Findings from China Iron and Steel Research Institute Group Broaden Understandin g of Machine Learning (Prediction and Rational Design of Stacking Fault Energy o f Austenitic Alloys Based On Interpretable Machine Learning and Chemical Composi tion)

中国钢铁研究院集团的发现拓宽了机器学习的理解(基于可解释机器学习和化学解释的奥氏体合金层错能预测与合理设计)

Robotics & Machine Learning Daily News2024，Issue(Jul.3) ：97-98.

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Findings from China Iron and Steel Research Institute Group Broaden Understandin g of Machine Learning (Prediction and Rational Design of Stacking Fault Energy o f Austenitic Alloys Based On Interpretable Machine Learning and Chemical Composi tion)

中国钢铁研究院集团的发现拓宽了机器学习的理解(基于可解释机器学习和化学解释的奥氏体合金层错能预测与合理设计)

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摘要

由一名新闻记者兼机器人与机器学习的新闻编辑每日新闻-调查人员发布了关于马学习的新报告。根据NewsRx记者从中华人民共和国北京发回的新闻报道，研究表明：“准确预测堆积断层能量(SFE)是影响物质形成机制的关键因素之一，”本研究利用奥氏体合金文献中测得的S FE值，利用机器学习技术建立了化学成分与SFE关系的预测模型。本研究经费来源于国家重点研究开发项目。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting from Beijing, People’s Republic of C hina, by NewsRx journalists, research stated, “Accurately predicting the stackin g fault energy (SFE), as one of the crucial factors influencing the material def ormation mechanism, is a focal point in research. This study utilizes measured S FE values from the literature on austenitic alloys to establish a predictive mod el for the relationship between chemical composition and SFE using machine learn ing techniques.” Financial support for this research came from National Key Research and Developm ent Program of China.

Key words

Beijing/People's Republic of China/Asi a/Alloys/Cyborgs/Emerging Technologies/Machine Learning/China Iron and Stee l Research Institute Group

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出版年

2024

Robotics & Machine Learning Daily News

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