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

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

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With the projected global surge in hydrogen demand,driven by increasing applications and the impera-tive 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 tech-niques 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 sus-tainable 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.

Machine learningComputational modelingHER catalyst synthesisHydrogen energy,Hydrogen production processesAlgorithm development

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

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Institute of Sustainable Energy(ISE),Universiti Tenaga Nasional(UNITEN),Putrajaya Campus,Jalan Ikram-Uniten,43000 Kajang,Selangor,Malaysia

Department of Physics,Faculty of Science,Kaduna State University,Tafawa Balewa Way,PMB 2339,Kaduna 800283,Nigeria

Department of Engineering,School of Engineering and Technology,Sunway University,Jalan Universiti,Bandar Sunway,47500 Petaling Jaya,Selangor,Malaysia

Fuel Cell Institute,Universiti Kebangsaan Malaysia,43600 UKM Bangi,Selangor,Malaysia

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2024

10.1016/j.jechem.2024.07.045

能源化学
中国科学院大连化学物理研究所 中国科学院成都有机化学研究所

能源化学

CSTPCDEI
影响因子：0.654
ISSN：2095-4956
年,卷(期)：2024.99(12)