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Deep machine learning for STEM image analysis

Anna Vladimirovna Nartova 1, 2
Anna Vladimirovna Nartova
, 
Andrey Victorovich Matveev 1
Andrey Victorovich Matveev
, 
Larisa Mikhailovna Kovtunova 1, 2
Larisa Mikhailovna Kovtunova
, 
1 Department of Chemistry, Novosibirsk State University, Novosibirsk, Russian Federation
2 G.K. Boreskov Institute of Catalysis, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russian Federation
10.1016/j.mencom.2024.10.002
Published 2024-10-22
Communication , Volume 34, Issue 6, 774-775
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Nartova A. V. et al. Deep machine learning for STEM image analysis // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 774-775.
GOST all authors (up to 50) Copy
Nartova A. V., Matveev A. V., Kovtunova L. M., Okunev A. G. Deep machine learning for STEM image analysis // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 774-775.
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TY - JOUR
DO - 10.1016/j.mencom.2024.10.002
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.002
TI - Deep machine learning for STEM image analysis
T2 - Mendeleev Communications
AU - Nartova, Anna Vladimirovna
AU - Matveev, Andrey Victorovich
AU - Kovtunova, Larisa Mikhailovna
AU - Okunev, Aleksey Grigoryevich
PY - 2024
DA - 2024/10/22
PB - Mendeleev Communications
SP - 774-775
IS - 6
VL - 34
ER -
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@article{2024_Nartova,
author = {Anna Vladimirovna Nartova and Andrey Victorovich Matveev and Larisa Mikhailovna Kovtunova and Aleksey Grigoryevich Okunev},
title = {Deep machine learning for STEM image analysis},
journal = {Mendeleev Communications},
year = {2024},
volume = {34},
publisher = {Mendeleev Communications},
month = {Oct},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.002},
number = {6},
pages = {774--775},
doi = {10.1016/j.mencom.2024.10.002}
}
MLA
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Nartova, Anna Vladimirovna, et al. “Deep machine learning for STEM image analysis.” Mendeleev Communications, vol. 34, no. 6, Oct. 2024, pp. 774-775. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.002.

Keywords

automatic recognition of objects
deep machine learning
image analysis.
microscopy
neural network
STEM
supported catalysts

Abstract

The universal, user-friendly online iOk Platform for automatic recognition of any type of objects in images based on deep machine learning is presented. Services aggregated in the iOk Platform significantly reduce the time spent on quantitative image analysis, decrease the influence of the subjective factor and increase the accuracy of the analysis by expanding the set of data that can be analyzed automatically. It is shown how the services can be used to analyze scanning transmission electron microscopy images obtained in heterogeneous catalysis studies, allowing for measurements of thousands of objects in an image, as well as simultaneous analysis of objects of different types, namely: nanoparticles and single sites.

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