Abstract We present a quantitative method which allows us to reliably measure dynamic changes in the atomic structure of monatomic crystalline nanomaterials from a time series of atomic resolution annular dark field scanning transmission electron microscopy images. The approach is based on the so-called hidden Markov model and estimates the number of atoms in each atomic column of the nanomaterial in each frame of the time series. We discuss the origin of the improved performance for time series atom-counting as compared to the current state-of-the-art atom-counting procedures, and show that the so-called transition probabilities that describe the probability for an atomic column to lose or gain one or more atoms from frame to frame are particularly important. Using these transition probabilities, we show that the method can also be used to estimate the probability and cross section related to structural changes. Furthermore, we explore the possibilities for applying the method to time series recorded under variable environmental conditions. The method is shown to be promising for a reliable quantitative analysis of dynamic processes such as surface diffusion, adatom dynamics, beam effects, or in situ experiments.

中文翻译：

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用于从连续 ADF STEM 图像进行原子计数的隐马尔可夫模型：方法论、可能性和局限性

摘要 我们提出了一种定量方法，使我们能够从原子分辨率环形暗场扫描透射电子显微镜图像的时间序列中可靠地测量单原子晶体纳米材料的原子结构的动态变化。该方法基于所谓的隐马尔可夫模型，并在时间序列的每一帧中估计纳米材料的每个原子列中的原子数。我们讨论了与当前最先进的原子计数程序相比时间序列原子计数性能改进的起源，并表明描述原子列丢失概率的所谓转移概率或者逐帧获得一个或多个原子尤为重要。使用这些转移概率，我们表明该方法还可用于估计与结构变化相关的概率和横截面。此外，我们探索了将该方法应用于在可变环境条件下记录的时间序列的可能性。该方法被证明有望用于动态过程的可靠定量分析，例如表面扩散、吸附原子动力学、光束效应或原位实验。