| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Advanced Data SCiENce Toolkit for Non-data Scientists (ASCENDS)
- A Case Study of the Oxidation Kinetics of NiCr-based Alloys |
| Author(s) |
Jian Peng, Rishi Pillai, Marie Romedenne, Sangkeun Lee, Govindarajan Muralidharan, Bruce A. Pint, J. Allen Haynes, Dongwon Shin |
| On-Site Speaker (Planned) |
Jian Peng |
| Abstract Scope |
We introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), developed to enable data-driven materials research. The toolkit can analyze the correlation between input features and target properties, train machine learning (ML) models, and make predictions with the trained surrogate models. We introduce the use of ASCENDS to predict the oxidation kinetics of NiCr-based alloys as a function of alloy chemistry and temperature. We compare two different oxidation models (a simple parabolic law and a statistical cyclic-oxidation model) to represent the high-temperature oxidation kinetics of NiCr-based alloys in dry- and wet-air within the context of data analytics. Understanding the oxidation characteristics correlated with the features will support and promote new alloy development with further improved performance. This research was sponsored by the U. S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Propulsion Materials Program. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Temperature Materials, Machine Learning, Computational Materials Science & Engineering |