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

Density functional theory (DFT) is a significant computational
tool that has substantially influenced chemistry, physics, and materials
science. DFT necessitates parametrized 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 the BO in optimizing functional parameters. Particularly, Koopmans’
theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to
the experimental UV-absorption values. Furthermore, we prepared an
optimized parameter dataset of KTLC-BLYP for over 3000 molecules through
BO for satisfying Koopmans’ theorem. We have developed a machine
learning model on this dataset to predict the parameters of the LC-BLYP
functional for a given molecule. The prediction model automatically
predicts the appropriate parameters for a given molecule and calculates
the corresponding values. The approach in this paper would be useful
to develop new functionals and to update the previously developed
functionals.