Data-Driven Enhancement of ZT in SnSe-Based Thermoelectric Systems

Abstract

Doping and alloying are fundamental strategies to improve the thermoelectric performance of bare materials. However, identifying outstanding elements and compositions for the development of high-performance thermoelectric materials is challenging. In this study, we present a data-driven approach to improve the thermoelectric performance of SnSe compounds with various doping. Based on the newly generated experimental and computational dataset, we built highly accurate predictive models of thermoelectric properties of doped SnSe compounds. A well-designed feature vector consisting of the chemical properties of a single atom and the electronic structures of a solid plays a key role in achieving accurate predictions for unknown doping elements. Using the machine learning predictive models and calculated map of the solubility limit for each dopant, we rapidly screened high-dimensional material spaces of doped SnSe and evaluated their thermoelectric properties. This data-driven search provided overall strategies to optimize and improve the thermoelectric properties of doped SnSe compounds. In particular, we identified five dopant candidate elements (Ge, Pb, Y, Cd, and As) that provided a high ZT exceeding 2.0 and proposed a design principle for improving the ZT by Sn vacancies depending on the doping elements. Based on the search, we proposed yttrium as a new high-ZT dopant for SnSe with experimental confirmations. Our research is expected to lead to novel high-ZT thermoelectric material candidates and provide cutting-edge research strategies for materials design and extraction of design principles through data-driven research.