Machine learning in photocatalysis: accelerating design, understanding, and environmental applications

Abstract

Photocatalysis is a transformative strategy with wide applications in environmental remediation, energy conversion, and chemical synthesis. However, optimizing photocatalysts is challenging due to the complex interplay of factors like material composition, light absorption, and surface reactivity. Traditional trial-and-error approaches are time-consuming and resourceintensive, often requiring extensive experimentation under varied conditions. Machine learning (ML) has recently emerged as a powerful tool to accelerate photocatalyst discovery and optimization. By analyzing large datasets, ML algorithms can predict material properties, identify optimal reaction conditions, and reduce the need for exhaustive experimentation. This data-driven approach enables faster exploration of complex chemical spaces and reaction environments. This review focuses on recent advancements in integrating ML into photocatalysis, emphasizing its role in catalyst design and environmental applications. It also addresses key challenges such as data quality and model interpretability while highlighting future research directions to fully harness the potential of ML in photocatalytic systems.