A temperature-dependent length-scale for transferable local density potentials

Area of Science:

• Computational chemistry
• Materials science
• Statistical mechanics

Background:

• Coarse-grained (CG) models are essential for simulating large molecular systems.
• Conventional CG models often use pair potentials, but local density (LD) potentials offer improved accuracy.
• Understanding the temperature dependence of LD potentials is crucial for their broader application.

Purpose of the Study:

• To investigate the temperature-dependence of local density (LD) potentials in multiscale coarse-graining (MS-CG) models.
• To develop accurate and transferable CG potentials for molecular liquids.
• To establish a method for predicting potentials at new state points without extensive simulations.

Main Methods:

• Utilized the multiscale coarse-graining (MS-CG) force-matching variational principle.
• Parameterized pair and LD potentials for one-site CG models of molecular liquids at ambient pressure.
• Analyzed the sensitivity of LD potential accuracy to the local density length-scale (rc).

Main Results:

• The accuracy of MS-CG LD potentials depends sensitively on the chosen length-scale (rc).
• An optimal length-scale (rc*) allows MS-CG potentials to accurately describe reference state points and transfer across temperatures.
• At ambient pressure, the optimal LD length-scale varies linearly with temperature.
• A temperature-dependent LD length-scale makes the MS-CG LD potential temperature-independent, while the pair potential varies linearly.
• Predicted potentials sometimes outperform potentials optimized for specific state points.

Conclusions:

• A temperature-dependent optimal length-scale simplifies the parameterization of MS-CG potentials for molecular liquids.
• This approach enables accurate prediction of potentials for new state points, reducing the need for additional atomistic simulations.
• The findings offer a computationally efficient strategy for modeling molecular liquids across various temperatures.