Övgü Yelgel

By Övgü Ceyda Yelgel

Over the past decade, there has been significant interest in the field of thermoelectric materials (TEs) owing to their use in clean and sustainable energy sources for cooling and/or power generation applications. Especially, Mg2XIV (XIV = Si, Ge, Sn) based TEs are promising candidates for middle-temperature range energy conversion due to their high thermoelectric performance, environmentally harmless, abundant raw materials, non-toxicity, and relatively inexpensive cost of modules. In this book chapter, we present an overview of the theoretical background of the thermoelectric transport properties (Seebeck coefficient, electrical conductivity, thermal conductivity, and thermoelectric figure of merit ZT) of magnesium-based bulk and low dimensional systems (i.e., quantum wells and quantum wires). A detailed description of the temperature-dependent Fermi level both in extrinsic and intrinsic regimes will be provided whereby it is the primary step in deriving the thermoelectric transport parameters of materials. Following the linearized Boltzmann transport equations temperature-dependent electronic transport properties (Seebeck coefficient, electrical conductivity, and electronic thermal conductivity) of materials under the energy-dependent relaxation time approximation will be defined. By employing Debye’s isotropic continuum model within the single mode relaxation time approximation including various phonon relaxation rates contributed by different scattering mechanisms the lattice contribution to the thermal conductivity will be included.

Part of the book: Magnesium Alloys

By Ebrar Yildirim and Övgü Ceyda Yelgel

Thermoelectric materials can be utilized to build devices that convert waste heat to power or vice versa. In the literature, the best-known thermoelectrics, however, are based on rare, costly or even hazardous materials, limiting their general usage. New types of effective thermoelectric materials are thus required to enable worldwide deployment. Although theoretical models of transport characteristics can aid in the creation of novel thermoelectrics, they are currently too computationally costly to be used simply for high-throughput screening of all conceivable candidates in the wide chemical space. Machine learning (ML) has been viewed as a promising technique to aid materials design/discovery because of its quick inference time. In this book chapter, we provide the whole workflow for machine learning applications to the identification of novel thermoelectric materials, predicting electrical and thermal transport properties and optimizing processes for materials and structures using cutting-edge ML methods.

Part of the book: New Materials and Devices for Thermoelectric Power Generation