首页|Findings from Lawrence Livermore National Laboratory Provide New Insights into M achine Learning (Machine-learning-assisted Deciphering of Microstructural Effect s On Ionic Transport In Composite Materials: a Case Study of ...)
Findings from Lawrence Livermore National Laboratory Provide New Insights into M achine Learning (Machine-learning-assisted Deciphering of Microstructural Effect s On Ionic Transport In Composite Materials: a Case Study of ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Livermore, California, b y NewsRx journalists, research stated, “The effective diffusivity of ionic speci es in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting mat erials but also on their topological arrangement; nevertheless, these coupled co ntributions are oversimplified in most analytical models.”
LivermoreCaliforniaUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesMachine LearningLawre nce Livermore National Laboratory
NETL
NSTL
