Establishing Quantitative Structure–Activity Relationships for the Degradation of Aromatic Organics by UV–H₂O₂ Using Machine Learning

Industrial & Engineering Chemistry Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 26, 2025

The degradation of aromatic organic compounds in aquatic environments is critical due to their persistence and toxicity. This study establishes a machine learning (ML)-driven quantitative structure–activity relationship model predict the pseudo-first-order reaction rate constants (K) for UV–H2O2 organics. A data set comprising 134 experimental observations 30 was constructed, integrating conditions, quantum chemical parameters, physicochemical properties. Among six ML algorithms evaluated, gradient boosting decision tree emerged as optimal model, with feature importance analysis identifying H2O2 concentration, topological polar surface area, q(C)min dominant factors. Theoretical calculations supported by linking higher reactivity o,p'-dicofol lower energy gaps elevated electrophilic susceptibility. Additionally, establishment interpretable expressions not only provides transparency clarity predictions but also aids economic analysis, which highlighted that mildly acidic pH low UV light intensity, along suitable concentrations, are cost-effective conditions process. work bridges chemistry elucidate mechanisms, offering rapid resource-efficient tool optimizing advanced oxidation processes.

Language: Английский

Design and Application of Electrocatalyst Based on Machine Learning

Interdisciplinary materials, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

ABSTRACT Data‐driven artificial intelligence provides strong technical support for addressing global energy and environmental issues. The powerful data processing analysis capabilities of machine learning (ML) can quickly predict electrocatalytic performance, improving the efficiency catalyst design time‐consuming inefficient nature traditional design. By integrating ML with theoretical calculations experiments, catalytic reaction processes be precisely regulated. This not only accelerates discovery new catalysts but also drives development more efficient environmentally friendly sustainable technologies. In this article, we discuss approaches to discovering novel driven by ML, focusing on activity prediction, barrier optimization, innovative materials. We systematically application in field electrocatalysis explore future prospects domain. provide a comprehensive in‐depth its potential development.

Language: Английский