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The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
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Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
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Polymers that are made up of identical monomer units are called homopolymers. Only one repeating unit is involved in the construction of the homopolymer structure. For example, as depicted in Figure 1, polypropylene is a homopolymer constituted of propylene monomers. Here, the only repeating unit in the polymer chain is propylene.
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Protocol for creating representations of molecular structures using a polymer-specific decoder.

Yannik Köster1, Julian Kimmig1, Stefan Zechel1

  • 1Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldstr. 10, 07743 Jena, Germany; Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany.

STAR Protocols
|May 3, 2024
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Summary
This summary is machine-generated.

This study introduces a polymer-specific decoder to generate polymer fingerprints (PFPs) for machine learning. This method enables predicting polymer properties using artificial neural networks.

Keywords:
ChemistryComputer sciencesMaterial sciences

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Area of Science:

  • Polymer Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Machine learning requires standardized chemical structure representations.
  • Existing methods may not adequately capture polymer-specific structural nuances.

Purpose of the Study:

  • To present a protocol for generating polymer fingerprints (PFPs).
  • To enable the application of machine learning in polymer science.
  • To facilitate the prediction of polymer properties.

Main Methods:

  • Developing and utilizing a polymer-specific decoder.
  • Outlining software installation and application.
  • Describing data processing, analysis, and integration for machine learning.
  • Explaining the use of artificial neural networks for property prediction.

Main Results:

  • A protocol for generating polymer fingerprints (PFPs) is established.
  • The method allows for the processing and analysis of polymer structure data.
  • Integration with machine learning workflows is demonstrated.
  • Artificial neural networks can predict polymer properties based on PFPs.

Conclusions:

  • The developed protocol provides a robust method for generating polymer representations.
  • This facilitates the application of machine learning for polymer property prediction.
  • The protocol supports advancements in data-driven polymer research.