Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13227-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-13227-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
06 Sep 2021
Research article | Highlight paper |
| 06 Sep 2021
| 06 Sep 2021
Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning
Emma Lumiaro
Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland
Milica Todorović
Department of Mechanical and Materials Engineering, University of Turku, 20014, Turku, Finland
Theo Kurten
Department of Chemistry, Faculty of Science, P.O. Box 55, 00014 University of Helsinki, Helsinki, Finland
Hanna Vehkamäki
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, P.O. Box 64, 00014 University of Helsinki, Helsinki, Finland
Patrick Rinke
CORRESPONDING AUTHOR
Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland
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Total article views: 7,331 (including HTML, PDF, and XML)
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- Advancing Aerosol Chemistry with Machine Learning: A Short Review Y. Wang et al. https://doi.org/10.1021/acsestair.5c00001
- Predicting Composition Evolution for a Sulfuric Acid-Dimethylamine System from Monomer to Nanoparticle Using Machine Learning Y. Liu & Y. Jiang https://doi.org/10.1021/acs.jpca.4c06062
- Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation J. Kubečka et al. https://doi.org/10.1021/acs.estlett.1c00997
- Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions N. Hyttinen et al. https://doi.org/10.1021/acs.jpclett.2c02612
- Evaluating the Impact of Vehicular Aerosol Emissions on Particulate Matter (PM2.5) Formation Using Modeling Study O. Sánchez-Ccoyllo et al. https://doi.org/10.3390/atmos13111816
- Correlation gas chromatography and two-dimensional volatility basis methods to predict gas-particle partitioning for e-cigarette aerosols L. Tian et al. https://doi.org/10.1080/02786826.2024.2326547
- Profiling the Photosensitizing Properties of Atmospheric Brown Carbon Y. Chang et al. https://doi.org/10.1021/acsestair.5c00098
- An interpretable molecular descriptor for machine learning predictions in atmospheric science L. Lind et al. https://doi.org/10.1063/5.0308548
- The effect of atmospherically relevant aminium salts on water uptake N. Hyttinen https://doi.org/10.5194/acp-23-13809-2023
- From receptor models to machine learning: Advancing source apportionment of carbonaceous aerosol A. Oshakbayeva et al. https://doi.org/10.1016/j.aeaoa.2026.100470
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke https://doi.org/10.5194/gmd-18-2701-2025
- Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules V. Besel et al. https://doi.org/10.1038/s41597-023-02366-x
- Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid M. Engsvang et al. https://doi.org/10.1063/5.0152517
- Current and future machine learning approaches for modeling atmospheric cluster formation J. Kubečka et al. https://doi.org/10.1038/s43588-023-00435-0
- Accurate modeling of the potential energy surface of atmospheric molecular clusters boosted by neural networks J. Kubečka et al. https://doi.org/10.1039/D4VA00255E
- Improved vapor pressure predictions using group contribution-assisted graph convolutional neural networks (GC2NN) M. Krüger et al. https://doi.org/10.5194/gmd-18-7357-2025
- Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models Y. Liu & Z. Li https://doi.org/10.1021/acs.jcim.2c01321
- The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds V. Besel et al. https://doi.org/10.1016/j.jaerosci.2024.106375
- Active learning of molecular data for task-specific objectives K. Ghosh et al. https://doi.org/10.1063/5.0229834
- Compositional engineering of perovskites with machine learning J. Laakso et al. https://doi.org/10.1103/PhysRevMaterials.6.113801
- Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning J. Kubečka et al. https://doi.org/10.1021/acsomega.3c07412
- Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. https://doi.org/10.5194/gmd-16-2037-2023
- Unified representation of molecules and crystals for machine learning H. Huo & M. Rupp https://doi.org/10.1088/2632-2153/aca005
- Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning F. Bortolussi et al. https://doi.org/10.5194/acp-25-685-2025
- Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters L. Seppäläinen et al. https://doi.org/10.1021/acs.jpca.5c06950
- Atmospheric Sulfuric Acid–Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning J. Kubečka et al. https://doi.org/10.1021/acs.jpca.3c00068
- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. https://doi.org/10.1186/s13321-024-00825-0
- Characterization of a new Teflon chamber and on-line analysis of isomeric multifunctional photooxidation products F. Löher et al. https://doi.org/10.5194/amt-17-4553-2024
Saved (final revised paper)
Latest update: 11 Jun 2026
Short summary
The study of climate change relies on climate models, which require an understanding of aerosol formation. We train a machine-learning model to predict the partitioning coefficients of atmospheric molecules, which govern condensation into aerosols. The model can make instant predictions based on molecular structures with accuracy surpassing that of standard computational methods. This will allow the screening of low-volatility molecules that contribute most to aerosol formation.
The study of climate change relies on climate models, which require an understanding of aerosol...
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