The defining feature of reticular chemistry is the tinkertoy principle, i.e., the construction of extensive crystal structures from small molecular building blocks. Interestingly, this principle is seldom exploited in machine learning studies for reticular materials such as MOFs. One possible explanation for this is that it is not trivial to extract the building blocks from a crystal structure into a form such that they can be used with featurizers that are conventionally used for molecules (e.g., as the ones implemented in the RDKit program ). To facilitate the use of BU-centered featurization we implement an adaptor that can convert any featurization function that accepts a molecule object from the RDKit library (that can easily be gener- Fig. | Impact of element encodings on predictive performance. Here, we compare revised autocorrelation functions with different element encodings as input for a gradient-boosted decision tree. We train the model on CoRE-MOF data reported by Moosavi et al. to predict the logarithm of the CO 2 Henry coefficient. The top row shows the number of top-100 materials we retrieve in the top-100 predictions of 20 independent runs. The bottom row shows estimated Hedge's g effect sizes (a suitable effect size metric in the case of little data which can be thought of as a normalized mean difference) with respect to the performance of the RACs using electronegativity (EN) as element encoding. This figure also compares the performance of feature sets augmented with scalar geometric properties (pore diameters, accessible surface area, and void fraction). We find similar very large trends using other metrics. ated from a SMILES string) into one that can be used along with all other mofdscribe featurizers.