A research team at Japan's Tohoku University believes it's found a valuable shortcut on the road to the batteries of the future. The team is using AI to help speed up and inform the hunt for the ideal materials for solid-state batteries that could unlock 1,000-mile ranges for EVs and lightning-quick charging.
It published its findings in the Angewandte Chemie International Edition journal and described the potential uses for its model in a news release.
One of the key challenges the team hopes its model can address is the painstaking nature of testing out the best solid-state electrolyte candidates. It touts its model's ability to save time in identifying promising materials and also predicting the specifics of how they will interact.
"The model essentially does all of the trial-and-error busywork for us," Professor Hao Li revealed in the press release. "It draws from a large database from previous studies to search through all the potential options and find the best SSE candidate."
The AI model is built off past experiments and computational data. Armed with an array of inputs, the model projects how reactions will take place and what scientists can expect from each SSE candidate.
That can save valuable time and resources from conventional approaches, which include experimenting on each material and pathway. The key to unleashing all of the benefits of SSBs will be finding the best materials that can form the foundation of safe, durable, and powerful batteries.
As the researchers allude to in the study, the race for SSBs is competitive and being waged on many fronts. A slew of auto brands like Toyota, Stellantis, Mercedes-Benz, and BYD are all in the mix to create EVs powered by the new-age batteries.
Meanwhile, the Department of Energy has its own series of initiatives that could not only go into EVs but also power laptops and cellphones. Another approach is a semi-solid-state battery, which China's IM Motors is pursuing.
Even as conventional lithium-ion batteries do see improvements like faster charging times, improved safety, and greater range, there is a push for improved batteries that will be safer and more durable while storing enough energy to completely quell range anxiety.
While the thrust of the research from Tohoku University's team is ultimately a positive in seeking better batteries for green tech, there is a drawback in using AI as the tool to do so. AI does require an enormous amount of computing power that drains a hefty amount of water and electricity, as MIT News explained.
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Absolutely 💯 However, there's no doubt that if the model does speed up the search for the best SSE materials, it can be a net positive for the planet. Better batteries can bolster EV adoption, which sharply reduces tailpipe pollution and our reliance on the dirty energy that heats the planet.
The team at Tohoku University hopes to help achieve these goals by expanding the model to more electrolyte families. It also believes its predictive power can be optimized by expanding its purview to areas like reaction mechanisms.
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