The molecular mechanisms governing the formation and growth of atmospheric molecular clusters into aerosol particles constitute one of the largest uncertainties in global climate modelling. Aerosol particles directly counteract the warming potential of greenhouse gases by scattering sunlight back into space. The largest source of new aerosol particles in the atmosphere is from gas-to-particle conversion. However, the chemical identity, relative significance of participating vapours and even the basic physiochemical properties of these new particles remain unknown and are not possible to directly study experimentally.
Our group applies quantum chemical and machine learning methods to understand atmospheric processes at the molecular level. We are specifically interested in:
