Direct Machine Learning Predictions of C3 Pathways

Abstract The C 3 pathways of CO 2 reduction reaction (CO 2 RR) lead to the generation of high‐value‐added chemicals for broad industrial applications, which are still challenging for current electrocatalysis. Only limited electrocatalysts have been reported with the ability to achieve C 3 products while the corresponding reaction mechanisms are highly unclear. To overcome such challenges, the first‐principle machine learning (FPML) technique on graphdiyne‐based atomic catalysts (GDY‐ACs) is introduced to directly predict the reaction trends for the key C─C─C coupling processes and the conversions to different C 3 products for the first time. All the prediction results are obtained only based on the learning dataset constructed by density functional theory (DFT) calculation results for C 1 and C 2 pathways, offering an efficient approach to screen promising electrocatalyst candidates for varied C 3 products. More importantly, the ML predictions not only reveal the significant role of the neighboring effect and the small–large integrated cycle mechanisms but also supply important insights into the C─C─C coupling processes for understanding the competitive reactions among C 1 to C 3 pathways. This work has offered an advanced breakthrough for the complicated CO 2 RR processes, accelerating the future design of novel ACs for C 3 products with high efficiency and selectivity.