  Machine learning interatomic potentials have become a powerful tool to
achieve molecular dynamics (MD) simulations with the accuracy of ab initio
methods while beyond their length and timescale limitations. Here, we develop
an efficient moment tensor potential (MTP) for high-entropy diborides (HEBs)
based on unary and binary diborides with Ti-V-Cr-Zr-Nb-Mo-Hf-Ta-W principal
elements. Notably, the trained MTP exhibits exceptional generalization across
both equimolar and non-equimolar HEBs, with testing errors in energy and force
of 2.6 meV/atom and 155 meV/{\AA} for equimolar HEBs, and 3.7 meV/atom and 172
meV/{\AA} for non-equimolar HEBs, respectively, indicating its remarkable
accuracy and transferability. The reliability of the established MTP is further
confirmed by a comparative analysis with first-principles calculations, where
our MTP accurately reproduces the structural and mechanical properties of
various HEBs. The work presents a significant advancement in the simulation of
high-entropy ceramics with enhanced efficiency and accuracy.
