High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling

High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling | 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 High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling Basita Das, William E. Heymann, Yueming Wang, Uwe Rau, Thomas Kirchartz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7115972/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2026 Read the published version in npj Computational Materials → Version 1 posted 9 You are reading this latest preprint version Abstract BIAS (Bayesian Inference with Accelerated Sampling) is a high-throughput parameter estimation framework designed to rapidly infer the root causes of device underperformance in real time. It integrates a deep neural network surrogate model with accelerated Markov Chain Monte Carlo (MCMC) sampling to efficiently explore high-dimensional parameter spaces and identify needle-like regions corresponding to the ground truth values of key physical parameters. BIAS is scalable to complex systems and has been used to infer eight underlying parameters in perovskite solar cell stacks with a speedup of 4800× compared to conventional Bayesian inference methods. Its rapid and robust inference capabilities make it suitable for integration into high-throughput fabrication workflows, enabling real-time feedback that links process variations to changes in material properties and their impact on device performance. By embedding BIAS in high-throughput fabrication cycles, researchers can accelerate the transition from novel materials to devices and obtain real-time insight into how novel material properties translate in the context of a device, and the root cause of performance limitations. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Supplementary Files BIASSupplementaryinformationBDNPJ2025.docx Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2026 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 02 Oct, 2025 Reviews received at journal 06 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 06 Aug, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 13 Jul, 2025 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. 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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-7115972","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497400977,"identity":"31d95c83-cc4b-4977-899c-53deaa035477","order_by":0,"name":"Basita Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYDCCAwxsDB8YJEBMA+K1MM5gkJAgTQszDwMDCVr4jh9/9tg2x6KOgb15mwRRWiTP5Jgb524DOoznWBlxWgwO5LBJg7VI5JgRqeX882fSliAt8m+I1XIjwUyaEWwLD5FaJG+8MTfs3SYh2caTVmxBlBa+8+nPHvzcVsfPz3544w2itMABG2nKR8EoGAWjYBTgBQDeiSiCrFKTbAAAAABJRU5ErkJggg==","orcid":"","institution":"Massachusetts Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Basita","middleName":"","lastName":"Das","suffix":""},{"id":497400978,"identity":"53081a16-b723-4fa6-ab1c-50df06249bd9","order_by":1,"name":"William E. 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