Insight into the Density-Dependence of Pair Potentials for Predictive Coarse-Grained Models

Area of Science:

• Computational chemistry
• Statistical mechanics

Background:

• Developing accurate coarse-grained (CG) models for industrial solvents like 1,4-dioxane and tetrahydrofuran is crucial for efficient molecular simulations.
• Understanding the factors influencing effective pair potentials in these solvents is key to improving model transferability and predictive power.

Purpose of the Study:

• To investigate the temperature and density dependence of effective pair potentials for 1-site CG models of 1,4-dioxane and tetrahydrofuran.
• To develop a robust and predictive CG model for industrial solvents applicable across a wide range of liquid states.

Main Methods:

• Utilized the generalized Yvon-Born-Green framework to analyze many-body correlations.
• Developed a density-independent interaction potential combined with a configuration-independent volume potential.
• Created a single global potential to model intermolecular structure and the pressure-volume equation of state.

Main Results:

• Effective pair potentials showed higher sensitivity to density than temperature.
• Density dependence of pair potentials was primarily attributed to many-body correlations.
• Density dependence was found to be non-critical for accurately modeling intermolecular structure.
• A single global potential successfully modeled liquid state points and the equation of state.

Conclusions:

• The study provides fundamental insights into the density dependence of effective pair potentials in CG models.
• The developed global potential offers a significant advancement for efficiently modeling industrial solvents with predictive CG models.