Machine Learning‐Guided Discovery of Copper(I)‐Iodide Cluster Scintillators for Efficient X‐ray Luminescence Imaging

Abstract Developing efficient scintillators with environmentally friendly compositions, adaptable band gaps, and robust chemical stability is crucial for modern X‐ray radiography. While copper(I)‐iodide cluster crystals show promise, the vast design space of inorganic cores and organic ligands poses challenges for conventional approaches. In this study, we present machine learning‐guided discovery of copper(I)‐iodide cluster scintillators for efficient X‐ray luminescence imaging. Our findings reveal that combining base learning models with fused features enhances model generalization, achieving an impressive determination coefficient of 0.88. By leveraging this approach, we obtain a high‐performance Cu(I)‐I cluster scintillator, named copper iodide‐(1‐Butyl‐1,4‐diazabicyclo[2.2.2]octan‐1‐ium) 2 , which exhibit radioluminescence 56 times stronger than that of PbWO 4 , and enables a detection limit for X‐rays of 19.6 nGy air s −1 . Furthermore, we demonstrate the versatility of these scintillators by incorporating them as microfillers in the fabrication of flexible composite scintillators for X‐ray imaging, achieving a static resolution of 20 lp mm −1 and demonstrating promising performance for dynamic X‐ray imaging.