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

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.