Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid

Abstract

Atmospheric molecular cluster formation is the first stage toward aerosol particle formation. Despite intensive progress in recent years, the relative role of different vapors and the mechanisms for forming clusters is still not well-understood. Quantum chemical (QC) methods can give insight into the cluster formation mechanisms and thereby yield information about the potentially relevant compounds. Here, we summarize the QC literature on clustering involving species such as sulfuric acid, methanesulfonic acid, and nitric acid. The importance of iodine species such as iodous acid (HIO2) and iodic acid (HIO3) in atmospheric cluster formation is an emerging topic, and we critically review the recent literature and give our view on how to progress in the future. We outline how machine learning (ML) methods can be used to enhance cluster configurational sampling, leading to a massive increase in the cluster compositions that can be modeled. In the future, ML-boosted cluster formation could allow us to comprehensively understand complex cluster formation with multiple pathways, leading us one step closer to implementing accurate cluster formation mechanisms in atmospheric models.