Atmospheric aerosol nucleation contributes to more than half of cloud condensation nuclei globally and nucleated particles can then grow larger to impact air quality. However, the understanding of aerosol nucleation is still limited due to various precursors and the corresponding complex interaction mechanism. Here we propose a general framework to tackle nucleation mechanisms based on fully ab initio kinetics. The kinetics derived from general dynamic equations is applied to sulfuric acid-dimethylamine cluster, one of the most concerned system. Collision rate constants from deep neural network force field based molecular dynamics in addition to static quantum chemistry thermodynamics based evaporation rates make the kinetics simulation towards fully ab initio. The framework is transferable to other nucleation systems, so we believe it will provide innovative understandings of atmosphere aerosol nucleation mechanism.
 

aerosol nucleation,quantum chemsitry,kinetics,deep learning