Comprehensive elemental screening of solid-solution copper alloys

被引:1
作者
Yamaguchi, Kenji [1 ]
Ishigaki, Takuya [1 ]
Inoue, Yuki [1 ]
Arisawa, Shuhei [2 ]
Matsunoshita, Hirotaka [2 ]
Ito, Yuki [2 ]
Mori, Hiroyuki [2 ]
Suehiro, Ken'ichiro [2 ]
Maki, Kazunari [2 ]
Nagata, Kenji [3 ]
Demura, Masahiko [3 ]
机构
[1] Mitsubishi Mat Corp, Innovat Ctr, 1002-14 Mukohyama, Naka, Ibaraki 3110102, Japan
[2] Mitsubishi Mat Corp, Innovat Ctr, Kitamoto Branch, Kitamoto, Japan
[3] Natl Inst Mat Sci, Res & Serv Div Mat Data & Integrated Syst, Tsukuba, Japan
来源
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS | 2023年 / 3卷 / 01期
关键词
Alloy design; copper alloys; mechanical properties; electrical resistivity/conductivity; density functional theory (DFT); modelling; INITIO MOLECULAR-DYNAMICS; TOTAL-ENERGY CALCULATIONS; STRENGTH; CONDUCTIVITY;
D O I
10.1080/27660400.2023.2250704
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Significantly improving the balance between the mechanical strength and electrical conductivity of solid-solution copper alloys is considered difficult. In this study, a comprehensive elemental screening framework is proposed to predict the solid-solution strengthening and electrical resistivity of copper alloys. Electrical resistivities are predicted by first-principles calculations, and a high degree of accuracy is obtained. Two models are considered to predict the solid-solution strengthening. One of them uses the generalized critical resolved shear stress formula and provides a reasonable accuracy for a testing set of our experimental data. The other model (using the first model as a feature with elemental features) has a high prediction performance for the testing set. Combining the predicted electrical resistivity and solid-solution strengthening, we establish a figure-of-merit formula for the comprehensive elemental screening. The formula provides reasonable results using the two models. The models predicted the known Cu-Ag (Cd, In, Mg) as high-performance copper alloys. All solute elements, H to Rn, including hypothetical copper alloys are ranked, and the less studied Cu-Au, -Hg, and -Tl are predicted to be high-performance structures. From economic, environmental, and healthcare perspectives, Cu-Mg is an appropriate choice according to the results.
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页数:9
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共 38 条
  • [31] COHERENT-POTENTIAL MODEL OF SUBSTITUTIONAL DISORDERED ALLOYS
    SOVEN, P
    [J]. PHYSICAL REVIEW, 1967, 156 (03): : 809 - &
  • [32] Performance on molecules, surfaces, and solids of the Wu-Cohen GGA exchange-correlation energy functional
    Tran, Fabien
    Laskowski, Robert
    Blaha, Peter
    Schwarz, Karlheinz
    [J]. PHYSICAL REVIEW B, 2007, 75 (11)
  • [33] Theory of electronic transport in random alloys with short-range order:: Korringa-Kohn-Rostoker nonlocal coherent potential approximation
    Tulip, P. R.
    Staunton, J. B.
    Lowitzer, S.
    Koedderitzsch, D.
    Ebert, H.
    [J]. PHYSICAL REVIEW B, 2008, 77 (16)
  • [34] Modelling of solid solution strengthening in multicomponent alloys
    Walbruhl, Martin
    Linder, David
    Agren, John
    Borgenstam, Annika
    [J]. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2017, 700 : 301 - 311
  • [35] A property-oriented design strategy for high performance copper alloys via machine learning
    Wang, Changsheng
    Fu, Huadong
    Jiang, Lei
    Xue, Dezhen
    Xie, Jianxin
    [J]. NPJ COMPUTATIONAL MATERIALS, 2019, 5 (1)
  • [36] Matminer: An open source toolkit for materials data mining
    Ward, Logan
    Dunn, Alexander
    Faghaninia, Alireza
    Zimmermann, Nils E. R.
    Bajaj, Saurabh
    Wang, Qi
    Montoya, Joseph
    Chen, Jiming
    Bystrom, Kyle
    Dylla, Maxwell
    Chard, Kyle
    Asta, Mark
    Persson, Kristin A.
    Snyder, G. Jeffrey
    Foster, Ian
    Jain, Anubhav
    [J]. COMPUTATIONAL MATERIALS SCIENCE, 2018, 152 : 60 - 69
  • [37] Investigation of solid solution hardening in molybdenum alloys
    Wesemann, I.
    Hoffmann, A.
    Mrotzek, T.
    Martin, U.
    [J]. INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS, 2010, 28 (06) : 709 - 715
  • [38] Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening
    Zhang, Hongtao
    Fu, Huadong
    He, Xingqun
    Wang, Changsheng
    Jiang, Lei
    Chen, Long-Qing
    Xie, Jianxin
    [J]. ACTA MATERIALIA, 2020, 200 : 803 - 810