Computational prediction of nanostructured alloys with enhanced thermoelectric properties

The Materials Genome Initiative calls for a dramatic increase in the rate of materials discovery and development. High-throughput (HT) calculations can advance this goal by efficiently screening a large search space for candidate materials to study in more depth. Thermoelectric materials (TEs) are prime candidates for such HT calculations: The properties required to achieve good performance are known, but systematic ways of improving these properties are scarce. Furthermore, known HT methods for TEs only address bulk crystals—screening realistic multicomponent alloys for their TE properties has yet to be accomplished. In this paper, we use a density functional theory driven HT screening-and-sorting procedure to search for new multicomponent bulk-nanostructured thermoelectric materials. We make maximum use of minimal calculations to obtain eight descriptors of the thermodynamics and TE performance of five-element semiconductor alloy systems from combinations of ternary additions in binary compounds. We use these descriptors to reduce a search space of 29 700 five-element systems to a set of 130 candidates. Lastly, we screen these candidates using TE descriptors to identify several existing high-performance thermoelectrics as well as promising new material systems awaiting further experimental verification.