Establishing C-C electro-coupling mechanism via big data analyses of reaction networks

Abstract C-C coupling is important in electrocatalytic CO2 reduction for production of green chemicals. However, understanding of the underlying coupling mechanism(s) is poor because of the complexity of the reaction network. Here, we report a method to establish the structure-activity relationship in C-C coupling reactions via statistical analyses on a big dataset. The dataset contains a total of 45,738 adsorption data, which are generated through a combination of quantum chemical computations and machine-learning and encompasses 378 active site structures formed by 27 transition metals together with 6 potential coupling precursors. Analyses of these data establishes that, 1) asymmetric coupling mechanisms exhibit potential for greater efficiency compared with symmetric coupling, agreeing well with reported experiment, and 2) C-C coupling selectivity of Cu-based catalysts can be boosted through bimetallic doping. The three-component combination of CuAgNb exhibited superior coupling selectivity compared with both Cu and CuAg catalysts. Importantly, we experimentally substantiate the CuAgNb catalyst selected based on data screening, to demonstrate actual boosted performance in C-C coupling. We conclude that this finding evidences practicality of our theoretical model, and that combining big data with complex catalytic reaction mechanisms will become a basis or a new paradigm for accelerating optimal catalyst design for multi-carbon chemicals production.