Active-learning accelerated computational screening of A2B@NG catalysts for CO2 electrochemical reduction
Xinyu Li, Haobo Li, Zhen Zhang, Qinfeng Shi, Yan Jiao, Shi‐Zhang Qiao
Abstract
Few-atom catalysts, due to the unique coordination structure compared with metal particles and single-atom catalysts, are potential to be applied for efficient electrochemical CO2 reduction (CRR). In this study, we designed a class of triple-atom clusters A2B catalysts, with two A metal atoms and one B metal atom either horizontally or vertically embedded in the nitrogen-doped graphene plane. Metals A and B were selected from 17 elements across 3d to 5d transition metals. The structural stability and CRR activity of the 257 constructed A2B catalysts were evaluated. The active-learning approach was applied to predict the adsorption site of key reaction intermediates *CO, which only used 40% computing resources in comparison to “brute force” calculation and greatly accelerated the large amount of computation brought by the large number of A2B catalysts. Our results reveal that these triple atom catalysts can selectively produce more valuable hydrocarbon products while preserving high reactivity. These findings provide a theoretical understanding of the experimentally synthesized Fe3 and Ru3-N4 catalysts and propose six triple-atom catalysts as potential CRR catalysts, and provide a foundation for future discovery of few-atom catalysts and carbon materials in other applications. A new machine learning method, masked energy model, was also proposed which outperforms existing methods by approximately 5% when predicting low-coverage adsorption sites.