Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB2 near the hydrogen liquefaction temperature
Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB2 near the hydrogen liquefaction temperature
复制标题
DOI：
10.1038/s41427-020-0214-y
复制
发表时间：
2020-05-12
影响因子：
9.7
通讯作者：
Takano, Yoshihiko
中科院分区：
文献类型：
--
作者：
de Castro, Pedro Baptista;Terashima, Kensei;Takano, Yoshihiko
磁制冷利用磁热效应，这是在材料中施加和去除磁场时的熵变化，为传统气体循环以外的制冷提供了另一种途径。虽然深入的研究已经发现了大量表现出大磁热效应的磁性材料，但对于大量化合物来说，这些特性仍然未知。为了在这个未知的空间中探索新的功能材料，机器学习被用作选择可以表现出大磁热效应的材料的指南。通过这种方法，HoB 2被挑选出来并合成，并评估其磁热性质，导致实验发现，在居里温度为15 K的铁磁二级相变附近，当磁场变化为5 T时，其巨大的磁熵变为40.1 J kg（-1）K-1（0.35 J cm（-3）K-1）。据我们所知，这是迄今为止报道的最高值，接近氢液化温度;因此，HoB 2是一种非常适合氢液化和低温磁冷却应用的材料。
Magnetic refrigeration exploits the magnetocaloric effect, which is the entropy change upon the application and removal of magnetic fields in materials, providing an alternate path for refrigeration other than conventional gas cycles. While intensive research has uncovered a vast number of magnetic materials that exhibit a large magnetocaloric effect, these properties remain unknown for a substantial number of compounds. To explore new functional materials in this unknown space, machine learning is used as a guide for selecting materials that could exhibit a large magnetocaloric effect. By this approach, HoB2 is singled out and synthesized, and its magnetocaloric properties are evaluated, leading to the experimental discovery of a gigantic magnetic entropy change of 40.1 J kg(-1) K-1 (0.35 J cm(-3) K-1) for a field change of 5 T in the vicinity of a ferromagnetic second-order phase transition with a Curie temperature of 15 K. This is the highest value reported so far, to the best of our knowledge, near the hydrogen liquefaction temperature; thus, HoB2 is a highly suitable material for hydrogen liquefaction and low-temperature magnetic cooling applications.