Better capacitors - a task for Machine Learning

March 07, 2019 // By Nick Flaherty
Better capacitors - a task for Machine Learning
Researchers at Georgia Institute of Technology are using machine learning to explore new combinations of materials to boost the energy capacity of supercapacitors.

The method, described in npj Computational Materials and sponsored by the US Office of Naval Research, involves teaching a computer to analyse 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.

Polyethylene is a very good insulator with a large band gap, but if it has a defect, unwanted charge carriers are allowed into 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 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.

Using the new machine learning method produces similar results eight orders of magnitude faster than using 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," said Ramprasad. "Basically we can say, 'Here are defects with this material that will really diminish the efficiency of its electronic structure.' And once we

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