Predicting polymorphism in molecular crystals using orientational entropy

Significance Small organic molecules often exhibit an amazing polymorphism. Since most drugs are based on organic molecules, this has important practical consequences. Not only does the deliverability of the drugs depend on the crystal structure but also different polymorphs can be separately patented. We address this problem by appropriately designed enhanced sampling simulations that start from the liquid and let the system crystallize spontaneously at the freezing temperature. In such a way entropy effects are automatically included. We successfully apply the method to the cases of urea and naphthalene and discover that entropy plays an important role. 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 timescale much longer than that of molecular simulations. 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 a collective variable an entropy surrogate based on an extended pair correlation function that includes the correlation between the orientations 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 apply our method to urea and naphthalene. We find different polymorphs for both substances, and one of them is stabilized at finite temperature by entropic effects.