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%0 Journal Article
%1 ZaKaHoStYYa
%A Zaverkin, V.
%A Kästner, J.
%A Holzmüller, D.
%A Steinwart, I.
%D 2021
%J J. Chem. Theory Comput.
%K
%R https://doi.org/10.1021/acs.jctc.1c00527
%T Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
@article{ZaKaHoStYYa,
added-at = {2022-12-13T14:52:24.000+0100},
author = {Zaverkin, V. and K{\"a}stner, J. and Holzm{\"u}ller, D. and Steinwart, I.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21d5d119c47b73557f01fbd0bd3fc619a/davidholzmller},
doi = {https://doi.org/10.1021/acs.jctc.1c00527},
interhash = {906a8189ca261404a8e915bee594b61f},
intrahash = {1d5d119c47b73557f01fbd0bd3fc619a},
journal = {J. Chem. Theory Comput.},
keywords = {},
note = {\url{https://arxiv.org/abs/2002.04861}},
timestamp = {2022-12-13T13:52:24.000+0100},
title = {Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on {G}aussian Moments},
year = 2021
}