Machine Learning-Assisted Catalysts for Advanced Oxidation Processes: Progress, Challenges, and Prospects

Le résumé fourni par la source

Advanced oxidation processes (AOPs) are recognized as one of the most effective methods in the field of wastewater treatment, and the selection of catalysts in the oxidation process is very important. In the face of the traditional test trial-and-error method, the method of screening advanced oxidation catalysts is time-consuming and inefficient. This paper examines approximately two decades’ worth of literature pertaining to the development of catalysts facilitated by machine learning. A synopsis of the various advanced oxidation processes and reactive oxygen species (ROS) is provided. Subsequently, it is posited that the swift advancement of machine learning (ML) and its algorithmic classification has significantly propelled the progress in ML-assisted catalyst screening, active site prediction, the discovery of acceleration mechanisms, and catalyst structural research, which are subsequently elucidated. Despite ML’s proven efficacy as a tool within the domain of AOPs’ catalysis, the article concludes by presenting challenges and outlining future development strategies, particularly in light of issues pertaining to data quality and quantity, as well as inherent model limitations.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.