期刊
CHEMICAL PHYSICS LETTERS
卷 734, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.cplett.2019.136732
关键词
-
资金
- Japan Science and Technology Agency (JST)
- JSPS [JP18K14184]
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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