To begin this section, consider the dataset presented here. In the context of popular datasets for MLPs, e.g., MD17 the DFT dataset presented here for C 14 H 30 is probably the most extensive one reported. The energy range used for training and testing extends from near zero (the global minimum) to more than 80,000 cm -1 (229 kcal/mol). In addition, the data set spans a very large range of configurations of this alkane. This is of course a consequence of the well-known flexibility of alkanes. The extensive range of energies and configurations, as well as the large number of atoms, present an opportunity to test various machine-learning methods mentioned in the Introduction and the plethora of new ones. We have previously critiqued the rMD17 datasets as basically being limited to the lowest energy minimum of a potential. Clearly the "interesting" science related to large molecules is the complexity of the energy landscape which exhibits numerous low-lying minima (and of course barriers separating them). If MLPs are to make a major contribution to this science, they need to be tested on such rugged potentials.