Development of Coarse-Grained Models for Polymers by Trajectory Matching.

Area of Science:

• Polymer Science
• Computational Chemistry
• Materials Science

Background:

• Coarse-grained (CG) models are essential for simulating polymers at relevant time and length scales.
• Developing accurate CG force fields remains challenging, as CG models may not fully reproduce atomistic properties.
• Statistical trajectory matching has shown promise for small molecule force field development.

Purpose of the Study:

• To extend the statistical trajectory matching method for developing coarse-grained (CG) models of polymers.
• To investigate the impact of model design choices on the structure and dynamics of bulk polymers.
• To quantitatively compare different CG methods across various properties and polymer systems.

Main Methods:

• Extension of statistical trajectory matching from small molecules to polymeric systems.
• Systematic study of modeling choices and their influence on polymer structure and dynamics.
• Quantitative comparison of CG model performance for different properties and polymers.

Main Results:

• The proposed method successfully extends statistical trajectory matching to polymeric systems.
• Model design choices significantly affect the resulting polymer structure and dynamics.
• The study provides a framework for evaluating and comparing different CG modeling approaches.

Conclusions:

• Statistical trajectory matching offers a viable route for developing accurate CG polymer models.
• Careful consideration of model design is crucial for achieving reliable CG simulations of polymers.
• This work facilitates the development of improved CG force fields for polymer research.