The Symmetry Adapted Gaussian Process Regression (SA-GPR) machine learning framework, combines covariant atomic descriptors with symmetry adapted kernels in order to learn tensors of any dimension with Gaussian process regression . We have previously shown that by using tensorial learning via the SA-GPR frame-work, we are able to predict quadrupolar frequencies (! Q ) for the 7 Li nucleus within the experimental sensitivity of SAE NMR . We couple the workflow for tensorial learning to the active learning procedure used for training the UFP, as shown in Figure , in order to train a model for predicting the 7 Li EFG tensors of -and am-LPS. The final set of structures from the active learning procedure for the UFP is used as the training set for validating the ML-EFG model.