Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit...

Area of Science:

• Computational Chemistry
• Materials Science
• Statistical Mechanics

Background:

• Bottom-up multiscale techniques are crucial for developing coarse-grained (CG) models for large-scale simulations.
• Conventional CG models often use pair potentials, which can be computationally efficient but may lack accuracy due to neglecting multibody environmental effects.
• This limitation restricts the transferability of CG potentials to different system conditions.

Purpose of the Study:

• To develop a novel approach for incorporating multibody effects into CG potentials.
• To enhance the accuracy and transferability of CG models beyond simple pair interactions.
• To explore the application of this method in implicit solvation strategies.

Main Methods:

• Proposing additional nonbonded terms in CG potentials that depend on local atomic densities in a mean-field manner.
• Utilizing the relative entropy coarse-graining framework for systematic parameterization of these local density potentials.
• Characterizing the approach through implicit solvation of model hydrophobes in aqueous environments.

Main Results:

• Demonstrated a systematic route for parameterizing local density potentials using relative entropy.
• Successfully incorporated multibody effects into CG potentials, improving upon traditional pair potentials.
• Validated the approach in the context of implicit solvation for hydrophobic interactions.

Conclusions:

• The proposed method effectively integrates multibody effects into CG potentials, overcoming limitations of traditional pair potentials.
• The relative entropy framework provides a robust method for parameterizing these advanced CG potentials.
• This work offers a significant advancement for developing accurate and transferable multiscale simulation models, particularly for solvation studies.