Direct Machine Learning Predictions of C3 Pathways

Abstract

The C3 pathways of CO2 reduction reaction (CO2RR) 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 C3 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 C3 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 C1 and C2 pathways, offering an efficient approach to screen promising electrocatalyst candidates for varied C3 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 C1 to C3 pathways. This work has offered an advanced breakthrough for the complicated CO2RR processes, accelerating the future design of novel ACs for C3 products with high efficiency and selectivity.