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

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 M...

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Main Authors: Salehmin M.N.I., Tiong S.K., Mohamed H., Umar D.A., Yu K.L., Ong H.C., Nomanbhay S., Lim S.S.
Other Authors: 55628787200
Format: Review
Published: Elsevier B.V. 2025
Subjects:
Algorithms development
Catalyst synthesis
Computational modelling
Hydrogen Energy
Hydrogen energy, hydrogen production process
Hydrogen evolution reaction catalyst synthesis
Hydrogen evolution reactions
Hydrogen production process
Machine-learning
]+ catalyst
Hydrogen evolution reaction
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id my.uniten.dspace-36205
record_format dspace
spelling my.uniten.dspace-362052025-03-03T15:41:34Z Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development Salehmin M.N.I. Tiong S.K. Mohamed H. Umar D.A. Yu K.L. Ong H.C. Nomanbhay S. Lim S.S. 55628787200 15128307800 57136356100 57218304981 57539404500 55310784800 57217211137 36608404200 Algorithms development Catalyst synthesis Computational modelling Hydrogen Energy Hydrogen energy, hydrogen production process Hydrogen evolution reaction catalyst synthesis Hydrogen evolution reactions Hydrogen production process Machine-learning ]+ catalyst Hydrogen evolution reaction 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. ? 2024 Science Press Final 2025-03-03T07:41:34Z 2025-03-03T07:41:34Z 2024 Review 10.1016/j.jechem.2024.07.045 2-s2.0-85201408874 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201408874&doi=10.1016%2fj.jechem.2024.07.045&partnerID=40&md5=c6d09cf8cf56e1f47ba0e0b9d5893564 https://irepository.uniten.edu.my/handle/123456789/36205 99 223 252 Elsevier B.V. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Algorithms development
Catalyst synthesis
Computational modelling
Hydrogen Energy
Hydrogen energy, hydrogen production process
Hydrogen evolution reaction catalyst synthesis
Hydrogen evolution reactions
Hydrogen production process
Machine-learning
]+ catalyst
Hydrogen evolution reaction
spellingShingle Algorithms development
Catalyst synthesis
Computational modelling
Hydrogen Energy
Hydrogen energy, hydrogen production process
Hydrogen evolution reaction catalyst synthesis
Hydrogen evolution reactions
Hydrogen production process
Machine-learning
]+ catalyst
Hydrogen evolution reaction
Salehmin M.N.I.
Tiong S.K.
Mohamed H.
Umar D.A.
Yu K.L.
Ong H.C.
Nomanbhay S.
Lim S.S.
Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
description 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. ? 2024 Science Press
author2 55628787200
author_facet 55628787200
Salehmin M.N.I.
Tiong S.K.
Mohamed H.
Umar D.A.
Yu K.L.
Ong H.C.
Nomanbhay S.
Lim S.S.
format Review
author Salehmin M.N.I.
Tiong S.K.
Mohamed H.
Umar D.A.
Yu K.L.
Ong H.C.
Nomanbhay S.
Lim S.S.
author_sort Salehmin M.N.I.
title Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
title_short Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
title_full Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
title_fullStr Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
title_full_unstemmed Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development
title_sort navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: beyond algorithm development
publisher Elsevier B.V.
publishDate 2025
_version_ 1825816264853946368
score 13.244109

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