Machine-learning guided discovery of a high-performance spin-driven thermoelectric material

Abstract

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.

25 pages, 4 figures, 1 table

References in corpus (10)

• Spin Caloritronics
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Direct electronic measurement of the spin Hall effect
• Spin Seebeck insulator
• Large anomalous Nernst effect at room temperature in a chiral antiferromagnet
• Theory of magnon-driven spin Seebeck effect
• Thermoelectric generation based on spin Seebeck effects
• Electrical detection of spin pumping due to the precessing magnetization of a single ferromagnet
• Magnon Polarons in the Spin Seebeck Effect
• Spectral non-uniform temperature, non-local heat transfer, and the spin Seebeck effect