At the global scale, atmospheric aerosol nucleation contributes nearly half of cloud condensation nuclei and therefore has a significant impact on global climate change. Under certain conditions, aerosol particles after nucleation will grow to haze. Despite the importance of atmospheric aerosol nucleation for climate change, air quality and human health, the understanding of atmospheric aerosol nucleation mechanisms is still lacking due to the wide variety of atmospheric nucleation precursor species.
Due to the rare event nature of atmospheric aerosol nucleation, the non-reactivity of existing molecular force fields and the high computational cost de novo hinder the understanding of atmospheric aerosol nucleation dynamics. Because of the wide size range and high-dimensional characteristics of potential energy surfaces of non-covalent hydrogen bonded nucleating molecular clusters, it is challenging to develop reactive molecular force fields for nucleating molecular clusters. Here, the research team proposes a universal workflow. By training the accurate reactive molecular force field based on deep neural network, the collision rate coefficient obtained based on the molecular dynamics of deep neural network and molecular force field is further coupled with the cluster dynamics model. The study found that the atmospheric acid-base nucleation rate reported in the previous literature is often seriously underestimated, especially under the conditions of compound pollution in China. This work points out the research direction for atmospheric aerosol nucleation simulation to finally move towards ab initio calculation.
The study, completed by the University of Science and Technology of China, was published in the international academic journal Nature Communications under the title “Towards fully ab initio simulation of atmospheric aerosol nucleation”. Jiang Shuai, a special associate researcher at the School of Information Science and Technology of the University of Science and Technology of China, is the first and corresponding author of this paper. The research work was supported by the National Natural Science Foundation of China.
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