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

XiaZihanZhaoChunYangZiningDuQiuyan

气象海洋与地球系统方向论文：Toward a learnable Artificial Intelligence Model for Aerosol Chemistry a

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内容导读Reading note

分类与摘要Category & abstract

面向天气、海洋、气溶胶或地球系统模拟，强调模型预测能力与科学解释。

Evidence note

y of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China 2Laoshan Laboratory, Qingdao, China 3CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, China 4School of Computer Science and Technology, University of Science and Technology of Chin || y of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software, University of Science and Technology of China, Hefei, China 2Laoshan Laboratory, Qingdao, China 3CAS Center for Excellence in Comparative Planetology, University of Science and Technology of China, Hefei, China 4School of Computer Science and Technology, University of Science and Technol || STC Research Funds of the Double First-Class Initiative (grant nos. YD2080002007, KY2080000114), and the Science and Technology Innovation Project of Laoshan Laboratory (grant no. LSKJ202300305). Review statement. This paper was edited by Minghuai Wang and reviewed by two anonymous referees. References Arﬁn, T., Pillai, A. M., Mathew, N., Tirpude, A., Bang, R., and Mon- dal, P.: An overview of atmospheric aero || STC Research Funds of the Double First-Class Initiative (grant nos. YD2080002007, KY2080000114), and the Science and Technology Innovation Project of Laoshan Laboratory (grant no. LSKJ202300305). Review statement. This paper was edited by Minghuai Wang and reviewed by two anonymous referees. References Arﬁn, T., Pillai, A. M., Mathew, N., Tirpude, A., Bang, R., and Mon- dal, P.: An overview of atmosph

Metadata

完整论文信息Full record

title: Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm
title_zh: 待补充
abstract: 待补充
abstract_zh: 待补充
keywords: Aerosol
year: 2025
published_date: 待补充
online_date: 待补充
paper_type: Journal
publication_status: Published
volume: 待补充
issue: 待补充
pages: 待补充
article_number: 待补充
publisher: 待补充
doi: 10.5194/acp-25-6197-2025
research_area: 气象海洋与地球系统
tags: Aerosol
category: 气象海洋与地球系统
summary: 面向天气、海洋、气溶胶或地球系统模拟，强调模型预测能力与科学解释。
authors: Xia, Zihan, Zhao, Chun, Yang, Zining, Du, Qiuyan
corresponding_authors: 否
affiliations: 崂山实验室, 待补充
funding: 崂山实验室项目

Research area

气象海洋与地球系统

Citation

Xia, Zihan; Zhao, Chun;Yang, Zining;Du, Qiuyan;Feng, Jiawang;Jin, Chen;Shi, Jun;An, Hong ,Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm, ATMOSPHERIC CHEMISTRY AND PHYSICS,VOL:25 NO:12 PP:6197-6218, 2025-06

BibTeX

@article{acsa2025_12,
  title = {Toward a learnable Artificial Intelligence Model for Aerosol Chemistry and Interactions (AIMACI) based on the Multi-Head Self-Attention algorithm},
  year = {2025},
  doi = {10.5194/acp-25-6197-2025}
}