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Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

Published online by Cambridge University Press:  24 April 2019

Jie Xiong Open the ORCID record for Jie Xiong [Opens in a new window] ,
Tong-Yi Zhang  and
San-Qiang Shi
Jie Xiong
Affiliation:
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Tong-Yi Zhang*
Affiliation:
Materials Genome Institute, Shanghai University, Shanghai, China
San-Qiang Shi
Affiliation:
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
*
Address all correspondence to Tong-Yi Zhang at mezhangt@ust.hk
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Abstract

There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 576 - 585
Copyright
Copyright © Materials Research Society 2019 

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