Robotics & Machine Learning Daily News2024,Issue(Jul.2) :147-147.

Investigators from Peking University Have Reported New Data on Machine Learning (Machine Learning Based Nonlocal Kinetic Energy Density Functional for Simple Me tals and Alloys)

北京大学的研究人员报告了机器学习的新数据(简单金属和合金基于机器学习的非局部动能密度泛函)

Robotics & Machine Learning Daily News2024,Issue(Jul.2) :147-147.

Investigators from Peking University Have Reported New Data on Machine Learning (Machine Learning Based Nonlocal Kinetic Energy Density Functional for Simple Me tals and Alloys)

北京大学的研究人员报告了机器学习的新数据(简单金属和合金基于机器学习的非局部动能密度泛函)

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摘要

机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据NewsRx记者源自中华人民共和国北京的新闻报道,研究称,“开发精确的动能密度泛函(KEDF)仍然是无ORBI密度泛函理论的一个主要障碍。我们提出了一个基于机器学习的物理约束非局部(MPN)KEDF,并使用ABACUS软件包中的体源局部赝势和平面波基集来实现它。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating in Beijing, Peo ple’s Republic of China, by NewsRx journalists, research stated, “Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbi tal -free density functional theory. We propose a machine -learning -based physi cal -constrained nonlocal (MPN) KEDF and implement it with the usage of the bulk -derived local pseudopotentials and plane wave basis sets in the ABACUS package .”

Key words

Beijing/People's Republic of China/Asi a/Alloys/Cyborgs/Emerging Technologies/Machine Learning/Peking University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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