Koopmans’ Theorem-Compliant Long-range Corrected (KTLC) Density Functional Mediated by Black-box Optimization and Data-Driven Prediction for Organic Molecules

Abstract

Density functional theory (DFT) is a significant computational tool that has sub- stantially influenced chemistry, physics, and materials science. DFT necessitates pa- rameterized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule. Herein, we optimize the parameters of range-separated functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO) to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness of BO in optimizing functional parameters. Particularly, Koopmans’ theorem-compliant LC-BLYP (KTLC- BLYP) shows results comparable to the experimental UV-absorption values. Further- more, we prepared an optimized parameter dataset of KTLC-BLYP for over 3,000 molecules through BO for satisfying Koopmans’ theorem. We have developed a ma- chine learning model on this dataset to predict the parameters of LC-BLYP functional for a given molecule. The prediction model automatically predicts appropriate param- eters for a given molecule and calculates the corresponding values. The fashion in this paper would be useful to develop new functionals and update the previously developed functionals.