Traditionally, methods for crystal structure prediction (CSP) have focussed on identifying and ranking local energy minima on the lattice energy surface, under the assumption that each local energy minimum corresponds to a possible crystal structure and those with lowest energy are most likely to be observable under normal experimental conditions. A large variety of methods have been developed for this purpose, but these rarely provide information on how the local minima are corrected: pairs of structures where transitions between structures are possible. In this work [1], we present an implementation of the Monte Carlo threshold algorithm for molecular crystals with the aim of identifying transitions between structures and characterizing energy barriers along these transitions. Results are presented for a series of polymorphic systems of rigid molecules to demonstrate the information that is gained from threshold calculations, focussing on the global structure of the energy landscape, which we present as a disconnectivity graph of the local minima. We show that the structure of the disconnectivity graph reflects the structure-directing intermolecular interactions, reinforcing our chemical intuition about the factors that control the depth of energy minima. We believe that the method will be powerful in identifying observable metastable structures from CSP studies.

Beyond local energy minima: Predicting the structure of molecular crystal energy landscapes

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ACS Fall 2022

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