The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning

Abstract

In this work, high-performance two-dimensional (2D) graphene-based single-atom electrocatalysts (ZZ/ZA-MN_xC_y) for the oxygen reduction reaction (ORR) were screened out using machine learning (ML). A model was built for the fast prediction of electrocatalysts and two descriptors valence electron correction (VEc) and degree of construction differences (DC) were proposed to improve the accuracy of the model prediction. Two evaluation criteria, high-performance catalyst retention rate r_R and high-performance catalyst occupancy rate r_O, were proposed to evaluate the accuracy of ML models in high-performance catalyst screening. The addition of VEc and DC in the model could change the mean absolute error (MAE_test) of the test set, the coefficient of determination (R²_test) of the test set, r_O, and r_R from 0.334 V, 0.683, 0.222, and 0.360 to 0.271 V, 0.774, 0.421, and 0.671, respectively. The partially screened potential high-performance ORR electrocatalysts such as ZZ-CoN₄ and ZZ-CoN₃C₁ were also further investigated using a Density Functional Theory (DFT) method, which confirmed the accuracy of the ML model (MAE = 0.157 V, R² = 0.821).