Mapping the configuration space of half-Heusler compounds <i>via</i> subspace identification for thermoelectric materials discovery
New high-throughput search strategies based on statistical identification of promising chemical subspaces show potential for accelerating half-Heusler thermoelectric materials discovery.
Authors
- Anna Pak
- Eric S. Toberer (ORCID: https://orcid.org/0000-0003-0826-2446)
- Elif Ertekin (ORCID: https://orcid.org/0000-0002-7816-1803)
- Kamil Ciesielski (ORCID: https://orcid.org/0000-0002-9787-5967)
- Maria Wróblewska
Institutions
- University of Illinois Urbana-Champaign (US)
- Colorado School of Mines (US)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta00397k
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- National Science Foundation
- Office of Advanced Cyberinfrastructure