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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title An informatics approach to predict shape memory effect in CrMnFeCoNi high-entropy alloys
Author(s) Jinsurang Lim, Je In Lee, Dongwon Shin
On-Site Speaker (Planned) Jinsurang Lim
Abstract Scope Shape memory effect (SME) has recently been reported in CrMnFeCoNi high-entropy alloys, suggesting potentials for functional applications beyond structural use. However, efficient exploration of the high-dimensional space remains challenging, limiting systematic understanding and optimization of shape memory behavior. Data-driven approaches have recently accelerated the discovery of multicomponent shape memory alloys (SMAs). Here, we developed an informatics framework for exploring CrMnFeCoNi SMAs. Owing to the limited SME data in the CrMnFeCoNi system, we used in-house datasets obtained from thermomechanical analysis (TMA). We found that models trained directly on SME metric showed poor predictive accuracy, likely due to the intrinsically complex nature of SME behaviors governed by phase stability and microstructures. To address this limitation, we instead trained models on dilatation curves, which improved predictive accuracy and enabled robust estimation of SME responses. The proposed informatics framework can be extended to the discovery of other multicomponent SMAs.
Proceedings Inclusion? Planned:
Keywords High-Entropy Alloys, Phase Transformations,

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