From STEM-EDXS data to phase separation and quantification using physics-guided NMF

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-n...

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Bibliographic Details
Main Authors: Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet, Hui Chen, Duncan T L Alexander, Guillaume Obozinski, Cécile Hébert
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
non-negative matrix factorization
physics modeling
scanning transmission electron spectro-microscopy
unsupervised learning
energy dispersive x-ray spectroscopy
Online Access:https://doi.org/10.1088/2632-2153/ad9192

Internet

https://doi.org/10.1088/2632-2153/ad9192

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