We calculated feature importances from the training dataset using Shapley additive explanation generated by the SHAP v0.44.0 module. We targeted our analysis of CoRE structures to those with higher confidence model predictions, as determined by structural latent space distance. We calculated the average latent space Euclidean distance between each structure in CoRE and the closest ten structures in the ethane and ethylene model training sets. We then repeated the process for each structure in the test set of each model. Using the known test set MAE values, we plotted the average MAE of all test set structures within a given min-max scaled latent space distance (Supporting Information Figure ). We also plotted the fraction of structures in CoRE that would remain within a given cutoff. We used the plots to determine latent space distance cutoffs below which model predictions will have a higher degree of confidence. We set the cutoffs at 0.5 and 0.15 for the ethane and ethylene models, respectively, due to the drop-off in average test set MAE (Supporting Information Figure ).