Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses

Author:

Zhang Jingzi12ORCID,Zhao Mengkun12,Zhong Chengquan12,Liu Jiakai12,Hu Kailong123ORCID,Lin Xi123

Affiliation:

1. School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, P. R. China

2. Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen 518055, P. R. China

3. State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, P. R. China

Abstract

The data-driven machine learning approach has greatly improved the predictive accuracy of Tg and Dmax values. The governing rules for GFA have been successfully established through feature significance analysis.

Funder

Putian University

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen Science and Technology Innovation Program

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

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