Unsupervised learning for the segmentation of small crystalline particles at the atomic level

Bárcena-González, Guillermo ; Hernández-Robles, Andrei ; Mayoral, Álvaro (Universidad de Zaragoza) ; Martinez, Lidia ; Huttel, Yves ; Galindo, Pedro L. ; Ponce, Arturo

Resumen: Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D-STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images that could prevent the need of large volumes of data to train deep learning models. The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filters are convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded with aberration-corrected microscopy, offering new possibilities of analysis at nanometric scale.

Idioma: Inglés

DOI: 10.1002/crat.202200211

Año: 2023

Publicado en: CRYSTAL RESEARCH AND TECHNOLOGY 58, 3 (2023), 2200211 [8 pp.]
ISSN: 0232-1300

Factor impacto JCR: 1.5 (2023)

Categ. JCR: CRYSTALLOGRAPHY rank: 18 / 33 = 0.545 (2023) - Q3 - T2

Factor impacto CITESCORE: 2.5 - Condensed Matter Physics (Q3) - Chemistry (all) (Q3) - Materials Science (all) (Q3)

Factor impacto SCIMAGO: 0.346 - Chemistry (miscellaneous) (Q3) - Materials Science (miscellaneous) (Q3) - Condensed Matter Physics (Q3)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E13-20R
Financiación: info:eu-repo/grantAgreement/EC/H2020/823717/EU/Enabling Science and Technology through European Electron Microscopy/ESTEEM3
Financiación: info:eu-repo/grantAgreement/ES/MCIU/RYC-2018-024561-I

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Física Materia Condensada (Dpto. Física Materia Condensa.)