Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution Phase Absorption Energy Prediction

Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ-tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 997 molecules with experimental solution-phase absorption spectra. Three γ-tuning methods, the gas phase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our data set, and we explained the origin of this phenomenon. TDDFT calculations with PVγT and SVγT tuned γ-values achieve superior performance compared to those using GPγT-tuned or default γ. Furthermore, we found that the smaller γ values from SVγT captured the expected 1/εr asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations. These results show that SVγT is the best among those three schemes for UV/vis absorption calculations with the ωPBEh functional from a data-driven perspective.