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 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
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 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
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.
中图分类号: (Magnetocaloric effect, magnetic cooling)
- 75.30.Sg