New Algorithm Can More Quickly Predict LED Materials

Researchers from the University of Houston have devised a new machine learning algorithm that is efficient enough to run on a personal computer and predict the properties of more than 100,000 compounds in search of those most likely to be efficient phosphors for LED lighting.

They then synthesized and tested one of the compounds predicted computationally – sodium-barium-borate – and determined it offers 95 percent efficiency and outstanding thermal stability.

Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications.