Additional deep neural network (DNN) methods, like ANI and BAND NN, used training data beyond optimized single points to better evaluate the potential surface for dynamics and geometry optimizations. These methods utilize the ANI-1 data set, or ANI-2 data set in the case of ANI-2x, for training as they contain both equilibrium and non-equilibrium structures of up to eight heavy atoms containing H, C, N, and O with the non-equilibrium structures being generated from normal-mode sampling. The training set for ANI-2x adds the additional elements of F, Cl, and S while providing additional torsion sampling data. The BAND NN model uses a subset of the ANI-1 data set with only nonequilibrium geometries with energies within 30 kcal/mol of the equilibrium energy. Although these methods have been shown to perform adequately in their respective papers, the range for bond stretch applications has been limited to the harmonic portion of the potential energy curve, rarely examining the potential energy curves further from equilibrium.