Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 20
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

Viewed

Total article views: 6,394 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,239 1,821 334 6,394 1,172 328 316
  • HTML: 4,239
  • PDF: 1,821
  • XML: 334
  • Total: 6,394
  • Supplement: 1,172
  • BibTeX: 328
  • EndNote: 316
Views and downloads (calculated since 11 Apr 2025)
Cumulative views and downloads (calculated since 11 Apr 2025)

Viewed (geographical distribution)

Total article views: 6,394 (including HTML, PDF, and XML) Thereof 6,336 with geography defined and 58 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 23 Oct 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint