Designing High‐Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning

ABSTRACT Low stacking fault energy (SFE) CoCrFeNiMn‐based high entropy alloys (HEAs) have garnered widespread attention due to their excellent mechanical properties. These outstanding mechanical properties result from multiple deformation mechanisms during tensile deformation, such as stacking faults, deformation twinning, and martensitic transformation. However, the vast and complex compositional space presents a significant challenge for the design of low SFE HEAs. To address this issue, this study developed an interpretable machine learning (ML) ensemble algorithm framework that integrates three high‐accuracy ML models (multilayer perceptron regressor, support vector regressor, extreme gradient boosting regressor, R 2 > 0.9). In the alloy composition screening stage, the Valence Electron Concentration (VEC) and the proposed ML scoring parameter (Score = A*Mean + B*Std) were employed to constrain the phase composition and screen for low SFE alloy compositions. Ultimately, multiple No‐BCC phase CoCrFeNiMn‐based HEAs with twinning‐induced plasticity/transformation‐induced plasticity effects were successfully designed. To overcome the challenge of insufficient model accuracy in data‐driven design, correlation‐based and importance‐based feature selection methods were combined. This approach efficiently processed additional descriptors generated from atomic compositions, improving model accuracy by 13%. Furthermore, the Shapley additive explanation method revealed the influence of individual elements on the SFE, providing valuable guidance for designing low‐SFE HEAs.