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 journalArticlepeer-review

83 Scopus citations


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
Issue number27
StatePublished - 2017

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Condensed Matter Physics


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