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.
Skip Nav Destination
Article navigation
21 October 2019
Research Article|
October 15 2019
Unsupervised learning for local structure detection in colloidal systems
Emanuele Boattini
;
Emanuele Boattini
a)
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
Search for other works by this author on:
Marjolein Dijkstra
;
Marjolein Dijkstra
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
Search for other works by this author on:
Laura Filion
Laura Filion
Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University
, Utrecht, The Netherlands
Search for other works by this author on:
a)
Electronic mail: e.boattini@uu.nl
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
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Sign in via your Institution
Sign in via your InstitutionPay-Per-View Access
$40.00
Citing articles via
Related Content
Application of autoencoder to traffic noise analysis
Proc. Mtgs. Acoust. (December 2019)
Application of autoencoder to traffic noise analysis
J Acoust Soc Am (October 2019)
Intelligent autoencoder for space-time-coding digital metasurfaces
Appl. Phys. Lett. (April 2023)
Analysis of chaotic dynamical systems with autoencoders
Chaos (October 2021)
Preserving entanglement in a solid-spin system using quantum autoencoders
Appl. Phys. Lett. (September 2022)