Wang, Y.; Xu, W.; Zhong, H.; Chen, B.; Yang, L.; Wang, L.; Ren, J.; Duan, J.; Lin, C.; Huang, R.-J. Advancing Aerosol Chemistry with Machine Learning: A Short Review. ACS ES&T Air 2025, 2 (11), 2323-2341.

Machine learning (ML) models have emerged as powerful tools for advancing aerosol chemistry research, offering the ability to effectively analyze large-scale, high-dimensional, and non-linear datasets. This review highlights recent advancements in the application of ML models to aerosol chemistry, including predictions of aerosol burdens, precursors and oxidants, sources, formation mechanisms and physicochemical properties. Additionally, we explore common limitations of current ML approaches and propose potential improvements to enhance their future applications in aerosol chemistry studies. This review aims to provide a comprehensive understanding of the opportunities and challenges at the intersection of ML and aerosol chemistry, paving the way for future developments in this field.