A Machine-Learning Approach to Better Batteries

Today’s rechargeable batteries are a wonder, but they’re still far from perfect. Eventually, they wear out and need recycling and (sometimes expensive) replacement. But what if batteries were indestructible? A new analytical approach to building better batteries could help speed the arrival of that day.

Researchers from Stanford University and collaborators at Berkeley Lab, MIT, and other institutions recently developed a machine-learning method to quantitatively analyze high-resolution microscopy images from a battery cathode material. Their image-learning framework was able to extract the underlying physical relationship between strain and composition, a fundamental material property relevant to battery failure. Eventually, the researchers say, the revelations could lead to batteries that last much longer than today’s.

A material’s performance is a function of both its chemistry and the physical interactions occurring at the atomic scale, what scientists refer to as “chemo-mechanics.” What’s more, the smaller things get and the more diverse the atoms making up the material are, the harder it is to predict how the material will behave.

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