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Bayesian learning of chemisorption for bridging the complexity of electronic descriptors.

ABSTRACT: Building 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.

SUBMITTER: Wang S

PROVIDER: S-EPMC7705683 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Bayesian learning of chemisorption for bridging the complexity of electronic descriptors.

Wang Siwen S Pillai Hemanth Somarajan HS Xin Hongliang H

Nature communications 20201130 1

Building 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, ...[more]

PMID: 33257689

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