首页|Studies from University of Chicago Update Current Data on Machine Learning (Impr oved Rate Capability for Dry Thick Electrodes Through Finite Elements Method and Machine Learning Coupling)
Studies from University of Chicago Update Current Data on Machine Learning (Impr oved Rate Capability for Dry Thick Electrodes Through Finite Elements Method and Machine Learning Coupling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingout of Chicago, Illinois, by NewsRx editors, research stated, “A coupled finite elements method (FEM) andmachine le arning (ML) workflow is presented to optimize the rate capability of thick posit ive electrodes(ca. 150 mu m and 8 mAh/cm(2)). An ML model is trained based on t he geometrical observables ofindividual LiNi0.8Mn0.1Co0.1O2 particles and their average state of discharge (SOD) predicted from FEMmodeling.”
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