Prediction of mechanical properties of high-entropy ceramics by deep learning with compositional descriptors

High-entropy ceramics (HECs) have garnered considerable research attention due to their unique characteristics and potential applications in extreme environmental conditions. As numerous elements can act as constituent materials for HECs, the trial-and-error approach as well as the computational approach are time-consuming and expensive for developing new HECs. In this study, we developed a deep learning model using a convolutional neural network (CNN) based on only the compositional descriptors of the constituent elements, e.g., rock-salt carbides, nitrides, and carbonitrides, to predict the corresponding mechanical properties of bulk (K), shear (G) and Young (Y) moduli. For comparison, XGboost, one of the ensemble algorithms, and an artificial neural network (ANN) were applied to predict the mechanical properties of HECs. The CNN models demonstrated the highest performance metrics of determination coefficient (R2 =0.856, 0.909, and 0.921 for K, G, and Y, respectively) and low root–mean–square errors (RMSE = 19.129, 14.540, and 31.929 GPa for K, G, and Y, respectively), which were consistent with the experimental results reported in previous studies. We further employed the CNN models to high-hardness HECs, evaluated their hardness (H) based on the empirical formulae of Tian, Chen, and Teter’s models, and compared them with the results reported in the existing literature. The hardness calculated using Teter’s model displayed excellent agreement with the experimental results for higher hardness HECs (H ≥ 27 GPa), while the estimated values by Tian’s models are consistent with the experimental results for lower hardness HECs (H < 27 GPa).