Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential
Jacob T. Sivak, Saeed S. I. Almishal, Mary Kathleen Caucci, Yueze Tan, Dhiya Srikanth, Joseph Petruska, Matthew Furst, Long‐Qing Chen, Christina M. Rost, Jon‐Paul Maria, Susan B. Sinnott
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 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.