Charting the low-loss region in electron energy loss spectroscopy with machine learning.
Bandgap
Electron energy loss spectroscopy
Machine learning
Neural networks
Transition metal dichalcogenides
Transmission electron microscopy
Journal
Ultramicroscopy
ISSN: 1879-2723
Titre abrégé: Ultramicroscopy
Pays: Netherlands
ID NLM: 7513702
Informations de publication
Date de publication:
Mar 2021
Mar 2021
Historique:
received:
10
09
2020
revised:
22
12
2020
accepted:
05
01
2021
pubmed:
17
1
2021
medline:
17
1
2021
entrez:
16
1
2021
Statut:
ppublish
Résumé
Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS
Identifiants
pubmed: 33453606
pii: S0304-3991(21)00001-2
doi: 10.1016/j.ultramic.2021.113202
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
113202Informations de copyright
Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.