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We present a new method for parameterizing generalized Langevin equation (GLE) thermostats to accurately simulate coarse-grained (CG) polymer dynamics. This approach improves polymer diffusion predictions in CG models by incorporating memory effects.

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Area of Science:

  • Computational chemistry
  • Polymer physics
  • Statistical mechanics

Background:

  • Coarse-grained (CG) models are essential for simulating large molecular systems.
  • Accurate dynamics in CG models are crucial but challenging to achieve.
  • Existing methods often fail to capture the full dynamic behavior of polymers.

Purpose of the Study:

  • To develop a parameterization route for generalized Langevin equation (GLE) thermostats.
  • To correct the dynamics of CG models with pairwise conservative interactions.
  • To improve the quantitative agreement between CG simulations and fine-grained simulations.

Main Methods:

  • Utilizing the Mori-Zwanzig formalism.
  • Deriving memory kernels from Q-projected time correlation functions.
  • Investigating bottom-up informed (GLE and LE) thermostats for a CG star-polymer melt.

Main Results:

  • Inclusion of memory in CG simulations leads to quantitative agreement with fine-grained simulations for polymer diffusion.
  • Memory effects were observed even in the diffusive regime.
  • Previously neglected cross-correlations between irrelevant and CG degrees of freedom were identified as key.

Conclusions:

  • The proposed parameterization route for GLE thermostats effectively corrects CG model dynamics.
  • Incorporating memory effects and cross-correlations is vital for accurate polymer diffusion prediction.
  • This work provides a more reliable approach for CG simulations of polymer systems.