Green hydrogen (H2) production via water electrolysis offers a sustainable pathway to decarbonize various industries, driven by its potential to replace fossil fuels and achieve carbon neutrality. Traditional approaches to catalyst development for H2 production, such as electrochemical catalysis (EC), photoelectrochemical catalysis (PEC), and photocatalysis (PC), have predominantly relied on empirical, trial-and-error methods. While significant progress has been made, these methods are time-consuming, costly, and limited by the complexity of multicomponent catalysts and reaction systems. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools for accelerating catalyst discovery and optimization. AI-driven approaches enable high-throughput screening of materials, prediction of catalyst performance, and real-time reaction mechanisms, offering a more efficient alternative to conventional experimentation. This review examines the current state of catalyst development for green H2 production, highlighting the role of AI in optimizing hydrogen evolution and oxygen evolution reactions (HER/OER). We explore advancements in electrochemical, photoelectrochemical, and photocatalytic systems, emphasizing the potential of AI to revolutionize the field. By integrating AI with experimental techniques, researchers are poised to achieve breakthroughs in efficiency, scalability, and cost-effectiveness, accelerating the transition toward a sustainable, hydrogen-powered future.
Zhang et al. (Mon,) studied this question.
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