Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
V. Zaverkin, D. Holzmüller, I. Steinwart, and J. Kästner. Software, (2021)Related to: 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). doi: 10.1021/acs.jctc.1c00527.
DOI: 10.18419/darus-2136
Related to: 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). doi: 10.1021/acs.jctc.1c00527
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%0 Generic
%1 zaverkin2021sampleefficient
%A Zaverkin, Viktor
%A Holzmüller, David
%A Steinwart, Ingo
%A Kästner, Johannes
%D 2021
%K
%R 10.18419/darus-2136
%T Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
%X Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab
@misc{zaverkin2021sampleefficient,
abstract = {Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab},
added-at = {2023-08-31T16:15:40.000+0200},
affiliation = {Zaverkin, Viktor/Universität Stuttgart, Holzmüller, David/Universität Stuttgart, Steinwart, Ingo/Universität Stuttgart, Kästner, Johannes/Universität Stuttgart},
author = {Zaverkin, Viktor and Holzmüller, David and Steinwart, Ingo and Kästner, Johannes},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ad1789f7f3ff88200eff8c2124032786/puma-wartung},
doi = {10.18419/darus-2136},
howpublished = {Software},
interhash = {58fe862868d5399bce418e1d62223db9},
intrahash = {ad1789f7f3ff88200eff8c2124032786},
keywords = {},
note = {Related to: 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). doi: 10.1021/acs.jctc.1c00527},
orcid-numbers = {Zaverkin, Viktor/0000-0001-9940-8548, Holzmüller, David/0000-0002-9443-0049, Steinwart, Ingo/0000-0002-4436-7109, Kästner, Johannes/0000-0001-6178-7669},
timestamp = {2023-08-31T14:15:40.000+0200},
title = {Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments},
year = 2021
}
