Herein, we present a solid-state NMR Al benchmarking set with both DFT calculated NMR parameters and their experimentally-measured counterparts, reported in the literature. It is worth noting that the most common way for these NMR tensor elements to be reported is using expressions that employ the "Haeberlen" convention. We follow that convention here, for ready comparison between computed and experimentally-measured quantities, we report both computed values and their experimental complements for isotropic chemical shielding (σ iso ), quadrupolar coupling constant (C Q ), and quadrupolar asymmetry parameter (η Q ) as well as the computed EFG tensor principal components in the Supporting Information Table . The full definition of NMR parameters can be found in the Supporting Information Section V. The DFT calculations were performed by two popular DFT packages: Vienna Ab initio Simulation Package (VASP) and Cambridge Serial Total Energy Package (CASTEP) . The reliability of DFT predictions of σ iso and C Q for Al materials was confirmed. We further trained a "random-forest" machine learning model to predict the quadrupolar coupling constant C Q for compounds containing 4-, 5-and 6coordinate Al sites based on a larger DFT calculated dataset with 1681 aluminum-containing crystalline solid materials. To train the model, we constructed two sets of features, structural features and elemental features (or sometimes termed "alchemical features" in machine learning literatures) , based on the crystal structure to represent the 27 Al local environment. We have found the Al C Q value is closely correlated with the geometric properties of the next-neighbor bonding environment (surprisingly, regardless of the chemical identity of the bonded species). The nextneighbor bonding environment is typically depicted as a space-filling polyhedron. Distortions to 6 the polyhedron given by variance of bond lengths and bond angles, in combination with other features denoting elemental variance, produce a simple but effective model with a predictive accuracy of R 2 = 0.98 and RMSE = 0.61 MHz expressed with a correlation between VASP computed C Q and the model predicted C Q .