Towards fully ab initio simulation of atmospheric aerosol nucleation

Atmospheric aerosol nucleation contributes to around half of cloud condensation nuclei (CCN) globally and the nucleated particles can grow larger to impact air quality and consequently human health. Despite the decades' efforts, the detailed nucleation mechanism is still poorly understood. The ultimate goal of theoretical understanding aerosol nucleation is to simulate nucleation in ambient condition. However, there is lack of accurate reactive force field. Here for the first time, we propose the reactive force field with good size scalability for nucleation systems based on deep neural network and further bridge the simulation in the limited box with cluster kinetics towards boosting the aerosol simulation to be fully ab initio. We found that the formation rates based on hard sphere collision rate constants tend to be underestimated. Besides, the framework here is transferable to other nucleation systems, potentially revolutionizing the atmospheric aerosol nucleation field.