Robotics & Machine Learning Daily News2024，Issue(Dec.5) ：142-142.

New Findings on Machine Learning from Washington University St. Louis Summarized (Structure-driven Prediction of Magnetic Order In Uranium Compounds)

华盛顿大学圣路易斯分校机器学习的新发现总结(铀化合物磁序的结构驱动预测)

Robotics & Machine Learning Daily News2024，Issue(Dec.5) ：142-142.
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New Findings on Machine Learning from Washington University St. Louis Summarized (Structure-driven Prediction of Magnetic Order In Uranium Compounds)

华盛顿大学圣路易斯分校机器学习的新发现总结(铀化合物磁序的结构驱动预测)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在可用。根据新闻报道由NEWSRX记者发起于密苏里州圣路易斯，研究称，“机器的进步”学习技术已经彻底改变了材料性能的搜索和优化。se算法数据输入通常依赖于理论计算，如密度泛函理论(DFT)由于铀基材料具有很强的电子性质，这种方法对铀基材料并不总是有效相关性。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news reportingoriginating in St. Louis, Missouri, by N ewsRx journalists, research stated, “The advancement of machinelearning technol ogies has revolutionized the search and optimization of material properties. The se algorithmsoften rely on theoretical calculations, such as density functional theory (DFT), for data inputsand validation, which are not always effective fo r uranium-based materials due to their strong electroncorrelations.”

Key words

St. Louis/Missouri/United States/Nort h and Central America/Actinoid Series Elements/Cyborgs/Emerging Technologies/Inorganic Chemicals/Machine Learning/Uranium/Uranium Compounds/Washington U niversity St. Louis

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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