MIT, Nature Communications 2022)

Active9691 week ago
Python
MIT

Highly scalable equivariant deep learning interatomic potentials enabling million-atom molecular dynamics simulations with ab initio accuracy, building on E(3)-equivariant architectures for large-scale atomistic modeling (mir-group, MIT License, 480+ stars)

Active4984 months ago
Python
MIT

Deep learning atomistic model across elements, temperatures, and pressures

Active5991 month ago
Python
MIT

A benchmark for ML-guided high-throughput materials discovery.

Active2551 week ago
Python
MIT

Provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks.

Active1.2K4 days ago
Jupyter Notebook
MIT

PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)

Active9412 weeks ago
Python
NOASSERTION