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%0 Journal Article
%1 zaverkin2021sampleefficient
%A Zaverkin, Viktor
%A Holzmüller, David
%A Steinwart, Ingo
%A Kästner, Johannes
%D 2021
%I ACS Publications
%J Journal of Chemical Theory and Computation
%K
%N 10
%P 6658-6670
%R 10.1021/acs.jctc.1c00527
%T Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
%V 17
@article{zaverkin2021sampleefficient,
added-at = {2023-08-31T16:29:32.000+0200},
author = {Zaverkin, Viktor and Holzmüller, David and Steinwart, Ingo and Kästner, Johannes},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/216972cc8e03472cd6246198ad7b3c1a6/puma-wartung},
doi = {10.1021/acs.jctc.1c00527},
interhash = {ded689e5dca5f542b34bdd372cb59103},
intrahash = {16972cc8e03472cd6246198ad7b3c1a6},
issn = {{1549-9618} and {1549-9626}},
journal = {Journal of Chemical Theory and Computation},
keywords = {},
language = {eng},
number = 10,
pages = {6658-6670},
publisher = {ACS Publications},
timestamp = {2023-08-31T14:29:32.000+0200},
title = {Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments},
volume = 17,
year = 2021
}
