%0 Journal Article %1 zaverkin2020gaussian %A Zaverkin, Viktor %A Kästner, Johannes %D 2020 %I American Chemical Society %J Journal of Chemical Theory and Computation %K %N 8 %P 5410-5421 %R 10.1021/acs.jctc.0c00347 %T Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials %V 16

@article{zaverkin2020gaussian, added-at = {2023-08-31T16:22:54.000+0200}, affiliation = {Kastner, J (Corresponding Author), Univ Stuttgart, Inst Theoret Chem, D-70569 Stuttgart, Germany. Zaverkin, V; Kastner, J., Univ Stuttgart, Inst Theoret Chem, D-70569 Stuttgart, Germany.}, author = {Zaverkin, Viktor and Kästner, Johannes}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22e4501cddb0c728672ca0aaa8efa0a9d/puma-wartung}, doi = {10.1021/acs.jctc.0c00347}, interhash = {89c0c895c5d976af269a91f5b342073c}, intrahash = {2e4501cddb0c728672ca0aaa8efa0a9d}, issn = {{1549-9618} and {1549-9626}}, journal = {Journal of Chemical Theory and Computation}, keywords = {}, language = {eng}, number = 8, orcid-numbers = {Kastner, Johannes/0000-0001-6178-7669 Zaverkin, Viktor/0000-0001-9940-8548}, pages = {5410-5421}, publisher = {American Chemical Society}, research-areas = {Chemistry; Physics}, timestamp = {2023-08-31T14:22:54.000+0200}, title = {Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials}, unique-id = {ISI:000562139200054}, volume = 16, year = 2020 }