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.

基于表面化学和催化作用中的d波段反应性理论，我们开发了一种贝叶斯学习方法来探测原子定制金属位点的化学吸附过程。对于具有代表性的物质，例如* O和* OH，经过过渡金属从头算吸附性能训练的贝叶斯模型预测了在各种金属间化合物和近表面合金范围内的位反应性，同时自然地提供了后验采样的不确定性量化。更重要的是，该概念框架阐明了在吸附位为d时化学键的轨道性质。态特征从块状半椭圆形带到自由原子状的离散能级，弥合了电子描述符的复杂性，可用于预测新型催化材料。