What Has Been Achieved: A high-throughput framework leveraging simplistic computational descriptors and experimental characterization has been developed that correctly distinguishes the single-phase stability of all known rock salt high-entropy oxides to date, leading to the discovery of a novel non-equimolar composition. Dozens more compositions await experimental discovery.
Importance of the Achievement: High-entropy materials discovery has largely relied on laborious, trial-and-error synthesis attempts, as the inherent disorder makes high-throughput density functional theory predictions daunting, even with advancing computing resources. We demonstrate that modern machine-learning interatomic potentials are now accurate enough to rapidly navigate these complex thermodynamic landscapes without being fit explicitly. Leveraging these universal potentials to compute interpretable descriptors such as and computationally drives the exploration, discovery, and eventual implementation of chemical disordered materials in more complex crystal structures such as spinels and perovskites.
How is the achievement related to the IRG, and how does it help it achieve its goals? By their nature, high-entropy oxides inherently possess multi-dimensional thermodynamic landscapes that are difficult to quantify and understand. By utilizing modern advances in machine-learning we are able to efficiently compute the phase stability of these materials using very interpretable descriptors. While we only explore rock salt systems here, this self-consistent framework is currently being extended to other chemistries and bonding environments where emerging properties likely exist.
Where the findings are published: Discovering High-Entropy Oxides with a Machine-Learning Interatomic Potential. Physical Review Letters – In Press. Jacob T. Sivak, Saeed S. I. Almishal, Mary K. Caucci, Yueze Tan, Dhiya Srikanth, Joseph Petruska, Matthew Furst, Long-Quin Chen, Christina M. Rost, Jon-Paul Maria, Susan B. Sinnott. https://doi.org/10.1103/PhysRevLett.134.216101