Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, the spin-driven thermoelectric effects (STEs), which employ emerging phenomena such as the spin-Seebeck effect (SSE) and the anomalous Nernst effect (ANE), have 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 mechanism and materials parameters that govern the STEs. To address this problem, we enlist machine learning modeling to establish the key physical parameters controlling SSE. Guided by these models, we have carried out a high-throughput experiment which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation STE devices.
@article{arxiv.1805.02303,
title = {Machine-learning guided discovery of a high-performance spin-driven thermoelectric material},
author = {Yuma Iwasaki and Ichiro Takeuchi and Valentin Stanev and Aaron Gilad Kusne and Masahiko Ishida and Akihiro Kirihara and Kazuki Ihara and Ryohto Sawada and Koichi Terashima and Hiroko Someya and Ken-ichi Uchida and Shinichi Yorozu and Eiji Saitoh},
journal= {arXiv preprint arXiv:1805.02303},
year = {2018}
}