We find the machine learning model gives excellent accuracy when tested against the training set by cross-validation. The relatively high accuracy and low variance are demonstrated in Figure where the DFT training set is plotted against the results of our model obtained by cross-validation, for 121 pure silica zeolites. Notably, the root-mean-squared error (RMSE) of 0.102 ± 0.0341 and 0.0847 ± 0.0222 for log(K) and log(G), respectively, compare well to the predictive accuracy achieved for simple k-nary inorganic solids; 0.0750 and 0.1378. This is excellent accuracy considering that the model is constructed from a small training set.