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Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB₂ near the hydrogen liquefaction temperature

Physics

Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB₂ near the hydrogen liquefaction temperature

P. B. D. Castro, K. Terashima, et al.

This paper reveals groundbreaking use of machine learning to uncover materials with an enormous magnetocaloric effect, showcasing the remarkable HoB₂ which exhibits a magnetic entropy change of 40.1 J kg⁻¹ K⁻¹. This discovery has significant implications for hydrogen liquefaction and low-temperature magnetic cooling applications, conducted by a team including Pedro Baptista de Castro, Kensei Terashima, Takafumi D Yamamoto, and others.

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Playback language: English
Abstract
This paper reports the use of machine learning to predict materials with a large magnetocaloric effect (MCE), leading to the experimental discovery of a gigantic MCE in HoB₂ near the hydrogen liquefaction temperature. HoB₂ exhibits a 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, making it a promising candidate for hydrogen liquefaction and low-temperature magnetic cooling applications.
Publisher
NPG Asia Materials
Published On
Mar 17, 2020
Authors
Pedro Baptista de Castro, Kensei Terashima, Takafumi D Yamamoto, Zhufeng Hou, Suguru Iwasaki, Ryo Matsumoto, Shintaro Adachi, Yoshito Saito, Peng Song, Hiroyuki Takeya, Yoshihiko Takano
DOI
https://doi.org/10.1038/s41427-020-0214-y
Tags
machine learning
magnetocaloric effect
HoB₂
hydrogen liquefaction
magnetic entropy
low-temperature cooling

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