using the mean µ(V ZZ ) and standard deviation σ(V ZZ ) for each nucleus. CQ thus preserves the magnitude of the original tensor while centering the values around zero. In turn, η reduces the noise introduced in η by the division by V ZZ , cf. Eq. 4, and reflects the fact that NMR measurements will only probe an average property over the system, such as µ(V ZZ ), rather than an individual V ZZ . At first glance, the polar and azimuthal angles θ and ϕ appear as useful additional metrics for ω Q , cf. Eq. 5, as they are naturally species insensitive, and have a fixed range. However, their discontinuity renders them a non-ideal representation. In tensorial learning, where the tensor orientation is explicitly available, this limitation can be overcome by the unit quaternion. This defines the orientation of any rank-2 tensor in terms of a normalized vector (q = (q 0 , q 1 , q 2 , q 3 )) and the dot product between two quaternions defines how closely oriented the two respective tensors are. When using quaternions as metric, the performance would thus be evaluated by the calculation of q = q data • q ML , (