Efficient technique for computational design of thermoelectric materials

TL;DR

This paper introduces a comprehensive first-principles method combining evolutionary algorithms, multiobjective optimization, DFT, and Boltzmann calculations to efficiently identify promising thermoelectric materials in multicomponent systems.

Contribution

It presents a novel integrated computational framework for predicting high-performance thermoelectric compounds, enhancing the search process for new materials.

Findings

Successfully applied to Bi2Te3-Sb2Te3 system

Demonstrates reliable prediction of thermoelectric efficiency

Provides a scalable approach for multicomponent systems

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 Bi$_2$Te$_3$-Sb$_2$Te$_3$.