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 establ...

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Номзүйн дэлгэрэнгүй
Үндсэн зохиолч:	Fangning Ren (7831376) (author)
Бусад зохиолчид:	Pinyuan Li (14060412) (author), Xu Chen (432759) (author), Lechen Dong (20634095) (author), Fang Liu (13271) (author)
Хэвлэсэн:	2025
Нөхцлүүд:	Biophysics Neuroscience Space Science vis absorption spectra state property predictions polarizable continuum model lesser computational cost >) asymptotic behavior computationally economical approach phase ct excitations strict vertical γ partial vertical γ slightly outperform srsh mediated tuning procedure separated hybrid functionals separated hybrid combined optimal tuning scheme separated hybrid tuning procedure optimal γ best scheme phase γ tuning methods phase uv pcm approach three γ smaller γ default γ best γ ωpbeh functional well established throughput uv throughput excited study created solvent effect significantly smaller results show r </ findings revealed driven recommendation driven perspective additional benchmarks accurate prediction
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Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction

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