lanl/hippynn
python library for atomistic machine learning
This tool helps researchers in materials science and chemistry to build and train machine learning models for atomistic simulations. You input atomic structure data, and it outputs predictions for material properties like energy, forces, and charge. It is ideal for computational chemists, physicists, and materials scientists working with molecular dynamics or quantum chemistry simulations.
Use this if you need to efficiently train machine learning models to predict properties of atomic systems, especially for large datasets or complex architectures.
Not ideal if you are looking for a pre-built, ready-to-use model for a specific material without needing to train custom architectures.
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94
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34
Language
Python
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Last pushed
Mar 04, 2026
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