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

Reactive bromine species (RBS) such as bromine atom (Br) and dibromine radical (Br) 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 Cl) to improve model performance. The ML-based models (RMSE = 0.476 -0.712) outperformed the multiple linear regression-based models (RMSE = 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 = 0.476 -0.712) compared to those developed by MFs (RMSE = 0.524 -0.834) or QDs (RMSE = 0.572 -0.806) alone. E and E 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, Br, Cl and Cl) was further developed (R = 0.802), which showed better predictive performance than the individual models (R = 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/Br and Cl/Cl, but worse for Br/Cl. This study provides useful tools for predicting k of RHS in aqueous environments.