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 C 2 products is a significant challenge for research on the CO 2 reduction reaction (CO 2 RR). Although significant efforts have been devoted to this endeavor, the understanding of C 2 pathways and the influences of metal selection and active sites on the CO 2 RR still remain unclear. Herein, this work presents a comprehensive theoretical exploration of full C 2 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 C 2 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 CH 3 CH 2 OH, CH 3 COOH, CH 3 CHO, and CH 2 OHCH 2 OH while the formation of C 2 H 4 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 CO 2 RR mechanisms, which is expected to aid the development of novel atomic catalysts for efficient C 2 product generation.