Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm
<p>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 p...
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Copernicus Publications
2025-06-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/6197/2025/acp-25-6197-2025.pdf |
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| author | Z. Xia C. Zhao C. Zhao C. Zhao Z. Yang Q. Du J. Feng C. Jin J. Shi H. An H. An |
| author_facet | Z. Xia C. Zhao C. Zhao C. Zhao Z. Yang Q. Du J. Feng C. Jin J. Shi H. An H. An |
| author_sort | Z. Xia |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">∼</span>5<span class="inline-formula">×</span> speedup with a single CPU and <span class="inline-formula">∼</span>277<span class="inline-formula">×</span> 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.</p> |
| format | Article |
| id | doaj-art-72ef9acbd7b54be09fc3b3a968fa44b9 |
| institution | Kabale University |
| issn | 1680-7316 1680-7324 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj-art-72ef9acbd7b54be09fc3b3a968fa44b92025-08-20T03:26:35ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242025-06-01256197621810.5194/acp-25-6197-2025Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithmZ. Xia0C. Zhao1C. Zhao2C. Zhao3Z. Yang4Q. Du5J. Feng6C. Jin7J. Shi8H. An9H. An10Deep 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, ChinaDeep 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, ChinaLaoshan Laboratory, Qingdao, ChinaCAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, ChinaDeep 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, ChinaDeep 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, ChinaDeep 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, ChinaDeep 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, ChinaSchool of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, ChinaLaoshan Laboratory, Qingdao, ChinaSchool of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, China<p>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 <span class="inline-formula">∼</span>5<span class="inline-formula">×</span> speedup with a single CPU and <span class="inline-formula">∼</span>277<span class="inline-formula">×</span> 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.</p>https://acp.copernicus.org/articles/25/6197/2025/acp-25-6197-2025.pdf |
| spellingShingle | Z. Xia C. Zhao C. Zhao C. Zhao Z. Yang Q. Du J. Feng C. Jin J. Shi H. An H. An Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm Atmospheric Chemistry and Physics |
| title | Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm |
| title_full | Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm |
| title_fullStr | Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm |
| title_full_unstemmed | Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm |
| title_short | Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm |
| title_sort | toward a learnable artificial intelligence model for aerosol chemistry and interactions aimaci based on the multi head self attention algorithm |
| url | https://acp.copernicus.org/articles/25/6197/2025/acp-25-6197-2025.pdf |
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