Predicting Mechanical and Thermal Properties of High‐Entropy Ceramics via Transferable Machine‐Learning‐Potential‐Based Molecular Dynamics

Abstract The mechanical and thermal performance of high‐entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high‐entropy ceramics significantly hinders their development with desired mechanical and thermal properties. Herein, taking high‐entropy carbides (HECs) as the model, the efficiency and effectiveness of predicting mechanical and thermal properties via transferable machine‐learning‐potential‐based molecular dynamics (MD) have been demonstrated. Specifically, a transferable neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB‐VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well‐established transferable NEP, MD predictions on mechanical and thermal properties of different HECs have shown good agreement with the results of first‐principles calculations and experimental measurements, validating the accuracy, transferability, and reliability of using the transferable machine‐learning‐potential‐based MD simulations in investigating mechanical and thermal performance of HECs. This work provides a strategy to accelerate the search for high‐entropy ceramics with desirable mechanical and thermal properties.