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

Hongtao Zhang, Huadong Fu, Xingqun He, Changsheng Wang, Lei Jiang, Long Qing Chen, Jianxin Xie

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)803-810
Number of pages8
JournalActa Materialia
Volume200
DOIs
StatePublished - Nov 2020

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Ceramics and Composites
  • Polymers and Plastics
  • Metals and Alloys

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