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Lookup NU author(s): Professor Thomas Penfold, Dr Luke Watson, Clelia Middleton, Dr Tudur David, Dr Sneha Verma, Dr Thomas PopeORCiD, Conor Rankine
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Computational spectroscopy has emerged as a critical tool for researchers looking to achieve both qualitative and quantitative interpretations of experimental spectra. Over the past decade, increased interactions between experiment and theory have created a positive feedback loop that has stimulated developments in both domains. In particular, the increased accuracy of calculations has led to them becoming an indispensable tool for the analysis of spectroscopies across the electromagnetic spectrum. This progress is especially well demonstrated for short-wavelength techniques, e.g. core-hole (X-ray) spectroscopies, whose prevalence has increased following the advent of modern X-ray facilities including third-generation synchrotrons and X-ray free-electron lasers (XFELs). While calculations based on well-established wavefunction or density-functional methods continue to dominate the greater part of spectral analyses in the literature, emerging developments in machine-learning algorithms are beginning to open up new opportunities to complement these traditional techniques with fast, accurate, and affordable 'black-box' approaches. This Topical Review recounts recent progress in data-driven/machine-learning approaches for computational X-ray spectroscopy. We discuss the achievements and limitations of the presently-available approaches and review the potential that these techniques have to expand the scope and reach of computational and experimental X-ray spectroscopic studies.
Author(s): Penfold TJ, Watson L, Middleton C, David T, Verma S, Pope T, Kaczmarek J, Rankine C
Publication type: Article
Publication status: Published
Journal: Machine Learning: Science and Technology
Year: 2024
Volume: 5
Online publication date: 07/06/2024
Acceptance date: 24/05/2024
Date deposited: 06/06/2024
ISSN (electronic): 2632-2153
Publisher: Institute of Physics Publishing Ltd
URL: https://doi.org/10.1088/2632-2153/ad5074
DOI: 10.1088/2632-2153/ad5074
Data Access Statement: The data that support the findings of this study are openly available at the following URL/DOI: https://gitlab. com/team-xnet. Accompanying materials including software, training and testing sets, and tutorials are freely available at: https://gitlab.com/team-xnet.
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