Fitting Pair Distribution Function with Backpropagation

Fitting Pair Distribution Function with Backpropagation | 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 Method Article Fitting Pair Distribution Function with Backpropagation Ziping Yang, Zhihong Luo, Xinjian Ouyang, Ping Wei, Laijun Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7236673/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pair distribution function (PDF) analysis is a powerful technique for characterizing both long-range structures and local distortions in materials, gaining significant importance in materials science. However, conventional PDF modeling approaches—including real-space Rietveld refinement for small-box models and Reverse Monte Carlo (RMC) for big-box models—often suffer from efficiency limitations. We propose a novel approach us- ing backpropagation algorithms to fit neutron and X-ray PDF data of ferroelectric perovskites. Our results demonstrate that this method achieves fitting accuracy comparable to RMC while offering potential efficiency advantages across various temperature ranges. By simultaneously optimizing tens of thousands of parame- ters, our approach can overcome unstable convergence inherent in RMC’s random perturbation. This method shows particular promise for determining local structures in materials with correlated disorder and illustrates the broader potential of integrating physical formulas within neural network frameworks for materials analysis. Materials Theory and Modeling Artificial Intelligence and Machine Learning Crystallography Computational Physics Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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-7236673","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":492177584,"identity":"e2a490d9-bd90-452f-8102-84fd68a97411","order_by":0,"name":"Ziping Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ziping","middleName":"","lastName":"Yang","suffix":""},{"id":492177984,"identity":"6d1a0711-5ee7-422c-82b0-7614bb3cfd39","order_by":1,"name":"Zhihong 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