The formation of atmospheric molecular clusters is an important step in atmospheric new particle formation. Despite much research within the field, the exact contribution of different vapors remains highly uncertain. Quantum chemical (QC) calculations and kinetics modelling allow the detailed mapping of cluster formation pathways, but such calculations are extremely computationally demanding. In this lecture I will present our recent work on the comprehensive screening of atmospherically relevant cluster systems. This allows us to gain direct insight into the cluster formation potential of each system and hence their ability to grow to larger sizes. Furthermore, I will present how to apply quantum machine learning (QML) models to accelerate the exploration of the configurational space in cluster formation studies.<br/> <br/>There is a significant gap in size between the currently modeled clusters using highly accurate QC methods (around 1 nm) and aerosol particles routinely measured in the field and the laboratory (around 2 nm). Hence, the transition from small clusters to freshly nucleated particles (FNPs) remain completely unexplored. I will outline our future work involving the studying of FNPs using QC methods. We will study the potential FNP compositions, molecular interactions with the surrounding environment and chemical reactions occurring at the surface or inside the FNPs. This future research will advance the field by yielding explicit molecular level insight into FNP properties, which can be directly implemented into atmospheric aerosol process models.

Quantum machine learning approach for studying atmospheric cluster formation

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.