Efficient technique for computational design of thermoelectric materials

Abstract

Efficient thermoelectric materials are highly desirable, and the quest for finding them has intensified as they could be promising alternatives to fossil energy sources. Here we present a general first-principles approach to predict, in multicomponent systems, efficient thermoelectric compounds. The method combines a robust evolutionary algorithm, a Pareto multiobjective optimization, density functional theory and a Boltzmann semi-classical calculation of thermoelectric efficiency. To test the performance and reliability of our overall framework, we use the well-known system Bi2Te3-Sb2Te3.

Roles of Carrier Doping, Band Gap, and Electron Relaxation Time in the Boltzmann Transport Calculations of a Semiconductor’s Thermoelectric Properties

“…In our calculations this limit was observed for almost any semiconductors with steep band edges. This is consistent with past theoretical calculations that included ¬ P 9,3437) or used the Wiedemann-Franz law 18) to successfully achieve z el T > 1, while studies that only used ¬ 0 7,3841) only found values of z el T below 1. The origin of this false "wall of z el T = 1" can be explained as follows.…”

Structure prediction drives materials discovery

“…The second option is the optimization of the physical property of interest (such as the hardness [78,79], density [80], band gap [81] or the thermoelectric figure of merit [82]). These should be extremized, or some target value must be approached (for example, for absorption of sunlight a direct gap as close as possible to 1.34 eV is desirable).…”

“…The third type of global optimization is the multiobjective (Pareto) optimization, in which two or more properties are simultaneously optimized. In our opinion, this type of optimization is most directly related to practical applications: for example, simultaneously optimizing the stability and physical properties of interest (as done for superhard materials [83,84] and thermoelectrics [82]), leads to the identification of materials that have attractive properties and at the same time can potentially be synthesized. The solution of a multiobjective optimization problem is, in general, not one material, but a set of materials forming the so-called first Pareto front (Box 2).…”

Electronic Structure and Thermoelectric Properties of Co‐, Fe‐, Mn‐, and Cr‐Doped Ba2LuTaO6 from Spin‐Polarized Calculations

“…The effect of temperature on the main thermoelectric parameters, such as electrical conductivity ( σ / τ ), Seebeck coefficient ( S ), thermal conductivity ( κ / τ ), power factor (PF = σS 2 / τ ), and figure of merit ( ZT ), has been reported in the previous studies. [ 60,61 ] The thermoelectric materials have a high electrical conductivity ( σ ) due to the flow of electrons that circulate from high‐temperature regions toward low‐temperature regions. The temperature dependence of electrical conductivity ( σ ) for both spin channels is shown in Figure 5.…”