Viscosity is a crucial property of liquid lubricants, and it is theoretically a well-defined quantity in molecular dynamics (MD) simulations. However, no standardized protocol has been defined for calculating this property from equilibrium MD simulations. While best practices do exist, the actual calculation depends on several ad hoc decisions during the post-processing of the raw MD data. A common protocol for calculating the viscosity with equilibrium MD simulations is called the time decomposition method (TDM). Although the TDM attempts to standardize the viscosity calculation using the Green–Kubo method, it still relies on certain empirical rules and subjective user observations, e.g., the plateau region of the Green–Kubo integral or the integration cut-off time. It is known that the TDM works reasonably well for low-viscosity fluids, e.g., at high temperatures. However, modified heuristics have been proposed at high pressures, indicating that no single set of rules works well for all circumstances. This study examines the effect of heuristics and ad hoc decisions on the predicted viscosity of a short, branched lubricant molecule, 2,2,4-trimethylhexane. Equilibrium molecular dynamics simulations were performed at various operating conditions (high pressures and temperatures), followed by post-processing with three levels of uncertainty quantification. A new approach, “Enhanced Bootstrapping”, is introduced to assess the effects of individual ad hoc parameters on the viscosity. The results show a strong linear correlation (with a Pearson correlation coefficient of up to 36%) between the calculated viscosity and an ad hoc TDM parameter, which determines the integration cut-off time, under realistic lubrication conditions, particularly at high pressures. This study reveals that ad hoc decisions can lead to potentially misleading conclusions when the post-processing is performed ambiguously.

STACIE (STable AutoCorrelation Integral Estimator) is a novel algorithm and Python package that delivers robust, uncertainty-aware estimates of autocorrelation integrals from time-correlated data. While its primary application is deriving transport properties from equilibrium molecular dynamics simulations, STACIE is equally applicable to time-correlated data in other scientific fields. A key feature of STACIE is its ability to provide robust and accurate estimates without requiring manual adjustment of hyperparameters. Additionally, one can follow a simple protocol to prepare sufficient simulation data to achieve a desired relative error of the transport property. We demonstrate its application by estimating the ionic electrical conductivity of a NaCl-water electrolyte solution. We also present a massive synthetic benchmark data set to rigorously validate STACIE, comprising 15,360 sets of time-correlated inputs generated with diverse covariance kernels with known autocorrelation integrals. STACIE is open source and available on GitHub and PyPI, with comprehensive documentation and examples.