The researchers 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.
The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency.
LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host – the interaction between the two materials determines the performance. The paper focused on rapidly predicting the properties of the host materials.
Brgoch said the project offers strong evidence of the value that machine learning can bring to developing high-performance materials, a field traditionally guided by trial-and-error and simple empirical rules. “It tells us where we should be looking and directs our synthetic efforts,” he said.
Brgoch collaborates with the UH Data Science Institute and has used the computing resources at the UH Center for Advanced Computing and Data Science for previous work. The algorithm used for this work, however, was run on a personal computer.
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