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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

Transformer-Based Prediction of Mechanical Properties in CoCrCuFeNi High-Entropy Alloys

Author(s)

Soonyeong Jung, Hongchul Shin, Jongwook Kim, Taeyoung Yoon

On-Site Speaker (Planned)

Soonyeong Jung

Abstract Scope

High-entropy alloys (HEAs) offer remarkable potential for mechanical performance optimization through compositional tuning. However, predicting properties such as ultimate tensile strength (UTS), Young’s modulus, and dislocation density from chemical composition remains challenging due to complex, nonlinear interactions among elements. This study proposes a Transformer-based deep learning model to predict these properties of equiatomic CoCrCuFeNi HEAs using elemental composition as input. A dataset of 233 unique compositions enables the model to learn inter-element dependencies via attention mechanisms. The model predicts multiple properties simultaneously, including dislocation density, providing a unified and scalable estimation approach. Attention scores enhance interpretability by revealing each element’s contribution to target properties and guiding rational alloy design. This approach highlights the potential of Transformer architectures as effective tools for accelerating inverse design and high-throughput screening in materials informatics, bridging composition and mechanical behavior in complex alloys.

Proceedings Inclusion?

Planned:

Keywords

Modeling and Simulation, High-Entropy Alloys, Modeling and Simulation

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