Issue 41, 2015

Machine learning assembly landscapes from particle tracking data

Abstract

Bottom-up self-assembly offers a powerful route for the fabrication of novel structural and functional materials. Rational engineering of self-assembling systems requires understanding of the accessible aggregation states and the structural assembly pathways. In this work, we apply nonlinear machine learning to experimental particle tracking data to infer low-dimensional assembly landscapes mapping the morphology, stability, and assembly pathways of accessible aggregates as a function of experimental conditions. To the best of our knowledge, this represents the first time that collective order parameters and assembly landscapes have been inferred directly from experimental data. We apply this technique to the nonequilibrium self-assembly of metallodielectric Janus colloids in an oscillating electric field, and quantify the impact of field strength, oscillation frequency, and salt concentration on the dominant assembly pathways and terminal aggregates. This combined computational and experimental framework furnishes new understanding of self-assembling systems, and quantitatively informs rational engineering of experimental conditions to drive assembly along desired aggregation pathways.

Graphical abstract: Machine learning assembly landscapes from particle tracking data

Supplementary files

Article information

Article type
Paper
Submitted
09 Aug 2015
Accepted
24 Aug 2015
First published
25 Aug 2015

Soft Matter, 2015,11, 8141-8153

Machine learning assembly landscapes from particle tracking data

A. W. Long, J. Zhang, S. Granick and A. L. Ferguson, Soft Matter, 2015, 11, 8141 DOI: 10.1039/C5SM01981H

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