4D STEM data analysis with KMeans clustering

When using 4D STEM methods to study various material characteristics, large amounts of diffraction images are created for each sample studied. To determine different characteristics of the material locally from the data obtained from the diffraction patterns, it has been considered to use clustering machine learning algorithms that will be able to quickly read and classify all diffraction images. A KMeans algorithm has been adapted to classify this type of data. The method has been found to work satisfactorily when applied to an experimental example

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutores: Vanessa Costa, Sònia Estradé

Country

Spain

Related Organizations

University of Barcelona
Spain

Keywords

Difracció, Bachelor's theses, Algorisme k-means, Treballs de fi de grau, k-means clustering, Diffraction

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average