About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu
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Presentation Title |
Coupling Physics in Data-driven High-temperature Alloys Design via High-throughput CALPHAD |
Author(s) |
Dongwon Shin, Jian Peng, Yukinori Yamamoto, Michael Brady, J. Allen Haynes, Sunyong Kwon |
On-Site Speaker (Planned) |
Dongwon Shin |
Abstract Scope |
It was recently demonstrated that augmenting raw experimental high-temperature alloy datasets consisting of only elemental compositions and measured properties with CALPHAD-derived synthetic features can efficiently introduce physics into data science. We will present a modern data-driven alloy design workflow that streamlines experimental data curation, high-throughput CALPHAD data augmentation, quantitative correlation analysis for feature selection, surrogate model training, predicting properties of hypothetical alloys, and rapidly screening alloy compositions worthy of experimental validation. We will also discuss challenges and opportunities to further improve the proposed alloy design workflow. This research was supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy (DOE) and the Vehicle Technologies Office Materials Program, U.S. DOE. |
Proceedings Inclusion? |
Planned: |