On the choice of acceleration amplitude for predicting shear viscosity using the periodic perturbation method.

Abstract

Shear viscosity is a crucial property for optimizing industrial processes. While data are available for common fluids under ambient conditions, viscosity often needs to be estimated at different temperatures and pressures or for systems lacking experimental measurements. Compared to empirical correlations, molecular simulation offers a molecular-level approach to determine viscosity that not only provides accurate viscosity estimates but also yields insight into the underlying molecular mechanisms and can be readily extended to mixtures. Of the available simulation approaches, the non-equilibrium molecular dynamics periodic perturbation method (PPM) has demonstrated both computational efficiency and accuracy in viscosity prediction. However, implementing PPM requires significant trial and error when selecting acceleration amplitude in order to accurately determine the zero-perturbation viscosity, which increases computational demands and reduces the appeal of the method. In this work, we demonstrate that Quentrec's local order theory provides a superior fit for the dependence of viscosity on acceleration amplitude, enabling accurate extrapolation to zero-perturbation viscosity. The method is applied to a diverse set of 144 organic solvents and yields results that show good agreement with both experimental data and equilibrium molecular dynamics simulations. We further show that data-driven models can accurately estimate the acceleration amplitude corresponding to a given relative deviation from zero-perturbation viscosity. By specifying a reasonable deviation, the estimated acceleration amplitude exhibits lower statistical noise while simultaneously enabling precise reproduction of the computed zero-perturbation viscosity after compensating for the deviation. This approach circumvents the need for sampling multiple acceleration amplitudes and thereby facilitates the implementation of the PPM method.