To showcase the influence of noise on a machine learning model, we use the noise-free dataset of artifical, additive enthalpies to train a d-MPNN model. The respective model performance with different sizes of the dataset is depicted in Fig. for different levels of noise. For a clean, noise-free training, validation and test set (labeled "clean/clean", left panel), a standard d-MPNN can learn the target property to seemingly arbitrary accuracy, because the task is simple and learnable. Adding Gaussian noise with standard deviation, i.e. magnitude, of 1 kcal/mol to the training data but not the test data (labeled "noisy/clean", left panel) leads to a loss in performance, diverging after the RMSE for the clean model approaches the noise level. The model continues to learn with added data and could still achieve reasonable accuracies, requiring more data for the same performance compared to the model trained on the clean data. Though noise-based, the error from noise introduced while training is not irreducible. However, when noise also affects the test set (labeled "noisy/noisy", left panel), it leads to an additional perceived loss in observed performance. The trained model is the exact same for the "noisy/clean" and "noisy/noisy" curves, only the test set differs in the addition of noise. The true model performance is thus described by the "noisy/clean" curve, but instead the noise in the test set causes the "noisy/noisy" curve to be observed. The learning curve of the noisy test and training set approaches an asymptote at 1 kcal/mol, which is the standard deviation of the employed noise distribution. Upon addition of more data, no further improvement in observed performance is perceived. The aleatoric limit is reached, where the observed test set error is dominated by noise. The effect of noise in the test set on the perceived model error is irreducible. This aleatoric limit is not a true limit of the model performance, however, but a property of the test set used to evaluate the model. Users who observe this sort of asymptotic behavior with respect to dataset size, should consider test set noise as a possible cause.