Furthermore, we performed extensive testing to demonstrate how QTAIM can functionalize machine learning models to perform better on out-of-domain tasks and smaller datasets. In the case of QM8, our test showed that QTAIM features helped both our architectures and Chemprop improve model performance given identical datasets -suggesting our featurization package could be used with outside machine learning models as well. In the future, we plan on writing more "translation" functionalities to allow users of other architectures to leverage QTAIM features for their learning tasks.