Machine Learning For OLED Displays

[New System For Cheaper, More Efficient Displays]

OLED Displays are currently considered the future of imaging technology. This claim has been bolstered by a variety of tech companies who have in recent years been shifting towards using these displays for their devices.

While there are plenty of things that are known characteristics of these OLED displays, but what really defines them is the fact that they produce different colors using red, green and blue materials.

While the RGB technology does gives a whole lot of versatility to the display, it also comes with one specific limitation that holds back the display from really taking off.

What we are referring to is inefficiency and instability of the blue materials, which requires the development of organometallic molecules, who’s main job is to use metals like iridium to boost the molecules through phosphorescence.

This workaround is almost as expensive as it sounds. Besides, the materials produced as a result are still not as stable as they should be.

To take care of this limitation, a team of researchers from Harvard, MIT and Samsung decided to create a system that would help them identify similar, but more efficient materials than the blue ones.

To do that, they developed what they call the Molecular Space Shuttle, a machine learning process that can automatically screen a database of over 1.6 million potential molecules, in order to find just the right ones.

This is an extensive process that takes a lot of time, as each molecule has to go through extensive calculations used to predict if their color, brightness and stability would match the industry standards.

The results of this sorting are then further sorted to remove the weaker materials, thus leaving only a handful of materials that have been proved to perform as well as existing ones.

These new materials will now be used to created a new range of OLED displays which will not only be cheaper, but will also be able to perform as good as existing standards, and even more.

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