Find the Most Efficient Phosphors for LED Lighting with New Algorithm

Researchers from the University of Houston have devised a new machine learning algorithm that can identify suitable materials for LED lighting. The new algorithm can be 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. The result of the research was published on October 22nd in Nature Communications.

LED based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host. As the interaction between the two materials determines the performance, the research aims to rapidly predict the properties of the host materials.


(Image: University of Houston)

Jakoah Brgoch, assistant professor of chemistry at University of Houston, and his research team used machine learning to quickly scan huge numbers of compounds for key attributes. They synthesized and tested one of the compounds predicted computationally – sodium-barium-borate – and determined it offers 95 percent efficiency and outstanding thermal stability.

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.

The project started with a list of 118,287 possible inorganic phosphor compounds from the Pearson’s Crystal Structure Database; the algorithm whittled that to just over 2,000. Another 30 seconds and it had produced a list of about two dozen promising materials. According to Brgoch, without machine learning method, the process would have taken weeks to be done.

“It tells us where we should be looking and directs our synthetic efforts,” Brgoch said,
“Now we can to use the machine learning tools to find a luminescent material that emits in a wavelength that would be useful. Our goal is to make LED light bulbs not only more efficient but also improve their color quality, while reducing the cost.”

Disclaimers of Warranties
1. The website does not warrant the following:
1.1 The services from the website meets your requirement;
1.2 The accuracy, completeness, or timeliness of the service;
1.3 The accuracy, reliability of conclusions drawn from using the service;
1.4 The accuracy, completeness, or timeliness, or security of any information that you download from the website
2. The services provided by the website is intended for your reference only. The website shall be not be responsible for investment decisions, damages, or other losses resulting from use of the website or the information contained therein<
Proprietary Rights
You may not reproduce, modify, create derivative works from, display, perform, publish, distribute, disseminate, broadcast or circulate to any third party, any materials contained on the services without the express prior written consent of the website or its legal owner.
Display devices have been used for many years as a means of HMI (Human Machine Interface) to connect humans and machines interactively, and their usage are still expanding. Automotive interiors are no exception to this trend, with an increasing ... READ MORE
About LiDAR Automotive industry trends In recent years, many vehicles have been launched with ADAS (Advanced Driver Assistance Systems) as standard equipment. As the future evolves towards more automated driving, sensing around the vehicle i... READ MORE