Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics

Published

November 29, 2018

Author(s)

Edward Maginn, Richard A. Messerly, Daniel Carlson, Daniel Roe, J. R. Elliott

Abstract

The ability to predict transport properties (i.e. diffusivity, viscosity, conductivity) is one of the primary benefits of molecular simulation. Although most studies focus on the accuracy of the simulation output compared to experimental data, such a comparison primarily tests the adequacy of the force field (i.e. the model). By contrast, the reliability of different simulation methodologies for predicting transport properties is the focus of this manuscript. Unfortunately, obtaining reproducible estimates of transport properties from molecular simulation is not as straightforward as static properties. Therefore, this manuscript discusses the best practices that should be followed to ensure that the simulation output is reliable, i.e. is a valid representation of the force field implemented. We also discuss procedures to use so that the results are reproducible (i.e. can be obtained by other researchers following the same methods and procedures). There are two classes by which transport properties are predicted: equilibrium molecular dynamics (EMD) and non-equilibrium molecular dynamics (NEMD). This manuscript presents the best practices for EMD, leaving NEMD for a future publication. As self-diffusivity and shear viscosity are the most prevalent transport properties found in the literature, the discussion will also be limited to these properties with the expectation that future publications will discuss best practices for thermal conductivity, ionic conductivity, and transport diffusivity.

Citation

Living Journal of Computational Molecular Science

Pub Type

Journals

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Citation

Maginn, E. , Messerly, R. , Carlson, D. , Roe, D. and Elliott, J. (2018), Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics, Living Journal of Computational Molecular Science, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=925953 (Accessed January 17, 2025)