The Green 2022 dataset represents a comprehensive compilation consisting of approximately 12,000 gas-phase reactions, meticulously calculated at high-level theory (CCSD(T)-F12a 46 /def2-TZVP ). This dataset was constructed to facilitate transfer learning approaches by incorporating two lower levels of theory (ωB97X-D3 62 /def2-TZVP , and B97-D3 63 /def2-mSVP ). Remarkably, our experimental results demonstrate a performance on par with the original authors' findings, achieving comparable results without necessitating a transfer learning approach at lower levels of theory . Notably, the original authors employ significantly higher levels of theory for transfer learning, specifically ωB97X-D3/def2-TZVP and B97-D3 63 /def2-mSVP . In contrast, our descriptors are limited to the TPSS 33 /def2-SVP level, yet they enable us to attain comparable performance. It would be intriguing for the original authors to explore and compare the transfer learning process from the lowest level of theory to the highest level of theory (without the intermediate-level of theory). This would effectively simulate the relative performance of QTAIM versus transfer-learning labels at inference time. Furthermore, when evaluating their non-transfer learned models, it's observed that those roughly align (4.17 kcal/mol versus 4.07 kcal/mol) with our Bondnet training without QTAIM integration (Table ). The incorporation of QTAIM features with Bondnet, however, elevates its performance, surpassing the non-transfer learned models with a reduced mean absolute error (MAE) of 2.6 kcal/mol (Figure , S16). This discrepancy underscores the advantageous impact of QTAIM integration in enhancing model accuracy and predictive capabilities. Beyond a measure of train/test performance, we wanted to demonstrate whether QTAIM could functionalize machine learning models to make out-of-domain predictions. We conducted two sets of experiments here. First, we trained/tested models with/without QTAIM features on the LIBE dataset where Table Test performance of our geometric learning algorithms on formation energies in LIBE OOD.