A data-driven machine learning approach for interpretable prediction of desalination stability of carbon materials for capacitive deionization

Eight features were employed to predict the desalination stability of carbon materials using different ML models. Experimental validation confirmed the reliability of the models, demonstrating the effectiveness of ML in the CDI field.