As computers get faster, researchers -- not hardware or algorithms -- become
the bottleneck in scientific discovery. Computational study of colloidal
self-assembly is one area that is keenly affected: 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 excruciating. We demonstrate how machine learning can
be applied to discover interesting areas of parameter space in colloidal self
assembly. We create numerical fingerprints -- inspired by bond orientational
order diagrams -- of structures found in self-assembly studies and use these
descriptors to both find interesting regions in a phase diagram and identify
characteristic local environments in simulations in an automated manner for
simple and complex crystal structures. Utilizing these methods allows analysis
methods to keep up with the data generation ability of modern high-throughput
computing environments.Comment: Fixed typo, added missing acknowledgment, added supplementary
informatio
