Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

dc.contributor.authorWang, Siwenen
dc.contributor.authorPillai, Hemanth Somarajanen
dc.contributor.authorXin, Hongliangen
dc.date.accessioned2020-12-04T13:51:21Zen
dc.date.available2020-12-04T13:51:21Zen
dc.date.issued2020en
dc.description.abstractBuilding upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.en
dc.description.sponsorshipS.W., H.S.P., and H.X. acknowledge the financial support from the NSF CAREER program (CBET-1845531).en
dc.identifier.doihttps://doi.org/10.1038/s41467-020-19524-zen
dc.identifier.urihttp://hdl.handle.net/10919/101007en
dc.identifier.volume11en
dc.language.isoen_USen
dc.publisherSpringer Natureen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleBayesian learning of chemisorption for bridging the complexity of electronic descriptorsen
dc.title.serialNature Communicationsen
dc.typeArticle - Refereeden
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