%0 Journal Article %1 zav20 %A Zaverkin, V. %A Kästner, J. %D 2020 %J Journal of Chemical Theory and Computation %K EXC2075 PN3 PN3-4 PN6 PN6A-1 selected %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 = {2024-03-26T11:56:32.000+0100}, author = {Zaverkin, V. and K\"astner, J.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/258ec9e653315a43276d606fd7f50dcbb/exc2075}, doi = {10.1021/acs.jctc.0c00347}, interhash = {a90a7763f9eaeac3225954baa22183c7}, intrahash = {58ec9e653315a43276d606fd7f50dcbb}, journal = {Journal of Chemical Theory and Computation}, keywords = {EXC2075 PN3 PN3-4 PN6 PN6A-1 selected}, pages = {5410-5421}, timestamp = {2024-03-27T15:18:19.000+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 }