Date of Award
Doctor of Philosophy (PhD)
Solid-state magnetic resonance (SSNMR) spectroscopy is a powerful tool for obtaining precise information about the local bonding and morphology of materials. The detailed local structure of crystalline materials cannot be easily solved by traditional experimental methods such as X-ray diffraction (XRD). SSNMR combined with first principal calculation methods such as density functional theory (DFT) can be of great use in this research area. The methodology that is called “NMR crystallography” today has been widely applied to the determination of a wide range of solid materials with an increasing amount of computationally simulated NMR spectra. The construction of a well-established computational NMR dataset is thus getting more important. The research presented in this dissertation serves the purpose of applying NMR crystallography to investigate the local structural distortion of 51V in Ag2V2PO4 (SVPO), building well-benchmarked SSNMR datasets for both spin ½ species such as 29Si and quadrupolar species (spin > ½) such as 27Al, and constructing machine learning mode for NMR parameters prediction utilizing computational simulated NMR database.
Chair and Committee
Sophia E Hayes
Sun, He, "Local Spectroscopy Data Infrastructure: Solid State NMR Crystallography with Experiment, First-principal Analysis and Machine learning" (2022). Arts & Sciences Electronic Theses and Dissertations. 2784.