A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts

Abstract

Electroreduction of CO 2 is one of the most potential ways to realize CO 2 recycle and energy regeneration. The key to promoting this technology is the development of high-performance electrocatalysts. Generally, high-throughput computational screening contributes a lot to materials innovation, but still consumes much time and resource. To achieve efficient exploration of electrocatalysts for CO 2 reduction, we created a machine learning model based on an extreme gradient boosting regression (XGBR) algorithm and simple features. Our screening model successfully and rapidly predicted the Gibbs free energy change of CO adsorption (Δ G CO ) of 1060 atomically dispersed metal–nonmetal codoped graphene systems, and greatly reduced the research cost. The competitive reaction, the hydrogen evolution reaction (HER), is also discussed with respect to such a screening model. This work demonstrates the potential of machine learning methods and provides a convenient approach for the effective theoretical design of electrocatalysts for CO 2 reduction.