TY - JOUR
T1 - Semi-local machine-learned kinetic energy density functional demonstrating smooth potential energy curves
AU - Seino, Junji
AU - Kageyama, Ryo
AU - Fujinami, Mikito
AU - Ikabata, Yasuhiro
AU - Nakai, Hiromi
N1 - Funding Information:
Some of the present calculations were performed at the Research Center for Computational Science (RCCS), Okazaki Research Facilities, National Institutes of Natural Sciences (NINS). Author J.S. received support from the PRESTO program “Advanced Materials Informatics through Comprehensive Integration among Theoretical, Experimental, Computational, and Data-Centric Sciences” sponsored by the Japan Science and Technology Agency (JST) . Author M.F. is supported by a Grant-in-Aid for the Japan Society for the Promotion of Science (JSPS) Research Fellows . Author Y.I. received support from the JSPS (KAKENHI Grant Number JP18K14184 ).
Funding Information:
Some of the present calculations were performed at the Research Center for Computational Science (RCCS), Okazaki Research Facilities, National Institutes of Natural Sciences (NINS). Author J.S. received support from the PRESTO program “Advanced Materials Informatics through Comprehensive Integration among Theoretical, Experimental, Computational, and Data-Centric Sciences” sponsored by the Japan Science and Technology Agency (JST). Author M.F. is supported by a Grant-in-Aid for the Japan Society for the Promotion of Science (JSPS) Research Fellows. Author Y.I. received support from the JSPS (KAKENHI Grant Number JP18K14184).
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/11
Y1 - 2019/11
N2 - This letter investigates the accuracy of the semi-local machine-learned kinetic energy density functional (KEDF) for potential energy curves (PECs) in typical small molecules. The present functional is based on a previously developed functional adopting electron densities and their gradients up to the third order as descriptors (Seino et al., 2018). It further introduces new descriptors, namely, the distances between grid points and centers of nuclei, to describe the non-local nature of the KEDF. The numerical results show a reasonable performance of the present model in reproducing the PECs of small molecules with single, double, and triple bonds.
AB - This letter investigates the accuracy of the semi-local machine-learned kinetic energy density functional (KEDF) for potential energy curves (PECs) in typical small molecules. The present functional is based on a previously developed functional adopting electron densities and their gradients up to the third order as descriptors (Seino et al., 2018). It further introduces new descriptors, namely, the distances between grid points and centers of nuclei, to describe the non-local nature of the KEDF. The numerical results show a reasonable performance of the present model in reproducing the PECs of small molecules with single, double, and triple bonds.
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U2 - 10.1016/j.cplett.2019.136732
DO - 10.1016/j.cplett.2019.136732
M3 - Article
AN - SCOPUS:85072201964
SN - 0009-2614
VL - 734
JO - Chemical Physics Letters
JF - Chemical Physics Letters
M1 - 136732
ER -