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: 4,685 (including HTML, PDF, and XML)
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- Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid M. Engsvang et al. 10.1063/5.0152517
- Current and future machine learning approaches for modeling atmospheric cluster formation J. Kubečka et al. 10.1038/s43588-023-00435-0
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- Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models Y. Liu & Z. Li 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. 10.1016/j.jaerosci.2024.106375
- Compositional engineering of perovskites with machine learning J. Laakso et al. 10.1103/PhysRevMaterials.6.113801
- Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning J. Kubečka et al. 10.1021/acsomega.3c07412
- Correlation gas chromatography and two-dimensional volatility basis methods to predict gas-particle partitioning for e-cigarette aerosols L. Tian et al. 10.1080/02786826.2024.2326547
- Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. 10.5194/gmd-16-2037-2023
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- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
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- Gas-to-Particle Partitioning of Cyclohexene- and α-Pinene-Derived Highly Oxygenated Dimers Evaluated Using COSMOtherm N. Hyttinen et al. 10.1021/acs.jpca.0c11328
19 citations as recorded by crossref.
- Opinion: Challenges and needs of tropospheric chemical mechanism development B. Ervens et al. 10.5194/acp-24-13317-2024
- Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules V. Besel et al. 10.1038/s41597-023-02366-x
- Quantum Machine Learning Approach for Studying Atmospheric Cluster Formation J. Kubečka et al. 10.1021/acs.estlett.1c00997
- Machine Learning for Predicting Chemical Potentials of Multifunctional Organic Compounds in Atmospherically Relevant Solutions N. Hyttinen et al. 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. 10.3390/atmos13111816
- Quantum chemical modeling of atmospheric molecular clusters involving inorganic acids and methanesulfonic acid M. Engsvang et al. 10.1063/5.0152517
- Current and future machine learning approaches for modeling atmospheric cluster formation J. Kubečka et al. 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. 10.1039/D4VA00255E
- Predict Ionization Energy of Molecules Using Conventional and Graph-Based Machine Learning Models Y. Liu & Z. Li 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. 10.1016/j.jaerosci.2024.106375
- Compositional engineering of perovskites with machine learning J. Laakso et al. 10.1103/PhysRevMaterials.6.113801
- Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning J. Kubečka et al. 10.1021/acsomega.3c07412
- Correlation gas chromatography and two-dimensional volatility basis methods to predict gas-particle partitioning for e-cigarette aerosols L. Tian et al. 10.1080/02786826.2024.2326547
- Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks T. Berkemeier et al. 10.5194/gmd-16-2037-2023
- Unified representation of molecules and crystals for machine learning H. Huo & M. Rupp 10.1088/2632-2153/aca005
- The effect of atmospherically relevant aminium salts on water uptake N. Hyttinen 10.5194/acp-23-13809-2023
- Atmospheric Sulfuric Acid–Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning J. Kubečka et al. 10.1021/acs.jpca.3c00068
- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 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. 10.5194/amt-17-4553-2024
Latest update: 25 Dec 2024
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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