Analysis of mapping atomic models to coarse-grained resolution | JoVE Visualize

Area of Science:

Computational physics and chemistry
Materials science
Statistical mechanics

Background:

Low-resolution coarse-grained (CG) models offer computational and conceptual benefits for simulating soft materials.
The accuracy of CG models is highly sensitive to the mapping function (M) that translates atomic configurations (r) to CG configurations (R).
The mapping determines how atomic configurational information is distributed between the CG ensemble and the lost atomic configurations.

Purpose of the Study:

To investigate how the mapping function partitions atomic configuration space into CG and intra-site components.
To analyze the role of the Jacobian factor and labeling entropy in information transfer.
To identify criteria for high-quality CG representations, exemplified by actin dynamics.

Main Methods:

Analysis of the coordinate transformation and its Jacobian factor.
Introduction and application of labeling entropy to quantify uncertainty in atom-site association.
Numerical illustration using a Gaussian network model for actin equilibrium fluctuations.
Evaluation of spectral quality (Q) as a metric for CG representation quality.

Main Results:

The coordinate transformation in mapping introduces a nontrivial Jacobian factor, defining a labeling entropy.
Labeling entropy quantifies the uncertainty in atom-site association, transferring information from the lost to the mapped ensemble.
Resonant mappings can effectively separate atomic potentials into CG and intra-site contributions.
Spectral quality (Q) is a useful metric for high-quality actin representations, correlating with adjusted information loss when labeling uncertainty is considered.

Conclusions:

The choice of mapping function significantly influences the information content and quality of CG models.
Labeling entropy provides a crucial measure of uncertainty and improves CG model representation.
Optimizing CG models requires considering labeling uncertainty, not just maximizing or minimizing mapped information content.
The developed framework and metrics offer a pathway to more accurate and reliable CG simulations of soft materials like actin.