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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Efficient Exploration of Shape Memory Alloy Process-Composition Space Using Multi-Objective Bayesian Optimization

Author(s)

Dylan Winer, Jihye Hur, Michael Buzzy, Surya Kalidindi, Aaron Stebner

On-Site Speaker (Planned)

Dylan Winer

Abstract Scope

Designing Nickel-Titanium-Hafnium (NiTiHf) Shape Memory Alloys (SMAs) for aerospace actuation requires minimizing both thermal hysteresis (ΔT) and mean transformation temperature (T). To accelerate discovery beyond costly, time-intensive trial-and-error methods, we developed a scalable multi-objective Bayesian optimization (MOBO) framework using BoTorch and NASA’s SMA dataset. Our approach integrates noisy expected hypervolume improvement acquisition, dynamic reference point adjustment, and Upper Confidence Bound filtering to down-select alloy-processing candidates from millions of possibilities. The Gaussian Process models provide meaningful predictions, converging to the known ΔT–T Pareto front with minimal data. Selected candidates were fabricated via arc melting, wire EDM, and heat treatment, and are undergoing differential scanning calorimetry testing. This work demonstrates the effectiveness of data-efficient MOBO for exploring high-dimensional alloy design spaces and advancing next-generation SMAs for aerospace applications, enabling more adaptive and thermally responsive actuation systems that perform reliably under extreme environmental conditions encountered in flight.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Phase Transformations, High-Temperature Materials

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