The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

中文翻译：

---

使用循环分析检测材料的原子分辨率图像中的缺陷

自动检测高角度环形暗场Z对比度（HAADF）扫描透射电子显微镜（STEM）图像中的缺陷已成为一项重大挑战。在这里，我们报告了一种基于材料的局部原子几何形状的变化自动检测和分类结构缺陷的方法。该方法将几何图论应用于已发现的原子列中心位置，并且能够检测和分类薄二元结构（即“ 2D材料”）中的任何缺陷以及厚二元结构中的大缺陷子集（即， ，3D或类似块的材料）。尽管该方法在检测和分类较厚的块状材料中的缺陷方面的适用性有限，但它可提供有关缺陷存在的潜在信息。缺陷的分类可用于筛选大量数据，并提供有关材料中缺陷分布的统计数据。该方法适用于从任何类型的高分辨率图像中提取的原子列位置，但是在这里我们将其用于HAADF STEM图像进行演示。