首页|Reports from San Francisco State University Add New Data to Findings in Machine Learning (Effects of Nonequilibrium Atomic Structure On Ionic Diffusivity In Llz o: a Classical and Machine Learning Molecular Dynamics Study)
Reports from San Francisco State University Add New Data to Findings in Machine Learning (Effects of Nonequilibrium Atomic Structure On Ionic Diffusivity In Llz o: a Classical and Machine Learning Molecular Dynamics Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on Machine Learning are discuss ed in a new report. According to news originating from San Francisco, California , by NewsRx correspondents, research stated, "To improve the performance of elec trochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strain ed regions, on atomic-scale transport and stability. Molecular dynamics simulati ons are used to explore the combined effect of far-from-equilibrium atomic struc tures and the choice of interatomic potential on ionic diffusivity predictions f or Li7La3Zr2O12 (LLZO), a promising solid electrolyte for all-solid-state batter ies." Funders for this research include NSF - Directorate for Mathematical & Physical Sciences (MPS), United States Department of Energy (DOE), United States Department of Energy (DOE), Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program via the Argonne Leadership Computing Facility, College of Engineering and Department of Mechanical Engineering at Boston Univer sity, National Science Foundation (NSF).
San FranciscoCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningM olecular DynamicsPhysicsSan Francisco State University
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