About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Uncertainty and Domain Quantification in Machine Learning Regression Models for Materials Properties |
Author(s) |
Dane Morgan, Glenn Palmer, Lane Schultz, Yiqi Wang, Ryan Jacobs |
On-Site Speaker (Planned) |
Dane Morgan |
Abstract Scope |
In this talk we discuss our recent work on assessing uncertainties and domain for machine learning regression models that predict materials properties. We demonstrate that a simple calibrated ensemble model approach is quite accurate for predicting the standard deviation of a target value prediction for new data reasonably close to the training data (Palmer et al, npj Computation Materials, 2022). We further demonstrate that a simple distance metric on feature space can be used in conjunction with these error bars to predict when a new data point will be within the domain of a model. These approaches can be applied in a fully automated way to almost any materials property prediction model providing practical guidance on model errors and domain. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, |