COF26: A new on-top functional for multiconfiguration pair-density functional theory
Authors: Yuhao Chen, Donald G. Truhlar, Xiao He
Organizations: Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China · Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455-0431, USA · Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing 401120, China · New York University–East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai, 200062, China
Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient and accurate framework for computing electronic energies in strongly correlated molecular systems, with the quality of the on-top functional being a key determinant of its predictive accuracy. Here, we introduce MMCDDB26, a rigorously curated benchmark database comprising 76 datasets and 1,495 reactions. We further propose a constrained, large-language-model-assisted optimization workflow for the development and assessment of MC-PDFT functionals. Using this workflow, we optimized the parameters of the MC23/MC25 functionals on MMCDDB26 to obtain MC26. Compared with earlier functionals of the same class, MC26 improves the accuracy on the training set and achieves a more balanced overall performance. In addition, we developed the hybrid meta-functional COF26. We find that COF26 delivers superior performance for both strongly and weakly correlated systems, and therefore recommend COF26 for future MC-PDFT calculations.