Rapid Discovery of Efficient Long-Wavelength Emission Garnet:Cr NIR Phosphors via Multi-Objective Optimization

High-efficiency long-wavelength emission near-infrared
(NIR) phosphors
are the key to next-generation LED light sources. However, high-efficiency
phosphors usually exhibit narrow-band emission at shorter wavelengths
due to the crystal field intensity. In this paper, we utilize multi-objective
optimization to discover the NIR phosphor Gd3Mg0.5Al1.5Ga2.5Ge0.5O12:0.04Cr3+. It exhibits a broadband NIR emission from 650 to 1100 nm
peaking at 763 nm, with a full width at half maximum (FWHM) of 150
nm, an internal quantum efficiency (IQE)/external quantum efficiency
(EQE) of 90%/53.1%, and good thermal stability (85.3% @ 150 °C).
The packaging results show that ∼53.2 mW of output power is
achieved at 915 mW input power, which suggests promising applications
for NIR pc-LED. Our approach is based on the data of emission wavelength
(WL) and IQE for garnet:Cr NIR phosphors to construct machine learning
models. An active learning strategy is used to make tradeoffs between
WL and IQE, and we are able to find the targeted phosphor after only
four iterations of synthesis and characterization.