Using just the structural features, we trained a random forest model for Al C Q which derives the target value by performing data segmentation with an ensemble of decision trees . Figure shows the correlation between the calculated DFT Al C Q and the model predicted C Q . The plot shows that the set of simple structural features can already predict C Q with a R 2 of 0.95 and RMSE of 0.77 MHz. We do note that there are still a number of outliers suggesting characteristics other than structural features can play a significant role in dictating NMR properties. Also, since the majority of the Al sites in the dataset are only coordinated with oxygens, to further test the ability of the structural features with more atomic variance, we rebalance the data using SMOTE to oversample the minority group (sites with non-oxygen neighbors) and undersample the majority group (sites with pure oxygen neighbors) then compared the model before and after the rebalance. more details can be found in the supporting information section VIII. The EFG tensor is not only related to the geometry of the local environment but is also strongly influenced by the properties of surrounding atomic species because it is derived from the electronic density distribution. To further improve the prediction of C Q , we need to represent the variation in local chemical composition into consideration. We selected twelve elemental properties such as atomic numbers, electron affinity, and other properties (Supporting Information Table ) and constructed three sets of elemental features. Figure shows the general procedure of feature construction. We first get the twelve elemental properties for each atom in the first coordinate shell around the Al sites. The first set of features is simple statistics of each of the elemental properties: its maximum, its minimum, standard deviation, and average values. The second set of features measures the differences between the neighbor atoms and the core atom (aluminum in our case).