Machine learning prediction of glass-forming ability in bulk metallic glasses

Jie Xiong, San Qiang Shi, Tong Yi Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

The critical casting diameter (Dmax) quantitatively represents glass-forming ability (GFA) of bulk metallic glasses (BMGs). The present work constructed a dataset of two subsets, L-GFA subset of 376 BMGs with 1 mm ≤Dmax < 5 mm and G-GFA subset of 319 BMGs with Dmax ≥ 5 mm. The sequential backward selector and exhaustive feature selector are introduced to select key features. The trained XGBoost classifier with four selected features is able to successfully classify the L-GFA and G-GFA BMGs. Furthermore, the trained XGBoost regression model with another four selected features predicts the Dmax of G-GFA samples with a cross-validated correlation coefficient of 0.8012. The correlation between features and Dmax will provide the guidance in the design and discovery of novel BMGs.

Original languageEnglish
Article number110362
JournalComputational Materials Science
Volume192
DOIs
Publication statusPublished - May 2021

Keywords

  • Bulk metallic glasses
  • Glass-forming ability
  • Machine learning
  • XGBoost

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

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