Artificial intelligence-driven advances in photocatalytic hydrogen production

Abstract

This perspective provides an overview of recent studies on the use of photocatalysis for hydrogen production, with a particular focus on water splitting. It examines the developments in this field that have been facilitated by artificial intelligence tools, especially machine learning algorithms. The photocatalytic generation of hydrogen has been the subject of extensive study in recent years, as the necessity for higher efficiency and hydrogen production rates represents a crucial step in the development of this technology for its mass deployment. The known difficulties of these systems pertain to the complexities associated with the photocatalysts, including the effect of reactants, the synthesis process, and their efficiency, particularly in harvesting sunlight. Moreover, the design of the reactor is a challenging undertaking, particularly in light of the dynamic behavior and the interaction between photons, solutions, photocatalysts, and co-catalysts that must be considered in the photocatalytic production of hydrogen. Research on this subject must consider the use of green materials and processes for synthesis, avoid extensive experimentation to reduce the carbon footprint, and seek efficient and less resource-intensive computational resources. To surmount the challenges inherent to the synthesis and development process, while simultaneously enabling the establishment of structure–performance relationships for knowledge acquisition, high-performing computational methods are key. This article concludes by potential avenues for improvement by highlighting strategies that have been successfully implemented in other related fields and could be beneficial for this field.