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첨단연성물질 연구단
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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(s)
Andrew W. Long; Jie Zhang; Steve Granick; Andrew L. Ferguson
Publication Date
2015-11
Journal
SOFT MATTER, v.11, no.41, pp.8141 - 8153
Publisher
ROYAL SOC CHEMISTRY
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. © The Royal Society of Chemistry 2015
URI
https://pr.ibs.re.kr/handle/8788114/4849
ISSN
1744-683X
Appears in Collections:
Center for Soft and Living Matter(첨단연성물질 연구단) > Journal Papers (저널논문)
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