Single-atom catalysts offer high reactivity and selectivity while maximizing utilization of the expensive active metal component. However, they are susceptible to sintering, where single metal atoms agglomerate into thermodynamically stable clusters. Tuning the binding strength between single metal atoms and oxide supports is essential to prevent sintering. We apply density functional theory, together with a statistical learning approach based on least absolute shrinkage and selection operator regression, to identify property descriptors that predict interaction strengths between single metal atoms and oxide supports. Here, we show that interfacial binding is correlated with readily available physical properties of both the supported metal, such as oxophilicity measured by oxide formation energy, and the support, such as reducibility measured by oxygen vacancy formation energy. These properties can be used to empirically screen interaction strengths between metal–support pairs, thus aiding the design of single-atom catalysts that are robust against sintering.

单原子催化剂提供了高反应性和选择性，同时最大限度地利用了昂贵的活性金属组分。但是，它们易于烧结，其中单个金属原子会聚集成热力学稳定的簇。调整单个金属原子与氧化物载体之间的结合强度对于防止烧结至关重要。我们将密度泛函理论与基于最小绝对收缩和选择算子回归的统计学习方法一起使用，以识别可预测单个金属原子与氧化物载体之间相互作用强度的特性描述符。在这里，我们表明界面结合与负载金属的容易获得的物理特性（例如通过氧化物形成能测量的亲氧性）和载体相关，如通过氧空位形成能测量的还原性。这些特性可用于凭经验筛选金属-载体对之间的相互作用强度，从而有助于设计抗烧结的单原子催化剂。