摘要
一位新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者从加州旧金山发回的新闻，研究表明：“为了改善电化学装置的性能，必须了解固体中的非平衡基序的影响，例如晶界、非晶相和高应变ED区，本文利用分子动力学模拟研究了远离平衡态原子结构和原子间势的选择对离子扩散率预测的影响。Li7La3Zr2O12(LLZO)是一种有前途的全固态电解质。这项研究的资助者包括NSF-数学和物理科学局(MPS)、美国能源部(DOE)、美国能源部(DOE)，通过波士顿大学工程学院和机械工程系阿贡领导计算设施的创新和新型计算对理论和实验的影响(INCITE)计划，国家科学基金会(NSF)。
Abstract
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).
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