  Thermal boundary conductance (TBC) across metal diamond interfaces plays a
critical role in the thermal management of future diamond based ultrawide
bandgap semiconductor devices. Molecular dynamics is a sophisticated method to
predict TBC but is limited by the lack of reliable potential describing metal
diamond interfaces. In this work, we report the development of machine learning
interatomic potentials and the prediction of TBCs of several technologically
promising metal diamond interfaces using nonequilibrium molecular dynamics. The
predicted TBCs of Al, Zr, Mo, and Au-diamond interfaces are approximately 316,
88, 52, and 55 MW/m2K, respectively, after quantum corrections. The
corresponding thermal boundary resistances are equivalent to 0.8 {\mu}m thick
of Al, 1.4 {\mu}m Mo, 0.3 {\mu}m Zr, and 5.3 {\mu}m Au, respectively. We also
find that the conventional simple models, such as the acoustic mismatch model
and diffuse mismatch model, even including the full-band phonon dispersion from
first principles, largely misestimate the TBC values because of their inability
to include inelastic transmission as well as interfacial structural and bonding
details. The quantum-corrected TBC values for the metal diamond interfaces
correlate well with the quantum corrected phonon specific heat of metals,
instead of diamond. Additionally, our comparative analysis of Debye temperature
and elastic modulus in these systems reveals that the former parameter
correlates more strongly with the TBC than the latter. These low TBC values
need to be considered in future diamond based semiconductor devices.
