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Unsupervised learning for local structure detection in colloidal systems.
- Source :
-
Journal of Chemical Physics . 10/21/2019, Vol. 151 Issue 15, pN.PAG-N.PAG. 12p. 7 Color Photographs, 1 Diagram, 1 Chart, 6 Graphs. - Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00219606
- Volume :
- 151
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Journal of Chemical Physics
- Publication Type :
- Academic Journal
- Accession number :
- 139227954
- Full Text :
- https://doi.org/10.1063/1.5118867