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Backmapping coarse-grained macromolecules: An efficient and versatile machine learning approach.

Wei Li1, Craig Burkhart2, Patrycja Polińska3

  • 1Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.

The Journal of Chemical Physics
|August 6, 2020
PubMed
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This summary is machine-generated.

This study introduces an image-based machine learning method for structural backmapping in polymers. It efficiently reconstructs atomistic details from coarse-grained models, crucial for multiscale polymer simulations.

Area of Science:

  • Polymer Science
  • Computational Chemistry
  • Materials Science

Background:

  • Multiscale modeling of polymers requires linking coarse and fine molecular representations.
  • Generating fine-scale details from coarse representations is challenging, demanding methods balancing accuracy and efficiency.
  • Existing techniques often struggle with general applicability and computational cost.

Purpose of the Study:

  • To develop an image-based approach for structural backmapping from coarse-grained to atomistic polymer models.
  • To utilize machine learning, specifically conditional generative adversarial networks, for this backmapping process.
  • To demonstrate the approach's efficiency and transferability using cis-1,4 polyisoprene melts.

Main Methods:

  • Training conditional generative adversarial networks on paired coarse-grained and atomistic configurations.

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  • Applying the trained model to predict atomistic structures from coarse-grained inputs.
  • Investigating the impact of different data representation schemes on model performance.

    • Main Results:

    • Achieved efficient and accurate structural backmapping from coarse-grained to atomistic models for cis-1,4 polyisoprene melts.
    • Demonstrated remarkable transferability across different molecular weights using training sets from oligomeric compounds.
    • The image-based machine learning approach proved effective in generating high-fidelity atomistic configurations.

    Conclusions:

    • The proposed image-based backmapping approach offers a versatile and efficient solution for multiscale polymer modeling.
    • This method facilitates the generation of high-fidelity initial configurations with minimal human intervention.
    • The approach is readily extendable to other complex polymer systems.