Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Aug 4, 2026

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Computational spectroscopy has become essential for qualitative and quantitative interpretation of experimental spectra, driven by closer interaction between experiment and theory. Progress in calculation accuracy has made computational methods indispensable for spectroscopies across the electromagnetic spectrum, particularly short-wavelength core-hole x-ray techniques enabled by modern facilities. Traditional wavefunction and density-functional approaches remain dominant, while recent machine-learning advances enable fast, accurate, and more affordable “black-box” alternatives. This Topical Review surveys data-driven methods for computational x-ray spectroscopy, covering current achievements, limitations, and future potential.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages43

Reading Time1 h 48 min

AccessFree to download

Introduction

Computational spectroscopy and x-ray spectroscopy

Machine learning as data-driven complement

Review scope and organization

This is a repository copy of Machine Learning Strategies for the Accurate and Efficient Analysis of X-ray Spectroscopy. White Rose Research Online URL for this paper: [https://eprints.whiterose.ac.uk/214042/](https://eprints.whiterose.ac.uk/214042/) Version: Published Version Article: Penfold, Thomas J, Watson, Luke, Middleton, Clelia et al. (5 more authors) (2024) Machine Learning Strategies for the Accurate and Efficient Analysis of X-ray Spectroscopy. Machine Learning: Science and Technology. 021001. ISSN 2632-2153 Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/) Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request. [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/) Machine Learning: Science and Technology TOPICAL REVIEW • OPEN ACCESS Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy To cite this article: Thomas Penfold et al 2024 Mach. Learn. : Sci. Technol. 5 021001 View the article online for updates and enhancements. You may also like -Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets Woo Seok Lee, Taegeun Song and Kyoung-Min Kim - Investigating the ability of PINNs to solve Burgers’ PDE near finite-time blowup Dibyakanti Kumar and Anirbit Mukherjee -The twin peaks of learning neural networks Elizaveta Demyanenko, Christoph Feinauer, Enrico M Malatesta et al. This content was downloaded from IP address [144.32.225.234](144.32.225.234) on 27/06/2024 at 13:22 Mach. Learn.: Sci. Technol. 5 (2024) 021001 [https://doi.org/10.1088/2632-2153/ad5074](https://doi.org/10.1088/2632-2153/ad5074) OPEN ACCESS RECEIVED 24 November 2023 REVISED 17 April 2024 ACCEPTED FOR PUBLICATION 24 May 2024 PUBLISHED 7 June 2024 Original Content from this work may be used under the terms of the Creative Commons Attribution 4 .0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. TOPICAL REVIEW Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy Thomas Penfold1, 􀀃 􀁂, Luke Watson1􀁂, Clelia Middleton1, Tudur David1, Sneha Verma1, Thomas Pope1􀁂 , Julia Kaczmarek1􀁂 and Conor Rankine2􀁂 1 Chemistry, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom 2 Department of Chemistry, University of York, York YO10 5DD, United Kingdom 􀀃 Author to whom any correspondence should be addressed. E-mail: [tom.penfold@newcastle.ac.uk](tom.penfold@newcastle.ac.uk) Keywords: x-ray spectroscopy, deep neural networks, multiple scattering theory, uncertainty, interpretability Abstract 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.

FAQs

Why is computational spectroscopy important for analyzing experimental spectra?

Computational spectroscopy enables both qualitative and quantitative interpretations of experimental spectra and supports predictive modeling of spectroscopic observables across the electromagnetic spectrum.

What has driven recent advances in x-ray spectroscopy analysis?

Improvements in instrumentation and theoretical analysis, together with next-generation light sources (including synchrotrons and x-ray free-electron lasers), have expanded accuracy and capabilities for short-wavelength core-hole techniques.

How do machine-learning approaches complement traditional wavefunction or density-functional methods?

Machine-learning algorithms open up opportunities for fast, accurate, and more affordable “black-box” analysis that can complement established wavefunction and density-functional calculations.

Aug 4, 2026

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