Identifying an efficient, thermally robust inorganic phosphor host via machine learning.

Area of Science:

• Materials Science
• Solid-State Chemistry
• Computational Materials Design

Background:

• Rare-earth substituted inorganic phosphors are essential components in modern solid-state lighting applications.
• Traditional methods for discovering new phosphors rely on chemical intuition and empirical synthesis, which are time-consuming and limit the exploration of novel materials.

Purpose of the Study:

• To develop a computational platform that integrates machine learning and high-throughput calculations to accelerate the discovery of high-performance inorganic phosphors.
• To identify novel phosphor materials with desirable properties, such as high Debye temperature (proxy for quantum yield) and large band gap.

Main Methods:

• A support vector machine regression model was employed to predict the Debye temperature of phosphor host crystal structures.
• High-throughput density functional theory (DFT) calculations were utilized to evaluate the electronic band gap of candidate materials.
• The combined platform enabled the screening and identification of promising phosphor candidates that might be overlooked by traditional methods.

Main Results:

• The computational platform successfully identified NaBaB9O15 as a phosphor host with high Debye temperature and a large band gap.
• Synthesized NaBaB9O15:Eu2+ exhibited UV excitation bands and a narrow violet emission at 416 nm.
• The material demonstrated a high quantum yield of 95% and excellent thermal stability, attributed to its rigid [B3O7]5- polyanionic backbone.

Conclusions:

• The integrated computational approach significantly enhances the efficiency of discovering novel inorganic phosphors.
• NaBaB9O15:Eu2+ represents a promising new phosphor material for solid-state lighting, offering high efficiency and stability.
• This work demonstrates the potential of merging machine learning with high-throughput DFT for materials discovery in phosphors.