  Tungsten (W) is a material of choice for the divertor material due to its
high melting temperature, thermal conductivity, and sputtering threshold.
However, W has a very high brittle-to-ductile transition temperature and at
fusion reactor temperatures ($\geq$1000K) may undergo recrystallization and
grain growth. Dispersion-strengthening W with zirconium carbide (ZrC) can
improve ductility and limit grain growth, but much of the effects of the
dispersoids on microstructural evolution and thermomechanical properties at
high temperature are still unknown. We present a machine learned Spectral
Neighbor Analysis Potential (SNAP) for W-ZrC that can now be used to study
these materials. In order to construct a potential suitable for large-scale
atomistic simulations at fusion reactor temperatures, it is necessary to train
on ab initio data generated for a diverse set of structures, chemical
environments, and temperatures. Further accuracy and stability tests of the
potential were achieved using objective functions for both material properties
and high temperature stability. Validation of lattice parameters, surface
energies, bulk moduli, and thermal expansion is confirmed on the optimized
potential. Tensile tests of W/ZrC bicrystals show that while the
W(110)-ZrC(111) C-terminated bicrystal has the highest ultimate tensile
strength (UTS) at room temperature, observed strength decreases with increasing
temperature. At 2500K, the terminating C layer diffuses into the W, resulting
in a weaker W-Zr interface. Meanwhile, the W(110)-ZrC(111) Zr-terminated
bicrystal has the highest UTS at 2500K.
