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SLAC, MIT, TRI researchers advance machine learning to accelerate battery development; insights on fast-charging Scientists have made a major advance in harnessing machine learning to accelerate the design for better batteries. Instead of using machine learning just to speed up scientific analysis by looking for patterns in data as typically done the researchers combined it with knowledge gained from experiments and equations guided by physics to discover and explain a process that shortens the lifetimes of fast-charging lithium-ion batteries. It was the first time this approach known as “scientific machine learning” has been applied to battery cycling, said Will Chueh, an associate professor at Stanford University and investigator with the Department of Energy’s SLAC National Accelerator Laboratory who led the study. He said the results overturn long-held assumptions about how lithium-ion batteries charge and discharge and give researchers a new set of rules for engineerin

Stephen kangStephen dongmin kangDepartment of energyMassachusetts institute of technologyBattery materials research programToyota research instituteOffice of scienceLawrence berkeley national laboratory advanced light sourceStanford universityNational accelerator laboratoryWill chuehAccelerator laboratoryNature materialsMassachusetts institutePatrick herringToyota research