Structure prediction in high-entropy alloys with machine learning

被引:36
|
作者
Zhao, D. Q. [1 ]
Pan, S. P. [1 ,2 ]
Zhang, Y. [3 ]
Liaw, P. K. [4 ]
Qiao, J. W. [1 ,2 ]
机构
[1] Taiyuan Univ Technol, Coll Mat Sci & Engn, Taiyuan 030024, Peoples R China
[2] Taiyuan Univ Technol, Key Lab Interface Sci & Engn Adv Mat, Minist Educ, Taiyuan 030024, Peoples R China
[3] Univ Sci & Technol Beijing, State Key Lab Adv Met & Mat, Beijing 100083, Peoples R China
[4] Univ Tennessee, Dept Mat Sci & Engn, Knoxville, TN 37996 USA
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
PHASE PREDICTION; SOLID-SOLUTION; STABILITY;
D O I
10.1063/5.0051307
中图分类号
O59 [应用物理学];
学科分类号
摘要
High-entropy alloy is an alloy design concept without a principal component. This concept not only refers to the complexity of alloy compositions but also means that when the high-entropy alloy transits from a high-energy state to low-energy state, there will be more intermediate metastable states. Corresponding to different states are the changes in the degree and manner of order in the microstructure. In this study, we used machine learning to combine elemental characteristics with long-term ordering and established 87% of prediction accuracy. This data-driven method can correlate elemental characteristics and metastable states and accelerate the discovery of potential compositions.
引用
收藏
页数:5
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