There are three limitations worth mentioning. Our models utilize point charges to account for long range interactions and to distinguish molecules of differing net charge and in this article we do not differentiate charges by level of theory; our model predicts only one set of point charges and multiple energies, essentially assuming that all DFT functionals yield approximately equivalent densities. Future work may distinguish charges between different levels of theory. Second, we do not define a mechanism besides the latent space for sharing information between output heads. This may be of interest if training to two levels of theory that are highly correlated, for example one training set which utilizes some approximation to four index integrals and one that does not. In such a case harmonically tethering the parameters of the outputs may be a successful strategy. The final limitation is that the chemical elements found in any dataset should ideally be present in all datasets. In an HDNNP trained with separate neural networks for each chemical element this seems to be a requirement for successful prediction on molecules containing a specific element. It is possible that models utilizing an atom embedding featurization scheme could infer the behavior of elements on a level of theory for which there is no training data.