| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Understanding High Entropy Materials via Data Science and Computational Approaches
|
| Presentation Title |
Screening High-Entropy Oxide Compositions Using Machine Learned Interatomic Potential |
| Author(s) |
Jacob Tyler Sivak, Saeed S.I. Almishal, Yueze Tan, Mary Kate Caucci, Matthew Furst, Dhiya Srikanth, Long-Quin Chen, Christina Rost, Jon-Paul Maria, Susan B Sinnott |
| On-Site Speaker (Planned) |
Jacob Tyler Sivak |
| Abstract Scope |
High-entropy oxides (HEOs) are characterized by populating the cation sublattice with many elements at random, resulting in extreme chemical disorder at the atomic scale. Discovery of new HEOs has been a laborious endeavor largely driven by experimental trial-and-error, where commonly-used computational approaches such as density functional theory are still far too expensive for adequate exploration of composition space. We have leveraged recent advances in machine learning interatomic potentials to circumnavigate the high-dimensional composition space of rocksalt HEOs. Formulated descriptors using these simulations allow for all rocksalt HEO compositions to be correctly identified as both single- and multi-phase from experimental results. This work predicts a multitude of unexplored HEO compositions awaiting discovery, as well as provides a framework for extension to other crystal systems and elements. |