Articles | Volume 25, issue 12
https://doi.org/10.5194/acp-25-6197-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-6197-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jun 2025
Research article |
| 25 Jun 2025
| 25 Jun 2025
Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm
Zihan Xia
Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China
Chun Zhao
CORRESPONDING AUTHOR
Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China
Laoshan Laboratory, Qingdao, China
CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, China
Zining Yang
Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China
Qiuyan Du
Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China
Jiawang Feng
Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China
Chen Jin
Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China
Jun Shi
School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China
Hong An
Laoshan Laboratory, Qingdao, China
School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China
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Short summary
Traditional numerical schemes of aerosol chemistry and interactions (ACI) in atmospheric models are computationally costly and are often simplified or omitted, introducing uncertainties. We use an AI scheme to achieve fast, accurate, and stable end-to-end simulation for full ACI within an atmospheric model, replacing numerical schemes. This innovation is expected to enhance the accuracy and efficiency of ACI simulations in climate models that would otherwise neglect or simplify ACI processes.
Traditional numerical schemes of aerosol chemistry and interactions (ACI) in atmospheric models...
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