Predicting reaction kinetics of reactive bromine species with organic compounds by machine learning: Feature combination and knowledge transfer with reactive chlorine species

Abstract

Reactive bromine species (RBS) such as bromine atom (Br^•) and dibromine radical (Br₂^•−) are important oxidative species accounting for the transformation of organic compounds in bromide-containing water. This study developed quantitative structure−activity relationship (QSAR) models to predict second order rate constants (k) of RBS by machine learning (ML) and conducted knowledge transfer between RBS and reactive chlorine species (RCS, e.g., Cl^• and Cl₂^•−) to improve model performance. The ML-based models (RMSE_test = 0.476−0.712) outperformed the multiple linear regression-based models (RMSE_test = 0.572−3.68) for predicting k of RBS. In addition, the combination of molecular fingerprints (MFs) and quantum descriptors (QDs) as input features improved the performance of ML-based models (RMSE_test = 0.476−0.712) compared to those developed by MFs (RMSE_test = 0.524−0.834) or QDs (RMSE_test = 0.572−0.806) alone. E_HOMO and E_gap were identified to be the most important features affecting k of RBS based on SHAP analysis. A unified model integrating the datasets of four reactive halogen species (RHS, e.g., Br^•, Br₂^•−, Cl^• and Cl₂^•−) was further developed (R²_test = 0.802), which showed better predictive performance than the individual models (R²_test = 0.521−0.776). Meanwhile, the model performance changed differently by employing knowledge transfer among RHS, which was improved for Br^•/Cl^•, mixed for Br^•/Br₂^•− and Cl^•/Cl₂^•−, but worse for Br₂^•−/Cl₂^•−. This study provides useful tools for predicting k of RHS in aqueous environments.