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This study introduces a hybrid method to improve coarse-grained simulations by periodically exchanging fine-grained particles. This ensures accurate diffusion rates and maintains dynamical consistency between simulation scales.

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Area of Science:

  • Computational chemistry
  • Molecular dynamics
  • Statistical mechanics

Background:

  • Coarse-grained (CG) simulations simplify complex systems by grouping fine-grained (FG) particles.
  • Representativity and dynamical consistency are key challenges in CG modeling.
  • Existing CG methods may not accurately capture FG particle behavior, especially diffusion.

Purpose of the Study:

  • To develop a hybrid procedure for enhancing representativity and dynamical consistency in CG simulations.
  • To ensure accurate simulation of FG particle dynamics, particularly diffusion rates.
  • To bridge the gap between FG and CG simulation scales.

Main Methods:

  • Implemented a hybrid procedure involving periodic exchange of FG particles between CG particles.
  • Utilized back-mapping of CG particles to FG particles for reassignment.
  • Ensured conservation of total mass and momentum during particle exchange.
  • Simulated FG particles in a hybrid CG framework and compared diffusion rates with all-atom molecular dynamics (AAMD).

Main Results:

  • An appropriate characteristic exchange time was found to yield correct effective diffusion rates for FG particles.
  • Without the exchange mechanism, FG particles remained localized within CG particles, reducing diffusion rates.
  • The proposed method demonstrated improved dynamical consistency compared to standard CG approaches, especially in compressed fluid regimes.

Conclusions:

  • The study confirms the necessity of addressing FG particle representativity within CG particles.
  • A simple FG particle exchange mechanism effectively retains dynamical consistency between FG and CG scales.
  • This approach offers a viable solution for improving the accuracy of CG simulations involving particle dynamics.