Accelerated discovery of eutectic compositionally complex alloys by generative machine learning  

作　　者：Z.Q.Chen Y.H.Shang X.D.Liu Y.Yang 

机构地区：[1]Department of Mechanical Engineering,City University of Hong Kong,Hong Kong,China [2]City University of Hong Kong(Dongguan),Dongguan,Guangdong,China [3]College of Mechatronics and Control Engineering,Shenzhen University,Shenzhen,China [4]Department of Materials Science and Engineering,City University of Hong Kong,Kowloon,China [5]Department of System Engineering,College of Engineering,City University of Hong Kong,Kowloon,China

出　　处：《npj Computational Materials》2024年第1期1091-1102,共12页计算材料学(英文)

基　　金：supported by Research Grants Council(RGC),the Hong Kong government through General Research Fund(GRF)with grant numbers of CityU 11206362 and CityU 11201721 and through NSFC-RGC Joint Research Schemewith grant number of N_CityU 109/21;YY also acknowledges the support by City University of Hong Kong through CityU new research initiative with grant number of 9610603。

摘　　要：Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties,as well as their technological relevance.However,the discovery of eutectic compositionally complex alloys(ECCAs)(e.g.high entropy eutectic alloys)remains a formidable challenge in the vast and intricate compositional space,primarily due to the absence of readily available phase diagrams.To address this issue,we have developed an explainable machine learning(ML)framework that integrates conditional variational autoencoder(CVAE)and artificial neutral network(ANN)models,enabling direct generation of ECCAs.To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design,we have incorporated thermodynamicsderived data descriptors and employed K-means clustering methods for effective data preprocessing.Leveraging our ML framework,we have successfully discovered dual-or even tri-phased ECCAs,spanning from quaternary to senary alloy systems,which have not been previously reported in the literature.These findings hold great promise and indicate that ourML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

关 键 词：EUTECTIC ALLOYS alloy 

分 类 号：TG1[金属学及工艺—金属学]