Further augmenting the base model with reciprocal-space multiplex information (Light+k) and simply expanding the model to 256-width (Large) yielded improvements of 4.25% and 13.7%, respectively. In terms of bandgap (E g ) prediction, COMPGNN models fail to score best, probably owing to the fact that their design and training process was refined on the formation energy task instead. Nevertheless they still maintain a competitive performance outperforming CGCNN, MEGNET and SchNET. In a more in-depth comparison of top-performing methods in formation energy, we estimate the quantity of predictions that would be considered practically useful. Specifically we calculate energy within threshold (EwT) which measures the percentage of predictions with absolute errors below a defined limit of the ground truth (in this case, 0.01eV and 0.02eV in-line with ), against the top three performers in formation eneregy of Table . Table shows that both lightweight variants of COMPGNN improve upon Matformer's score on [EwT 0.02] whilst falling just short in [EwT 0.01], suggesting a very competitive performance overall. The competitive performance of LiCOMPGNN against current state-of-the-art is noteworthy, especially when it requires only a fraction of parameters while most ML models are typically characterised by large memory footprints to achieve transduction