Structural and Electronic Features-Integrated Machine Learning Framework for High-Throughput Prediction of Organic Pollutant Reactivity

Environmental Science & Technology 2026

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Authors: Zonghao Liu, Shuang Wu, Ce-Hui Mo, Xiang Lei, Jianyu Han, Shaofeng Wang, Jie-Jie Chen, Han-Qing Yu, Min Song
DOI: 10.1021/acs.est.6c00875
Cited by: 1
References: 60

Abstract

= 0.811). This work provides a transparent and high-throughput predictive framework for reactivity prediction of organic pollutants, facilitating the bottom-up design of an advanced oxidation process tailored to specific pollutant profiles.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced oxidation water treatment Water Quality Monitoring and Analysis

Publication details

Journal / source
Environmental Science & Technology
Publisher
American Chemical Society (ACS)
Volume
60
Issue
25
Pages
17827–17838
Publication date
29 April 2026
Language
EN