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Machine-Learning Strategies for the Accurate and Efficient Analysis of X-ray Spectroscopy

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.

Publication metadata

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


DOI: 10.1088/2632-2153/ad5074


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Funder referenceFunder name
Leverhulme Trust