Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7357-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7357-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Model description paper
 | 
15 Oct 2025
Model description paper |  | 15 Oct 2025

Improved vapor pressure predictions using group contribution-assisted graph convolutional neural networks (GC2NN)

Matteo Krüger, Tommaso Galeazzo, Ivan Eremets, Bertil Schmidt, Ulrich Pöschl, Manabu Shiraiwa, and Thomas Berkemeier

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Short summary
This work uses machine learning to predict saturation vapor pressures of atmospherically-relevant organic compounds, crucial for partitioning of secondary organic aerosol (SOA). We introduce a new method using graph convolutional neural networks, in which molecular graphs enable the model to capture molecular connectivity better than with non-structural embeddings. The method shows strong agreement with experimentally determined vapor pressures, and outperforms existing estimation methods.
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