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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 Steel S-N curve prediction via domain knowledge guided LLM-assisted symbolic regression
Author(s) Luchen DENG, Junya Inoue
On-Site Speaker (Planned) Luchen DENG
Abstract Scope Accurate prediction of steel S–N curves remain a major challenge because fatigue behavior depends on complex interactions among composition, microstructure, processing conditions, and mechanical properties. Existing data-driven approaches primarily focus on predicting fatigue limits or estimating fatigue life using empirical equations, providing limited interpretability and little insight into the underlying fatigue mechanisms. In this research, a novel symbolic regression (SR) based machine learning approach integrated with the assistance of large language models is proposed to extract a more explainable and robust relationship for the prediction of steel S-N curves. Materials domain knowledge is incorporated into large language model (LLM) guided model selection process of symbolic regression to mitigate the overfitting problems and enhance the explainability of the final model. The resulting symbolic expressions are further analyzed to provide insights into the governing mechanisms of steel fatigue and to improve the interpretability of data-driven fatigue modeling.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Mechanical Properties, Iron and Steel

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