New Algorithm can More Quickly Predict LED (Light Emitting Diode) Materials.

Researchers from the Georgian Technical University  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 (Light Emitting Diode) lighting.

Researchers from the Georgian Technical University 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 (Light Emitting Diode) 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 (a non-SI metric unit) of electric dipole moment  Historically the debye was defined as the dipole moment resulting from two charges of opposite sign but an equal magnitude of 10−10 statcoulomb[note 3] (generally called e.s.u. (electrostatic unit) in older literature), which were separated by 1 ångström. This gave a convenient unit for molecular dipole moments) temperature and chemical compatibility. Brgoch previously demonstrated that Debye (a non-SI metric unit) of electric dipole moment  Historically the debye was defined as the dipole moment resulting from two charges of opposite sign but an equal magnitude of 10−10 statcoulomb (generally called e.s.u. (electrostatic unit) in older literature), which were separated by 1 ångström. This gave a convenient unit for molecular dipole moments) temperature is correlated with efficiency.

LED (Light Emitting Diode) 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. Focused on rapidly predicting the properties of the host materials.

X 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. The algorithm used for this work however was run on a personal computer. That process would have taken weeks without the benefit of machine learning X said.

His lab does machine learning and prediction as well as synthesis so after agreeing the algorithm-recommended sodium-barium-borate was a good candidate researchers created the compound.

It proved to be stable, with a quantum yield or efficiency of 95 percent but X said the light produced was too blue to be commercially desirable.

That wasn’t discouraging he 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 Emitting Diode) light bulbs not only more efficient but also improve their color quality, while reducing the cost”.

More to the point the researchers said they demonstrated that machine learning can dramatically speed the process of discovering new materials. This work is part of his research group’s broader efforts to using machine learning and computation to guide their discovery of new materials with transformative potential.