Knowledge of the structure and hydrogen-bonding motifs of amorphous solids would lead to structure-activity relations and could, for example, direct the optimization of pharmaceutical formulations. However, atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state NMR is among the most popular methods to characterize amorphous materials, and Molecular Dynamics (MD) simulations can generate models to describe the structure of disordered materials, but directly connecting MD to NMR experiments in molecular solids has been out of reach. Here, using a machine learning model of chemical shifts, we show how to bridge this gap, and we determine the atomic-level structure of an amorphous hydrate from dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.

Structure determination of amorphous molecular solids through large-scale NMR predictions

ACS Spring Meeting 2021

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