where R(r) is a learnable function of the distance between the two nodes and Y m l (r) are spherical harmonics taking in the orientation between the nodes. The W matrix has the form of a block diagonal matrix where the blocks correspond to the irreps selected for the network. Additionally, the embedding vectors used in the TFN framework contain blocks corresponding to the irreps. The W matrix along with the embedding vectors may then be convolved according to Clebsh-Grodan tensor products to ensure the symmetries of each irrep is preserved. So, the message passing phase of the TFN model uses equivariant embeddings.