Davis Thomas Daniel

* Developed a software suite in Python for analysis and inverse Laplace transform of relaxation data from magnetic resonance spectroscopy and impedance spectroscopy * Applied a wide range of nuclear magnetic resonance (NMR) spectroscopic methods to complex materials with energy applications for structure elucidation and investigation of dynamics.

* My expertise is in applying magnetic resonance techniques combined with computational methods to investigate materials. * Machine learning approaches are used to predict material properties and accelerate theoretical computation of spectroscopic observables. * Using a combination of experiment-backed computational protocols, degradation mechanisms and optimal material compositions are discerned.

The activities of my project involved, but were not limited to, learning and using chemical and biochemical techniques to form the complexes; characterization of the complexes using modern high-field EPR techniques; using these complexes to measure nanometer-scale molecular distances. In addition to EPR, I used different computational chemistry methods, such as Density Functional Theory and Molecular Dynamics to complement the experimental results.