Using AI to Design the Ideal Electrolyte

 Using AI to Design the Ideal Electrolyte

Designing the ideal electrolyte is one of the major bottlenecks for designing next-generation batteries for electric vehicles, phones, laptops and grid-scale energy storage. Researchers at the University of Chicago have approached the age-old chemical problem with modern techniques—AI and big data.

Pulling from a dataset compiled from 250 research papers going back to the earliest days of lithium-ion battery research, the researchers used AI to tally what they call the “eScore” for different molecules. The eScore balances those three criteria, identifying molecules that check all three boxes.

“The champion molecule in one property is not the champion molecule in another,” said Chibueze Amanchukwu, principal investigator.

The team has already tested their process, using AI to identify one molecule that performs as well as the best electrolytes on the market, a major advance in a field that often relies on trial-and-error.

“I don't want to find a molecule that was already in my training data. I want to look for molecules in very different chemical spaces. So, we tested how well these models predict when they see a molecule that they've never seen before,” said Amanchukwu.

The team also found that when a molecule was chemically similar to one from the training data, the AI predicted how good of an electrolyte it would make with high accuracy. But, it struggled to flag unfamiliar materials, marking the team’s next challenge in the quest to use AI to design next-generation batteries.

“Electrolyte optimization is a slow and challenging process where researchers frequently resort to trial-and-error to balance competing properties in multi-component mixtures,” said Northwestern University Assistant Professor of Chemical and Biological Engineering Jeffrey Lopez, who was not involved in the research. “These types of data-driven research frameworks are critical to help accelerate the development of new battery materials and to leverage advancements in AI-enabled science and laboratory automation.”

Information courtesy of University of Chicago

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