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-organization. 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 data set of real AFM images of self-organized nanoparticles. We do this regardless of feature-scale and without the need for manually labeled training data.

Combining nanoscale manipulation with macroscale relocation of single quantum dots.

We have controllably positioned, with nanometre precision, single CdSe quantum dots referenced to a registration template such that the location of a given nanoparticle on a macroscopic (≈1 cm(2)) sample surface can be repeatedly revisited. The atomically flat sapphire substrate we use is particularly suited to optical measurements of the isolated quantum dots, enabling combined manipulation-spectroscopy experiments on a single particle. Automated nanoparticle manipulation and imaging routines have been developed so as to facilitate the rapid assembly of specific nanoparticle arrangements.

A genetic algorithm approach to probing the evolution of self-organized nanostructured systems.

We present a new methodology, based on a combination of genetic algorithms and image morphometry, for matching the outcome of a Monte Carlo simulation to experimental observations of a far-from-equilibrium nanosystem. The Monte Carlo model used simulates a colloidal solution of nanoparticles drying on a solid substrate and has previously been shown to produce patterns very similar to those observed experimentally. Our approach enables the broad parameter space associated with simulated nanoparticle self-organization to be searched effectively for a given experimental target morphology.