Robotics & Machine Learning Daily News2024,Issue(Nov.25) :65-65.

Study Results from Harbin Institute of Technology Broaden Understanding of Machi ne Learning (Tuning Lattice Thermal ConductivityIn Nbmotaw Refractory High-entr opy Alloys: Insights From Molecular Dynamics Using Machine Learning Potential)

哈尔滨工业大学的研究成果拓宽了对机械学习的理解(Nbmotaw耐火高比合金晶格导热系数的调节:利用机器学习势从分子动力学中获得的启示)

Robotics & Machine Learning Daily News2024,Issue(Nov.25) :65-65.

Study Results from Harbin Institute of Technology Broaden Understanding of Machi ne Learning (Tuning Lattice Thermal ConductivityIn Nbmotaw Refractory High-entr opy Alloys: Insights From Molecular Dynamics Using Machine Learning Potential)

哈尔滨工业大学的研究成果拓宽了对机械学习的理解(Nbmotaw耐火高比合金晶格导热系数的调节:利用机器学习势从分子动力学中获得的启示)

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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新闻-调查人员发布关于机器学习的新报告。根据新闻报道在中华人民共和国哈尔滨,由NewsRx记者报道,研究表明:“难降解的高熵”(RHEAs)合金由于其优异的力学性能而引起了人们的广泛关注在极端条件下。然而,晶格导热系数的研究还不够深入。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Investigators publish new report on Machine Learn ing. According to news reporting originatingfrom Harbin, People’s Republic of C hina, by NewsRx correspondents, research stated, “Refractory highentropyalloys (RHEAs) have attracted increasing interest due to their excellent mechanical pr opertiesunder extreme conditions. However, the lattice thermal conductivity is still not well studied.”

Key words

Harbin/People’s Republic of China/Asia/Alloys/Cyborgs/Emerging Technologies/Machine Learning/Molecular Dynamics/Physics/Harbin Institute of Technology

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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