Machine learning-enhanced photocatalysis for environmental sustainability: Integration and applications

Abstract

Photocatalysis, an essential technology for sustainable fuel production and environmental remediation often encounters challenges due to the complexity and vastness of potential catalyst materials. Machine learning (ML), a branch of artificial intelligence, offers transformative potential to accelerate catalyst exploration by leveraging data-driven models to predict and optimize photocatalysts. Recent developments in artificial intelligence and data science hold enormous promise for accelerating the discovery of new materials in environmental science and photocatalysis technologies. This review delves into the integration of ML in photocatalysis, focusing on its role in improving light absorption, charge separation, and photoreactor design. In addition, the content emphasizes the importance of ML in photocatalytic applications such as drug degradation, water splitting, and organic dye degradation. ML techniques can enhance these applications by predicting the behavior of photocatalysts, improving their efficiency, and accelerating the discovery of new materials. With the help of ML, advanced next-generation catalysts can be developed, and the review serves as a guide for the scientific community regarding the use of ML in photocatalysis and environmental applications.