Accelerated Computer-Aided Screening of Optical Materials: Investigating the Potential of Δ-SCF Methods to Predict Emission Maxima of Large Dye Molecules

Abstract

Accurate simulation of electronic excited states of large chromophores is often difficult due to the computationally expensive nature of existing methods. Common approximations such as fragmentation methods that are routinely applied to ground-state calculations of large molecules are not easily applicable to excited states due to the delocalized nature of electronic excitations in most practical chromophores. Thus, special techniques specific to excited states are needed. Δ-SCF methods are one such approximation that treats excited states in a manner analogous to that for ground-state calculations, accelerating the simulation of excited states. In this work, we employed the popular initial maximum overlap method (IMOM) to avoid the variational collapse of the electronic excited state orbitals to the ground state. We demonstrate that it is possible to obtain emission energies from the first singlet (S₁) excited state of many thousands of dye molecules without any external intervention. Spin correction was found to be necessary to obtain accurate excitation and emission energies. Using thousands of dye-like chromophores and various solvents (12,318 combinations), we show that the spin-corrected initial maximum overlap method accurately predicts emission maxima with a mean absolute error of only 0.27 eV. We further improved the predictive accuracy using linear fit-based corrections from individual dye classes to achieve an impressive performance of 0.17 eV. Additionally, we demonstrate that IMOM spin density can be used to identify the dye class of chromophores, enabling improved prediction accuracy for complex dye molecules, such as dyads (chromophores containing moieties from two different dye classes). Finally, the convergence behavior of IMOM excited state SCF calculations is analyzed briefly to identify the chemical space, where IMOM is more likely to fail.