Predicting the stacking fault energy in FCC high-entropy alloys based on data-driven machine learning

Abstract

The properties of high-entropy alloys (HEAs) depend primarily on the composition and content of elements. However, getting the optimal composition of alloying elements through the traditional ''trial and error'' method is challenging, especially for non-equiatomic HEAs with a wide range of composition space. In this study, based on the knowledge that stacking fault energy (SFE) is the most crucial intrinsic property to determine the deformation mechanism and to optimize the mechanical properties of FCC HEAs, classical machine learning classification models including support vector classification (SVC) and random forest (RF), and deep learning regression model (Back Propagation Neural Network) were established to predict the stacking fault energy of Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloys. These models can obtain the SFE data of any atomic ratio composition of the FCC structured Co–Cr–Fe–Mn–Ni–V–Al high-entropy alloy quickly and accurately. The high accuracy of these models indicates that using the compositions as features to predict stacking fault energy is feasible. Meanwhile, the monotonic relationship between alloying elements and SFE makes it possible to change the SFE of high-entropy alloy by fine-tuning the composition to realize the control of material deformation mechanism and mechanical properties. Component-based machine learning models provide a new method for rapidly discovering high-entropy alloys with exceptional strength and flexibility.

Keywords

• High-entropy alloys
• Stacking fault energy
• Machine learning
• Alloy design