  Predictions of relative stabilities of (competing) molecular crystals are of
great technological relevance, most notably for the pharmaceutical industry.
However, they present a long-standing challenge for modeling, as often
minuscule free energy differences are sensitively affected by the description
of electronic structure, the statistical mechanics of the nuclei and the cell,
and thermal expansion. The importance of these effects has been individually
established, but rigorous free energy calculations for general molecular
compounds, which simultaneously account for all effects,have hitherto not been
computationally viable. Here we present an efficient "end to end" frame-work
that seamlessly combines state-of-the art electronic structure calculations,
machine-learning potentials, and advanced free energy methods to calculate ab
initio Gibbs free energies for general organic molecular materials. The facile
generation of machine-learning potentials for a diverse set of polymorphic
compounds, benzene, glycine, and succinic acid, and predictions of
thermodynamic stabilities in qualitative and quantitative agreement with
experiments highlights that predictive thermodynamic studies of
industrially-relevant molecular materials are no longer a daunting task.
