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.