Similarly, Tsuruta et al. used a representation of crystal structures as 3-dimensional tetrahedral meshes to predict average (isotropic) bulk and shear moduli. Finally, the last months have seen the application of equivariant graph neural networks, which are independent of the frame of reference and preserve material symmetry and therefore allow for the prediction of a full elastic tensor, going beyond average properties. All these approaches, while relying on the power of neural networks, differ in their encoding and representation of the materials' structure. Such tensorial approaches are powerful, but require training data sets of very large size (in this case, more than 10,000 elastic tensors): it remains to be seen how accurate they would for other tensorial materials properties, where such large data sets are not currently available.