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Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB$_{2}$ Near Hydrogen Liquefaction Temperature

Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB$_{2}$ Near Hydrogen Liquefaction Temperature

Authors :
Shintaro Adachi
Zhufeng Hou
Peng Song
Hiroyuki Takeya
Suguru Iwasaki
Takafumi Yamamoto
Pedro Baptista de Castro
Yoshito Saito
Kensei Terashima
Ryo Matsumoto
Yoshihiko Takano
Publication Year :
2020

Abstract

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.<br />12 pages including 3 figures and 1 table + 11 pages of supplementary information. Published version available at: https://rdcu.be/b36ep

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....1780ef640174d51317db681d112a534b