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Granick, Steve
CSLM(Center for Soft and Living Matter) Lab
Research Interests
  • Chemistry, physics, biology, engineering

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Machine learning assembly landscapes from particle tracking data

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Title
Machine learning assembly landscapes from particle tracking data
Author
Long, Andrew W.Zhang, JieGranick, SteveFerguson, Andrew L.
Keywords
NONLINEAR DIMENSIONALITY REDUCTION; FREE-ENERGY LANDSCAPES; JANUS PARTICLES; DIFFUSION MAPS; ELECTRIC-FIELDS; SKETCH-MAP; DYNAMICS; REPRESENTATION; NANOSTRUCTURES; ALIGNMENT
Issue Date
201508
Publisher
ROYAL SOC CHEMISTRY
Citation
SOFT MATTER, v.11, no.41, pp.8141 - 8153
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. © 2015 The Royal Society of Chemistry
URI
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DOI
http://dx.doi.org/10.1039/c5sm01981h
ISSN
1744-683X
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