  Molecular dynamics (MD) simulations are considered an efficient and low-cost
means to develop remarkable properties of high-entropy ceramics with vast
composition space, yet the lack of general potentials severely limits their
applications. Herein, taking high-entropy carbides (HECs) as the model, we
propose a strategy to efficiently construct a general neuroevolution potential
(NEP) with broad compositional applicability for HECs. Specifically, the small
dataset comprising unary and binary carbides with ten transition metal
principal elements is first identified as the most efficient choice to train
the general NEP for HECs, and then a highly accurate and transferable NEP is
constructed. Further MD predictions on structural, mechanical, and thermal
properties of HECs using the established NEP show good agreement with the
results of first-principles calculations and experimental measurements,
validating the accuracy, generalization, and reliability of our developed NEP.
Our work provides an efficient solution to accelerating the MD simulations
searching for high-entropy ceramics with desirable properties.
