Ceria-based single-atom catalysts present complex electronic structures due to the dynamic electron transfer between the metal atoms and the semiconductor oxide support. Understanding these materials implies retrieving all states in these electronic ensembles, which can be limiting if done via density functional theory. Here, we propose a data-driven approach to obtain a parsimonious model identifying the appearance of dynamic charge transfer for the single atoms (SAs). We first constructed a database of (701) electronic configurations for the group 9–11 metals on CeO₂(100). Feature Selection based on predictive Elastic Net and Random Forest models highlights eight fundamental variables: atomic number, ionization potential, size, and metal coordination, metal–oxygen bond strengths, surface strain, and Coulomb interactions. With these variables a Bayesian algorithm yields an expression for the adsorption energies of SAs in ground and low-lying excited states. Our work paves the way towards understanding electronic structure complexity in metal/oxide interfaces.

由于金属原子和半导体氧化物载体之间的动态电子转移，二氧化铈基单原子催化剂呈现出复杂的电子结构。了解这些材料意味着检索这些电子系综中的所有状态，如果通过密度泛函理论完成，这可能会受到限制。在这里，我们提出了一种数据驱动的方法来获得一个简单的模型，该模型可以识别单个原子 (SA) 的动态电荷转移的外观。_{我们首先为 CeO 2}上的 9-11 族金属构建了一个 (701) 电子配置数据库(100)。基于预测弹性网络和随机森林模型的特征选择突出了八个基本变量：原子序数、电离势、大小和金属配位、金属-氧键强度、表面应变和库仑相互作用。使用这些变量，贝叶斯算法产生了 SA 在地面和低位激发态的吸附能的表达式。我们的工作为理解金属/氧化物界面中的电子结构复杂性铺平了道路。