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A comparative study of coarse-graining methods for polymeric fluids: Mori-Zwanzig vs. iterative Boltzmann inversion
Zhen Li1, Xin Bian1, Xiu Yang2
1Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA.
The Journal of Chemical Physics
|August 1, 2016
Summary
We developed two coarse-graining (CG) strategies for polymeric fluids. Both forward (Mori-Zwanzig) and reverse (iterative Boltzmann inversion, stochastic parametric optimization) methods accurately model atomistic details, providing guidelines for CG model development.
Area of Science:
- Computational chemistry
- Polymer physics
- Statistical mechanics
Background:
- Developing accurate coarse-grained (CG) models is crucial for simulating large polymeric systems.
- Existing CG methods require careful parameterization or may oversimplify complex interactions.
Purpose of the Study:
- To construct and compare effective CG models for polymeric fluids using forward and reverse coarse-graining strategies.
- To provide general guidelines for developing reliable CG models for polymer melts.
Main Methods:
- Utilized molecular dynamics (MD) simulations of star polymer melts to generate atomistic data.
- Applied Mori-Zwanzig (MZ) projection for a forward CG procedure.
- Employed iterative Boltzmann inversion (IBI) and stochastic parametric optimization (SPO) for reverse CG procedures.
Main Results:
- Both forward (MZ) and reverse (IBI, SPO) CG methods accurately reproduce the atomistic dynamics.
- The forward method requires no parameter tuning, while reverse methods necessitate careful parameter selection for accuracy.
- Aggressive coarse-graining can introduce many-body effects, invalidating pairwise potentials at different densities.
Conclusions:
- Effective CG models for polymeric fluids can be constructed using either forward or reverse strategies.
- Proper parameterization is key for the success of reverse coarse-graining methods.
- Understanding many-body effects is essential for extending CG model validity beyond training conditions.
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