Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals

Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation. However, understanding the underlying chemical mechanisms still faces challenges due to the limited interpretability of the ML models. In this study, interpretable ML models were established to predict the second-order rate constants between hydroxyl radicals (^•OH) and organics (k_•OH). It was found that the energy of the highest occupied molecular orbital (E_HOMO), the number of aromatic rings (N_AR), and the number of carbon atoms of organics (N_C) have important impacts on k_•OH. The positive correlation between k_•OH and E_HOMO can be explained by the regularity of electrophilic reaction, while the relationship between k_•OH and N_AR and N_C seems to be related with reactive sites. Furthermore, a rapid judgment method for reaction mechanism was developed based on an unsupervised learning approach which automatically divided organics into three clusters. Additionally, this methodology was applied to the reaction between organics and sulfate radicals. This study offers a rational model for predicting reaction mechanisms and provides more insights into the impact of organic structures on the reaction mechanism from the perspective of big data.