Multi-Scale Modelling of the Epitaxial Growth of Organic Thin Films on Insulating Surfaces
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Abstract
Growth morphologies of epitaxially grown molecular thin films are of interest for a range of different technological applications. While the possibilities of epitaxial growth experiments are usually explored in the lab, it is desirable to be able to accurately simulate the process to more quickly explore a wide range of different experimental protocols. The kinetic Monte Carlo (KMC) method is a promising approach in that regard, as it can access the necessary time and length scales on which epitaxial growth can be observed. However, a KMC algorithm requires the transition rates of all implemented elementary transitions as input parameters and ideally also their dependence on experimental parameters like temperature. Experimental data can help to inform a model for the transition rates of a KMC simulation, but experiments alone are not sufficient to completely determine all the parameters. As a result, KMC rate models are often oversimplified to be able to work with a limited amount of experimental information. In this thesis, we present a bottom-up approach for the determination of a KMC rate model that is built on a foundation of transition rate data gathered in molecular dynamics (MD) simulations. The example system on which we apply this approach is the epitaxial growth of the buckminsterfullerene on a calcium fluoride substrate. We set up MD simulations of this system in a wide variety of configurations in which we can observe the elementary transitions, determine their transition rates in a range of temperatures and finally use the obtained data to derive a rate model for use in KMC simulations. To test the obtained models, we run KMC simulations and compare the results with experimental data. This thesis contributes to the ability to model and simulate the self-assembly processes of molecules on insulating substrates. Consequently, it advances the understanding of such systems and enables the development of new strategies to control the evolution of cluster morphologies in deposition experiments.