For the small MD17 datasets, we observe a decrease in MAE for Aspirin of 15.25% and 8.38% for SchNet and DimeNet++, respectively, for Benzene of 36.37% and 5.01%, for Ethanol of -9.58% and 6.45%, for Malonaldehyde of -5.23% and -0.27%, and for Toluene of 37.21% and 2.45%. For these smaller datasets, we used SchNet with 128 filters, 128 hidden channels, and 4 interaction blocks, and DimeNet++ models with 128 hidden channels, 4 interaction blocks, and an embedding size of 128 for the output blocks. For the cases where using ABP did not improve the MAE (e.g. SchNet on Ethanol), we noticed that different underlying architectures can help improve upon the sum pooling result (usually more complex models with more interaction blocks).