The electrical properties of grain boundaries in ionic conductors are studied most frequently and most easily by Electrochemical Impedance Spectroscopy (EIS). The resistance data obtained in this manner are typically analyzed with the Mott–Schottky space-charge model to extract a space-charge potential. In this study, taking CeO2 containing acceptor-dopant cations and oxygen vacancies as our model system, we calculate impedance spectra by solving the drift–diffusion equation for oxygen vacancies for a bicrystal geometry with space-charge layers at the grain boundary. Three different cases are considered for the behavior of the acceptor-dopant cations: a uniform distribution (Mott–Schottky), an equilibrium distribution (Gouy–Chapman), and a distribution frozen-in from a much higher temperature (restricted equilibrium). Analyzing our impedance data for the restricted-equilibrium case with the Mott–Schottky model, we find that the obtained space-charge potentials are substantially underestimated. In view of such a discrepancy not normally being apparent (the true values being unknown), we propose a specific set of EIS experiments that allow the Mott–Schottky model to be discounted.

Analysis of the mean squared displacement of species k, rk2, as a function of simulation time t constitutes a powerful method for extracting, from a molecular‐dynamics (MD) simulation, the tracer diffusion coefficient, Dk*. The statistical error in Dk* is seldom considered, and when it is done, the error is generally underestimated. In this study, we examined the statistics of rk2t curves generated by solid‐state diffusion by means of kinetic Monte Carlo sampling. Our results indicate that the statistical error in Dk* depends, in a strongly interrelated way, on the simulation time, the cell size, and the number of relevant point defects in the simulation cell. Reducing our results to one key quantity—the number of k particles that have jumped at least once—we derive a closed‐form expression for the relative uncertainty in Dk*. We confirm the accuracy of our expression through comparisons with self‐generated MD diffusion data. With the expression, we formulate a set of simple rules that encourage the efficient use of computational resources for MD simulations.