Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential
High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rocksalt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rocksalt compositions are correctly resolved, with dozens more compositions awaiting discovery.
Authors
- Jon‐Paul Maria (ORCID: https://orcid.org/0000-0003-3604-4761)
- Christina M. Rost (ORCID: https://orcid.org/0000-0002-6153-6066)
- Mary Kathleen Caucci (ORCID: https://orcid.org/0000-0003-4306-5114)
- Saeed S. I. Almishal (ORCID: https://orcid.org/0000-0003-2964-6000)
- Susan B. Sinnott (ORCID: https://orcid.org/0000-0002-3598-0403)
- Jacob T. Sivak (ORCID: https://orcid.org/0000-0001-9464-3821)
- Long‐Qing Chen (ORCID: https://orcid.org/0000-0003-3359-3781)
- Yueze Tan
- Dhiya Srikanth
- Matthew Furst
- Joseph Petruska
Institutions
- Pennsylvania State University (US)
- Virginia Tech (US)
Publication Details
- Journal
- Physical Review Letters
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1103/physrevlett.134.216101
- Citations
- 18
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 9.32
Funders
- National Science Foundation
- Pennsylvania State University