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Atmospheric aerosol nucleation contributes to more than half of cloudcondensation nuclei globally. The emissions, properties and concentrations ofatmospheric aerosols or aerosol precursors could respond significantly toclimate change. Despite the importance for climate, the detailed nucleationmechanisms are still poorly understood. The ultimate goal of theoreticalunderstanding aerosol nucleation is to simulate nucleation in ambientcondition, hindered by lack of accurate reactive force field. Here we proposethe reactive force field for nucleation systems with good size scalabilitybased on deep neural network. The huge computational costs from directmolecular dynamics in ambient conditions are surmounted by bridging thesimulation in the limited box with cluster kinetics, facilitating the aerosolnucleation simulation to be fully ab initio. We found that the acid-baseformation rates previously based on hard sphere collision rate constants tendto be underestimated up to several times. These findings show that the widelyrecognized acid-base nucleation observed in the CLOUD (Cosmics Leaving OUtdoorDroplets) chamber experiments, pristine and polluted environments should berevisited to considering the contribution of collision enhancement. Besides,the framework here is transferable to other nucleation systems, potentiallyboosting the nucleation parameterizations accuracy generally to effectivelyadvance the climate model predictions reliability.

Towards fully ab initio simulation of atmospheric aerosol nucleation