TY - JOUR

T1 - Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential

AU - Fujinami, Mikito

AU - Kageyama, Ryo

AU - Seino, Junji

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). This study was supported in part by the “ Elements Strategy Initiative for Catalysts & Batteries (ESICB)” project, Grant Number JPMXP0112101003 , supported by the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan. Author M.F. acknowledges to a Grant-in-Aid for the Japan Society for the Promotion of Science (JSPS) Research Fellows. 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 Y.I. received support from the JSPS (KAKENHI Grant Number JP18K14184 ).

PY - 2020/6

Y1 - 2020/6

N2 - This letter proposes a scheme of orbital-free density functional theory (OF-DFT) calculation for optimizing electron density based on a semi-local machine-learned (ML) kinetic energy density functional (KEDF). The electron density, which is represented by the square of the linear combination of Gaussian functions, is optimized using derivatives of electronic energy including ML kinetic potential (KP). The numerical assessments confirmed the accuracy of optimized density and total energy for atoms and small molecules obtained by the present scheme based on ML-KEDF and ML-KP.

AB - This letter proposes a scheme of orbital-free density functional theory (OF-DFT) calculation for optimizing electron density based on a semi-local machine-learned (ML) kinetic energy density functional (KEDF). The electron density, which is represented by the square of the linear combination of Gaussian functions, is optimized using derivatives of electronic energy including ML kinetic potential (KP). The numerical assessments confirmed the accuracy of optimized density and total energy for atoms and small molecules obtained by the present scheme based on ML-KEDF and ML-KP.

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U2 - 10.1016/j.cplett.2020.137358

DO - 10.1016/j.cplett.2020.137358

M3 - Article

AN - SCOPUS:85082101479

VL - 748

JO - Chemical Physics Letters

JF - Chemical Physics Letters

SN - 0009-2614

M1 - 137358

ER -