[1]赵鼎祺,乔珺威,吴玉程.机器学习辅助高熵合金设计的研究进展[J].中国材料进展,2021,40(07):508-517.[doi:10.7502/j.issn.1674-3962.202011011]
 ZHAO Dingqi,QIAO Junwei,WU Yucheng.Research Progress of Machine Learning Aided High Entropy Alloy Design[J].MATERIALS CHINA,2021,40(07):508-517.[doi:10.7502/j.issn.1674-3962.202011011]
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机器学习辅助高熵合金设计的研究进展()
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中国材料进展[ISSN:1674-3962/CN:61-1473/TG]

卷:
40
期数:
2021年第07期
页码:
508-517
栏目:
出版日期:
2021-07-30

文章信息/Info

Title:
Research Progress of Machine Learning Aided High Entropy Alloy Design
文章编号:
1674-3962(2021)07-0508-10
作者:
赵鼎祺乔珺威吴玉程
(太原理工大学材料科学与工程学院,山西 太原 030024)
Author(s):
ZHAO DingqiQIAO JunweiWU Yucheng
(College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China)
关键词:
高熵合金复杂成分合金机器学习人工神经元网络人工智能
Keywords:
High entropy alloy Complex concentrated alloys Machine learning Artificial neural network Artificial intelligence
分类号:
TG139;TP181
DOI:
10.7502/j.issn.1674-3962.202011011
文献标志码:
A
摘要:
近年来,高熵合金因其优异的性能和广阔的发展前景吸引了越来越多的关注,成为材料科学中的热门领域。由于高熵合金复杂的元素组成,使用传统方法对高熵合金进行计算不仅困难而且代价高昂,影响因素的多样性也为高熵合金的设计增加了困难,开发新方法加速对高熵合金成分空间的探索是当务之急。随着对高熵合金研究的不断深入,实验数据不断积累,人们尝试从数据的角度寻求解决方案。与此同时,人工智能的兴起极大改变了我们的生活方式,以数据为驱动的机器学习与高熵合金领域交叉融合,二者相得益彰并取得了一系列成果。人工神经元网络、支持向量机、主成分分析等方法被应用于高熵合金的分析和预测。除此之外,机器学习还与从头算和基于热力学数据库的方法相结合,在挖掘数据价值与指导实验设计方面展现出了优势。首先对材料科学中的机器学习和高熵合金两个领域做了简述,介绍了近年利用机器学习辅助高熵合金设计的典型研究成果。并对未来机器学习在高熵合金中的应用提出一些展望与建议。
Abstract:
In recent years, high entropy alloys have increasingly attracted attention due to their excellent properties and broad development prospects, become a hot field in materials science. Due to the complex element composition of high entropy alloy, it is difficult and expensive to use the traditional methods to calculate. The diversity of the influencing factors also makes the design of high entropy alloy more difficult. It is urgent to develop new strategies to accelerate the exploration of high entropy alloy composition space. With the development of research on high entropy alloys and the accumulation of experimental data, researchers try to find solutions from data. At the same time, the rise of artificial intelligence has dramatically changed our life. Machine learning and high entropy alloy field cross each other, and a series of achievements have been achieved. Artificial neural networks, support vector machine, principal component analysis, and other methods have been applied to analyzing and predicting high entropy alloys. Besides, machine learning combined with ab initio and thermodynamic library-based methods, has shown advantages in mining data value and guiding experimental design. In this paper, machine learning and high entropy alloys in materials science are briefly introduced, and recent researches on high entropy alloy design aided by machine learning are reviewed. Some prospects and suggestions for the application of machine learning in high entropy alloys in the future are put forward.

备注/Memo

备注/Memo:
收稿日期:2020-11-05修回日期:2021-03-16 第一作者:赵鼎祺,男,1995年生,博士研究生, Email:zhaodingqi@hotmailcom 通讯作者:乔珺威,男,1983年生,教授,博士生导师, Email:qiaojunwei@gmail.com
更新日期/Last Update: 2021-06-30