Prospects of AI in advancing green hydrogen production: From materials to applications
Date
2025
Authors
Zhang, D.
Pan, W.
Lu, H.
Wang, Z.
Gupta, B.
Oo, A.M.T.
Wang, L.
Reuter, K.
Li, H.
Jiang, Y.
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Journal article
Citation
Applied Physics Reviews, 2025; 12(3):031335-1-031335-23
Statement of Responsibility
Doudou Zhang, Weisheng Pan, Haijiao Lu, Zhiliang Wang, Bikesh Gupta, Aman Maung Than Oo, Lianzhou Wang, Karsten Reuter, Haobo Li, Yijiao Jiang, Siva Karuturi
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Abstract
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.
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© 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial- NoDerivs 4.0 International (CC BY-NC-ND) license (https://creativecommons.org/licenses/by-nc-nd/4.0/). https://doi.org/10.1063/5.0281416