Combining static experimental ssNMR spectra with first principles density functional theory (DFT) is already an established method for elucidating structure in crystalline and amorphous battery materials . Yet, up to now, this field has placed a strong focus on calculating chemical shielding (CSA) tensors, and a less prominent focus on calculating EFG tensors, mainly as quadrupolar interactions are only observed for nuclei with I > 1/2. While the calculation of static EFG tensors using DFT is a straightforward approach, a technique such as SAE requires computational methods that are capable of following dynamic processes over both long length and timescales. Studying these dynamics is of course impossible with DFT calculated ssNMR tensors (both CSA and EFG tensors), due to the computational constraints associated with the fact that DFT typically scales as O(N 3 ). Even recent applications of machine learning to NMR have been limited to static use-cases , incapable of capturing dynamical or time-dependent effects. A classical approach using the Sternheimer approximation has proven successful for tracking ion motion in liquid electrolytes, where the fast ion motion reduces the requirements for computing NMR observables to picosecond timescales . However, for slower ion motion (relative to the liquid state), this approach is not feasible, and therefore cannot be applied to study solid-state Li-ion motion, which requires simulations on the order of microseconds.