Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm

Abstract. Simulating aerosol chemistry and interactions (ACI) is crucial in climate and atmospheric modeling, yet conventional numerical schemes are computationally intensive due to the stiff differential equations and iterative methods involved. While artificial intelligence (AI) has demonstrated potential in accelerating photochemistry simulations, it has not been applied for simulating the full ACI processes, which encompass not only chemical reactions but also other processes, such as nucleation and coagulation. To bridge this gap, we develop a novel Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI), focusing initially on inorganic aerosols. Trained based on a conventional scheme, it has been validated in both offline and online modes (referring to whether it is coupled into a three-dimensional atmospheric model). Results demonstrate that AIMACI is not only comparable with conventional schemes in spatial distributions, temporal variations, and evolution of particle size distribution of main aerosol species, including water content in aerosols, but also exhibits robust generalization ability, reliably simulating one month under different environmental conditions across four seasons despite being trained on limited data from merely 16 d. Notably, it exhibits ∼5× speedup with a single CPU and ∼277× speedup with a single GPU, compared with conventional schemes. However, the stability of AIMACI for year-scale global simulations remains to be seen, requiring further testing. AIMACI's generalization capability and its modular design suggest potential for future coupling to global climate models, which are expected to enhance the precision and efficiency of ACI simulations in climate modeling that neglects or simplifies ACI processes.