首页|Carnegie Mellon University Reports Findings in Machine Learning (Accurate Surfac e and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials)
Carnegie Mellon University Reports Findings in Machine Learning (Accurate Surfac e and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials)
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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 out of Pittsburgh, Pennsylvan ia, by NewsRx editors, research stated, “The properties of lithium metal are key parameters in the design of lithium-ion and lithium-metal batteries. They are d ifficult to probe experimentally due to the high reactivity and low melting poin t of lithium as well as the microscopic scales at which lithium exists in batter ies where it is found to have enhanced strength, with implications for dendrite suppression strategies.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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