Machine-Learning-Driven Discovery of Mn4+-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays

The discovery of high-efficiency Mn4+-activated fluoride red phosphors with short excited-state lifetimes (ESLs) is urgent and crucial for high-quality, wide-color-gamut display applications. However, it is still a great challenge to design target phosphors with both short ESL and high luminescence efficiency. Herein, we propose an efficient machine learning approach based on a small dataset to establish the ESL prediction model, thereby facilitating the discovery of new Mn4+-activated fluorides with short ESLs. Such a model can not only accurately predict the ESLs of Mn4+ in fluorides but also quantify the impact of structure features on ESLs, therefore elucidating the “structure-lifetime” correlations. Guided by the correlations, two new Mn4+-doped tetramethylammonium (TMA)-based hybrid fluorides (TMA)2BF6:Mn4+ (B = Sn or Hf) with both short ESLs (τ ≤ 3.7 ms) and high quantum efficiencies (internal QEs > 92%, external QEs > 55%) have been discovered successfully. A prototype displayer with excellent performance (∼124% National Television Standards Committee (NTSC) color gamut) is assembled by employing a (TMA)2SnF6:Mn4+-based white Mini-LED backlight module, demonstrating its practical prospects in high-quality displays. This work not only brings promising candidates for Mn4+-doped fluoride phosphors but also provides a valuable reference for accelerating the discovery of new promising phosphors.