Polymorphism denotes the ability of a molecule to exist in more than one crystal structure. This phenomenon is commonly observed in a wide range of materials, including pharmaceuticals, polymers, minerals and metallic systems. This is of tremendous significance since, for instance, the bioavailability of pharmaceutical drugs, such as the anti-AIDS drug ritonavir, has been shown to depend strongly on polymorphism. Another well-known example is the case of carbon, which, when subjected to a high pressure, undergoes a polymorphic transformation from graphite to diamond. The transition from graphite to diamond is marked by a sharp change in standard molar entropy from 5.740 kJ/mol for graphite to 2.377 kJ/mol for diamond. This suggests that entropy can be an effective marker for polymorphic transitions, since it clearly distinguishes between the two crystal forms. Indeed, highly symmetric crystalline forms are associated with a low entropy, while more complex and less symmetric phases like liquids have a high entropy. As a result, entropy has recently emerged as a very useful order parameter, or collective variable (CV), to measure the progress of a system towards a new level of organization via, for instance, self-assembly, condensation, and crystallization. To achieve this, we combine Machine Learning (ML), to infer the canonical partition function and, in turn, the entropy of the solid forms and of the liquid, with rare-event sampling molecular simulations to investigate phase transitions and nucleation processes involving molecular crystals.

Statistical entropy: New collective variable for the study of polymorphism in molecular crystals

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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