Other benchmarks to note are the use of our QTAIM-enabled algorithms vs. those without on the LIBE, Green, and Tox21 datasets. Here we opted to remove the above benchmark datasets to avoid added complexities in treating classification tasks, reaction-property predictions, and spin/charge-varying molecules with algorithms that cannot encode this information. To gauge the effect of QTAIM on model learning, we benchmarked model test performance on LIBE, QM8, and QM9 given 10 2 , 10 3 , 10 4 , (and 10 5 for QM9) training data points. These learning curves are often used in machine learning to measure the learning capacity of a model and extrapolate to how accuracy varies with dataset size.