A hybrid meta on- top functional for multiconfiguration pair- density functional theory

• 成果类型: Article
• 署名作者: Baoa, JieJ.; Zhang, Dayou; Zhang, Shaoting; Gagliardid, Laura; Truhlara, Donald G.
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities; Nankai University; University of Chicago; University of Chicago
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8791
• DOI: 10.1073/pnas.2419413121
• 发表日期: 2024-12-30
• 关键词: 2nd-order perturbation-theory; correlated-participating-orbitals; model chemistries; exchange; accurate; thermochemistry; approximations; expansion; laplacian; energies

摘要

Multiconfiguration pair- density functional theory (MC-PDFT) was proposed a decade ago, but it is still in the early stage of density functional development. MC-PDFT uses functionals that are called on- top functionals; they depend on the density and the on- top pair density. Most MC-PDFT calculations to date have been unoptimized translations of generalized gradient approximations (GGAs) of Kohn-Sham density functional theory (KS-DFT). A hybrid MC-PDFT has also been developed, in which one includes a fraction of the complete active space self- consistent- field wave function energy in the total energy. Meta-GGA functionals, which use kinetic- energy densities in addition to GGA ingredients, have shown higher accuracy than GGAs in KS-DFT, yet the translation of meta-GGAs has not been previously proposed for MC-PDFT. In this paper, we propose a way to include kinetic energy density in a hybrid on- top functional for MC-PDFT, and we optimize the parameters of the resulting functional by training with a database developed as part of the present work that contains a wide variety of systems with diverse characters. The resulting hybrid meta functional is called the MC23 functional. We find that MC23 has improved performance as compared to KS-DFT functionals for both strongly and weakly correlated systems. We recommend MC23 for future MC-PDFT calculations.