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arXiv · 2408.06322

Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential

Jacob T. Sivak·Saeed S. I. Almishal·Mary K. Caucci·Yueze Tan·Dhiya Srikanth·Matthew Furst·Long-Quin Chen·Christina M. Rost·Jon-Paul Maria·Susan B. Sinnott
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Abstract

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 rock salt 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 rock salt compositions are correctly resolved, with dozens more compositions awaiting discovery.

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Jacob T. Sivak, Saeed S. I. Almishal, Mary K. Caucci, Yueze Tan, Dhiya Srikanth, Matthew Furst, Long-Quin Chen, Christina M. Rost, Jon-Paul Maria, Susan B. Sinnott. 2024-08-12. Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential. https://doi.org/10.1103/physrevlett.134.216101

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