MatterSim
github.com/microsoft/mattersimDeep learning atomistic model across elements, temperatures, and pressures
Sourced from
- GitHub — github.com/microsoft/mattersim
- Awesome AI for Science — github.com/microsoft/mattersim
Related resources
Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.
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)
Curated list of atomistic ML projects for materials science
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)
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)
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