Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses
Jingzi Zhang, Mengkun Zhao, Chengquan Zhong, Jiakai Liu, Kailong Hu, Xi Lin
Abstract
The data-driven machine learning approach has greatly improved the predictive accuracy of T g and D max values. The governing rules for GFA have been successfully established through feature significance analysis.
Topics & Concepts
Glass transitionMaterials scienceAmorphous metalFeature (linguistics)Artificial intelligenceMachine learningComputer scienceMetallurgyComposite materialPolymerPhilosophyAlloyLinguisticsMetallic Glasses and Amorphous AlloysGlass properties and applicationsPhase-change materials and chalcogenides