Encoder–decoder neural networks in interpretation of X-ray spectra

Encoder–decoder neural networks (EDNN) condense information most relevant to the output of the feedforward network to activation values at a bottleneck layer. We study the use of this architecture in emulation and interpretation of simulated X-ray spectroscopic data with the aim to identify key stru...

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Bibliographic Details
Published in:Journal of electron spectroscopy and related phenomena Vol. 277; p. 147498
Main Authors: Passilahti, Jalmari, Vladyka, Anton, Niskanen, Johannes
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2024
Subjects:
Encoder–decoder
Machine learning
Neural networks
X-ray spectroscopy
Encoder–decoder
X-ray spectroscopy
Neural networks
Machine learning
ISSN:0368-2048
Online Access:Get full text
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Summary:Encoder–decoder neural networks (EDNN) condense information most relevant to the output of the feedforward network to activation values at a bottleneck layer. We study the use of this architecture in emulation and interpretation of simulated X-ray spectroscopic data with the aim to identify key structural characteristics for the spectra, previously studied using emulator-based component analysis (ECA). We find an EDNN to outperform ECA in covered target variable variance, but also discover complications in interpreting the latent variables in physical terms. As a compromise of the benefits of these two approaches, we develop a network where the linear projection of ECA is used, thus maintaining the beneficial characteristics of vector expansion from the latent variables for their interpretation. These results underline the necessity of information recovery after its condensation and identification of decisive structural degrees of freedom for the output spectra for a justified interpretation. •Encoder–decoder neural network (EDNN) inherently compresses latent information hidden behind spectra.•Interpretation of EDNN is cumbersome. Simple encoder architectures may ease this task.•Identifications of decisive degrees of freedom is important for interpretation of spectra.
ISSN:0368-2048
DOI:10.1016/j.elspec.2024.147498