| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Expanding Materials Selection via Transfer Learning for High-temperature Oxide Selection |
| Author(s) |
Zachary D. Mcclure, Alejandro Strachan |
| On-Site Speaker (Planned) |
Zachary D. Mcclure |
| Abstract Scope |
Complex concentrated alloys (CCAs) with higher operating temperatures than today's current alloys can improve system performance in several applications. While the strength properties of many CCAs outperform Ni-based superalloys, the oxidation properties are not ideal. Selecting an appropriate oxide scale with high melting temperatures, thermodynamic stability, and low ionic diffusivity is critical for alloy development.
While some properties exist for many oxides, available melting temperature data is limited. The determination of melting temperatures is time consuming and costly, both experimentally and computationally. Instead we use data science tools to develop predictive models from existing data. The relatively small number of available melting temperature values precludes the use of standard tools; therefore, we use a multi-step approach via sequential learning where first principles data is leveraged to develop more appropriate models. The models are used to predict the desired properties for nearly 11,000 oxides and quantify uncertainties in the space. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, High-Temperature Materials, Machine Learning |