| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Elastic Properties Machine-learning-based Descriptor for a Refractory High Entropy Alloy |
| Author(s) |
Guillermo Vazquez Tovar, Prashant Singh, Daniel Sauceda, Raymundo Arroyave |
| On-Site Speaker (Planned) |
Guillermo Vazquez Tovar |
| Abstract Scope |
The discovery of new materials is crucial for the development of new and existing technologies, and the infinitely big material design space may hinder better options for modern demand. In this work, we address the need for a computational model for the elastic properties of the alloy system MoNbTaVW. The proposed accurate and computationally inexpensive model is based on using elastic data from density functional theory (DFT) stress-strain calculations to build a machine learning-based descriptor: SISSO (Sure Independence Screening Sparsifying Operator). SISSO does a feature selection of a space made from combinations of atomic features. The final descriptor has an accuracy similar to experimental characterization for the elastic constants C11, and bulk modulus K. Since this method relies on the combination of physical features, the final descriptor returns a physically meaningful expression that contains relevant atomic values to tune for the desired material design. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Machine Learning, |