Predicting polymorphism in molecular crystals using orientational entropy

TL;DR

This paper presents a novel computational approach using orientational entropy and enhanced sampling to discover and classify polymorphs in molecular crystals at finite temperature, predicting new stable forms.

Contribution

The method introduces an entropy-based collective variable and a similarity metric for automatic polymorph classification, enabling discovery of known and new polymorphs.

Findings

Successfully identified all relevant polymorphs for two substances

Predicted new polymorphs stabilized by entropy at finite temperature

Demonstrated the effectiveness of orientational entropy in polymorph prediction

Abstract

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.