Machine learning-based prediction of desalination capacity of electrochemical performance of nitrogen-doped for capacitive deionization

Abstract

Nitrogen doping has been widely applied in the field of capacitive deionization (CDI) desalination. However, the relationship between multiple forms of nitrogen doping, their proportions, and their effects on electrochemical and desalination performance remains unclear. Machine learning, as an emerging tool for handling large datasets, holds significant potential in optimizing CDI electrode performance. Hence, this study uses machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB) and Gradient Boosting Regressor (GBR), to clarify the nonlinear relationships between nitrogen doping and electrochemical performance, identifying the key influencing features. The GBR model demonstrates strong predictive accuracy with high goodness-of-fit. Additionally, the contributions of each feature to the model predictions is explained through Permutation Feature Importance (PFI), Embedded Feature Importance (EFI), and SHAP values, the results demonstrate the substantial impact of external conditions, such as concentration and voltage, along with specific capacitance as an intrinsic material property. Partial Dependence Plots (PDP) further illustrate the synergistic effects of different nitrogen forms and specific capacitance on desalination performance, with optimal doping levels identified as 1–1.5 at.% for N6, below 1 at.% for N5, and minimized N4 content to enhance electrochemical and salt adsorption properties. Finally, DFT calculations provide insights into the microscopic doping mechanisms, and a new dataset validates the accuracy of model. This study offers theoretical guidance for the design and optimization of CDI electrode materials and provides a strategic approach for machine learning applications in the CDI field.