Quantum-informed regression: local hybrid functionals enhance machine learning prediction of dye absorption maxima

Abstract Prediction of optical properties of molecules still remains a challenge in the design of dye-sensitized solar cells. By linking quantum-chemical accuracy with the flexibility of machine learning (ML), this work offers a glimpse into how data-driven approaches can reshape the way we design next-generation photovoltaic materials. In this work, we combine density functional theory (DFT) and time-dependent DFT (TDDFT) with ML models to assess the predictive performance of maximum absorption