Alex Voznyy, an assistant professor at U of T Scarborough, right, speaks with Zhaomin Hao, left, a visiting professor
Alex Voznyy, an assistant professor at U of T Scarborough, right, speaks with Zhaomin Hao, left, a visiting professor Researchers at the University of Toronto have developed a method of harnessing artificial intelligence to discover new and more efficient materials for clean energy technology. A team led by Alex Voznyy , an assistant professor in the department of physical and environmental sciences at University of Toronto Scarborough, used machine learning to significantly speed up the amount of time needed to find new materials with desired properties. "We are trying to find better alternatives to the materials we currently have," says Voznyy, whose research looks at developing new materials for lithium-ion batteries, hydrogen storage, CO2 capture and solar cells. "This could mean developing completely new materials or using materials we already know about but never considered using in clean energy applications." Voznyy says a major problem with the materials currently used in clean energy technologies is they are either expensive, inefficient or at the limit of their capabilities. The goal, he says, is to create new and better materials by combining elements of existing ones. The machine learning model relies on data found in the Materials Project , an open-source database of more than 140,000 known materials developed over the past decade. It contains information about the components of known materials, including crystal structure, molecular composition, density, energy conductivity and stability.
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