A reliable description is instead only achieved through the full tensorial learning and subsequent extraction of all quantities from the learned V to compute ω Q as shown in Fig. . When referring to reliable we hereby acknowledge that SAE-NMR experiments probe a dynamically averaged value of ω Q and for e.g. Li in LTO are sensitive to within roughly 10 kHz. As indicated in Fig. , with an MAE of 4.7 kHz only the average accuracy of the tensorial learning approach falls well within this experimental uncertainty range and thus allows to make meaningful predictions. In contrast, both scalar learning approaches to ω Q exhibit MAEs above 10 kHz. Intriguingly, the tensorial learning approach is not only more accurate, but also more data efficient. This extends to having to only train one versatile model to predict any tensor-derived observables, as well as to the required amount of training data. As shown in Fig. , the scalar learning approach for the four quantities CQ , η, θ, and ϕ exhibits a low learning rate across the number of Li data used in the training. This learning rate is much higher when predicting these quantities from the learned tensor V, which in turn lowers the amount of training data required to arrive at the same or even better model accuracy. The MAE for the individual tensor components V ij , which is additionally shown in Fig. , has a percent error in the same order tionals. At a plane wave cut-off of 1000 eV and a k-point spacing of 0.03×2πÅ -1 , the change in individual EFG tensor components ∆V ij between energy cutoffs are converged to within 4×10 -3