Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-2037-2023
https://doi.org/10.5194/gmd-16-2037-2023
Development and technical paper
 | 
14 Apr 2023
Development and technical paper |  | 14 Apr 2023

Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks

Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger

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Cited articles

Allotey, J., Butler, K. T., and Thiyagalingam, J.: Entropy-based active learning of graph neural network surrogate models for materials properties, J. Chem. Phys., 155, 174116, https://doi.org/10.1063/5.0065694, 2021. a
Almeida, L. B.: Multilayer Perceptrons, in: The Algebraic Mind: Integrating Connectionism and Cognitive Science, The MIT Press, https://doi.org/10.7551/mitpress/1187.003.0004, 2001. a, b
Berkemeier, T., Huisman, A. J., Ammann, M., Shiraiwa, M., Koop, T., and Pöschl, U.: Kinetic regimes and limiting cases of gas uptake and heterogeneous reactions in atmospheric aerosols and clouds: a general classification scheme, Atmos. Chem. Phys., 13, 6663–6686, https://doi.org/10.5194/acp-13-6663-2013, 2013. a
Berkemeier, T., Steimer, S. S., Krieger, U. K., Peter, T., Pöschl, U., Ammann, M., and Shiraiwa, M.: Ozone uptake on glassy, semi-solid and liquid organic matter and the role of reactive oxygen intermediates in atmospheric aerosol chemistry, Phys. Chem. Chem. Phys., 18, 12662–12674, https://doi.org/10.1039/C6CP00634E, 2016. a
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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.