A rotation-equivariant model was created using the Tensor Field Network (TFN) and e3nn 56 frameworks, as implemented in the MatTEN 57 package. In addition to the scalar NMR shift tensor parameters discussed above, this model can directly predict the full chemical shift tensors. An initial MatTEN model was implemented (details in Supplemental Information) to determine the optimal target to train on (symmetric vs. asymmetric and spherical vs. Cartesian tensors). A symmetric spherical tensor target was found to yield the best loss and a hyperparameter grid search was performed to optimize the MatTEN model for symmetric spherical tensor. To yield a useful model, the symmetric spherical tensor is then converted to a Cartesian tensor which may be processed as a shift tensor.