Composition design for (PrNd-La-Ce)2Fe(14)B melt-spun magnets by machine learning technique  

作　　者：Rui Li Yao Liu Shu-Lan Zuo Tong-Yun Zhao Feng-Xia Hu Ji-Rong Sun Bao-Gen Shen 李锐;刘瑶;左淑兰;赵同云;胡凤霞;孙继荣;沈保根(State Key Laboratory of Magnetism, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;University of Chinese Academy of Sciences, Beijing 100049, China)

机构地区：[1]State Key Laboratory of Magnetism, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China [2]University of Chinese Academy of Sciences, Beijing 100049, China

出　　处：《Chinese Physics B》2018年第4期460-464,共5页中国物理B（英文版）

基　　金：Project supported by the National Basic Research Program of China(Grant No.2014CB643702);the National Natural Science Foundation of China(Grant No.51590880);the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05);the National Key Research and Development Program of China(Grant No.2016YFB0700903)

摘　　要：Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity（H（cj）） and maximum magnetic energy product（BH）（max） via machine learning has been applied to（PrNd–La–Ce）2Fe（14）B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H（cj） and（BH）（max）,with high accuracies of R^2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H（cj） or（BH）max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2：14：1 structure melt-spun magnets.Combined with magnets＇ cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.Data-driven technique is a powerful and efficient tool for guiding materials design,which could supply as an alternative to trial-and-error experiments.In order to accelerate composition design for low-cost rare-earth permanent magnets,an approach using composition to estimate coercivity（H（cj）） and maximum magnetic energy product（BH）（max） via machine learning has been applied to（PrNd–La–Ce）2Fe（14）B melt-spun magnets.A set of machine learning algorithms are employed to build property prediction models,in which the algorithm of Gradient Boosted Regression Trees is the best for predicting both H（cj） and（BH）（max）,with high accuracies of R^2= 0.88 and 0.89,respectively.Using the best models,predicted datasets of H（cj） or（BH）max in high-dimensional composition space can be constructed.Exploring these virtual datasets could provide efficient guidance for materials design,and facilitate the composition optimization of 2：14：1 structure melt-spun magnets.Combined with magnets＇ cost performance,the candidate cost-effective magnets with targeted properties can also be accurately and rapidly identified.Such data analytics,which involves property prediction and composition design,is of great time-saving and economical significance for the development and application of La Ce-containing melt-spun magnets.

关 键 词：permanent magnet materials design machine learning property prediction 

分 类 号：TM273[一般工业技术—材料科学与工程]