Machine learning assembly landscapes from particle tracking data.

Citation data:

Soft matter, ISSN: 1744-6848, Vol: 11, Issue: 41, Page: 8141-53

Publication Year:
2015
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Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/19408
PMID:
26338295
DOI:
10.1039/c5sm01981h
Author(s):
Long, Andrew W.; Zhang, Jie; Granick, Steve; Ferguson, Andrew L.
Publisher(s):
Royal Society of Chemistry (RSC); The Royal Society of Chemistry; ROYAL SOC CHEMISTRY
Tags:
Chemistry; Physics and Astronomy; NONLINEAR DIMENSIONALITY REDUCTION; FREE-ENERGY LANDSCAPES; JANUS PARTICLES; DIFFUSION MAPS; ELECTRIC-FIELDS; SKETCH-MAP; DYNAMICS; REPRESENTATION; NANOSTRUCTURES; ALIGNMENT
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article description
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.