mir-group/allegro-pol
NequIP extension package that adapts the Allegro equivariant GNN architecture to predict the electric response of materials
This tool helps materials scientists and computational chemists predict the electrical behavior of materials. It takes structural information about a material (like atom positions) and outputs predictions for polarization, Born charges, and polarizability, along with energy and forces, using a single machine learning model. Researchers working on designing new materials with specific electrical properties would find this useful.
Use this if you need to simulate and predict the electric response (polarization, Born charges, polarizability) of materials alongside their energy and forces.
Not ideal if you are looking for a general-purpose machine learning library without a specific focus on materials science or electric response predictions.
Stars
34
Forks
2
Language
Python
License
MIT
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
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