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.

Automated real-space lattice extraction for atomic force microscopy images / Corrias M.; Papa L.; Sokolovic I.; Birschitzky V.; Gorfer A.; Setvin M.; Schmid M.; Diebold U.; Reticcioli M.; Franchini C.. - In: MACHINE LEARNING: SCIENCE AND TECHNOLOGY. - ISSN 2632-2153. - ELETTRONICO. - 4:1(2023), pp. 015015.1-015015.9. [10.1088/2632-2153/acb5e0]

Automated real-space lattice extraction for atomic force microscopy images

Schmid M.;Franchini C.
Ultimo
Supervision
2023

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.
2023
Automated real-space lattice extraction for atomic force microscopy images / Corrias M.; Papa L.; Sokolovic I.; Birschitzky V.; Gorfer A.; Setvin M.; Schmid M.; Diebold U.; Reticcioli M.; Franchini C.. - In: MACHINE LEARNING: SCIENCE AND TECHNOLOGY. - ISSN 2632-2153. - ELETTRONICO. - 4:1(2023), pp. 015015.1-015015.9. [10.1088/2632-2153/acb5e0]
Corrias M.; Papa L.; Sokolovic I.; Birschitzky V.; Gorfer A.; Setvin M.; Schmid M.; Diebold U.; Reticcioli M.; Franchini C.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/957786
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