The ability to predict transport properties (e.g., diffusivity, viscosity, and 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 multicomponent diffusivity.

Rosenfeld proposed two different scaling approaches to model the transport properties of fluids, separated by 22 years, one valid in the dilute gas, and another in the liquid phase. In this work, we demonstrate that these two limiting cases can be connected through the use of a novel approach to scaling transport properties and a bridging function. This approach, which is empirical and not derived from theory, is used to generate reference correlations for the transport properties of the Lennard-Jones 12-6 fluid of viscosity, thermal conductivity, and self-diffusion. This approach, with a very simple functional form, allows for the reproduction of the most accurate simulation data to within nearly their statistical uncertainty. The correlations are used to confirm that for the Lennard-Jones fluid the appropriately scaled transport properties are nearly monovariate functions of the excess entropy from low-density gases into the supercooled phase and up to extreme temperatures. This study represents the most comprehensive metastudy of the transport properties of the Lennard-Jones fluid to date.

In this study, we present an approach for rapid force field parameterization and uncertainty quantification of the non-bonded interaction parameters for classical force fields. The accuracy of most thermophysical properties, and especially vapor-liquid equilibria (VLE), obtained from molecular simulation depends strongly on the non-bonded interactions. Traditionally, non-bonded interactions are parameterized to agree with macroscopic properties by performing large amounts of direct molecular simulation. Due to the computational cost of molecular simulation, surrogate models (i.e., efficient models that approximate direct molecular simulation results) are an essential tool for high-dimensional parameterization and uncertainty quantification of non-bonded interactions. The present study compares two different configuration-sampling-based surrogate models, namely, Multistate Bennett Acceptance Ratio (MBAR) and Pair Correlation Function Rescaling (PCFR). MBAR and PCFR are coupled with the Isothermal Isochoric (ITIC) thermodynamic integration method for estimating vapor-liquid saturation properties. We find that MBAR and PCFR are complementary in their roles. Specifically, PCFR is preferred when exploring distant regions of the parameter space while MBAR is better in the local domain.

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