About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
High Entropy Alloys IX: Structures and Modeling
|
Presentation Title |
Extrapolation of Machine Learning Models for Designing Multi-principal Element Alloys |
Author(s) |
James Edward Saal, Chris Borg, Clara Nyby, Bryce Meredig |
On-Site Speaker (Planned) |
James Edward Saal |
Abstract Scope |
Machine learning is becoming an increasingly popular method for predicting metal alloy behavior, particularly for systems and properties where experimental data may be lacking. This is especially true for multi-principal element alloys (MPEAs), where the large compositional design space necessitates computational and data-driven methods to guide design. A model’s ability to extrapolate beyond the compositions on which it has been trained and predict properties for novel systems is a critical model performance question that is often overlooked and cannot simply be summarized by an R^2 value. In this work, we use MPEA data to explore metrics for model extrapolability and the importance of adding physical features to model training data. |
Proceedings Inclusion? |
Planned: TMS Journal: Metallurgical and Materials Transactions |
Keywords |
High-Entropy Alloys, |