Search for: All records

Radial distribution functions (RDFs) are widely used in molecular simulation and beyond. Most approaches to computing RDFs require assembling a histogram over inter-particle separation distances. In turn, these histograms require a specific (and generally arbitrary) choice of discretization for bins. We demonstrate that this arbitrary choice for binning can lead to significant and spurious phenomena in several commonplace molecular-simulation analyses that make use of RDFs, such as identifying phase boundaries and generating excess entropy scaling relationships. We show that a straightforward approach (which we term Kernel-Averaging Method to Eliminate Length-Of-Bin Effects) mitigates these issues. This approach is based on systematic and mass-conserving mollification of RDFs using a Gaussian kernel. This technique has several advantages compared to existing methods, including being useful for cases where the original particle kinematic data have not been retained, and the only available data are the RDFs themselves. We also discuss the optimal implementation of this approach in the context of several application areas. 

Ergodicity (or at least the tantalizing promise of it) is a core animating principle of molecular-dynamics (MD) simulations: Put simply, sample for long enough (in time), and you will make representative visits to states of a system all throughout phase space, consistent with the desired statistical ensemble. However, one is not guaranteed a priori that the chosen window of sampling in a production run is sufficiently long to avoid problematically non-ergodic observations; one is also not guaranteed that successive measurements of an observable are statistically independent of each other. In this paper, we investigate several particularly striking and troublesome examples of statistical correlations in MD simulations of nanoconfined fluids, which have profound implications on the quantification of uncertainty for transport phenomena in these systems. In particular, we show that these correlations can lead to confidence intervals on the fluid self-diffusion coefficient that are dramatically overconfident and estimates of this transport quantity that are simply inaccurate. We propose a simple approach—based on the thermally accelerated decorrelation of fluid positions and momenta—that ameliorates these issues and improves our confidence in MD measurements of nanoconfined fluid transport properties. We demonstrate that the formation of faithful confidence intervals for measurements of self-diffusion under nanoscale confinement typically requires at least 20 statistically independent samples, and potentially more depending on the sampling technique used.