中国物理B ›› 2018, Vol. 27 ›› Issue (6): 67503-067503.doi: 10.1088/1674-1056/27/6/067503

• SPECIAL TOPIC—Recent advances in thermoelectric materials and devices • 上一篇    下一篇

Machine learning technique for prediction of magnetocaloric effect in La(Fe, Si/Al)13-based materials

Bo Zhang(张博), Xin-Qi Zheng(郑新奇), Tong-Yun Zhao(赵同云), Feng-Xia Hu(胡凤霞), Ji-Rong Sun(孙继荣), Bao-Gen Shen(沈保根)   

  1. 1 State Key Laboratory of Magnetism, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
    2 School of Physical Sciences, University of the Chinese Academy of Sciences, Beijing 100049, China;
    3 School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
  • 收稿日期:2018-03-19 修回日期:2018-05-04 出版日期:2018-06-05 发布日期:2018-06-05
  • 通讯作者: Bao-Gen Shen E-mail:shenbg@iphy.ac.cn
  • 基金资助:

    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).

Machine learning technique for prediction of magnetocaloric effect in La(Fe, Si/Al)13-based materials

Bo Zhang(张博)1,2, Xin-Qi Zheng(郑新奇)3, 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. 1 State Key Laboratory of Magnetism, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
    2 School of Physical Sciences, University of the Chinese Academy of Sciences, Beijing 100049, China;
    3 School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
  • Received:2018-03-19 Revised:2018-05-04 Online:2018-06-05 Published:2018-06-05
  • Contact: Bao-Gen Shen E-mail:shenbg@iphy.ac.cn
  • Supported by:

    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).

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摘要：

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La(Fe,Si/Al)13-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree (GBRT) to essentially find the correlation between the Curie temperature (TC), maximum value of magnetic entropy change ((Δ SM)max), and chemical composition, all of which yield high accuracy in the prediction of TC and (Δ SM)max. The performance metric coefficient scores of determination (R2) for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.

关键词: La(Fe,Si/Al)13-based materials, composition design, machine learning, magnetic refrigeration

Abstract:

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La(Fe,Si/Al)13-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree (GBRT) to essentially find the correlation between the Curie temperature (TC), maximum value of magnetic entropy change ((Δ SM)max), and chemical composition, all of which yield high accuracy in the prediction of TC and (Δ SM)max. The performance metric coefficient scores of determination (R2) for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.

Key words: La(Fe,Si/Al)13-based materials, composition design, machine learning, magnetic refrigeration

中图分类号:  (Magnetocaloric effect, magnetic cooling)

  • 75.30.Sg
75.20.En (Metals and alloys)