  We introduce a computational method to discover polymorphs in molecular
crystals at finite temperature. The method is based on reproducing the
crystallization process starting from the liquid and letting the system
discover the relevant polymorphs. This idea, however, conflicts with the fact
that crystallization has a time scale much longer than that of molecular
simulations. In order to bring the process within affordable simulation time,
we enhance the fluctuations of a collective variable by constructing a bias
potential with well tempered metadynamics. We use as collective variable an
entropy surrogate based on an extended pair correlation function that includes
the correlation between the orientation of pairs of molecules. We also propose
a similarity metric between configurations based on the extended pair
correlation function and a generalized Kullback-Leibler divergence. In this
way, we automatically classify the configurations as belonging to a given
polymorph using our metric and a hierarchical clustering algorithm. We find all
relevant polymorphs for both substances and we predict new polymorphs. One of
them is stabilized at finite temperature by entropic effects.
