Automated real-space lattice extraction for atomic force microscopy images
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475657" target="_blank" >RIV/00216208:11320/23:10475657 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=I_V.vJs2cU" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=I_V.vJs2cU</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/2632-2153/acb5e0" target="_blank" >10.1088/2632-2153/acb5e0</a>
Alternative languages
Result language
angličtina
Original language name
Automated real-space lattice extraction for atomic force microscopy images
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10305 - Fluids and plasma physics (including surface physics)
Result continuities
Project
<a href="/en/project/GX20-21727X" target="_blank" >GX20-21727X: Ferroelectric Perovskites for Energy Applications</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Learning-Science and Technology
ISSN
2632-2153
e-ISSN
2632-2153
Volume of the periodical
4
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
015015
UT code for WoS article
000929298900001
EID of the result in the Scopus database
2-s2.0-85148236605