Statistical Mechanical Design Principles for Coarse-Grained Interactions across Different Conformational Free Energy...

Area of Science:

• Computational Chemistry and Physics
• Multiscale Modeling
• Statistical Mechanics

Background:

• Bottom-up coarse-graining (CG) simplifies molecular systems for multiscale simulations.
• Existing CG models often struggle with configuration-dependent interactions, limiting their applicability.
• Predictive power of CG models is hindered by the inability to capture diverse molecular configurations.

Purpose of the Study:

• To propose a novel statistical mechanical theory for designing robust coarse-grained (CG) interactions.
• To enable CG models to accurately represent molecular systems across various configurations and conditions.
• To develop a new protocol for creating predictive multiscale models applicable to diverse systems.

Main Methods:

• Utilized molecular collective variables to identify distinct classical CG free energy surfaces for characteristic configurations.
• Applied a statistical mechanical theory to systematically determine coupling interactions between different CG free energy surfaces.
• Drew analogies to quantum mechanical approaches for describing coupled states to model interaction dynamics.

Main Results:

• The proposed theory accurately captures many-body potentials of mean force in CG variables.
• Demonstrated applicability for various order parameters in liquids and at interfaces.
• Showcased potential for extension to complex systems like proteins, uncovering underlying coupling interactions.

Conclusions:

• The developed theory provides a robust framework for designing configuration-aware CG interactions.
• This approach enhances the predictive capabilities of multiscale models by addressing limitations in CG interaction design.
• Offers a new protocol for creating more versatile and accurate CG models for diverse scientific applications.