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Title: Controlling colloidal crystals via morphing energy landscapes and reinforcement learning

Abstract

We report a feedback control method to remove grain boundaries and produce circular shaped colloidal crystals using morphing energy landscapes and reinforcement learning–based policies. We demonstrate this approach in optical microscopy and computer simulation experiments for colloidal particles in ac electric fields. First, we discover how tunable energy landscape shapes and orientations enhance grain boundary motion and crystal morphology relaxation. Next, reinforcement learning is used to develop an optimized control policy to actuate morphing energy landscapes to produce defect-free crystals orders of magnitude faster than natural relaxation times. Morphing energy landscapes mechanistically enable rapid crystal repair via anisotropic stresses to control defect and shape relaxation without melting. This method is scalable for up to at least N = 10 3 particles with mean process times scaling as N 0.5 . Further scalability is possible by controlling parallel local energy landscapes (e.g., periodic landscapes) to generate large-scale global defect-free hierarchical structures.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Publication Date:
Research Org.:
Johns Hopkins Univ., Baltimore, MD (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
OSTI Identifier:
1724273
Alternate Identifier(s):
OSTI ID: 1755585
Grant/Contract Number:  
SC0017892
Resource Type:
Published Article
Journal Name:
Science Advances
Additional Journal Information:
Journal Name: Science Advances Journal Volume: 6 Journal Issue: 48; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; feedback control; Q-learning; field mediated assembly; perfect crystals; morphology control; grain boundary relaxation

Citation Formats

Zhang, Jianli, Yang, Junyan, Zhang, Yuanxing, and Bevan, Michael A. Controlling colloidal crystals via morphing energy landscapes and reinforcement learning. United States: N. p., 2020. Web. https://doi.org/10.1126/sciadv.abd6716.
Zhang, Jianli, Yang, Junyan, Zhang, Yuanxing, & Bevan, Michael A. Controlling colloidal crystals via morphing energy landscapes and reinforcement learning. United States. https://doi.org/10.1126/sciadv.abd6716
Zhang, Jianli, Yang, Junyan, Zhang, Yuanxing, and Bevan, Michael A. Wed . "Controlling colloidal crystals via morphing energy landscapes and reinforcement learning". United States. https://doi.org/10.1126/sciadv.abd6716.
@article{osti_1724273,
title = {Controlling colloidal crystals via morphing energy landscapes and reinforcement learning},
author = {Zhang, Jianli and Yang, Junyan and Zhang, Yuanxing and Bevan, Michael A.},
abstractNote = {We report a feedback control method to remove grain boundaries and produce circular shaped colloidal crystals using morphing energy landscapes and reinforcement learning–based policies. We demonstrate this approach in optical microscopy and computer simulation experiments for colloidal particles in ac electric fields. First, we discover how tunable energy landscape shapes and orientations enhance grain boundary motion and crystal morphology relaxation. Next, reinforcement learning is used to develop an optimized control policy to actuate morphing energy landscapes to produce defect-free crystals orders of magnitude faster than natural relaxation times. Morphing energy landscapes mechanistically enable rapid crystal repair via anisotropic stresses to control defect and shape relaxation without melting. This method is scalable for up to at least N = 10 3 particles with mean process times scaling as N 0.5 . Further scalability is possible by controlling parallel local energy landscapes (e.g., periodic landscapes) to generate large-scale global defect-free hierarchical structures.},
doi = {10.1126/sciadv.abd6716},
journal = {Science Advances},
number = 48,
volume = 6,
place = {United States},
year = {2020},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1126/sciadv.abd6716

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