Machine learning-aided design of LaCe(Fe,Mn,Si)13H-type magnetocaloric materials for room-temperature applications

Yibo Jin, Jun Wang, Ruihao Yuan, Hongchao Li, Tong Wei, Chao Li, Jinshan Li

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

La(Fe,Si)13 magnetocaloric materials have attracted extensive attention in recent years due to their excellent magnetocaloric properties and low price. In this study, we established and compared several machine models and used a support vector machine for predicting the influences of different element contents on the Curie temperature. Based on the results predicted by the machine learning model, a series of fully hydrogenated materials (La1.1-xCexFe13-y-zMnySizHF, y≈0.3–0.5x, z=1.1, 1.3, 1.5) with Curie temperatures between 290 and 310 K were designed. After material screening, a material with an element composition of La0.66Ce0.44Fe11.4Mn0.1Si1.5HF was proven to have good room temperature magnetocaloric properties, with a Curie temperature of 301 K, magnetic entropy change of 11.3 J/kgK under a 0–2 T magnetic field, and a relative cooling power of 119.3 J/kg, which performs well in the existing La(Fe,Si)13-type materials that can be applied near room temperature. This work not only deepens researchers' understanding of machine learning-aided design of materials with specific application environment restrictions but also greatly accelerates the design of near-room temperature La(Fe,Si)13 magnetocaloric materials.

源语言英语
文章编号175746
期刊Journal of Alloys and Compounds
1003
DOI
出版状态已出版 - 25 10月 2024

指纹

探究 'Machine learning-aided design of LaCe(Fe,Mn,Si)13H-type magnetocaloric materials for room-temperature applications' 的科研主题。它们共同构成独一无二的指纹。

引用此