Slip Spring-Based Mesoscopic Simulations of Polymer Networks: Methodology and the Corresponding Computational Code.

Area of Science:

• Materials Science
• Polymer Physics
• Computational Chemistry

Background:

• Previous work established a Brownian dynamics/kinetic Monte Carlo (BD/kMC) methodology for polymer melts.
• Simulating crosslinked polymer networks requires advanced computational techniques for accurate dynamical and thermodynamic predictions.
• Coarse-grained models simplify complex polymer structures into manageable units for efficient simulation.

Purpose of the Study:

• To extend the BD/kMC methodology for dynamical simulations of coarse-grained crosslinked polymer networks.
• To detail the C++ simulation code, Engine for Mesoscopic Simulations for Polymer Networks (EMSIPON).
• To validate the methodology by predicting thermodynamic and mechanical properties of a cis-1,4-polyisoprene network.

Main Methods:

• A coarse-grained representation models polymer chains as sequences of beads, each representing multiple Kuhn segments.
• Slip springs are employed to represent entanglements, with their dynamics tracked via kinetic Monte Carlo simulations.
• The Helmholtz energy is defined by entropic springs, slip springs, and non-bonded interactions derived from the Sanchez-Lacombe equation of state.

Main Results:

• The isothermal compressibility of the crosslinked polyisoprene network was accurately predicted from equilibrium density fluctuations.
• Predictions showed excellent agreement with both the Sanchez-Lacombe equation of state and experimental data.
• The EMSIPON code successfully simulated elongational deformations and predicted shear stress relaxation modulus.

Conclusions:

• The extended BD/kMC methodology and EMSIPON code provide a robust framework for simulating crosslinked polymer networks.
• The approach accurately captures both equilibrium thermodynamic properties and dynamic mechanical responses.
• This work enables detailed investigation of polymer network behavior under various conditions.