changzhiai/IANN
IANN (InterAtomic Neural Network Framework) is an equivariant interatomic neural network potential framework package
This framework helps materials scientists and computational chemists accurately predict energies and forces within atomic structures. By feeding in atomic structure data, you can train various neural network models to output precise energy and force predictions for both periodic and non-periodic systems. It's designed for researchers and engineers working with molecular dynamics simulations and materials design.
Use this if you need to perform high-accuracy energy and force predictions for atomic systems and integrate these predictions into molecular dynamics simulations.
Not ideal if your primary need is general machine learning model development outside of interatomic potentials or if you don't work with atomic structure data.
Stars
16
Forks
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Language
Python
License
MIT
Category
Last pushed
Feb 23, 2026
Commits (30d)
0
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