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

<p>Kinetic multi-layer models of aerosols and films have become the state-of-the-art method of describing complex aerosol processes at the particle and film level. We present MultilayerPy: an open-source framework for building, running and optimising kinetic multi-layer models – namely the kin...

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Main Authors: A. Milsom, A. Lees, A. M. Squires, C. Pfrang
Format: Article
Language:English
Published: Copernicus Publications 2022-09-01
Series:Geoscientific Model Development
Online Access:https://gmd.copernicus.org/articles/15/7139/2022/gmd-15-7139-2022.pdf
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author A. Milsom
A. Lees
A. M. Squires
C. Pfrang
C. Pfrang
author_facet A. Milsom
A. Lees
A. M. Squires
C. Pfrang
C. Pfrang
author_sort A. Milsom
collection DOAJ
description <p>Kinetic multi-layer models of aerosols and films have become the state-of-the-art method of describing complex aerosol processes at the particle and film level. We present MultilayerPy: an open-source framework for building, running and optimising kinetic multi-layer models – namely the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB) and the kinetic multi-layer model of gas–particle interactions in aerosols and clouds (KM-GAP). The modular nature of this package allows the user to iterate through various reaction schemes, diffusion regimes and experimental conditions in a systematic way. In this way, models can be customised and the raw model code itself, produced in a readable way by MultilayerPy, is fully customisable. Optimisation to experimental data using local or global optimisation algorithms is included in the package along with the option to carry out statistical sampling and Bayesian inference of model parameters with a Markov chain Monte Carlo (MCMC) sampler (via the emcee Python package). MultilayerPy abstracts the model building process into separate building blocks, increasing the reproducibility of results and minimising human error. This paper describes the general functionality of MultilayerPy and demonstrates this with use cases based on the oleic- acid–ozone heterogeneous reaction system. The tutorials in the source code (written as Jupyter notebooks) and the documentation aim to encourage users to take advantage of this tool, which is intended to be developed in conjunction with the user base.</p>
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spelling doaj.art-a2de3af275cb4da58847064dc31a25db2022-12-22T01:46:46ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-09-01157139715110.5194/gmd-15-7139-2022MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and filmsA. Milsom0A. Lees1A. M. Squires2C. Pfrang3C. Pfrang4School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UKSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UKDepartment of Chemistry, University of Bath, South Building, Soldier Down Ln, Claverton Down, Bath, UKSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UKDepartment of Meteorology, University of Reading, Whiteknights, Earley Gate, Reading, UK<p>Kinetic multi-layer models of aerosols and films have become the state-of-the-art method of describing complex aerosol processes at the particle and film level. We present MultilayerPy: an open-source framework for building, running and optimising kinetic multi-layer models – namely the kinetic multi-layer model of aerosol surface and bulk chemistry (KM-SUB) and the kinetic multi-layer model of gas–particle interactions in aerosols and clouds (KM-GAP). The modular nature of this package allows the user to iterate through various reaction schemes, diffusion regimes and experimental conditions in a systematic way. In this way, models can be customised and the raw model code itself, produced in a readable way by MultilayerPy, is fully customisable. Optimisation to experimental data using local or global optimisation algorithms is included in the package along with the option to carry out statistical sampling and Bayesian inference of model parameters with a Markov chain Monte Carlo (MCMC) sampler (via the emcee Python package). MultilayerPy abstracts the model building process into separate building blocks, increasing the reproducibility of results and minimising human error. This paper describes the general functionality of MultilayerPy and demonstrates this with use cases based on the oleic- acid–ozone heterogeneous reaction system. The tutorials in the source code (written as Jupyter notebooks) and the documentation aim to encourage users to take advantage of this tool, which is intended to be developed in conjunction with the user base.</p>https://gmd.copernicus.org/articles/15/7139/2022/gmd-15-7139-2022.pdf
spellingShingle A. Milsom
A. Lees
A. M. Squires
C. Pfrang
C. Pfrang
MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
Geoscientific Model Development
title MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
title_full MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
title_fullStr MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
title_full_unstemmed MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
title_short MultilayerPy (v1.0): a Python-based framework for building, running and optimising kinetic multi-layer models of aerosols and films
title_sort multilayerpy v1 0 a python based framework for building running and optimising kinetic multi layer models of aerosols and films
url https://gmd.copernicus.org/articles/15/7139/2022/gmd-15-7139-2022.pdf
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