One way to benefit from incompatible data is hinted at by the work of Smith et al. but we believe can be generalized such that many levels of theory can be simultaneously trained to. In their work, an HDNNP (ANI-1x) was pre-trained to the very large ANI-1x dataset (see above). Several of the layers of each atomic network were then frozen and the remaining unfrozen parameters were re-optimized by training to a much smaller dataset with labels from Figure : A multitask model shown for one element. The model transforms some input feature vector to a shared latent vector. Each output head transforms this shared vector into a different prediction. All of the parameters of the model are trained simultaneously on a collection of datasets with incompatible labels. There is a one-to-one mapping between output heads and datasets. a higher level of theory (DLPNO-CCSD(T)/CBS). We can re-interpret the work of Smith, not as a single transfer-learned model, but as a pair of models which utilize hard parameter sharing over the frozen layers in the transfer learning protocol. If one were to then merge the frozen layers of the two models (they are identical after all) then these two models could be interpreted as one single multitask model with two outputs. This type of architecture is depicted in Fig. . Here, for an atomic neural network with a total of N layers, the first M layers are shared and used to transform some input feature vector into an intermediate, or latent, representation which is finally transformed into two or more output energies. In an HDNNP these output atomic energies are summed independently to give molecular energies.