论文标题
用于原子力显微镜图像的自动真实空间晶格提取
Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images
论文作者
论文摘要
分析原子解决图像是一个耗时的过程，需要扎实的经验和大量的人类干预。此外，获得的图像包含大量信息，例如晶体结构，缺陷的存在和分布以及域的形成，这些信息需要解决以了解材料的表面结构。因此，在过去几年中，机器学习技术已应用于扫描探针和电子显微镜中，旨在进行自动化和有效的图像分析。这项工作引入了开发的免费开源工具（AISURF：自动识别表面图像），以通过比例不变特征变换（SIFT）和聚类算法（CA）检查原子解决图像。 Aisurf提取了原始晶格向量，单元单元和从原始图像中的结构变形，而没有预先提高晶格和最少的用户干预。该方法适用于具有不同级别复杂性的所选表面的各种原子分辨的非接触原子显微镜显微镜（AFM）图像：Aratase TiO2（101），氧气不足的金红石TiO2（110），带有和不具有Co Adsorbates，SRTIO3（001），srtio3（001），srtio3（001），带有SRTIO3（001），并带有sr facancies and Chapterene and Chapchene和Chapherene。该代码可提供出色的结果，并证明对原子错误分类和噪声非常有力，从而促进了解释扫描探针显微镜图像。
Analyzing atomically resolved images is a time-consuming process requiring solid experience and substantial human intervention. In addition, the acquired images contain a large amount of information such as crystal structure, presence and distribution of defects, and formation of domains, which need to be resolved to understand a material's surface structure. Therefore, machine learning techniques have been applied in scanning probe and electron microscopies during the last years, aiming for automatized and efficient image analysis. This work introduces a free and open source tool (AiSurf: Automated Identification of Surface Images) developed to inspect atomically resolved images via Scale-Invariant Feature Transform (SIFT) and Clustering Algorithms (CA). AiSurf extracts primitive lattice vectors, unit cells, and structural distortions from the original image, with no pre-assumption on the lattice and minimal user intervention. The method is applied to various atomically resolved non-contact atomic force microscopy (AFM) images of selected surfaces with different levels of complexity: anatase TiO2(101), oxygen deficient rutile TiO2(110) with and without CO adsorbates, SrTiO3(001) with Sr vacancies and graphene with C vacancies. The code delivers excellent results and has proved to be robust against atom misclassification and noise, thereby facilitating the interpretation scanning probe microscopy images.
