Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 10
Articles | Volume 26, issue 10
https://doi.org/10.5194/acp-26-7631-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-7631-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 10
Research article
 | 
29 May 2026
Research article |  | 29 May 2026

Machine learning interatomic potentials with accurate long-range interactions for molecular dynamics collision simulations of atmospherically-relevant molecules

Ivo Neefjes, Jakub Kubečka, and Jonas Elm

Viewed

Total article views: 1,613 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
983 534 96 1,613 163 66 138
  • HTML: 983
  • PDF: 534
  • XML: 96
  • Total: 1,613
  • Supplement: 163
  • BibTeX: 66
  • EndNote: 138
Views and downloads (calculated since 16 Feb 2026)
Cumulative views and downloads (calculated since 16 Feb 2026)

Viewed (geographical distribution)

Total article views: 1,613 (including HTML, PDF, and XML) Thereof 1,608 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 May 2026
Download
Short summary
Atmospheric particles impact climate and health. Most particles form through gas molecules colliding and sticking together. We use molecular dynamics accelerated by machine learning to study this process. We found that standard machine learning models often fail to capture the long-range forces driving collisions, and models with explicit long-range corrections are needed. This work provides a blueprint for accurate simulations of particle formation.
Read more
Share
Altmetrics
Final-revised paper
Preprint