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

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.