Abstract
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.
Highlights
Waste heat is ubiquitous in modern society, and thermoelectric technologies based on the Seebeck effect have been embraced as a central avenue to a sustainable future[1,2,3]
We have successfully leveraged the machine-learning-informed knowledge of the dependence of spin-Seebeck effect (SSE) on materials parameters to arrive at a novel and high-performance spin-driven thermoelectric (STE) material utilizing anomalous Nernst effect (ANE), which converts a heat current into an electrical current via the spin-orbit interaction in a single ferromagnetic material
We adopted a bilayer consisting of platinum (Pt) and rare-earth-substituted yttrium iron garnet (R1Y2Fe5O12, referred to as R:YIG), where R stands for a rare-earth element
Summary
Waste heat is ubiquitous in modern society, and thermoelectric technologies based on the Seebeck effect have been embraced as a central avenue to a sustainable future[1,2,3]. The emergence of novel thermoelectric devices based on the spin-driven thermoelectric (STE) phenomena offers a potential solution to this problem. Material synthesis guided by machine learning has been relatively rare so far[25], it is very likely going to become a commonplace in the future. Utilizing these methods, we have developed a systematic approach to uncovering the major materials variables governing the SSE. We have successfully leveraged the machine-learning-informed knowledge of the dependence of SSE on materials parameters to arrive at a novel and high-performance STE material utilizing ANE, which converts a heat current into an electrical current via the spin-orbit interaction in a single ferromagnetic material. Out of a number of proposed materials systems, a composition spread of one ternary system has led to the identification of Fe0.665Pt0.27Sm0.065, which exhibits thermopower as large as 11.12 μV/K
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
