Computational Methods in NMR Relaxometry

Nuclear magnetic resonance (NMR) relaxometry is a versatile technique with applications spanning material science, biophysics, and chemical analysis. Computational approaches, including machine learning and simulations, have significantly advanced the understanding and deconvolution of relaxometry data. In this work, we present three projects aimed at improving the analysis and modeling of relaxometry systems. The first project employs a sparsity-based method to extract meaningful insights from relaxometry data. The second introduces a novel sample-grouping approach based on relaxation decay profiles that eliminates the need for computation ally expensive calculations. Finally, the third utilizes molecular dynamic simulations to enhance the modeling of relaxation behaviors and to provide a more comprehensive understanding of multi-component systems