Machine Learning Speeds Discovery of New Host Materials for LED Lighting

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HOUSTON, Oct. 24, 2018 — A machine learning algorithm developed at the University of Houston was able to predict the properties of more than 100,000 compounds and determine those most likely to be efficient phosphors for LED lighting. When the researchers synthesized and tested one of the compounds predicted computationally — sodium-barium-borate — they found that it offered 95 percent efficiency and outstanding thermal stability.

Machine learning predicts most useful materials for LEDs, University of Houston.
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. Courtesy of the University of Houston.

Although the light produced by the sodium-barium-borate compound was too blue to be commercially desirable, the researchers were not discouraged. They said that they could now use the machine learning algorithm to find a luminescent material that emits in a wavelength that would be useful.

“Our goal is to make LED lightbulbs not only more efficient but also improve their color quality, while reducing the cost,” said professor Jakoah Brgoch.

The project started with a list of 118,287 possible inorganic phosphor compounds from the Pearson’s Crystal Structure Database. Machine learning was used to quickly scan the compounds for key attributes, including Debye temperature and chemical compatibility.

The algorithm whittled the list to just over 2000 compounds. Within 30 seconds, it singled out about two dozen promising materials from this list. Without the machine learning algorithm, this process would have taken weeks, said the researchers.

Brgoch said the project offers strong evidence of the value that machine learning can bring to the  development of high-performance materials, a field traditionally guided by trial-and-error and empirical rules.

“It tells us where we should be looking and directs our synthetic efforts,” he said.

The research was published in Nature Communications (

Published: October 2018
machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience or training. Instead of being explicitly programmed to perform a task, a machine learning system learns from data and examples. The primary goal of machine learning is to develop models that can generalize patterns from data and make predictions or decisions without being...
Research & TechnologyeducationUniversity of HoustonAmericasLEDsLight Sourcesmachine learningMaterialsphosphorsinorganic ledssolid-state chemistry

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