Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors

R. A. W. Ayyubi, Mei Xian Low, Salar Salimi, Majid Khorsandi, M. Mosarof Hossain, Hurriyat Arooj, Shoaib Masood, M. Husnain Zeb, Nasir Mahmood, Qiaoliang Bao, Sumeet Walia and Babar Shabbir ()
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R. A. W. Ayyubi: University of Illinois at Chicago
Mei Xian Low: RMIT University
Salar Salimi: Shahid Beheshti University
Majid Khorsandi: University of Illinois at Urbana–Champaign
M. Mosarof Hossain: Monash University
Hurriyat Arooj: Pakistan Institute of Engineering and Applied Sciences
Shoaib Masood: University of Illinois at Chicago
M. Husnain Zeb: Concordia University, 1455 Boul. de Maisonneuve Ouest
Nasir Mahmood: RMIT University
Qiaoliang Bao: University of Shanghai for Science and Technology
Sumeet Walia: RMIT University
Babar Shabbir: University of Illinois at Urbana–Champaign

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: 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 more efficient material’s performance under EUV radiation. Our predictive model, based on Extremely Randomized Trees, correlates physical descriptors with performance across different spectral regions using a comprehensive dataset of 1927 samples. Through this approach, we identified promising materials such as α-MoO3, MoS2, ReS2, PbI2, and SnO2, achieving responsivities varying from 20 to 60 A/W, exceeding conventional silicon photodiodes by ~225 times in EUV sensing applications. Monte Carlo simulations revealed double electron generation rates (~2×106 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.

Date: 2025
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DOI: 10.1038/s41467-025-60499-6

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