Machine Learning-Assisted Prediction of Ground- and Excited-State Redox Potentials in Iridium(III) Photocatalysts

This study introduces a data-driven framework that combines DFT calculations with machine learning to facilitate accurate and scalable predictions of ground- and excited-state redox potentials for iridium(III) photocatalysts. We first constructed independent models to identify key geometric and electronic descriptors governing redox behavior. Shapley additive explanations-based analyses revealed clear structure-activity relationships, offering mechanistic insights and rational guidance for tuning redox potentials. Based on these insights, we developed unified multi-output models-Model G for ground-state and Model E for excited-state redox potentials-to enable rapid, cost-effective, and high-throughput predictions. By modeling oxidation and reduction processes within a shared descriptor space, we can reduce computational overhead while maintaining high predictive accuracy. To assess cross-metal generalizability, residual transfer learning was applied to osmium (Os) photocatalysts. Using feature-similar complexes, the resulting transfer models (G-T, E-T) achieved performance comparable to Os-only baselines, demonstrating efficient few-shot cross-metal transfer. Collectively, this study establishes an interpretable and transferable machine-learning framework for photocatalyst discovery. This framework provides a foundation for large-scale screening and rational design across diverse transition-metal platforms, accelerating advancements in photoredox catalysis, solar fuel production, and broader sustainable energy technologies.