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Researchers Use Machine Learning To More Quickly Analyze Key Capacitor Materials
Capacitors, given their high energy output and recharging speed, could play a major role in powering the machines of the future, from electric cars to cell phones.
But the biggest hurdle for these energy storage devices is that they store much less energy than a battery of similar size.
Researchers at Georgia Institute of Technology are tackling that problem in a novel way, using machine learning to ultimately find ways to build more capable capacitors.
The method, which was described in February 18 in the journal npj Computational Materials and sponsored by the U.S. Office of Naval Research, involves teaching a computer to analyze at an atomic level two materials that make up some capacitors: aluminum and polyethylene.
The researchers focused on finding a way to more quickly analyze the electronic structure of those materials, looking for features that could affect performance.
“The electronics industry wants to know the electronic properties and structure of all of the materials they use to produce devices, including capacitors,” said Rampi Ramprasad, a professor in the School of Materials Science and Engineering.