%0 Journal Article %1 zav22 %A Zaverkin, Viktor %A Netz, Julia %A Zills, Fabian %A Köhn, Andreas %A Kästner, Johannes %D 2022 %J J. Chem. Theory Comput. %K EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische %P 1-12 %R 10.1021/acs.jctc.1c00853 %T Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments %U http://dx.doi.org/10.1021/acs.jctc.1c00853 %V 18

@article{zav22, added-at = {2022-01-14T11:50:46.000+0100}, author = {Zaverkin, Viktor and Netz, Julia and Zills, Fabian and Köhn, Andreas and K\"astner, Johannes}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2d85395aa51c2ef610be16a94ebb002f5/danielborn}, doi = {10.1021/acs.jctc.1c00853}, interhash = {8eb27e841bbc9c87f7553410f92e8039}, intrahash = {d85395aa51c2ef610be16a94ebb002f5}, journal = {J. Chem. Theory Comput.}, keywords = {EXC2075 chemie kaestner kästner pn6 stuttgart theochem theoretische}, pages = {1-12}, timestamp = {2022-01-14T10:50:46.000+0100}, title = {Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments}, url = {http://dx.doi.org/10.1021/acs.jctc.1c00853}, volume = 18, year = 2022 }