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Issue 41, 2015
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Machine learning assembly landscapes from particle tracking data

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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

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The article was received on 09 Aug 2015, accepted on 24 Aug 2015 and first published on 25 Aug 2015

Article type: Paper
DOI: 10.1039/C5SM01981H
Citation: Soft Matter, 2015,11, 8141-8153

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    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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