Reliable Viscosity Calculation from High-Pressure Equilibrium Molecular Dynamics: Case Study of 2,2,4-Trimethylhexane

Abstract

Viscosity is a fundamental property of liquid lubricants, yet it is challenging to determine accurately, especially at high pressures. Although equilibrium molecular dynamics (EMD) simulations are a promising alternative to resource-intensive experiments, practical challenges remain in assessing the sufficiency of simulation time and in controlling uncertainties in the Green-Kubo formalism due to the finite amount of trajectory data. In this work, we extend the STable AutoCorrelation Integral Estimator (STACIE), a recently developed algorithm for estimating transport properties. First, we introduce the Lorentz model to estimate the viscosity and the exponential correlation time from the low-frequency power spectrum of deviatoric pressure fluctuations. Second, we show how to supplement the three conventional off-diagonal elements of the pressure tensor ($P_{xy}$, $P_{yz}$ and $P_{zx}$) with two additional uncorrelated deviatoric pressure components for shear viscosity calculations. Using these improvements, we apply STACIE to calculate the shear viscosity of 2,2,4-trimethylhexane from EMD simulations. We demonstrate STACIE&#39;s capability to reliably calculate viscosity under high-pressure conditions, offering a robust and automated solution with validated uncertainty quantification. Our results, when compared to the outcomes of the 10th International Fluid Properties Simulation Challenge, underscore the need for long EMD simulations. Large deviations from experimental viscosities in previous works were primarily due to insufficient simulation times and ad hoc post-processing choices, rather than the limitations of the force fields used. Unlike previous studies, our viscosity estimates agree well with experimental results (relative error < 6%) up to the highest pressure of 1 GPa, highlighting the improved reliability and accuracy of STACIE&#39;s systematic approach to viscosity predictions.