Machine learning-guided plasticity model in refractory high-entropy alloys

Abstract

Refractory high-entropy alloys (RHEAs) represent a promising class of structural materials with significant potential for various applications. However, their limited plasticity at room temperature restricts their deformability, posing challenges for processing and industrial implementation. Traditional experimental methods for characterizing this property are time-consuming and resource-intensive, necessitating the development of efficient predictive models. In this study, we propose a machine learning approach to predict the fracture strain of RHEAs. A dataset comprising 128 RHEAs fracture strain samples is compiled from the literature and classified into two categories: “high plasticity” and “low plasticity.” Through feature selection techniques, a critical subset of features is identified, enabling a support vector classification model to achieve 96% prediction accuracy. Additionally, an interpretable machine learning algorithm is employed to derive explicit functional expressions describing the relationship between key features and fracture strain, achieving 88% accuracy. Although slightly less accurate, it provides valuable insights into the underlying mechanisms, making it a useful tool for materials design and optimization.