About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
2026 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-35: Computational Discovery of Medium-Entropy SMAs Using CALPHAD, Machine Learning, and Economic Analysis |
| Author(s) |
Sravya Josyula, Ayobami Oladipo, Ugochukwu Ochieze, Joshua Maile, Yuval Noiman, Nicholas Simpson, Eric Payton |
| On-Site Speaker (Planned) |
Yuval Noiman |
| Abstract Scope |
High-temperature shape memory alloys (SMAs) are essential for advanced actuation in aerospace and energy systems, yet their continued development is constrained by several challenges. This study presents a data-driven framework for discovery and multi-objective optimization of SMA compositions that integrates high-throughput CALPHAD with machine learning (ML). The framework is used to identify regions of B2 phase which have potential for exhibiting transitions to the B19’ phase upon cooling. An Extra Trees regression model was trained to predict martensitic and austenitic transformation temperatures. Compositions were selected for experimental validation using cost and supply chain risk. Selected compositions were synthesized via arc melting for experimental validation. The integrated approach enables rapid exploration of complex compositional spaces. Strengths and limitations of ML in the alloy optimization context are discussed, along with prospects of such a framework as a scalable pathway for accelerating the discovery and optimization of high-performance SMAs. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, Characterization |