Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation  

作　　者：Jie Xiong Tong-Yi Zhang 

机构地区：[1]School of Materials Science and Engineering,Harbin Institute of Technology,Shenzhen 518000,China [2]Hong Kong University of Science and Technology(Guangzhou),Guangzhou 511400,China [3]Material Genome Institute,Shanghai University,Shanghai 200444,China

出　　处：《Journal of Materials Science & Technology》2022年第26期99-104,共6页材料科学技术（英文版）

基　　金：the National Key R&D Program of China(No.2018YFB0704404);the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110798);the National Natural Science Foundation of China(Grant Nos.91860115);the Stable Supporting Fund of Shenzhen(GXWD20201230155427003-20200728114835006)。

摘　　要：A data augmentation technique is employed in the current work on a training dataset of 610 bulk metallic glasses(BMGs),which are randomly selected from 762 collected data.An ensemble machine learning(ML)model is developed on augmented training dataset and tested by the rest 152 data.The result shows that ML model has the ability to predict the maximal diameter Dmaxof BMGs more accurate than all reported ML models.In addition,the novel ML model gives the glass forming ability(GFA)rules:average atomic radius ranging from 140 pm to 165 pm,the value of TT/(T-T)(T-T)being higher than 2.5,the entropy of mixing being higher than 10 J/K/mol,and the enthalpy of mixing ranging from-32 k J/mol to-26 k J/mol.ML model is interpretative,thereby deepening the understanding of GFA.

关 键 词：Materials informatics Glass-forming ability Data augmentation Model interpretation Meta-ensemble model 

分 类 号：TG139.8[一般工业技术—材料科学与工程]