Designing and discovering new materials has always been a time-consuming and expensive task requiring a great deal of experimentation. Experiments to date have merely scratched the surface of graphene’s billions of structural possibilities. But by using deep learning models, researchers bypass this issue and are able to predict which configurations of boron and nitrogen atoms in graphene will produce the necessary properties at more than 95% accuracy.
That’s what a team of multi-disciplinary researchers at the University of Missouri are doing.
“You can train a computer to do what it would take many years for people to otherwise do,” said Yuan Dong, a research assistant professor of mechanical and aerospace engineering. Working with Jian Lin, an assistant professor of mechanical and aerospace engineering, Dong was able to determine a way to predict the billions of possibilities of material structures created when certain carbon atoms in graphene are replaced with non-carbon atoms.
“If you put atoms in certain configurations, the material will behave differently,” Lin said. “Structures determine the properties. How can you predict these properties without doing experiments? That’s where computational principles come in.”
By inputting a few thousand known combinations of graphene structures and their properties into deep learning models using a high-performance computer, the researchers were able to learn and predict the properties of the billions of other possible structures of graphene within two days, without having to test each one separately.
Researchers envision future uses of this artificial intelligence assistive technology in designing many different graphene-related or other two-dimensional materials. These materials could be applied to the construction of LED televisions, touch screens, smartphones, solar cells, missiles and explosive devices.
“Give an intelligent computer system any design, and it can predict the properties,” Cheng said. “This trend is emerging in the material science field. It’s a great example of applying artificial intelligence to change the standard process of material design in this field.”
Source: University of Missouri
