Computational prediction of nanostructured alloys with enhanced thermoelectric properties
Abstract
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.
- Publication Date:
- Research Org.:
- Northwestern Univ., Evanston, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1776853
- Grant/Contract Number:
- SC0014520
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review Materials
- Additional Journal Information:
- Journal Volume: 3; Journal Issue: 10; Journal ID: ISSN 2475-9953
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
Citation Formats
Doak, Jeff W., Hao, Shiqiang, Kirklin, Scott, and Wolverton, Christopher. Computational prediction of nanostructured alloys with enhanced thermoelectric properties. United States: N. p., 2019.
Web. doi:10.1103/physrevmaterials.3.105404.
Doak, Jeff W., Hao, Shiqiang, Kirklin, Scott, & Wolverton, Christopher. Computational prediction of nanostructured alloys with enhanced thermoelectric properties. United States. https://doi.org/10.1103/physrevmaterials.3.105404
Doak, Jeff W., Hao, Shiqiang, Kirklin, Scott, and Wolverton, Christopher. Tue .
"Computational prediction of nanostructured alloys with enhanced thermoelectric properties". United States. https://doi.org/10.1103/physrevmaterials.3.105404. https://www.osti.gov/servlets/purl/1776853.
@article{osti_1776853,
title = {Computational prediction of nanostructured alloys with enhanced thermoelectric properties},
author = {Doak, Jeff W. and Hao, Shiqiang and Kirklin, Scott and Wolverton, Christopher},
abstractNote = {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.},
doi = {10.1103/physrevmaterials.3.105404},
journal = {Physical Review Materials},
number = 10,
volume = 3,
place = {United States},
year = {Tue Oct 08 00:00:00 EDT 2019},
month = {Tue Oct 08 00:00:00 EDT 2019}
}
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