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

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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Cited by in corpus (6)

• Thermodynamics-Inspired High-Entropy Oxide Synthesis
• A statistical understanding of oxygen vacancies in distorted high-entropy oxides
• Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
• Maximizing solubility in rock salt high-entropy oxides
• Extreme disorder in crystalline perovskite oxide: a new paradigm in quantum materials research
• Anomalous Crystallinity and Magnetism in Chemically Disordered Coherent Heterostructures