Predicting the Artificial Acceleration in Coarse-Grained Molecular Dynamics Simulation of Polymer Melts

Area of Science:

• Polymer Science
• Computational Chemistry
• Materials Science

Background:

• Coarse-grained (CG) simulations accelerate molecular dynamics but introduce artificial increases in polymer mobility.
• The mobility acceleration factor (CG diffusion coefficient / all-atom diffusion coefficient) quantifies this simulation artifact.
• Accurate prediction of this factor is crucial for reliable CG simulations of polymer melts.

Purpose of the Study:

• To develop a predictive model for the mobility acceleration factor in polymer melts.
• To identify key parameters influencing the mobility acceleration factor.
• To incorporate temperature effects into the predictive model.

Main Methods:

• All-atom (AA) simulations of polymers at 450 K.
• Iterative Boltzmann Inversion (IBI) for coarse-graining.
• Calculation of AA and CG diffusion coefficients.
• Development and refinement of a predictive model based on environmental parameters and temperature.

Main Results:

• A predictive model was developed using key parameters and fitting constants.
• The initial model required refinement to include temperature effects.
• The final temperature-dependent model accurately predicts the mobility acceleration factor with an average absolute deviation of 5.6%.

Conclusions:

• The developed model reliably predicts artificial mobility increases in CG polymer systems.
• The model effectively utilizes local monomer environment descriptors and temperature.
• This work provides a valuable tool for enhancing the accuracy of coarse-grained polymer simulations.