We introduce a simple, fast, and easy to implement unsupervised learning algorithm for detecting different local environments on a single-particle level in colloidal systems. In this algorithm, we use a vector of standard bond-orientational order parameters to describe the local environment of each particle. We then use a neural-network-based autoencoder combined with Gaussian mixture models in order to autonomously group together similar environments. We test the performance of the method on snapshots of a wide variety of colloidal systems obtained via computer simulations, ranging from simple isotropically interacting systems to binary mixtures, and even anisotropic hard cubes. Additionally, we look at a variety of common self-assembled situations such as fluid-crystal and crystal-crystal coexistences, grain boundaries, and nucleation. In all cases, we are able to identify the relevant local environments to a similar precision as “standard,” manually tuned, and system-specific, order parameters. In addition to classifying such environments, we also use the trained autoencoder in order to determine the most relevant bond orientational order parameters in the systems analyzed.
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21 October 2019
Research Article|
October 15 2019
Unsupervised learning for local structure detection in colloidal systems Available to Purchase
Emanuele Boattini
;
Emanuele Boattini
a)
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
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Marjolein Dijkstra
;
Marjolein Dijkstra
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
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Laura Filion
Laura Filion
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
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Emanuele Boattini
a)
Marjolein Dijkstra
Laura Filion
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
a)
Electronic mail: [email protected]
J. Chem. Phys. 151, 154901 (2019)
Article history
Received:
July 08 2019
Accepted:
September 25 2019
Citation
Emanuele Boattini, Marjolein Dijkstra, Laura Filion; Unsupervised learning for local structure detection in colloidal systems. J. Chem. Phys. 21 October 2019; 151 (15): 154901. https://doi.org/10.1063/1.5118867
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