We are just at the beginning of this new era of NMR crystallography in which we are able to accurately model dynamical processes at the same temperatures and timescales as experiment. This workflow combining UFPs and experimental observables is a baseline on which the next generation of machine learning for materials methods can be based. We are now one step closer to bridging the gap between theory and experiment, and can tackle more dynamic in operando calculations, which were previously computationally infeasible.