The 67th JSAP Spring Meeting 2020

Presentation information

Oral presentation

Joint Session N "Informatics" » 23.1 Joint Session N "Informatics"

[15a-A205-1~10] 23.1 Joint Session N "Informatics"

Sun. Mar 15, 2020 9:30 AM - 12:15 PM A205 (6-205)

Yukari Katsura(Univ. of Tokyo), Toyohiro Chikyo(NIMS)

11:15 AM - 11:30 AM

[15a-A205-7] Search of magnetocaloric materials through machine-learning

Pedro Castro1,2, Kensei Terashima1, Takafumi D Yamamoto1, Suguru Iwasaki1, Ryo Matsumoto1,2, Shintaro Adachi1, Saito Yoshito1,2, Song Peng1,2, Hiroyuki Takeya1, Yoshihiko Takano1,2 (1.MANA, NIMS, 2.Tsukuba Univ.)

Keywords:magnetocaloric effect, machine learning

Magnetocaloric effect is emerging as a potential substitute for conventional gas-cycle based refrigerators, due to its enviromental friendliness and effectiveness, being suitable for cooling from room temperature to liquefaction of gases. However, for exploiting such effect, materials that exhibits remarkable magnetic entropy change are needed.
In this talk, we show our current efforts into modeling the magnetocaloric effect of magnetic materials through use of machine learning, discussing also ongoing experimental validation of such method and the challenges of this data-driven approach.