We also compare attention-based pooling with OWA on the OE62 dataset used by Chen et al. on both HOMO and LUMO energy. For this comparison, we use the provided OWA source code as a starting point, and use the same underlying SchNet implementation provided by the schnetpack 1.0.1 library , including the same SchNet hyperparameters (embedding size of 128, 128 filters, 64 Gaussian functions, 6 interaction blocks, a cutoff radius of 5, followed by 4 atom-wise layers with an input of 128 features). A batch size of 40 was used, as larger models would run out of memory when using hardware equipped with 32GB of video memory. For attention-based pooling, we modified the atom-wise component to output representations of the same dimensionality as the inputs, as described above. We also generated 5 random splits using the same ratio as Chen et al. (32,000 molecules for train, 19,480 for validation, and 10,000 for test), and report both the MAE and the RMSE (root mean squared error).