Issue 27, 2017

Machine learning for autonomous crystal structure identification

Abstract

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Graphical abstract: Machine learning for autonomous crystal structure identification

Article information

Article type
Paper
Submitted
12 May 2017
Accepted
10 Jun 2017
First published
16 Jun 2017

Soft Matter, 2017,13, 4733-4745

Machine learning for autonomous crystal structure identification

W. F. Reinhart, A. W. Long, M. P. Howard, A. L. Ferguson and A. Z. Panagiotopoulos, Soft Matter, 2017, 13, 4733 DOI: 10.1039/C7SM00957G

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