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

Abstract. Kinetic multi-layer models of aerosols and films have become the state-of-the-art method describing complex aerosol processes at particle film level. We present MultilayerPy: an open-source framework for building, running optimising kinetic – namely model surface bulk chemistry (KM-SUB) gas–particle interactions in clouds (KM-GAP). The modular nature this package allows user to iterate through various reaction schemes, diffusion regimes experimental conditions a systematic way. In way, can be customised raw code itself, produced readable way by MultilayerPy, is fully customisable. Optimisation data using local or global optimisation algorithms included along with option carry out statistical sampling Bayesian inference parameters Markov chain Monte Carlo (MCMC) sampler (via emcee Python package). MultilayerPy abstracts building process into separate blocks, increasing reproducibility results minimising human error. This paper describes general functionality demonstrates use cases based on oleic- acid–ozone heterogeneous system. tutorials source (written as Jupyter notebooks) documentation aim encourage users take advantage tool, which intended developed conjunction base.