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

ABSTRACT: Rare-earth substituted inorganic phosphors are critical for solid state lighting. New phosphors are traditionally identified through chemical intuition or trial and error synthesis, inhibiting the discovery of potential high-performance materials. Here, we merge a support vector machine regression model to predict a phosphor host crystal structure's Debye temperature, which is a proxy for photoluminescent quantum yield, with high-throughput density functional theory calculations to evaluate the band gap. This platform allows the identification of phosphors that may have otherwise been overlooked. Among the compounds with the highest Debye temperature and largest band gap, NaBaB₉O₁₅ shows outstanding potential. Following its synthesis and structural characterization, the structural rigidity is confirmed to stem from a unique corner sharing [B₃O₇]^5- polyanionic backbone. Substituting this material with Eu²⁺ yields UV excitation bands and a narrow violet emission at 416?nm with a full-width at half-maximum of 34.5?nm. More importantly, NaBaB₉O₁₅:Eu²⁺ possesses a quantum yield of 95% and excellent thermal stability.

SUBMITTER: Zhuo Y 

PROVIDER: S-EPMC6197245 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature