%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 exc2075 graduateschool myown peerReviewed pn6 %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 = {exc2075 graduateschool myown peerReviewed pn6}, 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 }