(142j) Machine Learning with Structural Fingerprints of Local Particle Environments

As the computational power available to scientists increases, researchers themselves â?? rather than hardware or algorithms â?? become the bottleneck in scientific discovery. The computational study of colloidal self-assembly is one area that keenly feels this effect: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what, if any, ordered structures occur in a large parameter space of many simulations can be a long, manual process. Here we demonstrate how â??off-the-shelfâ? machine learning algorithms can be applied to results of self-assembly studies both to find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated, high-throughput manner for both simple and complex crystal structures. These methods can form the foundation of an intelligent exploration of parameter space â?? a key component in the process of creating new materials by design.