Reports Outline Machine Learning Study Results from Guizhou Normal University (I dentifying Key Features for Predicting Glassforming Ability of Bulk Metallic Gl asses Via Interpretable Machine Learning)

贵州师范大学机器学习研究成果概要(I)——利用可解释性机器学习识别预测大块金属玻璃玻璃成形能力的关键特征

Robotics & Machine Learning Daily News2024，Issue(Jun.6) ：6-6.

来源：

NETL
NSTL

Reports Outline Machine Learning Study Results from Guizhou Normal University (I dentifying Key Features for Predicting Glassforming Ability of Bulk Metallic Gl asses Via Interpretable Machine Learning)

贵州师范大学机器学习研究成果概要(I)——利用可解释性机器学习识别预测大块金属玻璃玻璃成形能力的关键特征

扫码查看

摘要

一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者在贵阳的新闻报道，研究表明：“块状金属玻璃SES(BMGs)因其诱人的性能而受到物理和材料科学界的广泛关注，传统的试错法在设计好的BMG方面效率低下，因此有必要制定一套预测方案来加快BMG的发展。”本研究的资金支持单位包括国家重点研发项目、国家自然科学基金(NSFC)。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting from Guiyang, People’ s Republic of China, by NewsRx journalists, research stated, “Bulk metallic glas ses (BMGs) have been receiving extensive attention in the community of physics a nd materials science due to their attractive properties. The traditional trial-a nd-error approach is inefficient in designing good BMGs, then it is imperative t o elaborate a prediction scheme to accelerate the development of BGMs.” Financial supporters for this research include National Key R&D Pro gram of China, National Natural Science Foundation of China (NSFC).

Key words

Guiyang/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/Guizhou Normal University

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航