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SLAC, MIT, TRI researchers advance machine learning to accelerate battery development; insights on fast-charging


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 e ....

Stephen Kang , Stephen Dongmin Kang , Department Of Energy , Massachusetts Institute Of Technology , Battery Materials Research Program , Toyota Research Institute , Office Of Science , Lawrence Berkeley National Laboratory Advanced Light Source , Stanford University , National Accelerator Laboratory , Will Chueh , Accelerator Laboratory , Nature Materials , Massachusetts Institute , Patrick Herring , Toyota Research , Jungjin Park , Synchrotron Radiation Lightsource , Lawrence Berkeley National Laboratory , Advanced Light Source , Stanford Synchrotron Radiation Lightsource , Battery Materials Research , ஸ்டீபன் காங் , துறை ஆஃப் ஆற்றல் , மாசசூசெட்ஸ் நிறுவனம் ஆஃப் தொழில்நுட்பம் , மின்கலம் பொருட்கள் ஆராய்ச்சி ப்ரோக்ர்யாம் ,

In a leap for battery research, machine learning gets scientific smarts


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VIDEO: SLAC and Stanford researcher Will Chueh talks about a new way to incorporate scientific insight into machine learning for battery research - an approach that will speed up development of.
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Credit: Olivier Bonin/SLAC National Accelerator Laboratory
Menlo Park, Calif. Scientists have taken a major step forward in harnessing machine learning to accelerate the design for better batteries: Instead of using it just to speed up scientific analysis by looking for patterns in data, as researchers generally do, they 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. ....

United States , Stephen Dongmin Kang , Martin Bazant , Chongbo Zhao , Department Of Energy , Massachusetts Institute Of Technology , Battery Materials Research Program , Biden Administration , Toyota Research Institute , Office Of Science , Lawrence Berkeley National Laboratory Advanced Light Source , Stanford University , National Accelerator Laboratory , Menlo Park , Will Chueh , Accelerator Laboratory , Nature Materials , Massachusetts Institute , Patrick Herring , Toyota Research , Jungjin Park , Synchrotron Radiation Lightsource , Lawrence Berkeley National Laboratory , Advanced Light Source , Stanford Synchrotron Radiation Lightsource , Battery Materials Research ,

Getting the lead in


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IMAGE: Image shows a lithium-ion battery, a lead-based core-shell particle developed for the anode, the element lead in the periodic table, and a lead-acid battery for an automobile..
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Credit: Scapiens Inc., Argonne National Laboratory and Ulsan National Institute of Science and Technology
The lithium-ion battery powers everything from mobile phones to laptops to electric vehicles. Scientists worldwide are always on the hunt for new and improved components to build better batteries for these and other applications.
Scientists from the U.S. Department of Energy s (DOE) Argonne National Laboratory report a new electrode design for the lithium-ion battery using the low-cost materials lead as well as carbon. Contributors to this pivotal discovery also include scientists from Northwestern University, Brookhaven National Laboratory and the Ulsan National Institute of Science and Technology (UNIST). ....

United States , Johnb Goodenough , Vinayakp Dravid , Bak Brookhaven , Jinhyup Han , Chi Cheung Su , Christopher Johnson , Jihyeon Gim , Seungje Lee , Youngsik Kim , Argonne Chemical Sciences , Ulsan National Institute Of Science , Us Department Of Energy , Renewable Energy , National Synchrotron Light Source , Geosoilenviro Center , Office Of Science , University Of Chicago , Argonne National Laboratory , Vehicle Technologies Office Battery Materials Research , Office Of Energy Efficiency , Brookhaven National Laboratory , Uchicago Argonne , Nanoscale Characterization Center , Northwestern University Atomic , Office Of Science User Facility ,