Simulating highly entangled polymer melts using Gaussian soft-core potential

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.