Supervised Machine Learning Algorithms for Predicting Rate Constants of Ozone Reaction with Micropollutants

The second-order rate constants of organic contaminants degraded by ozone (k(O3)) are of great importance for evaluating their treatment efficiency and optimizing treatment processes. In this work, several supervised machine learning (ML) algorithms, including multiple linear regression (MLR), support vector machine with radial basis function kernels (SVM-RBF), decision tree (DT), random forest (RF), and deep neutral network (DNN) methods, were used to develop quantitative structure-property relationship (QSPR) models for the estimation of log k(O3). What is more, a series of quantum chemical and newly proposed norm descriptors was successfully used in developing ML models as inputs. The statistical parameters correlation coefficient (R-2), mean square error (MSE), mean absolute error (MAE), and external validation parameter (Q(ext)(2)) were used to evaluate the accuracy, robustness, and predictability of the as-developed models, suggesting that the nonlinear models (especially for the RF model) have better performance in predicting log k(O3) values than the linear model. It is expected that the proposed norm descriptors can be employed to evaluate other reaction rate constants or chemical properties.