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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

Influence of Microstructure on Mechanical Properties of High Entropy Alloys: A Physics-Informed Machine learning approach

Author(s)

Mrinalini Mulukutla, Wenle Xu, Bibhu Prasad Sahu, Shakti Prasad Padhy, Vahid Attari, Ibrahim Karaman, Raymundo Arroyave

On-Site Speaker (Planned)

Mrinalini Mulukutla

Abstract Scope

Predicting the mechanical performance of multi-component alloys requires models that integrate metallurgical principles while leveraging data-driven techniques. In this work, we develop and evaluate a suite of regression models, compare the strategies including a classical Hall-Petch model as a baseline, full multivariate ordinary least squares (OLS) with composition, processing and solid solution hardening (SSH) term, and PCA-based regression models using physics-informed alloy descriptors, to predict vickers hardness and yield strength of HEA system and compared them against non-linear benchmarks. All models are evaluated by 5-fold cross-validation of R^2 and MSE. The best-performing models incorporate physics-inspired features and dimensionality reduction: a PCA on selected-features model, while the high-dimensional physics-derived descriptor model overfits. Our results show that embedding metallurgical insights into regression yields transparent models and also illustrate how interpretable, physics-informed regression can balance model accuracy and statistical soundness, guiding alloy design by highlighting the interplay of microstructure and chemistry.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, High-Entropy Alloys, Mechanical Properties

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