Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images
- Author(s)
- Marco Corrias, Lorenzo Papa, Igor Sokolović, Viktor Birschitzky, Alexander Gorfer, Martin Setvín, Michael Schmid, Ulrike Diebold, Michele Reticcioli, Cesare Franchini
- 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 TiO
2(101), oxygen deficient rutile TiO
2(110) with and without CO adsorbates, SrTiO
3(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.
- Organisation(s)
- Computational Materials Physics
- External organisation(s)
- Technische Universität Wien, Charles University Prague, University of Bologna
- Journal
- Machine Learning: Science and Technology
- Volume
- 4
- No. of pages
- 9
- ISSN
- 2632-2153
- DOI
- https://doi.org/10.1088/2632-2153/acb5e0
- Publication date
- 01-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102019 Machine learning, 103009 Solid state physics
- Keywords
- ASJC Scopus subject areas
- Software, Artificial Intelligence, Human-Computer Interaction
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/automated-realspace-lattice-extraction-for-atomic-force-microscopy-images(04f766cb-08c6-4932-8f44-5e1394934d4e).html