  Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for
simulations beyond length and timescales of ab initio methods. Their
development for investigation of mechanical properties and fracture, however,
is far from trivial since extended defects -- governing plasticity and crack
nucleation in most materials -- are too large to be included in the training
set. Using TiB$_2$ as a model ceramic material, we propose a strategy for
fitting MLIPs suitable to simulate mechanical response of monocrystals until
fracture. Our MLIP accurately reproduces ab initio stresses and failure
mechanisms during room-temperature uniaxial tensile deformation of TiB$_2$ at
the atomic scale ($\approx{10}^3$ atoms). More realistic tensile tests (low
strain rate, Poisson's contraction) at the nanoscale ($\approx{10}^4$--10$^6$
atoms) require MLIP up-fitting, i.e. learning from additional ab initio
configurations. Consequently, we elucidate trends in theoretical strength,
toughness, and crack initiation patterns under different loading directions. To
identify useful environments for further up-fitting, i.e., making the MLIP
applicable to a wider spectrum of simulations, we asses transferability to
other deformation conditions and phases not explicitly trained on.
