Double‐Dependence Correlations in Graphdiyne‐Supported Atomic Catalysts to Promote CO₂RR toward the Generation of C₂ Products

Abstract Developing efficient and stable atomic catalysts (ACs) to achieve high faradaic efficiency and selectivity of C₂ products is a significant challenge for research on the CO₂ reduction reaction (CO₂RR). Although significant efforts have been devoted to this endeavor, the understanding of C₂ pathways and the influences of metal selection and active sites on the CO₂RR still remain unclear. Herein, this work presents a comprehensive theoretical exploration of full C₂ reaction pathway mapping based on graphdiyne (GDY)‐supported ACs with considerations of different metals and active sites for the first time. This work demonstrates the integrated large‐small cycle mechanism to explain the challenges for C₂ product generation, where the double‐dependence correlation with metal and active sites is identified. A series of novel transition metal based GDY‐SACs, GDY‐Pr, and GDY‐Pm SACs are demonstrated as promising electrocatalysts to generate CH₃CH₂OH, CH₃COOH, CH₃CHO, and CH₂OHCH₂OH while the formation of C₂H₄ is very difficult for all GDY‐ACs. First‐principle machine learning predicts the reaction energy for the first time, where the adsorptions of the intermediates are critical to achieving accurate predictions of multi‐carbon products. This work supplies an advanced understanding of the complicated CO₂RR mechanisms, which is expected to aid the development of novel atomic catalysts for efficient C₂ product generation.