Combination of Density Functional Theory and Machine Learning Provides Deeper Insight of the Underlying Mechanism in the Ultraviolet/Persulfate System

Abstract

The competition between radical and nonradical processes in the activated persulfate system is a captivating and challenging topic in advanced oxidation processes. However, traditional research methods have encountered limitations in this area. This study employed DFT combined with machine learning to establish a quantitative structure–activity relationship between contributions of active species and molecular structures of pollutants in the UV persulfate system. By comparing models using different input data sets, it was observed that the protonation and deprotonation processes of organic molecules play a crucial role. Additionally, the condensed Fukui function, as a local descriptor, is found to be less effective compared to the dual descriptor due to its imprecise definition of f⁰. The sulfate radical exhibits high selectivity toward local electrophilic sites on molecules, while global descriptors determined by their chemical properties provide better predictions for contribution rates of hydroxyl radicals. Interestingly, there exists a piecewise function relating the contribution rates of different active species to E_LU–HO, which is further supported by experimental data. Currently, this relationship cannot be explained by classical chemical theory and requires further investigation. Perhaps this is a new perspective brought to us by combining DFT with machine learning.