Steff Farley - Quantitative Data Integration with Computational Models of Dewetting Processes

The self-assembly of nanostructures has been of growing interest in materials science, with particular advancements in the development of computational models that describe this self-assembly. So far, however, the utility of these models have been limited by the absence of methods to integrate real experimental data with numerical simulations or the experimental and simulation conditions that generate them. We simulate images of the resulting nanostructures using two models, a kinetic Monte Carlo (KMC) model and a dynamical density functional theory (DDFT) model, and attempt to relate a dataset of 2625 real atomic force microscope (AFM) images with these models. We propose a map to a feature space of modified Minkowski functionals that meaningfully characterise the geometry of this image space as the first step of this task. These coordinate statistics show some promise of allowing us to successfully carry out the inverse problem but are insufficient for this task when used alone. We propose that drawing from the methods of Riemannian geometry in combination with Approximate Bayesian Computation should be considered as a more suitable approach to this problem.