Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/vis 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 997 937 molecules with experimental solution-phase UV/vis 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. Additional benchmarks are done for the optimally tuned screened RSH combined with the PCM approach (SRSH-PCM), and the solvation-mediated tuning procedure (sol-med-OT). 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 and default global Fock exchange fraction achieve superior performance compared to those using GPγT-tuned or default γ, and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. 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, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best among those three schemes for high-throughput UV/vis absorption spectrum calculations using with the ωPBEh functional from a data-driven perspective.