Publication
Structure elucidation of amorphous materials and microcrystalline solids is one of the key challenges in chemistry today. While techniques such as single crystal diffraction and cryo-electron microscopy are generally not able to characterize such materials, we will show how an approach based on measured NMR chemical shifts in combination with methods for large scale computation of shifts can rapidly determine full three-dimensional structures from powders. For example, using a machine learning model of chemical shifts, we determine the complete atomic-level structure of the amorphous form of a drug molecule by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with chemical shifts predicted using machine learning for MD simulations of large systems.(1,2) From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular interaction energies. In other examples, we will show how chemical shift driven NMR crystallography can be applied to determine the complete three-dimensional structures of challenging complex materials such as calcium-silicate-hydrates,(3,4,5) or hybrid organic- inorganic photovoltaic perovskites.(6,7)