Researchers use AI to optimize several flow battery properties simultaneously

Bringing new sources of renewable energy like wind and solar power onto the electric grid will require specially designed large batteries that can charge when the sun is shining and give energy at night. One type of battery is especially promising for this purpose: the flow battery. Flow batteries contain two tanks of electrically active chemicals that exchange charge and can have large volumes that hold a lot of energy.

For researchers working on flow batteries, their chief concern involves finding target molecules that offer the ability to both store a lot of energy and remain stable for long periods of time.

To find the right flow battery molecules, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have turned to the power of artificial intelligence (AI) to search through a vast chemical space of over a million molecules. Discovering the right molecules requires optimizing between several different characteristics. ​“In these batteries, we know that a majority of the molecules that we need will have to satisfy multiple properties,” said Argonne chemist Rajeev Assary. ​“By optimizing several properties simultaneously, we have a better shot of finding the best possible chemistry for our battery.”

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