Better capacitors - a task for Machine Learning: Page 2 of 2

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.
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 analyse the electronic structure of a wide range materials. Beyond analysing electronic structure, other aspects of material structure now analysed by quantum mechanics could also be hastened by the machine learning approach. 

"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," said Ramprasad.

The faster processing allowed by the machine learning method would also enable researchers to more quickly simulate how modifications to a material will impact its electronic structure, potentially revealing new ways to improve its efficiency.

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