acpype

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)

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)

Equivariant graph attention Transformer (ICLR2023)

A Package For Training SNAP Interatomic Potentials for use in the LAMMPS molecular dynamics package.

Tool to build force field input files for molecular simulation.

A package for creating fast and accurate interatomic potentials.