| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Domain and Uncertainty Quantification in Machine Learning Models of Alloy Properties |
| Author(s) |
Dane Morgan, Ryan Jacobs, Benjamin Blaiszik |
| On-Site Speaker (Planned) |
Dane Morgan |
| Abstract Scope |
Machine learning methods have the potential to dramatically expand alloy property databases through training on existing data and predicting properties for new compositions. However, it is essential to quantify the domains and uncertainties of machine learning models to develop reliable databases. In this talk, we explore a common Bayesian (Gaussian process regression) and ensemble (random forest decision trees) method for assessing domains and uncertainties using alloy diffusion data as an example. We show that Gaussian process regression is better at determining model domain than random forest, but that the random forest error bars are more accurate. We also describe the MAterials Simulation Toolkit – Machine Learning (MAST-ML) and Foundry environments that support efficient data management, machine learning model development, and accessible model dissemination. |
| Proceedings Inclusion? |
Planned: |