| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Powder and Ceramic Materials Science
|
| Presentation Title |
Discovery of Novel High-entropy Ceramics via Machine Learning |
| Author(s) |
Kevin Kaufmann, William Mellor, Tyler J. Harrington, Chaoyi Zhu, Alexander S. Rosengarten, Daniel Maryanovsky, Kenneth S. Vecchio |
| On-Site Speaker (Planned) |
Kevin Kaufmann |
| Abstract Scope |
Although high-entropy materials are attracting considerable interest due to a combination of useful properties and promising applications, predicting their formation remains a hindrance for rational discovery of new systems. Experimental approaches are based on intuition and/or expensive trial and error strategies. Most computational methods rely on the availability of sufficient experimental data and computational power. This work proposes a machine learning framework leveraging thermodynamic and compositional attributes of a given material for predicting the entropy-forming ability of disordered metal carbides. The approach’s suitability is demonstrated by comparing values calculated with density functional theory to ML predictions. Finally, the model is employed to predict the entropy-forming ability of new compositions; several of which are validated by additional density functional theory calculations and experimental synthesis. Compositions were specifically selected because they contain all three of the Group VI elements (Cr, Mo, and W), which do not form room temperature-stable rock-salt monocarbides. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Ceramics, Machine Learning, Computational Materials Science & Engineering |