Data-Driven Photoluminescence Tuning in Eu2+-Doped Phosphors.

Abstract

Discovery of rare earth phosphors has generally relied on the chemical intuition and time-intensive trial-and-error synthesis; therefore, finding new materials assisted by data-driven computations is urgent. Herein, we utilize a regression model to predict the emission wavelengths of Eu2+-doped phosphors by revealing the relationships between the crystal structure and luminescence property. The emission wavelengths of [Rb(1-x)K(x)]3LuSi2O7:Eu2+ (0 ≤ x ≤ 1) phosphors, as examples for the data-driven photoluminescence tuning, are successfully predicted on the basis of the existing data of only eight systems, also consistent with the experimental results. These phosphors can be excited by blue light and exhibit broad-band red and near-infrared emission ranging from 619 to 737 nm. These findings in Eu2+-doped silicate phosphors indicate that data-driven computations through the regression mode would have bright application in discovering novel phosphors with a target emission wavelengths.