We explored rotation-invariant GNNs as example ML models designed for scalar properties. Specifically, the DGL (deep graph library) implementation of the DimeNet++ invariant GNN is selected (we note that DimeNet++ is a strong baseline model in terms of accuracy). The model is customized to allow for the prediction of node properties. A hyperparameter grid sweep was performed to optimize the DimeNet++ model to predict the shift tensor eigenvalues. A total of six separate models are created, each trained on a different shift tensor convention (Eq. (1) -Eq. ( )), and the standard convention eigenvalues) using the three parameters of the convention as the target. See the Supplemental Information for further implementation details and the optimal hyperparameters.