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

43 Pages Posted: 10 Jul 2020 Publication Status: Accepted

See all articles by Hongtao Zhang

Hongtao Zhang

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology

Huadong Fu

University of Science and Technology Beijing - Beijing Advanced Innovation Center of Materials Genome Engineering

Xingqun He

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

Changsheng Wang

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

Lei Jiang

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

Long-Qing Chen

Pennsylvania State University - Department of Materials Science and Engineering; Pennsylvania State University - Materials Science Institute

Jianxin Xie

University of Science and Technology Beijing - Beijing Advanced Innovation Center of Materials Genome Engineering

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 novel 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.

Suggested Citation

Zhang, Hongtao and Fu, Huadong and He, Xingqun and Wang, Changsheng and Jiang, Lei and Chen, Long-Qing and Xie, Jianxin, Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening. Available at SSRN: https://ssrn.com/abstract=3646448 or http://dx.doi.org/10.2139/ssrn.3646448

Hongtao Zhang

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology

Huadong Fu

University of Science and Technology Beijing - Beijing Advanced Innovation Center of Materials Genome Engineering ( email )

Beijing
China

Xingqun He

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

Changsheng Wang

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

Lei Jiang

Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing

Long-Qing Chen (Contact Author)

Pennsylvania State University - Department of Materials Science and Engineering ( email )

University Park
State College, PA 16802
United States

Pennsylvania State University - Materials Science Institute ( email )

United States

Jianxin Xie

University of Science and Technology Beijing - Beijing Advanced Innovation Center of Materials Genome Engineering ( email )

Beijing
China

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