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) and (c) Data distribution for (ΔS_M)_max at 0 T–2 T and 0 T–5 T datasets, respectively. (b) and (d) Performances of (ΔS_M)_max at 0 T–2 T and 0 T–5 T prediction models trained with different parameters combinations, respectively. The best parameters combinations are marked with red star.