Facile ionic mobility within host frameworks is crucial to the design of high-energy-density batteries with high-power-densities, where the migration barrier (E_m) is the governing factor. Here, we assess the accuracy and computational performance of generalized gradient approximation (GGA), the strongly constrained and appropriately normed (SCAN), and their Hubbard U corrections, GGA+U and SCAN+U, within the density functional theory-nudged elastic band framework, in the prediction of E_m as benchmarked against experimental data. Importantly, we observe SCAN to be more accurate than other frameworks, on average, albeit with higher computational costs and convergence difficulties, while GGA is a feasible choice for “quick” and “qualitative” E_m predictions. Further, we quantify the sensitivity of E_m with adding uniform background charge and/or the climbing image approximation in solid electrolytes, and the Hubbard U correction in electrodes. Our findings will improve the quality of E_m predictions which will enable identifying better materials for energy storage applications.

主体框架内的离子迁移率对于设计具有高功率密度的高能量密度电池至关重要，其中迁移势垒 (E _m ) 是控制因素。在这里，我们在密度泛函理论推动的弹性带框架内评估广义梯度近似 (GGA)、强约束和适当归一化 (SCAN) 及其 Hubbard U校正、GGA+ U和 SCAN+ U的准确性和计算性能，在 E _{m的预测中}以实验数据为基准。重要的是，我们观察到 SCAN 平均而言比其他框架更准确，尽管计算成本和收敛困难更高，而 GGA 是“快速”和“定性”E _m预测的可行选择。此外，我们通过在固体电解质中添加均匀背景电荷和/或攀爬图像近似以及电极中的 Hubbard U校正来量化 E _{m的灵敏度。}我们的研究结果将提高 E _m预测的质量，这将有助于确定更好的储能应用材料。