Data-Driven Photoluminescence Tuning in Eu²⁺-Doped Phosphors

Area of Science:

• Materials Science
• Solid-State Chemistry
• Luminescence

Background:

• Traditional discovery of rare earth phosphors relies on chemical intuition and extensive trial-and-error synthesis.
• There is an urgent need for efficient, data-driven computational methods to discover new phosphors.

Purpose of the Study:

• To develop and apply a regression model for predicting the emission wavelengths of Europium-doped (Eu²⁺) phosphors.
• To demonstrate the utility of data-driven approaches in tuning photoluminescence properties.

Main Methods:

• Utilized a regression model to establish structure-property relationships for luminescence.
• Trained the model on existing data from eight phosphor systems.
• Predicted and experimentally validated emission wavelengths for [Rb_(1-x)K_x]₃LuSi₂O₇:Eu²⁺ phosphors.

Main Results:

• The regression model successfully predicted emission wavelengths for Eu²⁺-doped silicate phosphors.
• The synthesized [Rb_(1-x)K_x]₃LuSi₂O₇:Eu²⁺ phosphors exhibit broad-band red and near-infrared emission (619–737 nm) upon blue light excitation.
• Predictions aligned well with experimental outcomes.

Conclusions:

• Data-driven computational methods, specifically regression modeling, are effective for discovering novel phosphors.
• This approach facilitates the targeted design of phosphors with specific emission wavelengths.
• The study highlights the potential of computational materials science in accelerating phosphor development.