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.
@article{arxiv.2408.06322,
title = {Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential},
author = {Jacob T. Sivak and Saeed S. I. Almishal and Mary K. Caucci and Yueze Tan and Dhiya Srikanth and Matthew Furst and Long-Quin Chen and Christina M. Rost and Jon-Paul Maria and Susan B. Sinnott},
journal= {arXiv preprint arXiv:2408.06322},
year = {2025}
}