Literature DB >> 22722404

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

Emiliano Brini1, Claudia R Herbers, Gregor Deichmann, Nico F A van der Vegt.   

Abstract

In this work we study the transferability of systematically coarse-grained (CG) potentials for polymer-additive systems. The CG nonbonded potentials between the polymer (atactic polystyrene) and three different additives (ethylbenzene, methane and neopentane) are derived using the Conditional Reversible Work (CRW) method, recently proposed by us [Brini et al., Phys. Chem. Chem. Phys., 2011, 13, 10468-10474]. A CRW-based effective pair potential corresponds to the interaction free energy between the two atom groups of an atomistic parent model that represent the coarse-grained interaction sites. Since the CRW coarse-graining procedure does not involve any form of parameterisation, thermodynamic and structural properties of the condensed phase are predictions of the model. We show in this work that CRW-based CG models of polymer-additive systems are capable of predicting the correct structural correlations in the mixture. Furthermore, the excess chemical potentials of the additives obtained with the CRW-based CG models and the united-atom parent models are in satisfactory agreement and the CRW-based CG models show a good temperature transferability. The temperature transferability of the model is discussed by analysing the entropic and enthalpic contributions to the excess chemical potentials. We find that CRW-based CG models provide good predictions of the excess entropies, while discrepancies are observed in the excess enthalpies. Overall, we show that the CRW CG potentials are suitable to model structural and thermodynamic properties of polymer-penetrant systems.

Entities:  

Year:  2012        PMID: 22722404     DOI: 10.1039/c2cp40735c

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  2 in total

Review 1.  Recent advances in transferable coarse-grained modeling of proteins.

Authors:  Parimal Kar; Michael Feig
Journal:  Adv Protein Chem Struct Biol       Date:  2014-08-24       Impact factor: 3.507

2.  Bayesian calibration of coarse-grained forces: Efficiently addressing transferability.

Authors:  Paul N Patrone; Thomas W Rosch; Frederick R Phelan
Journal:  J Chem Phys       Date:  2016-04-21       Impact factor: 3.488

  2 in total

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