TY - JOUR

T1 - Machine-learned electron correlation model based on correlation energy density at complete basis set limit

AU - Nudejima, Takuro

AU - Ikabata, Yasuhiro

AU - Seino, Junji

AU - Yoshikawa, Takeshi

AU - Nakai, Hiromi

N1 - Funding Information:
Some of the present calculations were performed at the Research Center for Computational Science (RCCS), Okazaki Research Facilities, Institute of Natural Sciences (NINS). This study was supported by KAKENHI (Grant No. JP18K14184) from the Japan Society for the Promotion of Science (JSPS).
Publisher Copyright:
© 2019 Author(s).

PY - 2019/7/14

Y1 - 2019/7/14

N2 - We propose a machine-learned correlation model that is built using the regression between density variables such as electron density and correlation energy density. The correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total correlation energy. The numerical examination revealed that the correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present correlation model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient correlation model.

AB - We propose a machine-learned correlation model that is built using the regression between density variables such as electron density and correlation energy density. The correlation energy density of coupled cluster singles, doubles, and perturbative triples [CCSD(T)] is derived based on grid-based energy density analysis. The complete basis set (CBS) limit is estimated using the composite method, which has been reported to calculate the total correlation energy. The numerical examination revealed that the correlation energy density of the CCSD(T)/CBS level is appropriate for the response variable of machine learning. In addition to the density variables used in the exchange-correlation functionals of the density functional theory, the Hartree-Fock (HF) exchange energy density and electron density based on the fractional occupation number of molecular orbitals were employed as explanatory variables. Numerical assessments confirmed the accuracy and efficiency of the present correlation model. Consequently, the present protocol, namely, learning the CCSD(T)/CBS correlation energy density using density variables obtained by the HF calculation with a small basis set, yields an efficient correlation model.

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U2 - 10.1063/1.5100165

DO - 10.1063/1.5100165

M3 - Article

C2 - 31301697

AN - SCOPUS:85068784481

SN - 0021-9606

VL - 151

JO - Journal of Chemical Physics

JF - Journal of Chemical Physics

IS - 2

M1 - 024104

ER -