Deciphering the Interfacial Catalysis of Metal–Oxide Nanocatalysts in CO₂ Hydrogenation through a Machine Learning Approach

Understanding the structure–property relationship is of pivotal importance for the rational design of efficient solid catalysts yet persists as a significant challenge due to the inherent complexity of solid materials. Here, we present an efficient strategy to decipher the interfacial catalysis in metal–oxide nanocatalysts during CO2 hydrogenation through synergistically integrated experimental and theoretical investigations with machine learning (ML) algorithms. Using model Pd/CeO2 catalysts as a proof-of-concept system, the matched experimental and ML-predicted results demonstrate that given sufficient oxygen vacancy concentrations on the support matrix the catalytic performance is primarily governed by the nature of supported metals. Specifically, atomically dispersed Pd species exhibit exceptional intrinsic activity for CO production, which is attributed to its enhanced H spillover and hydrogenation capacities but weakened CO binding affinity. Further ML analysis indicates the sum surface d charge of supported metal as the principal factor governing catalytic performance among four types of typical intrinsic features influencing catalytic hydrogenation processes, which could be directly evaluated by a geometric descriptor of average coordination number of the metal on the support, associated with the metal particle size. This work provides a generalizable theoretical framework for understanding metal–oxide interfaces in CO2 hydrogenation and opens up a novel approach for catalytically fundamental studies to unravel the complex nature of heterogeneous catalysis.理解构效关系（structure–property relationship）对于理性设计高效固体催化剂至关重要，但由于固体材料固有的复杂性，该问题始终是一项重大挑战。在此，我们提出一种高效策略，通过将实验与理论研究同机器学习（machine learning, ML）算法协同整合，解析金属-氧化物纳米催化剂在CO₂加氢过程中的界面催化机制。以模型Pd/CeO₂催化剂作为概念验证体系，匹配的实验结果与机器学习预测结果表明，当载体基体具备充足的氧空位浓度时，催化性能主要由负载金属的固有性质决定。具体而言，原子级分散的Pd物种展现出优异的CO生成本征活性，这归因于其增强的氢溢流和加氢能力，同时CO结合亲和力被削弱。进一步的机器学习分析显示，在四类影响催化加氢过程的典型本征特征中，负载金属的表面总d电荷是主导催化性能的核心因素，该特征可通过负载金属的平均配位数这一几何描述符直接评估，而平均配位数与金属颗粒尺寸相关。本研究为理解CO₂加氢过程中的金属-氧化物界面提供了可推广的理论框架，并为揭示多相催化复杂本质的催化基础研究开辟了全新途径。