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Official PDF TranslationEnvironmental Chemistry

Explainable Machine Learning Model for Predicting Ozone Reaction Rate Constants of Aromatic Compounds in Water

Authors: SUN Ting; LIU Yang; WEI Chongzhi; REN Yueying

DOI: 10.7524/j.issn.0254-6108.2024103107Status: Verified Translated Edition
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Key Findings in This Report

• • The PPR model achieved R2=0.923, Q2LOO=0.836, and Q2ext=0.873, demonstrating high predictive accuracy and robustness for estimating ozone reaction rate constants of aromatic compounds in water. • • SHAP analysis identified that the presence of dssC (=C<) fragments and chlorine atoms significantly reduces ozone reactivity, guiding molecular design for enhanced degradability. • • The model was validated using Williams plots to define its applicability domain, ensuring reliable predictions within the chemical space of the training set. • • TMAP and ARKA analyses provided insights into structural diversity and activity cliffs, aiding in outlier detection and model refinement.