We also assess the predicted tensors to show that the MatTEN model is capable of learning both tensor magnitude and shape as well as the tensor orientation in a diverse set of silicon local structures. The Si NMR dataset used in this study is a subset of ab initio NMR chemical shift tensors of relaxed structures calculated by Sun et al. The dataset is composed of oxygencoordinated silicon tetrahedral networks consisting of SiO 2 along with silicates containing group 1 and 2 cations (Li, Na, Mg, etc. cations). It contains a wide variety of structures with different number of bridging oxygen atoms, n, commonly referred to as Q n , as shown in Fig. . Each Q n species has a different chemical environment and local point group symmetry due to the differing bond lengths to bridging oxygen (BO) and non-bridging oxygen (NBO), which in effect results in different chemical shift tensor symmetries. In total there are 421 unique silicate structures, consisting of 1387 unique silicon sites. The silicon sites consist of 874 Q 4 sites, 174 Q 3 sites, 172 Q 2 sites, 32 Q 1 sites, and 97 Q 0 sites. From each site, the raw calculated rank-two asymmetric chemical shift tensor was extracted and processed in accordance with each tensor space or tensor convention used in the training of the ML models outlined below.