中国物理学会期刊网
Chin.Phys.B  2018, Vol.27 Issue (6): 067503  DOI:10.1088/1674-1056/27/6/067503
Machine learning technique for prediction of magnetocaloric effect in La(Fe, Si/Al)13-based materials
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>

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.
收稿日期:2018-03-19

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).

引用本文

[英文]
Bo Zhang, Xin-Qi Zheng, Tong-Yun Zhao, Feng-Xia Hu, Ji-Rong Sun, Bao-Gen Shen. Machine learning technique for prediction of magnetocaloric effect in La(Fe, Si/Al)13-based materials[J]. Chin. Phys. B, 2018, 27(6): 067503.
使用本文
PACS
本文作者
阅读笔记
在左边选中内容后,点击→加入笔记。笔记内容将复制到下面文本框中,点击保存按钮可保存在个人文献中心中
              
[1]
Duc N H and Anh D T K 2002 J. Magn. Magn. Mater. 242 873
[2]
Giguere A, Foldeaki M, Schnelle W and Gmelin E 1999 J. Phys:Condens. Matter 11 6969
[3]
Zheng X Q, Zhang B, Wu H, Hu F X, Huang Q Z and Shen B G 2016 J. Appl. Phys. 120 163907
[4]
Pecharsky V K and Gschneidner K A 1997 Phys. Rev. Lett. 78 4494
[5]
Giguére A, Foldeaki M, Ravi G B, Chahine R, Bose T K, Frydman A and Barclay J A 1999 Phys. Rev. Lett. 83 2262
[6]
Wada H and Tanabe Y 2001 Appl. Phys. Lett. 79 3302
[7]
Tegus O, Brück E, Buschow K H J and de Boer F R 2002 Nature 415 150
[8]
Hu F X, Shen B G, Sun J R and Wu G H 2001 Phys. Rev. B 64 132412
[9]
Liu J, Gottschall T, Skokov K P, Moore J D and Gutfleisch O 2012 Nat. Mater. 11 620
[10]
Shen B G, Sun J R, Hu F X, Zhang H W and Cheng Z H 2009 Adv. Mater. 21 4545
[11]
Hu F X, Shen B G, Sun J R and Zhang X X 2000 Chin. Phys. 9 550
[12]
Balli M, Fruchart D and Gignoux D 2007 J. Phys. Condens. Matter 19 236230
[13]
Barcza A, Katter M, Zellmann V, Russek S, Jacobs S and Zimm C 2011 IEEE Trans. Magn. 47 3391
[14]
Fujieda S, Fujita A and Fukamichi K 2005 IEEE Trans. Magn. 41 2787
[15]
Fujieda S, Fujita A and Fukamichi K 2007 J. Appl. Phys. 102 023907
[16]
Shen J, Li Y X, Zhang J, Gao B, Hu F X, Zhang H W, Chen Y Z, Rong C B and Sun J R 2008 J. Appl. Phys. 103 07B317
[17]
Fujita A, Fujieda S, Hasegawa Y and Fukamichi K 2003 Phys. Rev. B 67 104416
[18]
Zhang H, Shen J, Xu Z Y, Zheng X Q, Hu F X, Sun J R and Shen B G 2012 J. Magn. Magn. Mater. 324 484
[19]
Chen X A, Chen Y G and Tang Y B 2011 J. Alloys Compd. 509 2864
[20]
Nosengo N 2016 Nature 533 23
[21]
Meredig B, Agrawal A, Kirklin S, Saal J E, Doak J W, Thompson A, Zhang K, Choudhary A and Wolverton C 2014 Phys. Rev. B 89 094429
[22]
Oliynyk A O, Antono E, Sparks T D, Ghadbeigi L, Gaultois M W, Meredig B and Mar A 2016 Chem. Mater. 28 7324
[23]
Xue D, Balachandran P V, Hogden J, Theiler J, Xue D and Lookman T 2016 Nat. Commun. 7 11241
[24]
Owolabi T O, Akande K O, Olatunji S O, Alqahtani A and Aldhafferi N 2016 AIP Adv. 6 105009
[25]
Friedman J H 2001 Ann. Stat. 29 1189
[26]
Franco V, Blázquez J S, Iplus J J, Law J Y, Moreno-Ramírez L M and Conde A 2018 Prog. Mater. Sci. 93 112
[27]
Sun Y T, Bai H Y, Li M Z and Wang W H 2017 J. Phys. Chem. Lett. 8 3434
数据正在加载中...
中国物理学会期刊网