Ab initio molecular dynamics (AIMD) simulation is widely employed in studying diffusion mechanisms and in quantifying diffusional properties of materials. However, AIMD simulations are often limited to a few hundred atoms and a short, sub-nanosecond physical timescale, which leads to models that include only a limited number of diffusion events. As a result, the diffusional properties obtained from AIMD simulations are often plagued by poor statistics. In this paper, we re-examine the process to estimate diffusivity and ionic conductivity from the AIMD simulations and establish the procedure to minimize the fitting errors. In addition, we propose methods for quantifying the statistical variance of the diffusivity and ionic conductivity from the number of diffusion events observed during the AIMD simulation. Since an adequate number of diffusion events must be sampled, AIMD simulations should be sufficiently long and can only be performed on materials with reasonably fast diffusion. We chart the ranges of materials and physical conditions that can be accessible by AIMD simulations in studying diffusional properties. Our work provides the foundation for quantifying the statistical confidence levels of diffusion results from AIMD simulations and for correctly employing this powerful technique.

从头算分子动力学（AIMD）模拟被广泛用于研究扩散机理和量化材料的扩散特性。但是，AIMD模拟通常仅限于几百个原子和短的亚纳秒物理时标，这导致仅包含有限数量的扩散事件的模型。结果，从AIMD模拟获得的扩散特性经常受到不良统计数据的困扰。在本文中，我们将重新检查从AIMD模拟得出扩散率和离子电导率的过程，并建立使拟合误差最小化的程序。另外，我们提出了从AIMD模拟过程中观察到的扩散事件数量量化扩散率和离子电导率统计方差的方法。由于必须采样足够数量的扩散事件，因此AIMD模拟应足够长，并且只能在扩散速度相当快的材料上执行。我们绘制了AIMD模拟可用于研究扩散特性的材料和物理条件范围的图表。我们的工作为量化AIMD模拟的扩散结果的统计置信度提供了基础，并且可以正确地采用这种强大的技术。