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

The second-order rate constants of organic contaminants degraded by ozone (kO3) 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 kO3. 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 (R2), mean square error (MSE), mean absolute error (MAE), and external validation parameter (Qext2) 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 kO3 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.臭氧降解有机污染物的二级反应速率常数（kO3），对于评估其处理效率与优化处理工艺具有重要意义。本研究采用多元线性回归（MLR）、带径向基函数核的支持向量机（SVM-RBF）、决策树（DT）、随机森林（RF）以及深度神经网络（DNN）等多种监督式机器学习（ML）算法，构建了用于预测log kO3的定量构效关系（QSPR）模型。此外，本研究将一系列量子化学描述符与新提出的范数描述符作为模型输入，成功搭建了机器学习模型。研究采用相关系数（R²）、均方误差（MSE）、平均绝对误差（MAE）以及外部验证参数（Qext²）等统计指标，对所构建模型的准确性、稳健性与可预测性进行评估，结果表明非线性模型（尤其是随机森林模型）在预测log kO3数值方面的性能优于线性模型。本研究所提出的范数描述符有望用于评估其他反应速率常数或化学性质。