Study Data from University of Wisconsin Update Understanding of Machine Learning (Studies of Ni-cr Complexation In Flibe Molten Salt Using Machine Learning Inte ratomic Potentials)

威斯康星大学的研究数据更新了机器学习的理解(利用机器学习Inte Ratomic势研究Flibe熔盐中Ni-Cr络合)

Robotics & Machine Learning Daily News2024，Issue(Jun.12) ：28-29.

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Study Data from University of Wisconsin Update Understanding of Machine Learning (Studies of Ni-cr Complexation In Flibe Molten Salt Using Machine Learning Inte ratomic Potentials)

威斯康星大学的研究数据更新了机器学习的理解(利用机器学习Inte Ratomic势研究Flibe熔盐中Ni-Cr络合)

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摘要

由一名新闻记者-机器人与机器学习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以获得。根据Ne wsRx记者从威斯康星麦迪逊发回的新闻报道，研究表明，“在核和/或太阳能应用中，在Volve熔盐中，杂质经常以裂变产物或腐蚀的形式进入盐中。杂质可以相互作用并形成络合物，但这种络合对盐的性质和腐蚀速率的影响尚不清楚。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available. According to news reporting originating in Madison, Wisconsin, by Ne wsRx journalists, research stated, “In nuclear and/or solar applications that in volve molten salts, impurities frequently enter the salt as either fission produ cts or via corrosion. Impurities can interact and make complexes, but the impact of such complexation on the properties of the salts and corrosion rates has not been understood.”

Key words

Madison/Wisconsin/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/University of Wisconsin

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出版年

2024

Robotics & Machine Learning Daily News

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