%0 Journal Article %1 zav20 %A Zaverkin, V. %A Kästner, J. %D 2020 %J J. Chem. Theory Comput. %K EXC2075 PN3 PN3-4 PN6 PN6A-1 curated %P 5410-5421 %R 10.1021/acs.jctc.0c00347 %T Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials %U http://dx.doi.org/10.1021/acs.jctc.0c00347 %V 16

@article{zav20, added-at = {2021-12-03T16:31:29.000+0100}, author = {Zaverkin, V. and K\"astner, J.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/21a7df57a93ccc846297d538bafc469d8/simtech}, doi = {10.1021/acs.jctc.0c00347}, interhash = {a90a7763f9eaeac3225954baa22183c7}, intrahash = {1a7df57a93ccc846297d538bafc469d8}, journal = {J. Chem. Theory Comput.}, keywords = {EXC2075 PN3 PN3-4 PN6 PN6A-1 curated}, pages = {5410-5421}, timestamp = {2023-11-08T16:37:05.000+0100}, title = {Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials}, url = {http://dx.doi.org/10.1021/acs.jctc.0c00347}, volume = 16, year = 2020 }