Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

岩崎 悠真*; 澤田 亮人*; Stanev, V.*; 石田 真彦*; 桐原 明宏*; 大森 康智*; 染谷 浩子*; 竹内 一郎*; 齊藤 英治; 萬 伸一*

npj Computational Materials (Internet), 5, p.103_1 - 103_6, 2019/10

https://doi.org/10.1038/s41524-019-0241-9

Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date.

Irradiation tolerance of spin-driven thermoelectric device against gamma-rays

家田 淳一; 岡安 悟; 武山 昭憲*; 針井 一哉*; 大島 武*; 石田 真彦*; 齊藤 英治

no journal, , 

We investigate irradiation tolerance against gamma-rays of a prototypical spin-driven thermoelectric (STE) device based on YFeO/Pt heterostructures. The past study has proved that the STE device was rad-hard against the gamma-irradiation dose up to 0.3 MGy. In this study, we assess the radiation hardness in severer environments, such as higher dose levels, higher ambient temperatures, and water steam, assuming the future RTG usage near the spent nuclear fuel storage. We show that the YFeO/Pt STE device has tolerance to irradiation of gamma rays up to 0.9 MGy even at 150 C. On the other hand, we observe the performance degradation of the STE device by the gamma-irradiation under the water steam. From a control experiment without gamma-rays, the irradiation is irrelevant, and the damage is caused solely by the water steam effect. To avoid the damage, it is found that the thicker Pt film (10 nm) in the STE device is effective. Therefore, it was clarified that the STE device has fundamentally high resistance to gamma rays.

持続可能な未来への一歩; 放射性廃棄物を用いた蓄電池開発、熱・放射線発電、元素利用研究

菅原 隆徳

no journal, , 

原子力機構では2023年より放射性廃棄物等の再資源化に関する研究開発を開始した。具体的には、「燃えないウランの蓄電池利用」、「熱・放射線による発電」、「使用済燃料内の元素利用」の研究開発を行っている。本発表では、各項目についての背景・目的、技術の内容、現状と今後の見通しを紹介し、これまでにない原子力利用の方向性を提示する。

熱電変換システム、熱電変換方法

岡安 悟; 家田 淳一; 針井 一哉

not registered

特願 2023-214458  公開特許公報

【課題】放射線による劣化が小さな熱電変換システムあるいは熱電変換方法を得る。 【解決手段】この熱電変換システム1においては、膜厚方向(Z方向)に沿って、基板10の上に強磁性絶縁層20、金属層30が順次形成される。この熱電変換システム1に温度勾配を付与する熱源40は、基板10の下側に接続されている。スピンゼーベック効果による電位差は図中でY軸方向において発生するため、金属層30におけるY軸方向における両端部側にそれぞれ第1電極31、第2電極32が接続される。上記の熱電変換システム1は高いγ線耐性をもつため、γ線の遮蔽のための重金属層を必要としない。