where I is the nuclear spin, and θ ∈ [0, π[ and ϕ ∈ [-π/2, π/2[ are the polar and azimuthal angles, respectively, between V PAS and the lab frame with z-axis parallel to the magnetic field B 0 as shown in Fig. . As ionic mobility can induce changes in ω Q , SAE has e.g. been repeatedly applied to battery materials like LTO. Introducing suitable performance metrics. The previous relations motivate to directly learn the EFG tensor as a whole, instead of separately or simultaneously learning a multitude of derived scalar observables or the individual EFG matrix elements. Consider, that even if each of the V ij components are learned individually, for example, there is no guarantee that the final nine components when recombined to form a (3 × 3) matrix will satisfy any of the properties of a rank-2 tensor. Furthermore, each of such trained models will have its own separate uncertainties with unspecified error propagation to the set of finally targeted NMR observables. Precisely, the latter limitation applies equally to the way tensorial learning models have thus far been evaluated and optimized though. While in recent years, several tensorial learning approaches have been introduced, with neural networks, 21 structure-property maps and with regression-based approaches 20,30 using a variety of descriptors, they are typically only evaluated on the mean absolute error (MAE) of predicting the individual elements, V ij , of the non-diagonalized tensor. For a total number of N data points for which reference values are available, MAE(V ij ) is given by