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 |
Alloy Design Based on Automated CALPHAD Composition Search and Machine Learning |
Author(s) |
Alan A. Luo |
On-Site Speaker (Planned) |
Alan A. Luo |
Abstract Scope |
The last few decades have seen alloy design approach migrated from experimental exploration to CALPHAD-based computational methods. This talk introduces a Python-based program to automatically search for compositions that can meet user-defined requirements, such as phase constitution, transformation temperatures and phase fractions. This program can be coupled to CALPHAD software to access and calculate all thermodynamic variables for automated composition search. This powerful tool can be applied to conventional and high entropy alloy design. Furthermore, a machine learning algorithm has been developed to design lightweight high entropy alloys with promising strength and ductility. |
Proceedings Inclusion? |
Planned: |
Keywords |
ICME, Computational Materials Science & Engineering, Machine Learning |