Rampi Ramprasad, a professor in the School of Materials Science and Engineering at Georgia Tech, holds an aluminum-based capacitor. Photo: Allison Carter, Georgia Tech.Given their high energy output and recharging speed, capacitors 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 now tackling this problem in a novel way, by using machine learning to ultimately find ways to build more capable capacitors.
Their method, which is reported in a paper in npj Computational Materials, involves teaching a computer to analyze at an atomic level two materials that make up certain capacitors: aluminum and polyethylene. The researchers focused on finding a way to more quickly analyze the electronic structure of these 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 at Georgia Tech.
A material like polyethylene is a very good insulator with a large band gap – an energy range forbidden to electrical charge carriers. But defects in the polyethylene can allow unwanted charge carriers to enter the band gap, reducing efficiency.
"In order to understand where the defects are and what role they play, we need to compute the entire atomic structure, something that so far has been extremely difficult," said Ramprasad. "The current method of analyzing those materials using quantum mechanics is so slow that it limits how much analysis can be performed at any given time."
Ramprasad and his colleagues, who specialize in using machine learning to help develop new materials, used a sample of data created from a quantum mechanics analysis of aluminum and polyethylene as an input to teach a powerful computer how to simulate that analysis.
Analyzing the electronic structure of a material with quantum mechanics involves solving the Kohn-Sham equation of density functional theory, which generates data on wave functions and energy levels. That data is then used to compute the total potential energy of the system and atomic forces.
The new machine learning method produces similar results, but eight orders of magnitude faster than the conventional technique based on quantum mechanics.
"This unprecedented speedup in computational capability will allow us to design electronic materials that are superior to what is currently out there," Ramprasad said. "Basically, we can say, 'Here are defects with this material that will really diminish the efficiency of its electronic structure'. And once we can address such aspects efficiently, we can better design electronic devices."
While the study focused on aluminum and polyethylene, machine learning could be used to analyze the electronic structure of a wide range of other materials. Beyond analyzing electronic structure, other aspects of material structure now analyzed by quantum mechanics could also be hastened by the machine learning approach, Ramprasad said.
"In part we selected aluminum and polyethylene because they are components of a capacitor, but it also allowed us to demonstrate that you can use this method for vastly different materials, such as metals that are conductors and polymers that are insulators," Ramprasad said.
The faster processing allowed by the machine learning method would also allow researchers to more quickly simulate how modifications to a material will impact its electronic structure, potentially revealing new ways to improve its efficiency.
This story is adapted from material from Georgia Institute of Technology, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.