4D STEM data analysis with KMeans clustering

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dc.contributor.advisorCosta Ledesma, Vanessa
dc.contributor.advisorEstradé Albiol, Sònia
dc.contributor.authorBach Siches, Núria
dc.date.accessioned2024-10-01T14:22:15Z
dc.date.available2024-10-01T14:22:15Z
dc.date.issued2024-06
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutores: Vanessa Costa, Sònia Estradéca
dc.description.abstractWhen 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 exampleca
dc.format.extent5 p.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2445/215509
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Bach, 2024
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física
dc.subject.classificationDifracciócat
dc.subject.classificationAlgorisme k-meanscat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherDiffractioneng
dc.subject.otherk-means clusteringeng
dc.subject.otherBachelor's theseseng
dc.title4D STEM data analysis with KMeans clusteringeng
dc.typeinfo:eu-repo/semantics/bachelorThesisca

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