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

Ya Zhuo, Aria Mansouri Tehrani, Anton O. Oliynyk, Anna C. Duke and Jakoah Brgoch ()
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Ya Zhuo: University of Houston
Aria Mansouri Tehrani: University of Houston
Anton O. Oliynyk: University of Houston
Anna C. Duke: University of Houston
Jakoah Brgoch: University of Houston

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-06625-z

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