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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

A Multi-objective Bayesian Optimization Framework for Predicting Grain-Boundary Segregation in High-Entropy Alloys

Author(s)

Shimanta Das, Noah Oyeniran, Joshua Walter, Aidan Gesch, Chongze Hu

On-Site Speaker (Planned)

Shimanta Das

Abstract Scope

High-entropy alloys (HEAs) exhibit concurrent segregation (co-segregation) of multiple elements at grain boundaries (GBs), which significantly influences microstructural evolution and different properties of HEAs. While many experimental studies and atomistic simulations confirm this behavior, limited efforts have been made to optimize HEA compositions for targeted co-segregation, due to vast compositional space and complex interactions among multiple solute elements. In this study, we developed a Scalarized Bayesian Optimization (SBO) framework combining Gaussian Process modeling and Thompson Sampling to identify the CrMnFeCoNi compositions that maximize or minimize co-segregation of Cr and Mn at GBs. The SBO predictions were validated using molecular dynamics simulations and first-principles calculations. Additionally, the SBO model predicted GB disorder, further verified through MD analysis. Although not universally transferable across all HEA systems, SBO demonstrates strong predictive capability when supported by suitable datasets.

Proceedings Inclusion?

Planned:

Keywords

High-Entropy Alloys, Machine Learning, Other

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