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

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.