High-throughput data-driven machine learning prediction of thermal expansion coefficients of high-entropy solid solution carbides

Recent advances in machine learning and the expanding availability of materials data have enabled significant developments in materials science. In this study, novel configurations of high-entropy ceramic (HEC) materials were explored by predicting their coefficient of thermal expansion (CTE) using machine learning (ML) and high-throughput screening. A machine learning model was built using 3360 datasets containing the thermodynamic, elastic, and thermophysical properties of HEC with carbide configurations of (Ti _0.2Ta _0.2X _0.2Y _0.2Z _0.2 )C. The high correlation of the bulk and Young`s moduli, and cohesive energy features with the CTE facilitated its prediction. The random forest (RF) and neural network (NET)-based models successfully reproduced the CTE reported in existing experimental and theoretical studies. Overall, first-principles calculation was implemented to configure a database for HEC and a new ML application method is proposed.