Examining the results of the equivariant MatTEN model, we observe that the model is able to accurately predict the tensor, not just in terms of shape and magnitude, but in most cases in the orientation as well. Closer inspection of the cases where the orientation seems to fail shows that these are often the cases of highly spherically symmetric tensors where an orientation is not meaningful. Most surprisingly, the model is able to capture the shape (anisotropy and asymmetry) of the tensor, even in cases where the tensor exhibits very little anisotropy, for example for Q 4 and Q 0 sites. Despite the successes of the model, there are still cases where it fails; however, these failures are likely associated with a lack of data in the training set. Future work will be focused towards expanding the dataset, particularly for the Q n species, which are less well sampled.