Articles | Volume 25, issue 20
https://doi.org/10.5194/acp-25-13379-2025
https://doi.org/10.5194/acp-25-13379-2025
Research article
 | 
22 Oct 2025
Research article |  | 22 Oct 2025

Implications of VOC oxidation in atmospheric chemistry: development of a comprehensive AI model for predicting reaction rate constants

Xin Zhang, Jiaqi Luo, Wenxiao Pan, Qiao Xue, Xian Liu, Jianjie Fu, Aiqian Zhang, and Guibin Jiang

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Short summary
Volatile organic compounds drive atmospheric chemistry via oxidation, forming PM2.5/ozone precursors. This study introduces Vreact, a graph-based deep learning model predicting reaction rate constants (ki) for multiple oxidants simultaneously. It achieves mean squared error = 0.299 and R² = 0.941 for log10ki , overcoming single-oxidant model limits. Vreact advances pollutant formation insights and supports emission control strategies, aiding global air quality and public health efforts.
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