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Chin. Phys. B, 2018, Vol. 27(4): 047501    DOI: 10.1088/1674-1056/27/4/047501
CONDENSED MATTER: ELECTRONIC STRUCTURE, ELECTRICAL, MAGNETIC, AND OPTICAL PROPERTIES Prev   Next  

Composition design for (PrNd-La–Ce)2Fe14B melt-spun magnets by machine learning technique

Rui Li(李锐)1,2, Yao Liu(刘瑶)1,2, Shu-Lan Zuo(左淑兰)1,2, Tong-Yun Zhao(赵同云)1,2, Feng-Xia Hu(胡凤霞)1,2, Ji-Rong Sun(孙继荣)1,2, Bao-Gen Shen(沈保根)1,2
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
Abstract  

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 (Hcj) and maximum magnetic energy product ((BH)max) via machine learning has been applied to (PrNd-La-Ce)2Fe14B 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 Hcj and (BH)max, with high accuracies of R2=0.88 and 0.89, respectively. Using the best models, predicted datasets of Hcj 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 LaCe-containing melt-spun magnets.

Keywords:  permanent magnet      materials design      machine learning      property prediction  
Received:  14 January 2018      Revised:  27 February 2018      Published:  05 April 2018
PACS:  75.47.Np (Metals and alloys)  
  75.50.Ww (Permanent magnets)  
Fund: 

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), and the National Key Research and Development Program of China (Grant No. 2016YFB0700903).

Corresponding Authors:  Bao-Gen Shen     E-mail:  shenbg@iphy.ac.cn

Cite this article: 

Rui Li(李锐), Yao Liu(刘瑶), Shu-Lan Zuo(左淑兰), Tong-Yun Zhao(赵同云), Feng-Xia Hu(胡凤霞), Ji-Rong Sun(孙继荣), Bao-Gen Shen(沈保根) Composition design for (PrNd-La–Ce)2Fe14B melt-spun magnets by machine learning technique 2018 Chin. Phys. B 27 047501

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