材料科学
三元运算
高熵合金
电负性
延展性(地球科学)
弹性模量
二进制数
熵(时间箭头)
统计物理学
刚度
合金
热力学
计算机科学
冶金
复合材料
数学
物理
蠕动
算术
量子力学
程序设计语言
作者
G. Vazquez,Prashant Singh,Daniel Sauceda,Richard Couperthwaite,Nicholas Britt,Khaled Youssef,Duane D. Johnson,Raymundo Arróyave
出处
期刊:Acta Materialia
[Elsevier]
日期:2022-06-01
卷期号:232: 117924-117924
被引量:37
标识
DOI:10.1016/j.actamat.2022.117924
摘要
We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of the elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. We also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.
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