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A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data

; ; ; ; ; ; ; ; ;

In
Microscopy and microanalysis 29(5), Seiten/Artikel-Nr.:1658-1670

ImpressumOxford : Oxford University Press

ISSN1435-8115

Online
DOI: 10.18154/RWTH-2024-05780
DOI: 10.1093/micmic/ozad086

URL: https://publications.rwth-aachen.de/record/987726/files/987726.pdf

Einrichtungen

  1. Juniorprofessur für Mathematische Bild- und Signalverarbeitung (112430)
  2. Lehrstuhl für Mathematik der Informationsverarbeitung (114510)
  3. Fachgruppe Mathematik (110000)


Thematische Einordnung (Klassifikation)
DDC: 500

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85172941070
WOS Core Collection: WOS:001186447000003
PubMed: pmid:37639387

Interne Identnummern
RWTH-2024-05780
Datensatz-ID: 987726

Beteiligte Länder
Germany, UK

 GO

Medline ; Creative Commons Attribution CC BY 4.0 ; OpenAccess ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; NationallizenzNationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2024-06-12, last modified 2025-02-07
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