Automated searching and identification of self-organized nanostructures

Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organisation. Here, we use a combination of Monte Carlo simulations, general statistics and machine learning to automatically distinguish several spatially-correlated patterns in a mixed, highly varied dataset of real AFM images of self-organised nanoparticles. We do this regardless of feature-scale and without the need for manually labelled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organised systems and datasets.