X-Ray Experiments, Machine Learning Could Trim Years Off Bat

X-Ray Experiments, Machine Learning Could Trim Years Off Battery R&D


X-Ray Experiments, Machine Learning Could Trim Years Off Battery R&D
An X-ray instrument at Berkeley Lab contributed to a battery study that used an innovative approach to machine learning to speed up the learning curve about a process that shortens the life of fast-charging lithium batteries.
Researchers used Berkeley Lab’s Advanced Light Source, a synchrotron that produces light ranging from the infrared to X-rays for dozens of simultaneous experiments, to perform a chemical imaging technique known as scanning transmission X-ray microscopy, or STXM, at a state-of-the-art ALS beamline dubbed COSMIC.
Researchers also employed “in situ” X-ray diffraction at another synchrotron – SLAC’s Stanford Synchrotron Radiation Lightsource – which attempted to recreate the conditions present in a battery, and additionally provided a many-particle battery model. All three forms of data were combined in a format to help the machine-learning algorithms learn the physics at work in the battery.

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Stanford , California , United States , Berkeley , Stephen Dongmin Kang , Berkeley Lab Advanced Light Source , Berkeley Lab , Young , Toyota Research Institute , Advanced Light Source , Synchrotron Radiation Lightsource , Accelerated Materials Design , ஸ்டான்போர்ட் , கலிஃபோர்னியா , ஒன்றுபட்டது மாநிலங்களில் , பெர்க்லி , பெர்க்லி ஆய்வகம் , இளம் , டொயோட்டா ஆராய்ச்சி நிறுவனம் , முடுக்கப்பட்ட பொருட்கள் வடிவமைப்பு ,

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