To illustrate the qualitative performance of potential energy surface predictions, we analyzed both small and larger molecules outside of the ANI-1 data set used for training for each ML method. We wish to focus on how the methods perform not only around the bond length at the energy minima, r 0 , but also in the attractive and repulsive regimes to gain a better understanding of how ML methods would behave if given less ideal starting geometries for a task such as geometry optimization. 1 Median mean absolute percent error over all 17 molecules from r 0 ± 0.25 Å. The number of molecules in which the lowest predicted energy point matches DFT. The number of times the method predicted a repulsive wall as the bond was compressed. The number of times the method predicted anharmonic attractive forces after r 0 . The number of molecules predicted to have a local or global minima after 2 Å. BAND-NN regularly would not predict energies for geometries with a bond stretch of 2 Å or greater.