| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
Machine Learning to Predict Oxidation Behavior of High-temperature Alloys |
| Author(s) |
Dongwon Shin, Rishi Pillai, Jian Peng, Marie Romedenne, Bruce Pint, J. Allen Haynes |
| On-Site Speaker (Planned) |
Dongwon Shin |
| Abstract Scope |
Due to the lack of a physics-based model and missing fundamental data on the diffusion kinetics of oxide scales, it is not yet possible to predict high-temperature oxidation of multi-component alloys in first-principles manners. We demonstrate a modern data analytic workflow that leverages high-quality experimental data, augmented with highly relevant thermodynamic and kinetic descriptors to predict alloy oxidation behavior as a function of composition and temperature. The presentation will discuss the challenges and opportunities in the proposed workflow in three aspects: 1) defining quantitative target properties to represent high-temperature alloy oxidation, 2) populating scientific alloy descriptors to capture underlying mechanisms, and 3) interrogating trained surrogate machine learning models to design advanced alloys. We use an example of cyclic oxidation of NiCr-based alloys, of which data have been consistently collected over the past few decades. The research was sponsored by the Department of Energy, Vehicle Technologies Office, Propulsion Materials Program. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |