Scanning transmission electron microscopy (STEM) has become the technique of choice for quantitative characterization of atomic structure of materials, where the minute displacements of atomic columns from high-symmetry positions can be used to map strain, polarization, octahedra tilts, and other physical and chemical order parameter fields. The latter can be used as inputs into mesoscopic and atomistic models, providing insight into the correlative relationships and generative physics of materials on the atomic level. However, these quantitative applications of STEM necessitate understanding the microscope induced image distortions and developing the pathways to compensate them both as part of a rapid calibration procedure for in situ imaging, and the post-experimental data analysis stage. Here, we explore the spatiotemporal structure of the microscopic distortions in STEM using multivariate analysis of the atomic trajectories in the image stacks. Based on the behavior of principal component analysis (PCA), we develop the Gaussian process (GP)-based regression method for quantification of the distortion function. The limitations of such an approach and possible strategies for implementation as a part of in-line data acquisition in STEM are discussed. The analysis workflow is summarized in a Jupyter notebook that can be used to retrace the analysis and analyze the reader's data.

扫描透射电子显微镜 (STEM) 已成为材料原子结构定量表征的首选技术，其中原子柱从高对称位置的微小位移可用于绘制应变、极化、八面体倾斜和其他物理和化学顺序参数字段。后者可以用作介观和原子模型的输入，提供对原子水平上材料的相关关系和生成物理的洞察。然而，这些 STEM 的定量应用需要了解显微镜引起的图像失真并开发补偿它们的途径，作为原位快速校准程序的一部分。成像和实验后数据分析阶段。在这里，我们使用图像堆栈中原子轨迹的多变量分析来探索 STEM 中微观失真的时空结构。基于主成分分析 (PCA) 的行为，我们开发了基于高斯过程 (GP) 的回归方法来量化失真函数。讨论了这种方法的局限性以及作为 STEM 中在线数据采集的一部分实施的可能策略。分析工作流程总结在 Jupyter 笔记本中，可用于回溯分析和分析读者的数据。