Publication Date:
2018-03-15
Description:
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 to keep up with the data generation ability of modern high-throughput computing environments. This article is protected by copyright. All rights reserved.
Print ISSN:
0001-1541
Electronic ISSN:
1547-5905
Topics:
Chemistry and Pharmacology
,
Process Engineering, Biotechnology, Nutrition Technology
