Machine learning and Shapley Additive Explanation-based interpretable prediction of the electrocatalytic performance of N-doped carbon materials

•Building a dataset of N-doped carbon materials for Microbial fuel cells.•The gradient boosting regression algorithm has good predictive power with R2 of 0.86.•Identify the importance of different characteristics to the target.•The influence of features is explained based on Shapley Additive Explanation theory. Enhancing the kinetic rate of cathodic oxygen reduction reaction (ORR) by catalysts is the key to improve the performance of microbial fuel cells (MFCs). Metal-free ORR catalysts represented by nitrogen-doped carbon materials have been extensively investigated and have shown excellent catalytic effects for oxygen reduction reaction. However, it is difficult to clarify the coupling effect physicochemical properties of nitrogen-doped carbon materials on their catalytic effect (i.e. electricity production performance of MFCs) by traditional experimental methods. Therefore, in this study, six machine learning models were combined with SHAP to develop prediction models for the power density ratio of MFCs for reflecting the catalytic performance of nitrogen-doped carbon materials by using physicochemical properties, such as elemental composition, functional group structure, and pore structure, as input features. The gradient boosting regression (GBR) model was found to have the highest prediction accuracy on the test set, with R2 and RMSE of 0.86 and 0.09, respectively. The SHAP method was used to interpret the output of the GBR model and reveal the mechanism of interaction between different characteristic variables. It was found that pyridine nitrogen is the most important input characteristic of the nitrogen elements, as its corresponding SHAP value reaches above 0.1. Surprisingly, an increase in the content of oxygen significantly attenuates the extent to which changes in nitrogen content affect the system, although its effect on power density prediction is inconsiderable. In addition, the optimal range of important physicochemical properties of nitrogen-doped carbon materials was obtained by the SHAP method. The carbon materials have relatively high catalytic performance under the conditions of C(at%) between 85% and 90%, N(at%) > 2.5%, Pyridine-N(at%) > 30%, Vtotal between 0.3 and 0.6 cm3g−1, and SBET > 1000 m2g−1. This study can provide theoretical guidance for subsequent experimental design of carbon-based nitrogen-doped electrocatalysts.