Prediction of elastic properties of face-centered cubic high-entropy alloys by machine learning

被引：17

作者：

Wang, Shen ^{[1
]}

LI, Da ^{[1
]}

Xiong, Jun ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, Coll Mat Sci & Engn, Key Lab Adv Technol Mat, Minist Educ, Chengdu 610031, Peoples R China

来源：

TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA | 2023年 / 33卷 / 02期

基金：

中国国家自然科学基金;

关键词：

elastic modulus; face-centered cubic high-entropy alloys; first-principles calculations; machine learning; MECHANICAL-PROPERTIES; MODEL; IRRADIATION; CONSTANTS; MODULUS; VERSION; GIBBS2;

D O I：

10.1016/S1003-6326(22)66124-7

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

The machine learning (ML) models were proposed for predicting elastic properties of face-centered-cubic (FCC) high-entropy alloys (HEAs). The data set was from the first-principles calculation, which contained 186 samples. The goodness-of-fit (R2) values of predicted bulk modulus (B) and shear modulus (G) in the test set were 0.81 and 0.84, respectively. According to the results of ML, CoNiCuMoW HEAs have the largest B, G, elastic modulus (Y) and good ductility (G/B <= 0.57) among the FCC HEAs with equal components. The first-principles calculation results show that the elastic anisotropy of (CoNiCuMo)1-xWx HEAs increases and ductility decreases with increasing W content. According to the analysis of charge density difference, there is obvious charge accumulation at W-W and W-Mo bonds, indicating the directional covalent bonds formed between W atoms and their neighboring atoms.

引用

页码：518 / 530

页数：13

共 35 条

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