Current and future machine learning approaches for modeling atmospheric cluster formation

The formation of strongly bound atmospheric molecular clusters is the first step towards forming new aerosol particles. Recent advances in the application of machine learning models open an enormous opportunity for complementing expensive quantum chemical calculations with efficient machine learning predictions. In this Perspective, we present how data-driven approaches can be applied to accelerate cluster configurational sampling, thereby greatly increasing the number of chemically relevant systems that can be covered. Although the number of quantum chemical studies on atmospheric cluster formation continue to rise, data-driven approaches can greatly expand the number of chemically relevant systems that can be covered and increase our understanding of the aerosol particle formation process.