Direct route to reproducing pair distribution functions with coarse-grained models via transformed atomistic cross
Svenja J Wörner1, Tristan Bereau1, Kurt Kremer1
1Max Planck Institute for Polymer Research, 55128 Mainz, Germany.
The Journal of Chemical Physics
|January 3, 2020
Summary
This study introduces novel noniterative methods for parameterizing coarse-grained (CG) models by directly using atomistic cross correlations. These approaches improve the accuracy of CG models for structural features, particularly in complex systems like water.
Area of Science:
- Computational chemistry and materials science
- Molecular modeling and simulation
Background:
- Coarse-grained (CG) models simplify complex systems but often struggle with accurate parameterization, especially for multicomponent systems.
- Current iterative methods, assuming uncorrelated interactions, have limitations in capturing essential cross-correlation information from atomistic models.
Purpose of the Study:
- To develop direct, noniterative methods for CG model parameterization using atomistic cross correlations.
- To improve the accuracy of CG models in reproducing structural features of atomistic (AA) models.
Main Methods:
- Proposed two distinct direct parameterization approaches leveraging high-resolution cross correlations.
- Adjusted atomistic cross correlations to better match expected CG model correlations.
- Applied methods to a one-site-per-molecule model of liquid water.
Main Results:
- Developed CG models that more accurately describe low-order structural features of the reference AA model.
- The new models generate distinct cross correlations compared to the AA reference.
- Demonstrated limitations of pairwise interactions in reproducing water's tetrahedral solvation structure.
Conclusions:
- Direct parameterization using cross correlations offers an alternative to iterative methods.
- Cross-correlation features significantly influence the accuracy of pair correlation functions.
- The placement and balance of correlation features are crucial for model fidelity.
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