Crystal structure prediction (CSP) aims to reduce the risk and improve the efficiency of pharmaceutical drug manufacturing by correctly predicting the low energy crystal polymorphs of organic molecules from minimal information, such as the chemical diagram. Traditionally, CSP approximates the solid form energy landscape at ambient temperature using energy-minimized crystal structures at 0 K. While this approach is often successful in predicting the low energy structures, it neglects the crucial role of thermal effects on polymorph stabilities. Moreover, it often leads to overprediction of polymorphism since crystal structures trapped in shallow lattice energy minima at 0 K often coalesce into the same ensemble of crystal structures at ambient temperature. To address these issues, we have developed a robust multi-stage approach that uses a combination of molecular dynamics, free energy calculations, information entropy clustering, and PXRD matching to characterize the relevant set of ambient temperature crystal structures. Enhanced sampling simulations are used to assess the likelihood of polymorphic transitions between crystal structures and identify unique metastable forms. We demonstrate our approach on the crystal polymorphs of sulfamerazine.

Characterization of the metastable forms of the finite-temperature crystal energy landscape

ACS Spring Meeting 2021

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