  Determining thermal and physical quantities across a broad temperature
domain, especially up to the ultra-high temperature region, is a formidable
theoretical and experimental challenge. At the same time it is essential for
understanding the performance of ultra-high temperature ceramic (UHTC)
materials. Here we present the development of a machine-learning force field
for ZrB2, one of the primary members of the UHTC family with a complex bonding
structure. The force field exhibits chemistry accuracy for both energies and
forces and can reproduce structural, elastic and phonon properties, including
thermal expansion and thermal transport. A thorough comparison with available
empirical potentials shows that our force field outperforms the competitors.
Most importantly, its effectiveness is extended from room temperature to the
ultra-high temperature region (up to ~ 2,500 K), where measurements are very
difficult, costly and some time impossible. Our work demonstrates that
machine-learning force fields can be used for simulations of materials in a
harsh environment, where no experimental tools are available, but crucial for a
number of engineering applications, such as in aerospace, aviation and nuclear.
