Researchers at Harvard University have developed new blue-light emitting molecules that could potentially lead to cheaper and more efficient organic light-emitting diode (OLED) displays. The team designed over 1,000 molecules, which could considerably enhance the displays for phones, televisions, phones, tablets, etc.
Organic molecules used by OLED screens produce light once an electric current is applied. A backlight is not required in the case of OLED screens, unlike the popular liquid crystal displays (LCDs). This means, the display can be made extremely thin and flexible like a plastic sheet.
Separate pixels can be turned on or off, which significantly improves the energy consumption and color contrast of the screen. In high-end consumer devices, LCDs are already replaced by OLEDs but due to lack of efficient and stable blue materials, OLEDs have become less viable in large displays like televisions.
A large-scale, computer-driven screening process known as the Molecular Space Shuttle has been developed by the interdisciplinary team of Harvard researchers, in association with Samsung and MIT. The process uses experimental ad hypothetical chemistry, cheminformatics, and machine learning to rapidly detect innovative OLED molecules that execute as well as or even better than present industry standards.
People once believed that this family of organic light-emitting molecules was restricted to a small region of molecular space. But by developing a sophisticated molecular builder, using state-of-the art machine learning, and drawing on the expertise of experimentalists, we discovered a large set of high-performing blue OLED materials.
Alán Aspuru-Guzik, Professor of Chemistry and Chemical Biology, Harvard
The study has been reported in the current issue of Nature Materials.
The emission of blue color presented the most major challenge in manufacturing low-cost OLEDs. OLEDs, similar to LCDs, depend on blue, red, and green subpixels to create each color on screen. However, organic molecules that efficiently produce blue light were difficult to find.
In order to enhance efficiency, OLED manufacturers developed organometallic molecules that contain expensive transition metals such as iridium to improve the molecule via phosphorescence. This solution was found to be expensive and was yet to achieve a consistent blue color.
Aspuru-Guzik with his research team tried to substitute these organometallic systems with organic molecules. The researchers initially constructed libraries of over 1.6 million candidate molecules.
In order to narrow down the field, a research team from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), created novel machine learning algorithms to estimate which molecules could deliver good results, and prioritize such molecules to be almost tested. This study was headed by Ryan Adams, Assistant Professor of Computer Science. This approach also considerably reduced the estimated cost of the study by a factor of ten.
This was a natural collaboration between chemistry and machine learning. Since the early stages of our chemical design process starts with millions of possible candidates, there’s no way for a human to evaluate and prioritize all of them. So, we used neural networks to quickly prioritize the candidates based on all the molecules already evaluated. Machine learning tools are really coming of age and starting to see applications in a lot of scientific domains. This collaboration was a wonderful opportunity to push the state of the art in computer science, while also developing completely new materials with many practical applications. It was incredibly rewarding to see these designs go from machine learning predictions to devices that you can hold in your hand.
David Duvenaud, Postdoctoral Fellow, SEAS
"We were able to model these molecules in a way that was really predictive,” said Rafael Gómez-Bombarelli, a postdoctoral fellow in the Aspuru-Guzik lab and first author of the paper. “We could predict the color and the brightness of the molecules from a simple quantum chemical calculation and about 12 hours of computing per molecule. We were charting chemical space and finding the frontier of what a molecule can do by running virtual experiments."
“Molecules are like athletes,” Aspuru-Guzik said. “It’s easy to find a runner, it’s easy to find a swimmer, it’s easy to find a cyclist but it’s hard to find all three. Our molecules have to be triathletes. They have to be blue, stable and bright.”
However, mere computing power is not enough for finding these super molecules, but requires human intuition, informed Tim Hirzel, a senior software engineer in the Department of Chemistry and Chemical Biology and the paper’s coauthor.
In an effort to close the gap between experimental practice and hypothetical modeling, Hirzel along with the research team developed a new web application for collaborators to study the outcomes of over half a million quantum chemistry simulations. Each month, coauthor Jorge Aguilera-Iparraguirre, also a postdoctoral fellow in the Aspuru-Guzik lab, and Gómez-Bombarelli chose the most viable molecules and then employed their software to produce “baseball cards,” profiles, which included critical data about individual molecules.
The process was able to detect as much as 2500 molecules. At MIT and Samsung, the team’s experimental collaborators voted the most promising molecules for application. The voting tool was dubbed as “molecular Tinder” following the trendy online dating app.
“We facilitated the social aspect of the science in a very deliberate way,” said Hirzel.
“The computer models do a lot but the spark of genius is still coming from people,” said Gómez-Bombarelli. “The success of this effort stems from its multidisciplinary nature,” said Aspuru-Guzik. “Our collaborators at MIT and Samsung provided critical feedback regarding the requirements for the molecular structures.”
The high throughput screening technique pioneered by the Harvard team significantly reduced the need for synthesis, experimental characterization, and optimization. It shows the industry how to advance OLED technology faster and more efficiently.
Marc Baldo, Professor of Electrical Engineering and Computer Science, MIT
Following this expedited design cycle, the researchers had countless number of molecules that excel just like, if not better than, advanced metal-free OLEDs. This kind of molecular screening can be used beyond OLEDs.
“This research is an intermediate stop in a trajectory towards more and more advanced organic molecules that could be used in flow batteries, solar cells, organic lasers, and more,” said Aspuru-Guzik. “The future of accelerated molecular design is really, really exciting.”
Besides the authors mentioned, the paper was coauthored by Dougal Maclaurin, Hyun Sik Chae, Martin A. Blood-Forsythe, Dong-Gwang Ha, Markus Einzinger, Georgios Markopoulos, Tony Wu, Hosuk Kang, Soonok Jeon, Hiroshi Miyazaki, Sunghan Kim, Masaki Numata, Seong Ik Hong, and Wenliang Huang.
The Samsung Advanced Institute of Technology funded the study.