典型文献
cardiGAN:A generative adversarial network model for design and discovery of multi principal element alloys
文献摘要：
Multi-principal element alloys(MPEAs),inclusive of high entropy alloys(HEAs),continue to attract sig-nificant research attention owing to their potentially desirable properties.Although MPEAs remain un-der extensive research,traditional(i.e.empirical)alloy production and testing are both costly and time-consuming,partly due to the inefficiency of the early discovery process which involves experiments on a large number of alloy compositions.It is intuitive to apply machine learning in the discovery of this novel class of materials,of which only a small number of potential alloys have been probed to date.In this work,a proof-of-concept is proposed,combining generative adversarial networks(GANs)with discrimi-native neural networks(NNs),to accelerate the exploration of novel MPEAs.By applying the GAN model herein,it was possible to directly generate novel compositions for MPEAs,and to predict their phases.To verify the predictability of the model,alloys designed by the model are presented and a candidate produced-as validation.This suggests that the model herein offers an approach that can significantly enhance the capacity and efficiency of development of novel MPEAs.
文献关键词：
中图分类号：
作者姓名：
Z.Li;W.T.Nash;S.P.O'Brien;Y.Qiu;R.K.Gupta;N.Birbilis
作者机构：
College of Engineering and Computer Science,The Australian National University,A.C.T.,Acton 2601,Australia;Department of Materials Science and Engineering,Monash University,Clayton,Victoria 3800,Australia;Department of Materials Science and Engineering North Carolina State University,Raleigh,NC 27695,USA
文献出处：
引用格式：
[1]Z.Li;W.T.Nash;S.P.O'Brien;Y.Qiu;R.K.Gupta;N.Birbilis-.cardiGAN:A generative adversarial network model for design and discovery of multi principal element alloys)[J].材料科学技术（英文版）,2022(30):81-96
A类：
cardiGAN,MPEAs,discrimi
B类：
generative,adversarial,model,discovery,multi,principal,element,alloys,Multi,inclusive,high,entropy,HEAs,continue,attract,research,attention,owing,their,potentially,desirable,properties,Although,remain,un,der,extensive,traditional,empirical,production,testing,are,both,costly,consuming,partly,due,inefficiency,early,process,which,involves,experiments,large,number,compositions,It,intuitive,machine,learning,this,novel,class,materials,only,small,have,been,probed,In,proof,concept,proposed,combining,networks,GANs,native,neural,NNs,accelerate,exploration,By,applying,herein,was,possible,directly,generate,phases,To,verify,predictability,designed,by,presented,candidate,produced,validation,This,suggests,that,offers,approach,significantly,enhance,capacity,development
AB值：
0.557414
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