Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments (doi:10.18419/darus-2136)

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Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

doi:10.18419/darus-2136

DaRUS

2021-10-15

1

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/DARUS-2136, DaRUS, V1

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

doi:10.18419/darus-2136

Zaverkin, Viktor (Universität Stuttgart)

Holzmüller, David (Universität Stuttgart)

Steinwart, Ingo (Universität Stuttgart)

Kästner, Johannes (Universität Stuttgart)

EXC 2075 - 390740016

info:eu-repo/grantAgreement/EC/H2020/646717

info:eu-repo/grantAgreement/EC/H2020/646717

DaRUS

Kästner, Johannes

Holzmüller, David

2021-09-15

https://doi.org/10.18419/DARUS-2136

Chemistry, Computer and Information Science, Physics, GM-NN, Gaussian Moments, Potential Energy Surface, Atomistic Machine Learning, Computational Chemistry

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found <a href="https://gitlab.com/zaverkin_v/gmnn">on GitLab</a>

Basic instructions for installing and running the software can be found in the README.md file.

V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021).

10.1021/acs.jctc.1c00527

V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner, “Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments,” J. Chem. Theory Comput. 17, 6658–6670 (2021).

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layers.py

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parameters.py

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pes_fit.py

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trainer.py

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utils.py

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__init__.py

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test_data_pipeline.py

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test_layers.py

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