20171130/Equivariant-NN-Zoo
A library for building equivariant neural networks and a zoo of implementations & examples.
This library provides tools to build specialized AI models for chemistry and materials science. It takes in atomic structures or molecular graphs and can predict properties like potential energy surfaces, dipole moments, or even generate new molecular conformations. Scientists, researchers, and engineers working in computational chemistry or drug discovery would find this useful for accelerating molecular simulations and design.
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Use this if you need to develop highly accurate machine learning models for predicting molecular and material properties or generating new molecular structures, particularly when rotational and translational symmetry are important.
Not ideal if your primary focus is on standard machine learning tasks that do not involve molecular or atomic structure data with inherent symmetries.
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32
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5
Language
Python
License
MIT
Category
Last pushed
Aug 09, 2022
Commits (30d)
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