Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening

Optimizing two conflicting properties such as mechanical strength and toughness or dielectric constant and breakdown strength of a material has always been a challenge. Here we propose a machine learning approach to dramatically enhancing the combined ultimate tensile strength (UTS) and electric conductivity (EC) of alloys by identifying a set of key features through correlation screening, recursive elimination and exhaustive screening of existing datasets. We demonstrate that the key features responsible for solid solution strengthened conductive Copper alloys are absolute electronegativity, core electron distance, and atomic radius, based on which, we discovered a series of new alloying elements that can significantly improve the combined UTS and EC. The predictions are then validated by experimentally fabricating four new Cu-In alloys which could potentially replace the more expensive Cu-Ag alloys currently used in railway wiring. We show that the same set of key features can be generally applicable to designing a broad range of conductive alloys.

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Work Title Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening
Access
Open Access
Creators
  1. Hongtao Zhang
  2. Huadong Fu
  3. Xingqun He
  4. Changsheng Wang
  5. Lei Jiang
  6. Long-Qing Chen
Keyword
  1. Machine learning
  2. Feature screening
  3. Alloy design
  4. Copper alloy
  5. Aluminum alloy
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Acta Materialia
Publication Date September 30, 2020
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.actamat.2020.09.068
Deposited August 10, 2022

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Version 1
published

  • Created
  • Updated
  • Added Creator Sandra Elder
  • Added Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening.pdf
  • Updated License Show Changes
    License
    • https://rightsstatements.org/page/InC/1.0/
  • Published
  • Updated Keyword, Publisher, Publisher Identifier (DOI), and 1 more Show Changes
    Keyword
    • Machine learning, Feature screening, Alloy design, Copper alloy, Aluminum alloy
    Publisher
    • Acta Materialia
    Publisher Identifier (DOI)
    • https://doi.org/10.1016/j.actamat.2020.09.068
    Publication Date
    • 2020-11
    • 2020-09-30
  • Deleted Creator Sandra Elder
  • Added Creator Hongtao Zhang
  • Added Creator Huadong Fu
  • Added Creator Xingqun He
  • Added Creator Changsheng Wang
  • Added Creator Lei Jiang
  • Added Creator Long-Qing Chen
  • Updated