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Machine learning algorithm finds the best LED phosphor in list of 100,000 compounds

05 Nov 2018

A machine learning algorithm that can swiftly identify the most desirable LED phosphor host compound out of a database of almost 120,000 materials has been developed by researchers in the US. Efficient enough to run on a PC, the program was created by Jakoah Brgoch and colleagues at the University of Houston and has predicted the relevant properties of a highly efficient, thermally stable compound in well under a minute. The team's algorithm could soon be used to speed up the discovery of new materials for use in commercially competitive LEDs.

Typically, LEDs are composed of an inorganic, luminescent "phosphor" material, doped with small quantities of rare earth elements. The interactions between phosphor hosts and the rare earth dopants are a strong indication of an LED's performance. The key properties of a good phosphor are its photoluminescent quantum yield (photon production efficiency) and its thermal stability (resistance to breaking down under high temperatures). Previously, discoveries of new, higher-performance phosphor compounds were made either through trial-and-error, or by the intuition of chemists, meaning many, more optimal materials were likely missed.


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