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.