We selected key datasets across varying levels of computational complexity and computed properties to highlight the flexibility of our package. Key considerations for these datasets and the level of theory for subsequent QTAIM calculations were the following: first, we wanted to highlight important features of our package such as support for reactions and spin/charge. Second, we informed the level of theory in our QTAIM calculations with the relative expense of computed properties. In other words, inexpensive orbital energies of organic molecules only justified a modest level of theory in our dataset construction. Conversely, more expensive vertical excitation or vibrationally-corrected free energies on metal-containing complexes justified more expensive calculations. We wanted to reflect real use cases where computing descriptors should be considerably less expensive than the properties they are used to predict. Finally, we sought to integrate datasets that are either already in use by the community or could be adopted readily to test the limits of new models on domains such as molecules with varying spins and charges, transition metals, and reactions. We briefly describe the datasets we based our QTAIM datasets on as well as the labels we used to test and validate the use case for QTAIM descriptors in machine learning: