[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123848-en":3,"doc-seo-123848-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123848,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy","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.","This is a repository copy of Machine Learning Strategies for the Accurate and Efficient Analysis of X-ray Spectroscopy.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/214042/](https://eprints.whiterose.ac.uk/214042/)  \nVersion: Published Version  \nArticle:  \nPenfold, Thomas J, Watson, Luke, Middleton, Clelia et al. (5 more authors) (2024)  \nMachine Learning Strategies for the Accurate and Efficient Analysis of X-ray Spectroscopy. Machine Learning: Science and Technology. 021001. ISSN 2632-2153  \nReuse  \nThis 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/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nMachine Learning: Science and  \nTechnology   \nTOPICAL REVIEW • OPEN ACCESS  \nMachine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy  \nTo cite this article: Thomas Penfold et al 2024 Mach. Learn. : Sci. Technol. 5 021001  \nView the article online for updates and enhancements.  \nYou may also like  \n-Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets  \nWoo Seok Lee, Taegeun Song and Kyoung-Min Kim  \n- Investigating the ability of PINNs to solve Burgers’ PDE near finite-time blowup  \nDibyakanti Kumar and Anirbit Mukherjee  \n-The twin peaks of learning neural networks Elizaveta Demyanenko, Christoph Feinauer, Enrico M Malatesta et al.  \nThis content was downloaded from IP address [144.32.225.234](144.32.225.234) on 27/06/2024 at 13:22  \n Mach. Learn.: Sci. Technol. 5 (2024) 021001 [https://doi.org/10.1088/2632-2153/ad5074](https://doi.org/10.1088/2632-2153/ad5074)  \nOPEN ACCESS  \nRECEIVED  \n24 November 2023  \nREVISED  \n17 April 2024  \nACCEPTED FOR PUBLICATION 24 May 2024  \nPUBLISHED  \n7 June 2024  \nOriginal Content from this work may be used under the terms of the  \nCreative Commons Attribution 4 .0 licence.  \nAny further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nTOPICAL REVIEW  \nMachine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy  \nThomas Penfold1, 􀀃 􀁂, Luke Watson1􀁂, Clelia Middleton1, Tudur David1, Sneha Verma1, Thomas Pope1􀁂 , Julia Kaczmarek1􀁂 and Conor Rankine2􀁂  \n1 Chemistry, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom  \n2 Department of Chemistry, University of York, York YO10 5DD, United Kingdom  \n􀀃 Author to whom any correspondence should be addressed.  \nE-mail: [tom.penfold@newcastle.ac.uk](tom.penfold@newcastle.ac.uk)  \nKeywords: x-ray spectroscopy, deep neural networks, multiple scattering theory, uncertainty, interpretability  \nAbstract  \nComputational 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.","cbCaieZaD32XN1Dd","https://ap.wps.com/l/cbCaieZaD32XN1Dd","pdf",4677370,1,43,"English","en",105,"# Introduction\n## Computational spectroscopy and x-ray spectroscopy\n## Machine learning as data-driven complement\n# Review scope and organization","[{\"question\":\"Why is computational spectroscopy important for analyzing experimental spectra?\",\"answer\":\"Computational spectroscopy enables both qualitative and quantitative interpretations of experimental spectra and supports predictive modeling of spectroscopic observables across the electromagnetic spectrum.\"},{\"question\":\"What has driven recent advances in x-ray spectroscopy analysis?\",\"answer\":\"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.\"},{\"question\":\"How do machine-learning approaches complement traditional wavefunction or density-functional methods?\",\"answer\":\"Machine-learning algorithms open up opportunities for fast, accurate, and more affordable “black-box” analysis that can complement established wavefunction and density-functional calculations.\"}]","Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy | 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