Machine Learning Accelerated Catalyst Design for Advanced Oxidation Processes: Efficient and Streamlined Development

Abstract

Advanced oxidation processes (AOPs) hold great potential in the degradation of pollutants and purification of water quality, but traditional AOPs face challenges, such as high costs, low efficiency, and environmental risks. Machine learning (ML), as a powerful tool, can facilitate the optimization and development of AOPs catalysts. This paper first introduces the development history and advantages of AOPs, then analyzes the dilemmas faced by traditional AOPs, and elaborates on how machine learning can address these issues through means, such as data mining, analysis of descriptor importance, and prediction of catalyst performance. Finally, the paper outlooks on future research directions of machine learning in the field of AOPs, including enhancing data quality, improving model algorithms, designing intelligent systems, and gaining a deeper understanding of mechanisms. (Figure presented.)