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(E...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.展开更多
The compositional design of metallic glasses(MGs)is a long-standing issue in materials science and engineering.However,traditional experimental approaches based on empirical rules are time consuming with a low efficie...The compositional design of metallic glasses(MGs)is a long-standing issue in materials science and engineering.However,traditional experimental approaches based on empirical rules are time consuming with a low efficiency.In this work,we successfully developed a hybrid machine learning(ML)model to address this fundamental issue based on a database containing~5000 different compositions of metallic glasses(either bulk or ribbon)reported since 1960s.Unlike the prior works relying on empirical parameters for featurization of data,we designed modeling guided data descriptors in line with the recent theoretical models on amorphization in chemically complex alloys for the development of the hybrid classification-regression ML algorithms.Our hybrid ML modeling was validated both numerically and experimentally.Most importantly,it enabled the discovery of MGs(either bulk or ribbon)through the ML-aided deep search of a multitude of quaternary to scenery alloy compositions.The computational framework herein established is expected to accelerate the design of MG compositions and expand their applications by probing the complex and multi-dimensional compositional space that has never been explored before.展开更多
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 109/21YY also acknowledges the support by City University of Hong Kong through CityU new research initiative with grant number of 9610603。
文摘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.
基金The research of YY is supported by the Research Grant Council,the Hong Kong Government,through the General Research Fund(GRF)with the grant numbers CityU11209317,CityU11213118,and CityU11200719Atom probe tomography research was conducted by Dr.JH LUAN at the Inter-University 3D Atom Probe Tomography Unit of City University of Hong Kong,which is supported by the CityU grant 9360161。
文摘The compositional design of metallic glasses(MGs)is a long-standing issue in materials science and engineering.However,traditional experimental approaches based on empirical rules are time consuming with a low efficiency.In this work,we successfully developed a hybrid machine learning(ML)model to address this fundamental issue based on a database containing~5000 different compositions of metallic glasses(either bulk or ribbon)reported since 1960s.Unlike the prior works relying on empirical parameters for featurization of data,we designed modeling guided data descriptors in line with the recent theoretical models on amorphization in chemically complex alloys for the development of the hybrid classification-regression ML algorithms.Our hybrid ML modeling was validated both numerically and experimentally.Most importantly,it enabled the discovery of MGs(either bulk or ribbon)through the ML-aided deep search of a multitude of quaternary to scenery alloy compositions.The computational framework herein established is expected to accelerate the design of MG compositions and expand their applications by probing the complex and multi-dimensional compositional space that has never been explored before.
