Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7139-2022
https://doi.org/10.5194/gmd-15-7139-2022
Model description paper
 | 
22 Sep 2022
Model description paper |  | 22 Sep 2022

MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films

Adam Milsom, Amy Lees, Adam M. Squires, and Christian Pfrang

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Cited articles

Abbatt, J. P. D. and Wang, C.: The atmospheric chemistry of indoor environments, Environ. Sci.-Proc. Imp., 22, 25–48, https://doi.org/10.1039/c9em00386j, 2020. 
Berkemeier, T., Ammann, M., Krieger, U. K., Peter, T., Spichtinger, P., Pöschl, U., Shiraiwa, M., and Huisman, A. J.: Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets, Atmos. Chem. Phys., 17, 8021–8029, https://doi.org/10.5194/acp-17-8021-2017, 2017. 
Berkemeier, T., Mishra, A., Mattei, C., Huisman, A. J., Krieger, U. K., and Pöschl, U.: Ozonolysis of Oleic Acid Aerosol Revisited: Multiphase Chemical Kinetics and Reaction Mechanisms, ACS Earth Sp. Chem., 5, 3313–3323, https://doi.org/10.1021/acsearthspacechem.1c00232, 2021. 
Dennis-Smither, B. J., Miles, R. E. H., and Reid, J. P.: Oxidative aging of mixed oleic acid/sodium chloride aerosol particles, J. Geophys. Res.-Atmos., 117, 1–13, https://doi.org/10.1029/2012JD018163, 2012. 
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Short summary
MultilayerPy is a Python-based framework facilitating the creation, running and optimisation of state-of-the-art kinetic multi-layer models of aerosol and film processes. Models can be fit to data with local and global optimisation algorithms along with a statistical sampling algorithm, which quantifies the uncertainty in optimised model parameters. This “modelling study in a box” enables more reproducible and reliable results, with model code and outputs produced in a human-readable way.