As stated in Methodology, the proposed attention-based pooling function can be applied to a variety of atomistic modelling algorithms. Here, we chose to evaluate our methods using two architectures: SchNet and DimeNet++, which were briefly discussed in the introduction. Both models are widely-known and used, making them easily accessible in general purpose deep learning libraries such as PyTorch Geometric (used here) . Furthermore, DimeNet++ is a particularly competitive model which outperforms both contemporary and newer models (e.g. E(n) GNNs ).