It can be generalized to many outputs, does not require pre-training to one output, both outputs can improve as information about different chemical spaces is incorporated into the latent space that is shared by the various output heads and no empirical selection of frozen layers is required. Finally, we point out that this architecture is not novel and in fact many MLFF methodologies already use multiple output heads to train to different properties such as energy, dipole moment, polarizability, HOMO-LUMO gap, etc. For such models there is no change in architecture required, one must simply update the loss function to include contributions from more than one energy. As far as we are aware multitask learning to multiple levels of theory has not been previously reported. A similar strategy has, however, been recently used by Takamoto et al. where the model is conditioned on the task to be predicted (i.e. used as a feature). While no detailed comparison on the effectiveness of that approach was reported it seems such an approach would have similar benefits as the one described in detail here.