Assessing machine learning predictions of austenitic steel compositions for optimum mechanical response

Understanding and predicting mechanical properties such as proof stress (yield strength), ultimate tensile strength, elongation to failure, and reduction in area are essential for screening the application of austenitic stainless steels in adverse chemomechanical environments. However, experimental determination of these properties is time-consuming, labor-intensive, and costly—especially under extreme conditions, which requires advanced experimental capabilities. In this study, we leverage a large-scale, curated dataset comprising 2180 experimental entries of austenitic alloys to develop machine learning (ML) models capable of predicting these key mechanical properties as functions of composition, solution treatment condition, and testing temperature . We systematically evaluate a range of ML algorithms, namely linear regression, kernel ridge regression, extreme gradient boosting, and artificial neural network. Among these, the extreme gradient boosting achieves the highest predictive accuracy, with R 2 scores of 0.946 and 0.985 for proof stress and ultimate tensile strength, respectively. To further enhance model performance, we explore ensemble learning and transfer learning strategies. The transfer learning approach that leverages interdependencies between mechanical properties reduces the mean percentage error by 21.0% and increases R 2 score by 3.1% in predicting reduction in area, compared to the original artificial neural network model. Our results show that ML models trained on well-structured experimental data can serve as an efficient and reliable tool for exploring the effect of composition on screening metrics. This work highlights the potential of data-driven approaches to accelerate the design and optimization of high-performance austenitic alloys. Furthermore, obtaining feature importance and insights from extreme gradient boosting using Shapley Additive Explanation tool provides valuable understanding of how features contribute to the prediction of mechanical properties. • Developed machine learning models to predict key mechanical properties of austenitic stainless steels as functions of compositions, solution treatment conditions, and testing temperature, leveraging 2180 curated experimental data points. • Achieved high predictive accuracy with R 2 scores of 0.95, 0.99, 0.90, and 0.88 for yield strength, ultimate tensile strength, elongation to failure, and reduction in area, respectively. • Implemented advanced ensemble and transfer learning techniques to further enhance predictive performance beyond conventional ML models. Transfer learning leveraging knowledge from ultimate tensile strength reduced the mean absolute percentage error in predicting reduction in area by 21% compared to the baseline Artificial Neural Network (ANN) model. • Demonstrated the capability of transfer learning to effectively transfer knowledge across related mechanical property domains. • Employed SHapley Additive exPlanations (SHAP) to interpret feature importance, identifying the roles of Nb, Ti, Mo, C, N, and B in influencing strength and ductility, consistent with metallurgical understanding. • Highlighted that the developed ML models successfully capture complex relationships between composition, processing, and mechanical properties, offering a pathway to accelerate austenitic alloy design.