Machine learning-accelerated density functional theory optimization of PtPd-based high-entropy alloys for hydrogen evolution catalysis

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dc.contributor.authorPatcharaporn Khajondetchairit
dc.contributor.authorSiriwimol Somdee
dc.contributor.authorTinnakorn Saelee
dc.contributor.authorAnnop Ektarawong
dc.contributor.authorBjörn Alling
dc.contributor.authorPiyasan Praserthdam
dc.contributor.authorMeena Rittiruam
dc.contributor.authorSupareak Praserthdam
dc.date.accessioned2026-05-08T19:17:15Z
dc.date.issued2025-11-1
dc.identifier.doi10.1007/s12613-025-3173-z
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15903
dc.publisherInternational Journal of Minerals Metallurgy and Materials
dc.subjectElectrocatalysts for Energy Conversion
dc.subjectHigh Entropy Alloys Studies
dc.subjectMachine Learning in Materials Science
dc.titleMachine learning-accelerated density functional theory optimization of PtPd-based high-entropy alloys for hydrogen evolution catalysis
dc.typeArticle

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