Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning

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dc.contributor.authorLi, X.
dc.contributor.authorShi, J.Q.
dc.contributor.authorPage, A.J.
dc.date.issued2023
dc.descriptionPublished: October 27, 2023
dc.description.abstractDespite today’s commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials.
dc.description.statementofresponsibilityXinyu Li, Javen Qinfeng Shi, and Alister J. Page
dc.identifier.citationNano Letters: a journal dedicated to nanoscience and nanotechnology, 2023; 23(21):9796-9802
dc.identifier.doi10.1021/acs.nanolett.3c02496
dc.identifier.issn1530-6984
dc.identifier.issn1530-6992
dc.identifier.orcidLi, X. [0000-0003-1332-9203]
dc.identifier.orcidShi, J.Q. [0000-0002-9126-2107]
dc.identifier.urihttps://hdl.handle.net/2440/140427
dc.language.isoen
dc.publisherAmerican Chemical Society
dc.relation.granthttp://purl.org/au-research/grants/arc/DP210100873
dc.rights© 2023 The Authors. Published by American Chemical Society. This article is licensed under CC-BY-NC-ND 4.0
dc.source.urihttps://doi.org/10.1021/acs.nanolett.3c02496
dc.subjectGraphene; catalyst; alloy; chemical vapor deposition; machine learning
dc.titleDiscovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning
dc.typeJournal article
pubs.publication-statusPublished

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