Developing high‐performance photocatalysts is essential for sustainable solar fuel production, offering a pathway to mitigate global warming and address pressing environmental challenges. With the rapid growth of data in materials science, machine learning (ML) has emerged as a transformative tool capable of revealing intricate structure–property relationships that are often inaccessible through conventional methods. This capability significantly accelerates the screening and rational design of photocatalysts with enhanced activity and durability. In this review, we provide a critical overview of recent advances in supervised ML applied to photocatalysis, with a particular focus on optimizing photocatalyst properties and designing materials for solar‐driven water splitting and CO 2 reduction. We first introduce the most commonly employed supervised ML techniques relevant to photocatalyst discovery. Key studies are then emphasized to illustrate how ML has been applied to fine‐tune material properties and guide the development of efficient photocatalysts for targeted solar‐to‐chemical conversion. In the final section, we highlight persistent challenges and future directions in leveraging ML to accelerate photocatalyst innovation. By focusing on domain‐specific applications, this review offers a practical framework for researchers aiming to integrate ML into the rational design of photocatalysts.
Regonia et al. (Sun,) studied this question.