Thermodynamic transferability of coarse-grained potentials for polymer-additive systems

Area of Science:

• Computational Chemistry
• Materials Science
• Polymer Science

Background:

• Coarse-grained (CG) potentials are essential for simulating large molecular systems like polymers.
• Systematic derivation of CG potentials is crucial for accurate predictions.
• Transferability of CG potentials across different conditions is a key challenge.

Purpose of the Study:

• To investigate the transferability of Conditional Reversible Work (CRW)-based coarse-grained potentials for polymer-additive systems.
• To assess the accuracy of CRW models in predicting structural and thermodynamic properties.
• To evaluate the temperature transferability of CRW-derived potentials.

Main Methods:

• Derivation of CG nonbonded potentials using the Conditional Reversible Work (CRW) method.
• Application of CRW to atactic polystyrene and three additives (ethylbenzene, methane, neopentane).
• Analysis of structural correlations and excess chemical potentials using CG and united-atom parent models.

Main Results:

• CRW-based CG models accurately predict structural correlations in polymer-additive mixtures.
• Excess chemical potentials from CRW models show satisfactory agreement with parent models.
• CRW models exhibit good temperature transferability, particularly for excess entropies.

Conclusions:

• CRW coarse-graining potentials are suitable for modeling structural and thermodynamic properties of polymer-penetrant systems.
• The CRW method provides a parameter-free approach for deriving effective potentials.
• CRW models demonstrate good predictive power and temperature transferability for polymer-additive interactions.