Machine-learning guided engineering of Mo4+ activated halide near-infrared phosphors for AI-augmented medical imaging

Developing highly efficient lead-free near-infrared (NIR) phosphors with strong thermal stability is a key challenge in material design and optoelectronics applications. Here, a machine-learning (ML) guided co-doping strategy to construct a broadband NIR-emitting phosphor, Cs 2 Zr(Cl 0.46 Br 0.54 ) 6 :12%Mo 4+ /3.6%Sb 3+ (SM-CZCB) is reported, achieving record-high internal and external quantum efficiencies of 92.4% and 65.9% at 920 nm, respectively. Guided by ML, Sb 3+ and Br - were selected to co-dope and synergistically enhance energy transfer through the spin-orbit coupling, d-d correlation, and lattice distortion to enhance NIR emission of Mo 4+ . Notably, a [SbCl 6 ] 3+ -[ZrCl 6 ] 2- -[MoCl 6 ] 2- sequential energy transfer chain form a near-resonant configuration to reach the emission centers. The fabricated NIR light-emitting diode using SM-CZCB exhibits a record-high power conversion efficiency of 27.07% with an operational T 50 exceeding 4000 hours at 450 nm excitation. Moreover, the AI-enhanced biomedical imaging was demonstrated using NIR light with high-resolution. This marks the integration of AI-guided material design with practical AI-enhanced medical imaging.