Nudejima Takuro, Ikabata Yasuhiro, Seino Junji, Yoshikawa Takeshi, Nakai Hiromi
Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.
J Chem Phys. 2019 Jul 14;151(2):024104. doi: 10.1063/1.5100165.
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.
我们提出了一种机器学习相关模型，该模型基于诸如电子密度和相关能量密度等密度变量之间的回归构建。耦合簇单双激发和微扰三激发[CCSD(T)]的相关能量密度是基于网格能量密度分析得出的。使用复合方法估计完全基组（CBS）极限，该方法已被报道用于计算总相关能量。数值检验表明，CCSD(T)/CBS水平的相关能量密度适用于机器学习的响应变量。除了密度泛函理论的交换相关泛函中使用的密度变量外，基于分子轨道分数占据数的哈特里-福克（HF）交换能量密度和电子密度也被用作解释变量。数值评估证实了当前相关模型的准确性和效率。因此，当前的方案，即使用通过小基组HF计算获得的密度变量来学习CCSD(T)/CBS相关能量密度，产生了一个有效的相关模型。
