Bottom-up approach to represent dynamic properties in coarse-grained molecular simulations.

Area of Science:

• Computational chemistry and polymer physics.
• Development of advanced simulation methodologies for materials science.

Background:

• Molecular coarse-graining (CG) methods create transferable force fields but often overestimate dynamic properties.
• Accurate representation of dynamic properties is crucial for predicting material behavior.

Purpose of the Study:

• To evaluate the effectiveness of a "bottom-up informed" dissipative particle dynamics (DPD) thermostat, specifically the Markovian Mori-Zwanzig (MZ-DPD) method.
• To assess the ability of MZ-DPD to accurately represent long-time dynamic properties in molecular CG systems.
• To extend the applicability of CG models for multicomponent systems.

Main Methods:

• Development of single-site and multiple-site CG models for 2,2-dimethyl propane (monomer, dimer, 24mer).
• Application of the MZ-DPD coarse-graining method and thermostat.
• Simulations of liquids, polymer solutions (24mers in dimer solvent), and polymer melts.
• Analysis of diffusive dynamics and penetrant diffusion in polymer networks.

Main Results:

• MZ-DPD achieved quantitative accuracy in predicting diffusive dynamics for single-component liquids and polymer solutions.
• Despite incomplete time scale separation in the CG model, MZ-DPD demonstrated predictive power.
• MZ-DPD simulations showed limitations in accurately modeling molecular penetrant diffusion in dense polymer networks, highlighting the need for barrier crossing and memory effect treatments.

Conclusions:

• The MZ-DPD thermostat significantly enhances the accuracy of molecular CG models for predicting diffusive dynamics in various systems.
• This method extends the scope of CG models, particularly for multicomponent systems requiring accurate relative diffusion rates.
• Further development is needed to model diffusion governed by activated barrier crossing in dense systems.