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

https://doi.org/10.1021/acsmaterialslett.4c00263

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.