Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Siwen Wang, Hemanth Somarajan Pillai and Hongliang Xin ()
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Siwen Wang: Virginia Polytechnic Institute and State University
Hemanth Somarajan Pillai: Virginia Polytechnic Institute and State University
Hongliang Xin: Virginia Polytechnic Institute and State University

Nature Communications, 2020, vol. 11, issue 1, 1-7

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-19524-z

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