MAML

github.com/materialsvirtuallab/maml
Active460updated 1 month ago
Jupyter Notebook
BSD-3-Clause

Aims to provide useful high-level interfaces that make ML for materials science as easy as possible.

Sourced from

  • Awesome Python Chemistrygithub.com/materialsvirtuallab/maml
  • GitHubgithub.com/materialsvirtuallab/maml

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)

Active9332 weeks ago
Python
MIT

Deep learning atomistic model across elements, temperatures, and pressures

Active5703 weeks 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)

Active4821 month ago
Python
MIT

Deep learning library for Chemistry based on Tensorflow

Active6.8K2 weeks ago
Python
MIT

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.

Archived5583 years ago
Jupyter Notebook
BSD-3-Clause

Materials informatics benchmark

Idle2071 year ago
Python
MIT