Bayesian learning of chemisorption for bridging the complexity of electronic descriptors
| dc.contributor.author | Wang, Siwen | en |
| dc.contributor.author | Pillai, Hemanth Somarajan | en |
| dc.contributor.author | Xin, Hongliang | en |
| dc.date.accessioned | 2020-12-04T13:51:21Z | en |
| dc.date.available | 2020-12-04T13:51:21Z | en |
| dc.date.issued | 2020 | en |
| dc.description.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. | en |
| dc.description.sponsorship | S.W., H.S.P., and H.X. acknowledge the financial support from the NSF CAREER program (CBET-1845531). | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | https://doi.org/10.1038/s41467-020-19524-z | en |
| dc.identifier.uri | http://hdl.handle.net/10919/101007 | en |
| dc.identifier.volume | 11 | en |
| dc.language.iso | en | en |
| dc.publisher | Springer Nature | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Bayesian learning of chemisorption for bridging the complexity of electronic descriptors | en |
| dc.title.serial | Nature Communications | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |