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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

A Comparative Study of Ensemble Machine Learning Models for Nanoindentation Hardness and Elastic Modulus of Ion-irradiated HEAs

Author(s)

Mohammad Shafinul Haque, Md Kamal Hossain, Christopher Antu, Breanna Mead, Abraham Burleigh, Kavin Ammigan, Adrien Couet, Nicholas Crnkovich

On-Site Speaker (Planned)

Mohammad Shafinul Haque

Abstract Scope

Next-generation multi-megawatt accelerator target facilities require novel materials that can perform at elevated beam power and be resistant to beam-induced radiation and thermal shock damage. Qualifying high-entropy alloys (HEAs) requires extensive mechanical testing; however, machine learning can accelerate this process by modeling mechanical behavior. This presentation compares the performance of several ensemble and neural-network machine learning models in predicting the mechanical properties (nanoindentation hardness and modulus) of a six-element AlCoCrMnTiV HEA. Building upon the Gradient Boosting (GB) baseline (R² = 0.84), the study explores Random Forest, XGBoost, LightGBM, and Neural Networks' performance using composition and displacement per atom (dpa) damage levels as inputs. Data pre-processing and correlation analysis are performed to optimize model inputs. Next, a stacked ensemble approach is evaluated to leverage these base learners and surpass the baseline model. Finally, the models are ranked using error metrics, learning curves, five-fold cross-validation, and predictions on a new validation dataset.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, High-Entropy Alloys, Mechanical Properties

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