High throughput screening of single atomic catalysts with optimized local structures for the electrochemical oxygen reduction by machine learning

Single atomic catalysts (SACs), especially metal-nitrogen doped carbon (M−NC) catalysts, have been extensively explored for the electrochemical oxygen reduction reaction (ORR), owing to their high activity and atomic utilization efficiency. However, there is still a lack of systematic screening and optimization of local structures surrounding active centers of SACs for ORR as the local coordination has an essential impact on their electronic structures and catalytic performance. Herein, we systematic study the ORR catalytic performance of M−NC SACs with different central metals and environmental atoms in the first and second coordination sphere by using density functional theory (DFT) calculation and machine learning (ML). The geometric and electronic informed overpotential model (GEIOM) based on random forest algorithm showed the highest accuracy, and its R2 and root mean square errors (RMSE) were 0.96 and 0.21, respectively. 30 potential high-performance catalysts were screened out by GEIOM, and the RMSE of the predicted result was only 0.12 V. This work not only helps us fast screen high-performance catalysts, but also provides a low-cost way to improve the accuracy of ML models.