Abstract
The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. The performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.
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Acknowledgements
This research was sponsored by the Department of Energy, Energy Efficiency and Renewable Energy, Vehicle Technologies Office (VTO), Powertrain Materials Core Program. This research used resources of the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory, which is supported by the DOE Office of Science under Contract No. DE-AC05-00OR22725. The authors thank Chris Layton for his support with using CADES.
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Peng, J., Yamamoto, Y., Brady, M.P. et al. Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys. JOM 73, 164–173 (2021). https://doi.org/10.1007/s11837-020-04423-x
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DOI: https://doi.org/10.1007/s11837-020-04423-x