Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2037-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2037-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
14 Apr 2023
Development and technical paper |
| 14 Apr 2023
| 14 Apr 2023
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany
Matteo Krüger
Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany
Aryeh Feinberg
Institute for Atmospheric and Climate Science, ETH Zürich, 8092 Zürich, Switzerland
Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092 Zürich, Switzerland
Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland
currently at: Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
Marcel Müller
Institute for Atmospheric and Climate Science, ETH Zürich, 8092 Zürich, Switzerland
Ulrich Pöschl
Multiphase Chemistry Department, Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany
Ulrich K. Krieger
Institute for Atmospheric and Climate Science, ETH Zürich, 8092 Zürich, Switzerland
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Cited
12 citations as recorded by crossref.
- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
- Molecular Self-Organization in Surfactant Atmospheric Aerosol Proxies A. Milsom et al. 10.1021/acs.accounts.3c00194
- Acoustic levitation with polarising optical microscopy (AL-POM): water uptake in a nanostructured atmospheric aerosol proxy A. Milsom et al. 10.1039/D3EA00083D
- Oxidation von Kohlenstoff‐Nanopartikeln durch NO2 und O2: Chemische Kinetik und Reaktionspfade T. Berkemeier & U. Pöschl 10.1002/ange.202413325
- Exploring the influence of particle phase in the ozonolysis of oleic and elaidic acid R. Kaur Kohli et al. 10.1080/02786826.2023.2226183
- Technological advances and operational challenges of the FAWAG (Foam-Assisted Water Alternating Gas) method in enhanced oil recovery: a comprehensive review of principles, applications, and future perspectives J. Ribeiro et al. 10.1016/j.geoen.2025.214206
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
- Carbon Nanoparticle Oxidation by NO2 and O2: Chemical Kinetics and Reaction Pathways T. Berkemeier & U. Pöschl 10.1002/anie.202413325
- Discovering deposition process regimes: Leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis G. Loachamín-Suntaxi et al. 10.1016/j.ceja.2024.100667
- Algorithms for Approximate Solution of ODE Ensembles Using Clustering and Sensitivity Matrices A. Penenko et al. 10.1134/S1995423925020053
- Opinion: Challenges and needs of tropospheric chemical mechanism development B. Ervens et al. 10.5194/acp-24-13317-2024
- Unveiling the Role of Carbonate Radical Anions in Dust‐Driven SO2 Oxidation Y. Liu et al. 10.1029/2023JD040017
12 citations as recorded by crossref.
- A numerical compass for experiment design in chemical kinetics and molecular property estimation M. Krüger et al. 10.1186/s13321-024-00825-0
- Molecular Self-Organization in Surfactant Atmospheric Aerosol Proxies A. Milsom et al. 10.1021/acs.accounts.3c00194
- Acoustic levitation with polarising optical microscopy (AL-POM): water uptake in a nanostructured atmospheric aerosol proxy A. Milsom et al. 10.1039/D3EA00083D
- Oxidation von Kohlenstoff‐Nanopartikeln durch NO2 und O2: Chemische Kinetik und Reaktionspfade T. Berkemeier & U. Pöschl 10.1002/ange.202413325
- Exploring the influence of particle phase in the ozonolysis of oleic and elaidic acid R. Kaur Kohli et al. 10.1080/02786826.2023.2226183
- Technological advances and operational challenges of the FAWAG (Foam-Assisted Water Alternating Gas) method in enhanced oil recovery: a comprehensive review of principles, applications, and future perspectives J. Ribeiro et al. 10.1016/j.geoen.2025.214206
- Similarity-based analysis of atmospheric organic compounds for machine learning applications H. Sandström & P. Rinke 10.5194/gmd-18-2701-2025
- Carbon Nanoparticle Oxidation by NO2 and O2: Chemical Kinetics and Reaction Pathways T. Berkemeier & U. Pöschl 10.1002/anie.202413325
- Discovering deposition process regimes: Leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis G. Loachamín-Suntaxi et al. 10.1016/j.ceja.2024.100667
- Algorithms for Approximate Solution of ODE Ensembles Using Clustering and Sensitivity Matrices A. Penenko et al. 10.1134/S1995423925020053
- Opinion: Challenges and needs of tropospheric chemical mechanism development B. Ervens et al. 10.5194/acp-24-13317-2024
- Unveiling the Role of Carbonate Radical Anions in Dust‐Driven SO2 Oxidation Y. Liu et al. 10.1029/2023JD040017
Latest update: 29 Oct 2025
Short summary
Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieved high correlations. The surrogate models run orders of magnitude faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Kinetic multi-layer models (KMs) successfully describe heterogeneous and multiphase atmospheric...
