Machine learning for crystal identification and discovery

TL;DR

This paper demonstrates how machine learning can efficiently analyze large datasets from colloidal self-assembly simulations, identifying interesting structures and local environments to accelerate scientific discovery.

Contribution

The authors introduce numerical fingerprints and automated analysis techniques that enable rapid identification of crystal structures and phase regions in large simulation datasets.

Findings

Machine learning effectively identifies phase regions in colloidal self-assembly.

Numerical fingerprints distinguish different crystal structures.

Automated analysis keeps pace with high-throughput data generation.

Abstract

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.