Near-infrared (NIR) phosphors based on Cr³⁺ doped garnets present great potential in the next generation of NIR light sources. Nevertheless, the huge searching space for the garnet composition makes the rapid discovery of NIR phosphors with high performance remain a great challenge for the scientific community. Herein, a generalizable machine learning (ML) strategy is designed to accelerate the exploration of innovative NIR phosphors via establishing the relationship between key parameters and emission peak wavelength (EPW). We propose a semi-supervised co-training model based on kernel ridge regression (KRR) and support vector regression (SVR), which successfully establishes an expanded dataset with unlabeled dataset (previously unidentified garnets), addressing the overfitting issue resulted from a small dataset and greatly improving the model generalization capability. The model is then interpreted to extract valuable insights into the contribution originated from different features. And a new type NIR luminescent material of Lu₃Y₂Ga₃O₁₂: Cr³⁺ (EPW~750 nm) is efficiently screened, which demonstrates a high internal (external) quantum efficiency of 97.1% (38.8%) and good thermal stability, particularly exhibiting promising application in the NIR phosphor-converted LEDs (pc-LED). These results suggest the strategy proposed in this work could provide new viewpoint and direction for developing NIR luminescence materials.

基于 Cr ³⁺掺杂石榴石的近红外 (NIR) 荧光粉在下一代 NIR 光源中展现出巨大的潜力。然而，石榴石成分的巨大搜索空间使得快速发现高性能近红外荧光粉仍然是科学界面临的巨大挑战。本文设计了一种可推广的机器学习（ML）策略，通过建立关键参数和发射峰值波长（EPW）之间的关系来加速创新近红外荧光粉的探索。我们提出了一种基于核岭回归（KRR）和支持向量回归（SVR）的半监督协同训练模型，成功地建立了未标记数据集（之前未识别的石榴石）的扩展数据集，解决了小数据集导致的过度拟合问题并大大提高了模型泛化能力。然后对该模型进行解释，以提取对源自不同特征的贡献的有价值的见解。并高效筛选出一种新型近红外发光材料Lu ₃ Y ₂ Ga ₃ O ₁₂ : Cr ³⁺ (EPW~750 nm)，该材料表现出高达97.1%(38.8%)的内(外)量子效率和良好的热学性能。稳定性，特别是在近红外荧光粉转换 LED (pc-LED) 中表现出有前景的应用。这些结果表明本文提出的策略可以为开发近红外发光材料提供新的观点和方向。