A chemical compound designed with the aid of a Harvard-created computer program has turned out to be one of the best organic electronic materials to date. This new material, an organic semiconductor, could be used to make new electronics such as colorful displays that roll up. It’s an important proof of principle for using computers to aid materials design.
Organic semiconductors could enable less expensive, lightweight electronics that can take new forms, such as flexible displays and printed solar cells. It’s hoped that the materials will also make solar power more widespread, because it should be less expensive to make solar cells from them than from silicon and other inorganic materials. But in the decades since chemists began working with organic semiconductors, progress has been slow, and these materials have found limited applications, such as in short-lived portable solar cells. The main challenge is that these materials just don’t conduct electrons and their positive counterparts, holes, nearly as fast as conventional semiconductors like silicon, making them much less efficient.
The new organic semiconductor, predicted using a computer modeling program developed by Harvard chemistry professor Alán Aspuru-Guzik, and then synthesized by researchers at Stanford University, conducts charge much faster than the silicon material used in most of today’s display electronics. That means it could be used to make brighter displays that provide crisper video. And the new material is sufficiently speedy to make electronics for organic light-emitting diode (OLED) displays used in cell phones and televisions or to control radio-frequency identification (RFID) tags used to track stuff.
For many years, scientists have talked about the potential of computational modeling to shorten the materials development process. When chemists develop new materials, “most work is based on intuition,” says Zhenan Bao, the Stanford professor of chemical engineering whose group made and tested the new material. Unfortunately, intuition is hit or miss—a new molecule that seems promising may not do what researchers expect it to. By prescreening potential materials using Aspuru-Guzik’s computer program, chemists can focus the months or years needed to synthesize and test new compounds on those that seem most promising.
Computational screening has been a great success in some areas, including energy storage. Gerbrand Ceder, a professor at MIT, has computationally predicted faster-charging battery materials that are currently being commercialized by the company A123 Systems. Until recently, computational methods hadn’t been applied to making better organic semiconductors, which pose a different set of challenges, says Aspuru-Guzik. But now theoretical chemists have generated enough foundational knowledge, and experimentalists enough data, to make successful predictive models.
Aspuru-Guzik took as a starting point an organic semiconductor called DTT, which has already demonstrated promise in the lab. First, the computer program generated several possible variations on this large carbon-based molecule by adding and subtracting components. The program then predicted how these variations would behave and screened for the most promising—those that seemed likeliest to conduct charges very fast. These predictions were informed by a huge amount of data on how similar molecules and molecular building blocks have performed in past experiments, and by theoretical physics and chemistry.
When Bao’s group synthesized the molecule predicted to have the best properties, it behaved as expected. Transistors made from the material operate 10 times faster than transistors made from amorphous silicon, the material used in today’s display electronics; the new material is the second-fastest organic semiconductor yet made. The work is described in the journal Nature Communications. While Bao and Aspuru-Guzik say the material could be used in industrial applications, the experiment has more significance as a proof of principle for their methods for using computers to develop new organic materials.
Aspuru-Guzik is using a similar computational approach for the Harvard Clean Energy Project, which aims to discover better solar material. For this endeavor, he has a lot of computational power at his fingertips: his calculations are being run on the almost two million computers of users signed on to the IBM World Community Grid. Aspuru-Guzik is taking advantage of this brute force to screen about 2.6 million molecules that haven’t been made for their solar potential, using experimental data on approximately 200 previously made molecules. The program predicts what color of light a material will absorb, and how strongly, as well as other factors that make a good solar material.
“There’s no way you can try all of the possible materials experimentally,” says Geoffrey Hutchison, a professor of chemistry at the University of Pittsburgh who’s also working on computationally predicting characteristics of possible solar materials. “As time goes on, the experimentalists are starting to rely more on prediction.” Results like Aspuru-Guzik’s should make them more confident in doing so, he says.
DeepMind’s cofounder: Generative AI is just a phase. What’s next is interactive AI.
“This is a profound moment in the history of technology,” says Mustafa Suleyman.
What to know about this autumn’s covid vaccines
New variants will pose a challenge, but early signs suggest the shots will still boost antibody responses.
Human-plus-AI solutions mitigate security threats
With the right human oversight, emerging technologies like artificial intelligence can help keep business and customer data secure
Next slide, please: A brief history of the corporate presentation
From million-dollar slide shows to Steve Jobs’s introduction of the iPhone, a bit of show business never hurt plain old business.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.