Figure presents the average RNE across three prediction tasks (E f , E g and EwT) for the top performers in MP18, namely ALIGNN, coGN and the COMPGNN suite. The Light variant appears to have the best trade-off between predictive performance and memory footprint in training, while the Light+k variant scores also very closely. This showcases the COMPGNN suite as an ideal candidate for practical applications of materials learning tasks both in terms of efficacy, but efficiency and sustainability as well.