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

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dc.citedby5
dc.contributor.authorSalehmin M.N.I.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorMohamed H.en_US
dc.contributor.authorUmar D.A.en_US
dc.contributor.authorYu K.L.en_US
dc.contributor.authorOng H.C.en_US
dc.contributor.authorNomanbhay S.en_US
dc.contributor.authorLim S.S.en_US
dc.contributor.authorid55628787200en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57136356100en_US
dc.contributor.authorid57218304981en_US
dc.contributor.authorid57539404500en_US
dc.contributor.authorid55310784800en_US
dc.contributor.authorid57217211137en_US
dc.contributor.authorid36608404200en_US
dc.date.accessioned2025-03-03T07:41:34Z
dc.date.available2025-03-03T07:41:34Z
dc.date.issued2024
dc.description.abstractWith 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 Pressen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.jechem.2024.07.045
dc.identifier.epage252
dc.identifier.scopus2-s2.0-85201408874
dc.identifier.spage223
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85201408874&doi=10.1016%2fj.jechem.2024.07.045&partnerID=40&md5=c6d09cf8cf56e1f47ba0e0b9d5893564
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36205
dc.identifier.volume99
dc.pagecount29
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Chemistry
dc.subjectAlgorithms development
dc.subjectCatalyst synthesis
dc.subjectComputational modelling
dc.subjectHydrogen Energy
dc.subjectHydrogen energy, hydrogen production process
dc.subjectHydrogen evolution reaction catalyst synthesis
dc.subjectHydrogen evolution reactions
dc.subjectHydrogen production process
dc.subjectMachine-learning
dc.subject]+ catalyst
dc.subjectHydrogen evolution reaction
dc.titleNavigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm developmenten_US
dc.typeReviewen_US
dspace.entity.typePublication
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