论文标题
机器学习指导发现hob $ _ {2} $在氢液化温度附近的巨型磁静电效应
Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB$_{2}$ Near Hydrogen Liquefaction Temperature
论文作者
论文摘要
磁制冷利用了磁电效应，即在材料中施用和去除磁场后的熵变化，为除常规气体周期以外的冷藏提供了替代路径。尽管密集的研究发现了大量磁性材料，这些磁性材料表现出巨大的磁性作用，但这些化合物的这些特性仍然未知。为了探索这个未知空间中的新功能材料，机器学习被用作选择可能表现出较大磁性效果的材料的指南。 By this approach, HoB$_{2}$ is singled out, synthesized and its magnetocaloric properties are evaluated, leading to the experimental discovery of gigantic magnetic entropy change 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据我们所知，这是迄今为止在氢液化温度附近报道的最高值，因此它是氢液化温度附近的最高值，因此它是一种非常适合氢液化和低温磁性冷却应用的材料。
Magnetic refrigeration exploits the magnetocaloric effect which is the entropy change upon application and removal of magnetic fields in materials, providing an alternate path for refrigeration other than the conventional gas cycles. While intensive research has uncovered a vast number of magnetic materials which exhibits large magnetocaloric effect, these properties for a large number of compounds still remain unknown. To explore new functional materials in this unknown space, machine learning is used as a guide for selecting materials which could exhibit large magnetocaloric effect. By this approach, HoB$_{2}$ is singled out, synthesized and its magnetocaloric properties are evaluated, leading to the experimental discovery of gigantic magnetic entropy change 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 our knowledge, near the hydrogen liquefaction temperature thus it is a highly suitable material for hydrogen liquefaction and low temperature magnetic cooling applications.
