Automated real-space lattice extraction for atomic force microscopy images. Issue 1 (1st March 2023)

• Record Type: Journal Article
• Authors: Corrias, Marco; Papa, Lorenzo; Sokolović, Igor; Birschitzky, Viktor; Gorfer, Alexander; Setvin, Martin; Schmid, Michael; Diebold, Ulrike; Reticcioli, Michele; Franchini, Cesare
• Is Part Of: Machine learning: science and technology. Volume 4:Issue 1(2023)
• Publication Date: 2023-03-01
• Subjects: machine learning -- clustering algorithm -- unsupervised learning -- computer vision -- atomic force microscopy -- scanning probe microscopy -- surface science; 006.31
• DOI: 10.1088/2632-2153/acb5e0
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: 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 and clustering algorithms. 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 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 is tested against atom misclassification and artifacts, thereby facilitating the interpretation of scanning probe microscopy images.