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

Abstract

Abstract 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.