High-Throughput Machine Learning and Experimental Validation Unveils Giant Responsivity for Extreme Ultraviolet Detectors

High-Throughput Machine Learning and Experimental Validation Unveils Giant Responsivity for Extreme Ultraviolet Detectors | 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 Machine Learning and Experimental Validation Unveils Giant Responsivity for Extreme Ultraviolet Detectors Babar Shabbir, Raja Abdul Wahab Ayyubi, Mei Xian Low, Salar Salimi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5783936/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Identifying materials with optimal optoelectronic properties for targeted applications represents both a critical need and a persistent challenge in optoelectronic device engineering. Machine learning models often depend on extensive datasets, which are typically lacking in specialized research domains such as extreme ultraviolet (EUV) radiation detection. Here, we demonstrate a Cross-Spectral Response Prediction framework that leverages existing visible and ultraviolet (UV) photoresponse data to predict much more efficient material’s performance under EUV radiation. Our predictive model, based on Extremely Randomized Trees, correlates physical descriptors with performance across spectral regions using a comprehensive dataset of 1385 samples. Through this approach, we identified promising materials such as α-MoO 3 , ReS 2 , Bi 2 Te 3 , and SnO 2 , achieving giant responsivities of 15 to 40 A/W, exceeding conventional silicon photodiodes by 800 times in EUV sensing applications. Monte Carlo simulations revealed double electron generation rates (~2×10 6 electrons per million EUV photons) compared to silicon, with experimental validation confirming the effectiveness of our prediction framework for accelerating the discovery of other high performing materials for diverse spectral applications. Physical sciences/Materials science/Materials for devices/Sensors and biosensors Physical sciences/Materials science/Nanoscale materials/Two-dimensional materials Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInfo.docx Supporting Information Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Nature Communications → 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. 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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-5783936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":406719048,"identity":"43293a00-c2ee-466a-9d7d-bdf3c01cfc2b","order_by":0,"name":"Babar Shabbir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACAwYeMJ3AIMHDcIChgoGBDcTlIV7LGaAWNlK0MDC2MUCswafFnP3swc8VDHV5/LN7Dx6unHcvmk++gfHB2zYGeYMD2LVY9uQlS55hOFwscedcwsGz24pz29gYmA3ntjEYbsChxeBAjoFkA8OBxIYbOQYHG7clgLSwSfO2MTDi1HL+jfHPBoa6xPlgLXPAWth/A7XY49RyI8cMaAtz4gawlgaILcxALYm4tFjOeJdm2WBwOHEjyC8Nx0BaEpsl55yTSJ6JQ4s5f+7hmw0VdYnzbvce/thQk5A7v/nwwQ9vymxs+3BogToPhcfYACQk8KkfBaNgFIyCUUAAAAB97GFB4rCXagAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8162-8013","institution":"RMIT University","correspondingAuthor":true,"prefix":"","firstName":"Babar","middleName":"","lastName":"Shabbir","suffix":""},{"id":406719049,"identity":"5a533257-14d0-43d7-98a2-687230bd5de8","order_by":1,"name":"Raja Abdul Wahab Ayyubi","email":"","orcid":"https://orcid.org/0000-0002-9887-6822","institution":"University of Illinois at Chicago","correspondingAuthor":false,"prefix":"","firstName":"Raja","middleName":"Abdul Wahab","lastName":"Ayyubi","suffix":""},{"id":406719050,"identity":"fa888ad3-0ec7-46e4-876e-3d8ebe065b23","order_by":2,"name":"Mei Xian Low","email":"","orcid":"","institution":"RMIT University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"Xian","lastName":"Low","suffix":""},{"id":406719051,"identity":"adc105bc-6e54-440d-bb66-abd4fe738b97","order_by":3,"name":"Salar Salimi","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Salar","middleName":"","lastName":"Salimi","suffix":""},{"id":406719052,"identity":"e8322013-973f-4b7b-a688-6341828f863a","order_by":4,"name":"Majid Khorsandi","email":"","orcid":"","institution":"University of Illinois, Urbana Champaign","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Khorsandi","suffix":""},{"id":406719053,"identity":"b1a2c340-b87f-4997-bd8b-ff2cf3b8b9c3","order_by":5,"name":"Mosarof Hossain","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Mosarof","middleName":"","lastName":"Hossain","suffix":""},{"id":406719054,"identity":"8c47079d-8bdc-436f-adf0-bac8222fb108","order_by":6,"name":"Hurriyat Arooj","email":"","orcid":"","institution":"PIEAS","correspondingAuthor":false,"prefix":"","firstName":"Hurriyat","middleName":"","lastName":"Arooj","suffix":""},{"id":406719055,"identity":"855ee60d-5072-46ed-8eaf-3da18f2ce76e","order_by":7,"name":"Shoaib Masood","email":"","orcid":"","institution":"University of Illinois Chicago","correspondingAuthor":false,"prefix":"","firstName":"Shoaib","middleName":"","lastName":"Masood","suffix":""},{"id":406719056,"identity":"9361d71e-d0e7-4fb2-b6ad-dec2c38778e4","order_by":8,"name":"M. 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