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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
被引用回数：47 パーセンタイル：87.87(Chemistry, Physical)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.
岩崎 悠真*; 竹内 一郎*; Stanev, V.*; Gilad Kusne, A.*; 石田 真彦*; 桐原 明宏*; 井原 和紀*; 澤田 亮人*; 寺島 浩一*; 染谷 浩子*; et al.
Scientific Reports (Internet), 9, p.2751_1 - 2751_7, 2019/02
被引用回数：61 パーセンタイル：92.99(Multidisciplinary Sciences)Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.
内田 健一*; 安立 裕人; 吉川 貴史*; 桐原 明宏*; 石田 真彦*; 萬 伸一*; 前川 禎通; 齊藤 英治*
Proceedings of the IEEE, 104(10), p.1946 - 1973, 2016/10
被引用回数：213 パーセンタイル：99.2(Engineering, Electrical & Electronic)The spin Seebeck effect (SSE) refers to the generation of a spin current as a result of a temperature gradient in magnetic materials including insulators. The SSE is applicable to thermoelectric generation because the thermally generated spin current can be converted into a charge current via spin-orbit interaction in conductive materials adjacent to the magnets. The insulator-based SSE device exhibits unconventional characteristics potentially useful for thermoelectric applications, such as simple structure, device-design exibility, and convenient scaling capability. In this article, we review recent studies on the SSE from the viewpoint of thermoelectric applications.
桐原 明宏*; 内田 健一*; 梶原 瑛祐*; 石田 真彦*; 中村 泰信*; 眞子 隆志*; 齊藤 英治; 萬 伸一*
Nature Materials, 11(8), p.686 - 689, 2012/08
被引用回数：239 パーセンタイル：98.59(Chemistry, Physical)Energy harvesting technologies, which generate electricity from environmental energy, have been attracting great interest because of their potential to power ubiquitously deployed sensor networks and mobile electronics. Of these technologies, thermoelectric (TE) conversion is a particularly promising candidate, because it can directly generate electricity from the thermal energy that is available in various places. Here we show a novel TE concept based on the spin Seebeck effect, called "spin-thermoelectric (STE) coating", which is characterized by a simple film structure, convenient scaling capability, and easy fabrication. The STE coating, with a 60-nm-thick bismuth-substituted yttrium iron garnet (Bi:YIG) film, is applied by means of a highly efficient process on a non-magnetic substrate. Notably, spin-current-driven TE conversion is successfully demonstrated under a temperature gradient perpendicular to such an ultrathin STE-coating layer (amounting to only 0.01% of the total sample thickness). We also show that the STE coating is applicable even on glass surfaces with amorphous structures. Such a versatile implementation of the TE function may pave the way for novel applications making full use of omnipresent heat.
