Predicting mechanical properties of non-equimolar high-entropy carbides using machine learning

Abstract

High-entropy carbides (HECs) have garnered significant attention due to their unique mechanical properties. However, the design of novel HECs has been limited by extensive trial-and-error strategies, along with insufficient knowledge and computational capabilities. In this work, the intrinsic correlations between elements in the high-dimensional compositional space of HECs are investigated using high-throughput density functional theory calculations and two machine learning models, which enable us to predict the Young's modulus, hardness and wear resistance with only a chemical formula provided. Our models demonstrate a low root mean square error (11.5 GPa) and mean absolute error (9.0 GPa) in predicting the elastic modulus of HECs with arbitrary non-equimolar compositions. We further established a database of 566 370 HECs and identified 15 novel HECs with the best mechanical properties. Our models can rapidly explore the mechanical properties of HECs with descriptor–property correlation analysis, and hence provide an efficient method for accelerating the design of non-equimolar high-entropy materials with desired performance.