  Calculating sublimation enthalpies of molecular crystal polymorphs is
relevant to a wide range of technological applications. However, predicting
these quantities at first-principles accuracy -- even with the aid of machine
learning potentials -- is a challenge that requires sub-kJ/mol accuracy in the
potential energy surface and finite-temperature sampling. We present an
accurate and data-efficient protocol based on fine-tuning of the foundational
MACE-MP-0 model and showcase its capabilities on sublimation enthalpies and
physical properties of ice polymorphs. Our approach requires only a few tens of
training structures to achieve sub-kJ/mol accuracy in the sublimation
enthalpies and sub 1 % error in densities for polymorphs at finite temperature
and pressure. Exploiting this data efficiency, we explore simulations of
hexagonal ice at the random phase approximation level of theory at experimental
temperatures and pressures, calculating its physical properties, like pair
correlation function and density, with good agreement with experiments. Our
approach provides a way forward for predicting the stability of molecular
crystals at finite thermodynamic conditions with the accuracy of correlated
electronic structure theory.
