We make a few important advances to the use of QTAIM in machine learning. First, we create a set of easy-to-use, pythonic tools for computing QTAIM descriptors at scale and using them for machine learning tasks. These tools include high-throughput jobrunners for calculating QTAIM values, utilities for descriptive/visual statistics, parsing utilities for compiling data into single data structures, and ready-to-use graph neural network architectures. These tools work together in an ecosystem for using QTAIM in geometric learning. We also compute QTAIM values on several datasets with a mind towards using standard datasets for benchmarking and/or pushing development towards algorithms to treat edge chemical domains with varying charges, spins, and reactivities. In addition, we benchmark the use of QTAIM features to demonstrate their ability to improve model performance, performance on smaller datasets, and out-of-domain predictivity. We hope that these contributions can serve as an important foundation for further studies using hybrid QTAIM/ML approaches to tackle machine learning in difficult chemical domains with experimental or small datasets. In addition, these tools can serve as an important basis for developing more advanced QTAIM-enabled machine learning algorithms.