Data Science in Order and Disorder of High-Entropy Materials

Abstract

In recent years, high-entropy materials (HEMs) have garnered significant attention due to their unique multi-principal element compositions, which endow them with remarkable properties distinct from traditional materials. The order and disorder in HEMs are particularly complex, influenced by factors such as temperature, pressure, and composition, and are closely related to their mechanical and physical properties. This review systematically summarizes the progress in understanding the order and disorder in HEMs, with a focus on the role of data science in this field. We introduce the basic concepts of order and disorder and the related research in HEMs, discuss the nonlinear behaviors of HEMs, and elaborate on the relevant applications of data science, including analysis by machine learning, molecular dynamics simulations, and Monte Carlo simulations. Challenges and future directions are also explored, aiming to provide comprehensive insights into materials science.