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Issue 27, 2017
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Machine learning for autonomous crystal structure identification

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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

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Publication details

The article was received on 12 May 2017, accepted on 10 Jun 2017 and first published on 16 Jun 2017

Article type: Paper
DOI: 10.1039/C7SM00957G
Citation: Soft Matter, 2017,13, 4733-4745
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    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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