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Simulating highly entangled polymer melts using Gaussian soft-core potential.

Shensheng Chen1, Zhen-Gang Wang2

  • 1Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.

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This study introduces a more efficient molecular dynamics (MD) simulation method for entangled polymers. The new approach enables simulations of highly entangled polymer melts, revealing key dynamics and scaling laws.

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Area of Science:

  • Polymer Physics
  • Computational Materials Science
  • Soft Matter Physics

Background:

  • The Kremer-Grest (KG) model is standard for simulating entangled polymer dynamics.
  • Simulating highly entangled polymers in the diffusive regime is computationally intensive.
  • Existing methods face challenges with long simulation times and large chain lengths.

Purpose of the Study:

  • To develop and validate a computationally efficient molecular dynamics (MD) simulation method for entangled polymer melts.
  • To investigate polymer dynamics over extended time scales, reaching the diffusive regime.
  • To explore the impact of chain length on entanglement properties and relaxation behavior.

Main Methods:

  • Employed molecular dynamics (MD) simulations utilizing a Gaussian soft-core potential.
  • Simulated polymer melts with chain lengths up to N = 2000 (approximately 80 entanglement strands).
  • Analyzed monomer mean-squared displacement (g1(t)) and stress relaxation function (G(t)).

Main Results:

  • The new method achieves a smaller entanglement length (Ne) and larger invariant degree of polymerization (N̄) compared to the KG model.
  • Achieved significant computational efficiency, enabling simulations across the full spectrum of entanglement dynamics.
  • Observed robust t1/4 scaling for monomer mean-squared displacement over three decades.
  • Identified a weak, power-law-like quasi-plateau in the stress relaxation function for long chains (N > 1000).
  • Chain diffusion (D) and zero-shear viscosity (η) follow experimental trends (D ∼ N-2.3, η ∼ N3.4) up to 80Ne.

Conclusions:

  • The Gaussian soft-core potential offers a computationally advantageous alternative to the KG model for simulating entangled polymers.
  • The simulations provide insights into the dynamics of highly entangled polymer melts, extending into the diffusive regime.
  • Results support established scaling laws for chain diffusion and viscosity, without evidence of crossover to theoretical limits within the simulated range.