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 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 range-separated
hybrid 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 dataset, 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 scheme for high-throughput UV/vis absorption spectrum
calculations using the ωPBEh functional from a data-driven perspective.