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Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials

Author

Listed:
  • Fabian Grünewald

    (University of Groningen)

  • Riccardo Alessandri

    (University of Groningen
    University of Chicago)

  • Peter C. Kroon

    (University of Groningen)

  • Luca Monticelli

    (UMR 5086 CNRS and University of Lyon)

  • Paulo C. T. Souza

    (UMR 5086 CNRS and University of Lyon)

  • Siewert J. Marrink

    (University of Groningen)

Abstract

Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that provides 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle.

Suggested Citation

  • Fabian Grünewald & Riccardo Alessandri & Peter C. Kroon & Luca Monticelli & Paulo C. T. Souza & Siewert J. Marrink, 2022. "Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-021-27627-4
    DOI: 10.1038/s41467-021-27627-4
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    Cited by:

    1. Samantha M. McDonald & Emily K. Augustine & Quinn Lanners & Cynthia Rudin & L. Catherine Brinson & Matthew L. Becker, 2023. "Applied machine learning as a driver for polymeric biomaterials design," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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