Wang, SiwenPillai, Hemanth SomarajanXin, Hongliang2020-12-042020-12-042020http://hdl.handle.net/10919/101007Building 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-USCreative Commons Attribution 4.0 InternationalArticle - Refereedhttps://doi.org/10.1038/s41467-020-19524-z11