Work Description

Title: Data for the manuscript titled "Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors" Open Access Deposited

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Attribute Value
Methodology
  • The data contains simulation, visualization and analysis scripts, and the corresponding results produced for the publication Agrawal, M, Glotzer SC. (2020). Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors. PNAS Journal URL:  www.pnas.org/cgi/doi/10.1073/pnas.1922635117

  • - Simulation: 2D Brownian dynamics simulations are performed using the HOOMD-blue simulation package. Simulation trajectories are in tar format that is read using the Glotzformats/Garnett software developed by the Glotzer lab at the University of Michigan.

  • - Visualization: Matplotlib is used to generate images in png format. FFMPEG is used along with the Matplotlib to generate movies in mp4 format.

  • - Analysis: Python Numpy is primarily used for writing the data analysis.

  • - Data management: Signac software, developed by the Glotzer lab, is used to generate and manage state points.

  • - Relevant File Names: run.py - simulation script for HOOMD-blue; visualize.py - visualization script to generate images and movies; dump.tar - simulation trajectory
Description
  • Micron-scale robots require systems that can morph into arbitrary target configurations controlled by external agents such as heat, light, electricity, and chemical environment. Achieving this behavior using conventional approaches is challenging because the available materials at these scales are not programmable like their macroscopic counterparts. To overcome this challenge, we propose a design strategy to make a robotic machine that is both programmable and compatible with colloidal-scale physics. Our strategy uses motors in the form of active colloidal particles that constantly propel forward. We sequence these motors end-to-end in a closed chain forming a two-dimensional loop that folds under its mechanical constraints. We encode the target loop shape and its motion by regulating six design parameters, each scale-invariant and achievable at the colloidal scale. The research dataset includes simulation, visualization, and analysis scripts and results generated for the 2D chain loops of self-propelling particles. File Description:

  • -- arrows_folding - Contains the data for the folded chain loop shapes resembling an arrowhead.

  • -- bending_vs_variation - Contains the data to study the stability of a particular shape in simulations as one of the segments of the shape bends and/or the distribution of propulsion on it varies.

  • -- curved_triangle - Contains the data to study motion and bending of a triangle shape made using chain loop.

  • -- example_shapes - Contains data for various examples of shapes that can be generated by designing the chain loops.

  • -- nskT_vs_fakT - Contains the data for a specific shape to study the effect of scaling up the number of particles (governed by ns) and the propulsion (governed by fa) in its chain.

  • -- stability - Contains the data and theoretical model (stability.py) to study the stability of the six different shapes.

  • -- tuning_design_forM - Contains the data for sequential tuning the design parameters to fold the shape "M" as described in the corresponding publication.

  • -- two_neighboring_cds_segments_ - Contains the data to study a system of two neighboring chain segments with respect to different parameters discussed in the publication.
Creator
Depositor
  • amayank@umich.edu
Contact information
Discipline
Funding agency
  • Department of Energy (DOE)
Keyword
Citations to related material
Resource type
Last modified
  • 03/26/2020
Published
  • 03/26/2020
Language
DOI
  • https://doi.org/10.7302/czgw-2x26
License
To Cite this Work:
Agrawal, M., Glotzer, S. C. (2020). Data for the manuscript titled "Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/czgw-2x26

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Files (Count: 9; Size: 763 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
README.md 2020-03-25 2020-03-25 1003 Bytes Open Access
arrows_folding.zip 2020-03-25 2020-03-25 54.2 MB Open Access
curved_triangle.zip 2020-03-23 2020-03-23 79.4 MB Open Access
bending_vs_variation.zip 2020-03-23 2020-03-23 108 MB Open Access
example_shapes.zip 2020-03-23 2020-03-23 167 MB Open Access
nskT_vs_fakT.zip 2020-03-23 2020-03-23 79.8 MB Open Access
stability.zip 2020-03-25 2020-03-25 194 MB Open Access
tuning_design_forM.zip 2020-03-23 2020-03-23 18.7 MB Open Access
two_neighbor_cds_segments_.zip 2020-03-23 2020-03-23 62.3 MB Open Access

Activeloops

Public data repository for the active loops paper published in PNAS journal.

The glotzerlab software packages used here are
hoomd-blue: https://glotzerlab.engin.umich.edu/hoomd-blue/
glotzformats: https://glotzerlab.engin.umich.edu/glotzformats/
signac: https://signac.io/
FFMPEG: https://www.ffmpeg.org

Python version : 3.4.6

Important files that work together in a folder are:

dump.tar is the trajectory file of a simulation | read using glotzformats (now garnett)
run.py is the simulation script that generates dump.tar using HOOMD-blue
visualize.py that reads dump.tar and generates image/movie using Matplotlib / FFMPEG

signac_script.py if present in a folder, is a signac script that generates statepoints,
which are hash-mapped folders inside the sub-folder . The script also copies the files and inside the statepoint folders to be used within those.
The three main funcitons that signac_script uses are initialize_jobs, edit_files and run.

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