| Autor: |
Marco Corrias, Lorenzo Papa, Igor Sokolović, Viktor Birschitzky, Alexander Gorfer, Martin Setvin, Michael Schmid, Ulrike Diebold, Michele Reticcioli, Cesare Franchini |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015015 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2632-2153 |
| DOI: |
10.1088/2632-2153/acb5e0 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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