Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

Unsupervised machine learning is applied to study the disordered aggregates of a model sequence defined macromolecule. Using these learned collective variables provides new insight into both the structure and kinetics of these aggregates.

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Work Title Unsupervised learning of sequence-specific aggregation behavior for a model copolymer
Access
Open Access
Creators
  1. Antonia Statt
  2. Devon C. Kleeblatt
  3. Wesley F. Reinhart
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Royal Society of Chemistry (RSC)
Publication Date 2021
Publisher Identifier (DOI)
  1. 10.1039/d1sm01012c
Source
  1. Soft Matter
Deposited May 27, 2022

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Version 1
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  • Created

    May 27, 2022 16:19 by Scholarly Communications and Copyright

  • Added arXiv-1.pdf

    May 27, 2022 16:19 by Scholarly Communications and Copyright

  • Added Creator Antonia Statt

    May 27, 2022 16:19 by Scholarly Communications and Copyright

  • Added Creator Devon C. Kleeblatt

    May 27, 2022 16:19 by Scholarly Communications and Copyright

  • Added Creator Wesley F. Reinhart

    May 27, 2022 16:19 by Scholarly Communications and Copyright

  • Published

    May 27, 2022 16:19 by Scholarly Communications and Copyright

  • Updated Description Show Changes

    June 01, 2022 13:40 by xzx1

    Description
    • <jats:p>Unsupervised machine learning is applied to study the disordered aggregates of a model sequence defined macromolecule. Using these learned collective variables provides new insight into both the structure and kinetics of these aggregates.</jats:p>
    • Unsupervised machine learning is applied to study the disordered aggregates of a model sequence defined macromolecule. Using these learned collective variables provides new insight into both the structure and kinetics of these aggregates.