matbench-discovery

github.com/janosh/matbench-discovery
Active246updated 1 week ago
Python
MIT

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

Sourced from

  • Awesome Python Chemistrygithub.com/janosh/matbench-discovery
  • GitHubgithub.com/janosh/matbench-discovery

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)

Active9541 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)

Active4922 months ago
Python
MIT

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)

Idle3996 months ago
Python
NOASSERTION

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

Active9343 weeks ago
Python
NOASSERTION

Crystal property prediction

Stale8854 years ago
Python
MIT

Deep learning atomistic model across elements, temperatures, and pressures

Active5853 weeks ago
Python
MIT