Machine learning for autonomous crystal structure identification

Wesley F. Reinhart, Andrew W. Long, Michael P. Howard, Andrew L. Ferguson, Athanassios Z. Panagiotopoulos

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)4733-4745
Number of pages13
JournalSoft Matter
Volume13
Issue number27
DOIs
StatePublished - Jan 1 2017

ASJC Scopus subject areas

  • Chemistry(all)
  • Condensed Matter Physics

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    Reinhart, W. F., Long, A. W., Howard, M. P., Ferguson, A. L., & Panagiotopoulos, A. Z. (2017). Machine learning for autonomous crystal structure identification. Soft Matter, 13(27), 4733-4745. https://doi.org/10.1039/c7sm00957g