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 mechanic 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 only using a chemical formula. Our models demonstrate low root mean square error (11.5GPa) and mean absolute errors (9.0GPa) in predicting the mechanic properties of HECs with arbitrary non-equimolar compositions. We further establish a database of 566,370 HECs and identified 15 novel HECs with 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.