Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development  

作　　者：Mohd Nur Ikhmal Salehmin Sieh Kiong Tiong Hassan Mohamed Dallatu Abbas Umar Kai Ling Yu Hwai Chyuan Ong Saifuddin Nomanbhay Swee Su Lim 

机构地区：[1]Institute of Sustainable Energy(ISE),Universiti Tenaga Nasional(UNITEN),Putrajaya Campus,Jalan Ikram-Uniten,43000 Kajang,Selangor,Malaysia [2]Department of Physics,Faculty of Science,Kaduna State University,Tafawa Balewa Way,PMB 2339,Kaduna 800283,Nigeria [3]Department of Engineering,School of Engineering and Technology,Sunway University,Jalan Universiti,Bandar Sunway,47500 Petaling Jaya,Selangor,Malaysia [4]Fuel Cell Institute,Universiti Kebangsaan Malaysia,43600 UKM Bangi,Selangor,Malaysia

出　　处：《Journal of Energy Chemistry》2024年第12期223-252,共30页能源化学（英文版）

基　　金：express their gratitude to the Higher Institution Centre of Excellence (HICoE) fund under the project code (JPT.S(BPKI)2000/016/018/015JId.4(21)/2022002HICOE);Universiti Tenaga Nasional (UNITEN) for funding the research through the (J510050002–IC–6 BOLDREFRESH2025);Akaun Amanah Industri Bekalan Elektrik (AAIBE) Chair of Renewable Energy grant,and NEC Energy Transition Grant (202203003ETG)。

摘　　要：With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.

关 键 词：Machine learning Computational modeling HER catalyst synthesis Hydrogen energy Hydrogen production processes Algorithm development 

分 类 号：TQ116.2[化学工程—无机化工] O643.36[理学—物理化学] TP181[理学—化学]