首页|Research Conducted at Lawrence Livermore National Laboratory Has Updated Our Kno wledge about Machine Learning (Probing Degradation At Solid-state Battery Interf aces Using Machinelearning Interatomic Potential)
Research Conducted at Lawrence Livermore National Laboratory Has Updated Our Kno wledge about Machine Learning (Probing Degradation At Solid-state Battery Interf aces Using Machinelearning Interatomic Potential)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According tonews reporting from Livermore, California, by NewsRx journalists, research stated, “Solid-state batteriesfeaturing fast i on-conducting solid electrolytes are promising next-generation energy storage te chnologies,yet challenges remain for practical deployment due to electro-chemo- mechanical instabilities at solid-solidinterfaces. These interfaces, which incl ude homogeneous/internal interfaces such as grain boundaries(GBs) and heterogen eous/external interfaces between solid-electrolyte and electrode materials, can impedeLiion transport, deteriorate performance, and eventually lead to cell fai lure.”
LivermoreCaliforniaUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesMachine LearningLawre nce Livermore National Laboratory
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