典型文献
Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation
文献摘要：
A data augmentation technique is employed in the current work on a training dataset of 610 bulk metal-lic 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 Dmax of BMGs more accurate than all re-ported 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 TgTx/(T1-Tg)(T1-Tx)being higher than 2.5,the en-tropy of mixing being higher than 10 J/K/mol,and the enthalpy of mixing ranging from-32 kJ/mol to-26 kJ/mol.ML model is interpretative,thereby deepening the understanding of GFA.
文献关键词：
中图分类号：
作者姓名：
Jie Xiong;Tong-Yi Zhang
作者机构：
School of Materials Science and Engineering,Harbin Institute of Technology,Shenzhen 518000,China;Hong Kong University of Science and Technology(Guangzhou),Guangzhou 511400,China;Material Genome Institute,Shanghai University,Shanghai 200444,China
文献出处：
引用格式：
[1]Jie Xiong;Tong-Yi Zhang-.Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation)[J].材料科学技术（英文版）,2022(26):99-104
A类：
BMGs,TgTx
B类：
Data,driven,forming,ability,criterion,bulk,amorphous,metals,augmentation,technique,is,employed,current,work,training,dataset,lic,glasses,which,are,randomly,selected,from,collected,An,ensemble,machine,learning,ML,developed,augmented,tested,rest,result,shows,that,has,predict,maximal,diameter,Dmax,more,accurate,than,all,ported,models,In,addition,novel,gives,GFA,rules,average,atomic,radius,ranging,pm,value,being,higher,tropy,mixing,enthalpy,kJ,interpretative,thereby,deepening,understanding
AB值：
0.554252
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
