In the figure below, panel a shows the adversarial validation scores for the datasets considered in Moosavi et al. . For the entries on the diagonal, we considered a random split into two equally sized parts. Scores closer to one indicate that the datasets are easily distinguishable. In this case, we see that a model can easily distinguish the datasets-in particular, the experimental ones from the in-silico assembled ones. Therefore, we cannot expect a model to necessarily generalize in this setting. In panel b we see that the analysis of the feature importance can reveal which features the model used to distinguish the datasets. Removing those features can help to mitigate data-drift features or also help to guide the generation of new materials that can mitigate those biases (or remove materials that are dissimilar from the target distribution, i.e, have a ROC-AUC score greater than 0.5). When we group the features into scopes, as in Moosavi et al. , we find that the dominating differences across databases vary. While linker feature contributions do not play a major role in distinguishing structures from the BW and CoRE databases, they do play an important role in distinguishing structures from the CoRE and ARABG database. BW denotes a database of hypothetical MOFs assembled by Boyd and Woo, (i.e., using the same features and labels) and use adversarial validation to estimate how difficult it is for a model two distinguish the datasets. a The heatmap shows the area under the receiver-operating characteristic curve (ROC-AUC). b Highlights the feature scopes (computed using the Gini impurity decrease estimate) that are most important for the model distinguishing the datasets.