Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events

Wang, Pan; Zhao, Yue; Wang, Jiandong; Kerminen, Veli-Matti; Jiang, Jingkun; Li, Chenxi

doi:https://doi.org/10.5194/acp-25-7431-2025

Articles | Volume 25, issue 13

https://doi.org/10.5194/acp-25-7431-2025

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

https://doi.org/10.5194/acp-25-7431-2025

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

Articles | Volume 25, issue 13

Research article

|

15 Jul 2025

Research article |

| 15 Jul 2025

Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events

Pan Wang, Yue Zhao, Jiandong Wang, Veli-Matti Kerminen, Jingkun Jiang, and Chenxi Li

Supplement

https://doi.org/10.5194/acp-25-7431-2025-supplement

Data sets

Machine Learning Assisted Inference of the Particle Charge Fraction and the Ion-induced Nucleation Rates during New Particle Formation Events Pan Wang https://doi.org/10.5281/zenodo.15024817

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Short summary

We developed a numerical model to investigate the evolution of the charge state of newly formed atmospheric particles. Based on the simulation results, we successfully employed neural networks to predict particle charge states and estimate ion-induced nucleation rates. This study provides new insights into the dynamics of particle charging and introduces advanced methods for evaluating ion-induced nucleation in atmospheric research.