Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al)<sub>13</sub>-based materials<xref rid="cpb_27_6_067503_fn1" ref-type="fn">*</xref> <fn id="cpb_27_6_067503_fn1"> <label>*</label> <p>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).</p> </fn>

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

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).

Zhang Bo^{1, 2}, Zheng Xin-Qi³, Zhao Tong-Yun^{1, 2}, Hu Feng-Xia^{1, 2}, Sun Ji-Rong^{1, 2}, Shen Bao-Gen^{1, 2, †}

(color online) (a) Data distribution for T_C dataset. (b) Performances of T_C prediction models trained with different parameter combinations. The best parameter combination is marked with red star.