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

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

doi:https://doi.org/10.5194/acp-25-13379-2025

Articles | Volume 25, issue 20

https://doi.org/10.5194/acp-25-13379-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-25-13379-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 20

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

Supplement

https://doi.org/10.5194/acp-25-13379-2025-supplement

Data sets

Code and data set for ``Implications of VOC oxidation in atmospheric chemistry: development of a comprehensive AI model for predicting reaction rate constants'' Xin Zhang and Jiaqi Luo https://doi.org/10.5281/zenodo.17141364

Model code and software

Code and data set for ``Implications of VOC oxidation in atmospheric chemistry: development of a comprehensive AI model for predicting reaction rate constants'' Xin Zhang and Jiaqi Luo https://doi.org/10.5281/zenodo.17141364

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Short summary

Volatile organic compounds drive atmospheric chemistry via oxidation, forming PM_2.5/ozone precursors. This study introduces Vreact, a graph-based deep learning model predicting reaction rate constants (k_i) for multiple oxidants simultaneously. It achieves mean squared error = 0.299 and R² = 0.941 for log₁₀k_i , overcoming single-oxidant model limits. Vreact advances pollutant formation insights and supports emission control strategies, aiding global air quality and public health efforts.