DFT-Assisted Machine Learning for Global Optimization of Fe–Carbon Catalyst: Persulfate Activation and Targeted Removal of Emerging Contaminants

Fe–carbon catalysts (FCCs) are extensively used for persulfate activation in advanced oxidation processes (PS-AOPs), an approach regarded as an efficient and cost-effective strategy for removing emerging contaminants (ECs). However, the quantitative structure–activity relationship between the degradation efficiency of ECs with diverse molecular characteristics and the microstructure of FCCs has not been clearly elucidated. This hinders the widespread practical implementation of FCCs. Herein, density functional theory (DFT)-derived molecular-descriptor-assisted machine learning models were employed to accurately predict the reaction rate constants for EC degradation in FCC-PS AOPs, mainly focusing on three aspects: performance prediction, operating condition optimization and mechanism interpretation. Additionally, DFT-derived descriptors are integrated with fabrication and operational parameters to facilitate the generative design of FCCs. The excellent fitting performance of the overall XGB model in predicting the reaction constants for EC degradation (Test R2 = 0.813) highlights the notable advantages of customized hyperparameter tuning for improving predictive accuracy. Subsequently, the submodels trained using different EC clusters (derived from t-SNE and K-means clustering methods) can offer specific strategies for selecting optimal parameters of FCC-PS AOPs that target ECs with distinct properties. The interpretability of the model was improved by using SHAP values and partial-dependence plots to clarify the internal relationships of the ML “black box”. Overall, a feasible and generalizable ML model is proposed to facilitate a paradigm shift in the inverse design of FCCs for the degradation of specific ECs.