|About this Abstract
||Materials Science & Technology 2019
||Ceramics and Glasses Simulations and Machine Learning
||Predicting Nuclear Magnetic Resonance Parameters in Ceramics Using Density Functional Theory
||Ryan J. McCarty, Arthur Pyuskulyan, Jingda Zhang, Kieron Burke
|On-Site Speaker (Planned)
||Ryan J. McCarty
Nuclear magnetic resonance is a useful tool for characterizing the atomic structures and arrangements of ceramic materials. In settings where the resulting observations are difficult to understand, a density functional theory (DFT) calculation using the GIPAW approach can be used as a predictive and interpretive tool. Using a variety of oxide and ionic solids, we have predicted nuclear magnetic properties and compared these to a database of experimental values. We explored both homogenous materials as well as materials containing dopants. During our work, we identified a smaller representative set of materials that may prove ideal for future benchmarking purposes. We will comment on the agreement between predictions and experimental results, as well as best practices and techniques to produce the most accurate predictions.