摘要
机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-关于机器学习的最新研究结果已经发表。根据NewsRx编辑在中国北京的新闻报道，研究表明：“在极端条件下获得准确的LiF晶格导热系数数据，不仅为材料的性能评价、预测和控制提供了重要的依据。”本文将动态机器学习力F IELDS(MLFFs)与green-kubo方法相结合,计算了固体LiF的高温声子性质和晶格导热系数(LT C)。本研究的资助者包括国家自然科学基金(NSFC)、国家自然科学基金(NSFC)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting out of Beijing, People’s Republic of C hina, by NewsRx editors, research stated, “Obtaining accurate lattice thermal co nductivity data of LiF under extreme conditions not only provides important refe rence for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measureme nts. The high-temperature phonon properties and lattice thermal conductivity (LT C) of solid LiF were calculated by combining on-the-fly machine learning force f ields (MLFFs) with the Green-Kubo method.” Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC).
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