期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2018
卷号：115
期号：41
页码：10251-10256
DOI：10.1073/pnas.1811056115
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：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.
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