Issue 9, 2018

Addressing the temperature transferability of structure based coarse graining models

Abstract

Systematically derived coarse grained (CG) models for molecular liquids do not inherently guarantee transferability to a state point different from its reference, especially when derived on the basis of structure based CG methods like Inverse Monte Carlo (IMC). Several efforts made in the past years to improve the transferability of these models focused on including thermodynamic constraints or on the application of multistate parametrization. Das and Andersen (DA) [Das et al., J. Chem. Phys., 2010, 132, 164106.] proposed a different Ansatz. They derived a correction term added to the system's Hamiltonian to reproduce the virial pressure and the volume fluctuations of the reference system in the CG resolution which does not require further adjustment of the effective pair potential. Herein, we discuss the possibility to achieve temperature transferability with IMC models for selected alkanes following the optimization of the DA approach as proposed by Dunn and Noid (DN) [Dunn et al., J. Chem. Phys., 2015, 143, 243148.]. The work focuses on a novel approach to determine the DN correction term for different state points by linear interpolation.

Graphical abstract: Addressing the temperature transferability of structure based coarse graining models

Article information

Article type
Paper
Submitted
08 Dec 2017
Accepted
12 Feb 2018
First published
12 Feb 2018

Phys. Chem. Chem. Phys., 2018,20, 6617-6628

Addressing the temperature transferability of structure based coarse graining models

D. Rosenberger and N. F. A. van der Vegt, Phys. Chem. Chem. Phys., 2018, 20, 6617 DOI: 10.1039/C7CP08246K

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