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The authors investigate the impact of finite trajectory length, limited cell size, and sampling errors on molecular-dynamics-predicted ionic conductivity using cubic $\text{Li}_7\text{La}_3\text{Zr}_2\text{O}_{12}$ as a benchmark solid electrolyte. Unchecked numerical errors frequently masquerade behind linear mean-squared displacements and standard Arrhenius fits, systematically misranking candidate materials in high-throughput computational screening pipelines. To resolve this, they establish the local mean-squared-displacement scaling exponent, $\alpha(t)$, as a rigorous convergence metric that dictates the minimum trajectory length needed to achieve bounded errors in diffusivity and Nernst-Einstein conductivity across temperatures.
Seemingly linear diffusion plots and pristine Arrhenius fits routinely mask large transport errors in solid electrolyte simulations, but tracking the local MSD exponent $\alpha(t)$ provides an exact threshold for true dynamical convergence.
Molecular dynamics is widely used to predict ionic conductivity in solid electrolytes, but the reliability of these predictions is often difficult to assess. Our analysis identifies finite trajectory length, limited cell size, and insufficient sampling as intrinsic limitations of finite atomistic ion-transport calculations. Cubic Li7La3Zr2O12 is used as a representative solid electrolyte to quantify their consequences. These limitations can remain hidden behind apparently linear mean-squared displacements and well-behaved Arrhenius relations, leading to inaccurate diffusivities, activation energies, and extrapolated ionic conductivities. Such inaccuracies can misrank candidate solid electrolytes and consequently misdirect computational screening and experimental validation. Here, we establish the local mean-squared-displacement exponent, {\alpha}(t), as a quantitative reliability criterion that links dynamical convergence to errors in diffusivity and Nernst-Einstein ionic conductivity. The criterion further determines the minimum trajectory length required to achieve a prescribed accuracy as a function of temperature. Independent replicas reduce statistical uncertainty, while selective single-axis expansion mitigates finite-size effects. By establishing when simulated ionic conductivity is quantitatively trustworthy, this approach enables more reliable materials ranking and more efficient use of computational and experimental resources, thereby accelerating the development of high-performance solid electrolytes for solid-state batteries.