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Systematic methods for structurally consistent coarse-grained models.

¹Department of Chemistry, The Pennsylvania State University, University Park, PA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|October 5, 2012

Summary

This summary is machine-generated.

This study reviews coarse-grained (CG) modeling theories and methods for effective potentials. It establishes a framework where CG models consistent with atomistic models minimize relative entropy, requiring many-body potentials.

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Area of Science:

Computational Chemistry
Statistical Mechanics
Materials Science

Background:

Coarse-grained (CG) modeling simplifies complex systems by reducing the number of degrees of freedom.
Developing accurate CG models requires effective potentials that capture essential physics from atomistic simulations.
Existing methods for deriving CG potentials vary, necessitating a unified theoretical framework.

Purpose of the Study:

To provide a comprehensive overview of coarse-grained (CG) modeling theories.
To review systematic methods for deriving effective potentials for CG models.
To establish a quantitative criterion for CG model consistency with atomistic models.

Main Methods:

Review of statistical mechanics framework relating atomistic and CG models.
Application of relative entropy minimization as a consistency criterion.
Exposition of theory and numerical methods for approximating many-body potential of mean force (PMF).

Main Results:

A statistical mechanics framework is presented for relating atomistic and CG models.
Relative entropy minimization is identified as a criterion for CG model consistency.
The necessity of many-body potentials of mean force (PMF) for consistent CG models is established.

Conclusions:

Systematic coarse-graining requires rigorous theoretical underpinnings.
Minimizing relative entropy provides a principled approach to deriving effective CG potentials.
Further research is needed to address outstanding challenges in systematic coarse-graining.