We evaluate our methods on all the properties of the QM7b and QM8 datasets, on HOMO and LUMO energy prediction for QM9 and QMugs, as well as on energy prediction tasks from MD17, which provide a challenging setting due to the limited amount of data. We have also considered total energy prediction for QMugs as an example of a non-local property on the largest and most diverse of the available datasets. Results are provided for both SchNet and DimeNet++, with sum and attention-based pooling, i.e. 4 results per (property, dataset) pair. A batch size of 128 is used for all models. To ensure an accurate and self-contained comparison, we randomly generate 5 different train, validation, and test splits for each dataset using a ratio of 80%/10%/10%, and report the average MAE ± standard deviation. The MAE is used as it is widely used in the literature to evaluate atomistic models on quantum property prediction. Since ABP introduces several hyperparameters compared to standard functions, we evaluate a small set of common hyperparameter choices and select the best configurations according to the validation set.