Since its inception, nuclear magnetic resonance (NMR) has been a valuable tool for determining chemical structure. In recent years, the field of NMR has been advanced forward by the ability to calculate theoretical parameters with increasing accuracy and efficiency. These calculations are compared to experimental data to produce high resolution structures. The progression of these applications has been made possible by improved instrumentation, data processing methods, probe and experiment design, better quality functionals and basis sets, as well as increased computational power. This research is especially relevant with the emergence of artificial intelligence, which has great potential to expedite steps of the process. Combining experimental NMR with theoretical calculations has applications in both solid state and solution NMR and has several advantages that are discussed herein. One advantage is to simplify the process of structure elucidation, illustrated in chapter three in which a single experiment yields the complete characterization of a structure, including connectivity, conformation, tautomeric form and dynamics. These parameters are provided unambiguously, simplifying the process leading from data to structure. In solid state NMR these techniques provide unusually high resolution and accuracy and provide a tool capable of both assisting traditional diffraction methods for crystallography, as well as independently solving crystal structures. This is particularly useful in cases where traditional diffraction methods fall short. Examples of such include cases in material sciences in which crystallite sizes are too small for conventional single crystal diffraction, disorder that disrupts the conversion from diffraction pattern to structure, inadequate placement of weakly diffracting hydrogen atoms, and isoelectric systems such as aluminosilicates often seen in material sciences. The application of these techniques with solid state NMR is discussed in chapter five.