Catalysis AI Agent Guides Discovering the Universal Design Principle of Cu-Based Single-Atom Alloy Catalysts for CO₂...

Copper (Cu)-based single-atom alloys (SAAs) represent a promising strategy for optimizing the electroreduction of CO₂ (CO₂R) to multi-carbon products (C₂₊). However, the diverse enhancement degrees of C₂₊ selectivity brought about by various dopants have not yet been rationalized, which lead to the absence of guidelines for further designing desired Cu-based SAAs. Herein, guided by the Catalysis AI Agent developed based on large-scale data + large language model, as well as the Digital Catalysis Platform (the DigCat experimental database), we performed first-principles calculations to evaluate C₂₊ products selectivity trends through identifying the energy barrier of rate-determining step (RDS) among diverse C-C coupling pathways. With first-principles results fed back, Catalysis AI Agent reveals that the element classification in the periodic table of guest metal dopant is essential for establishing robust structure-selectivity correlations among Cu-based SAAs. A structural descriptor (φ) is developed and helps to establish a strong correlation among the electronic-scale structural features, the adsorption strength of C-C coupling precursors, and the macroscopic C₂₊ products selectivity. A universal design principle based on φ for Cu-based SAAs enables the rapid and qualitative evaluation of C₂₊ selectivity, which is fully supported by most of the experimental references and our experimental verification.