Mapping Pair Distribution Functions of Zirconium in NaF-ZrF4 Molten Salt from X-ray Absorption Spectroscopy Data via Machine Learning

Mapping Pair Distribution Functions of Zirconium in NaF-ZrF4 Molten Salt from X-ray Absorption Spectroscopy Data via Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mapping Pair Distribution Functions of Zirconium in NaF-ZrF4 Molten Salt from X-ray Absorption Spectroscopy Data via Machine Learning Omar Oraby, Nicholas Marcella, Anubhav Wadehra, Uday Pal, Karl Ludwig, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124538/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Molten salts are central to advanced clean energy technologies yet elucidating their highly disordered local atomic environments is especially challenging. In this work, we apply a model-agnostic machine learning method, to invert experimental extended X-ray Absorption Fine Structure (EXAFS) of NaF–ZrF₄ (53–47 mol%) directly into pair distribution functions (PDFs). Thereby circumventing the difficulty of conventional analysis that requires structural assumptions or molecular dynamics (MD) simulations, which are limited by the accuracy of force fields and computational expense. Our workflow generates training data from diverse statistical PDFs, enabling robust learning across varied configurations. Our results resolve key structural features for local atomic coordination of Zr directly from experiments, that align with previous MD and X-ray scattering studies providing a rapid and scalable tool for characterizing disordered systems. This workflow offers a pathway to in-situ and operando monitoring of atomic structure evolution for various material classes. Physical sciences/Materials science/Techniques and instrumentation/Characterization and analytical techniques Physical sciences/Materials science/Materials for energy and catalysis Full Text Additional Declarations There is NO Competing Interest. Supplementary Files mlexafsManuscriptv3naturecssupplementary.pdf Supplemental Information Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7124538","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509909714,"identity":"9d3e8504-726f-46a1-a9f4-e0a541c1bac7","order_by":0,"name":"Omar 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