Structure‐Based Machine Learning Enables Discovery of Mn⁴⁺‐Activated Red‐Light Fluorides for Ultrawide‐Gamut Mini‐Light‐Emitting Diodes

Abstract Mn⁴⁺‐activated fluorides with a saturated red color and sharp line emission are ideal for applications in the light‐emitting diodes (LEDs) backlight for displays. However, the emissions attributed to ²E→⁴A₂ parity and spin‐forbidden transitions limit the design and adjustments of emission wavelength and chromaticity coordinates. Herein, machine learning algorithms are used to build a wavelength‐prediction model for Mn⁴⁺‐activated fluorides. The model precisely identifies the key structural features that affect wavelengths and discovers target materials. The predicted candidate Cs₂NaAlF₆:Mn⁴⁺ (CNAF) with a long‐wavelength zero‐phonon‐line emission at 628 nm exhibits a redshift in comparison with other reported Mn⁴⁺‐activated fluorides and commercial K₂SiF₆:Mn⁴⁺, but maintains narrow spectral emission with full‐width half maximum (FWHM) of 11.2 nm. The redshift and narrow spectra result in a color purity of 99.7% and Commission Internationale de L'Eclairage (CIE) chromaticity coordinate of (0.7032,0.2967) that is close to the pure red‐light point of Recommendation BT. 2020 (Rec. 2020). Moreover, CNAF is prepared as a transparent red‐light film, and the device fabricated using the blue‐light mini‐LEDs, green quantum‐dot film, and CNAF film exhibits a wide color‐gamut of 121.5% National Television Standards Committee (NTSC) or 90.6% Rec. 2020, suggesting that CNAF has potential for wide‐color‐gamut displays.