Author: M.Kim^†, J.Kim^†, H.Kim and J.Kim^*

Title: High-Throughput Data-Driven Machine Learning Prediction of Thermal Expansion Coefficients of High-Entropy Solid Solution Carbides

Journal: International Journal of Refractory Metals and Hard Materials

Year: 2024

Impact factor: 3.6

Abstract:

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 (Ti0.2Ta0.2 × 0.2Y0.2Z0.2)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.