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This study enhances coarse-grained (CG) models for molecular liquids, improving temperature transferability for alkanes. A novel linear interpolation method optimizes a correction term for more accurate simulations across different states.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Statistical mechanics

Background:

  • Coarse-grained (CG) models are essential for simulating molecular liquids but often lack transferability to new state points.
  • Structure-based methods like Inverse Monte Carlo (IMC) can limit model transferability.
  • Previous attempts to improve transferability involved thermodynamic constraints or multistate parametrization.

Purpose of the Study:

  • To investigate the temperature transferability of IMC-derived CG models for alkanes.
  • To optimize the Das-Andersen (DA) approach for enhanced model transferability.
  • To implement a novel linear interpolation method for determining the Dunn-Noid (DN) correction term.

Main Methods:

  • Systematic derivation of CG models using Inverse Monte Carlo (IMC).
  • Application of the Das-Andersen (DA) Ansatz, which adds a correction term to the Hamiltonian.
  • Optimization of the DA approach via the Dunn-Noid (DN) method using linear interpolation for the correction term.

Main Results:

  • Demonstrated improved temperature transferability for CG models of selected alkanes.
  • Successfully applied a novel linear interpolation strategy to determine the DN correction term.
  • Validated the effectiveness of the optimized DA approach for CG model transferability.

Conclusions:

  • The optimized DA approach, particularly with the DN correction term determined by linear interpolation, significantly enhances the temperature transferability of IMC-derived CG models.
  • This method offers a more robust way to develop transferable CG models for molecular liquids, reducing the need for re-parametrization.
  • The findings pave the way for more accurate and efficient molecular simulations across various thermodynamic conditions.