To minimize the loss function we use the AdamaxW optimizer (Adamax with decoupled weight decay) with a weight decay of 1.0e-4. We utilize early stopping with a patience of zero and a maximum of 100 cycles, where each cycle consists of training to 5 million examples in batches of 128. We do not optimize batches to balance the frequency of seeing the various labels. We simply select a random batch and update the loss function for the labels that we encounter. We train ensembles with 7 members and each ensemble member is trained to a 90/10 train/validation random split of the training data which is performed independently at run time (as such there is some degree of overlap between the training sets of the ensemble members). We use exponential moving averaged weights and biases to evaluate validation and test errors, which are updated every 10 batches with a smoothing factor of 0.001. As is standard practice we reduce the range of the energy labels and center them towards zero by subtracting an offset energy for each atom, defined by fitting a linear model to each level of theory. For purposes of multi-label training, this procedure has the further benefit of making the different labels more similar to each other by effectively removing the differences between levels of theory. All models are implemented and trained in a locally modified copy of the TorchANI open source software package, which is an implementation of HDNNP models in PyTorch. Each neural network is trained on a single GPU with single-precision floating point, and all test results are computed with double-precision inference. Hyperparameters defining the network dimensions and AEVs are taken from previous work. The output heads in this study are defined as a single layer, and as such, the multitask model has the same layer dimensions as the single task models described previously, and trained here. 3 Results The four datasets we use here (ANI-1x, ANI-ccx, OrbNet Denali and SPICE) will be abbreviated as 1X, CCX, D and S, respectively. We will denote a QRNN multitask model trained to datasets X, Y and Z as QRNN(X, Y, Z). (The order X, Y, Z does not matter as all are trained simultaneously.) Further, we will refer to predictions made by such a model using the output head corresponding to dataset Y as QRNN(X, Y, Z)[Y]. For singletask models we will often omit the square bracket notation since there is no ambiguity in this case. The properties of the four datasets relevant to this work and the abbreviations just described are summarized in Table .