Prediction and Rational Design of Stacking Fault Energy of Austenitic Alloys Based on Interpretable Machine Learning and Chemical Composition

Abstract

Accurately predicting the stacking fault energy (SFE), as one of the crucial factors influencing the material deformation mechanism, is a focal point in research. This study utilizes measured SFE values from the literature on austenitic alloys to establish a predictive model for the relationship between chemical composition and SFE using machine learning techniques. Among five compared machine learning algorithms, the extremely randomized trees algorithm demonstrates the highest prediction accuracy. Incorporating atomic features further enhances the model's performance. Subsequently, feature selection is conducted using correlation analysis, recursive elimination, and exhaustive search to obtain the optimal subset of features. The interpretability of the model is analyzed using Shapley additive explanations values, providing rankings of feature importance and critical thresholds for feature influence. Finally, rational design of SFE is demonstrated by adjusting alloy composition, exemplified by Fe–17.5Cr–15.6Ni–2.5Mo–0.02Si–0Co steel.