In this section we test the generalisability capabilities of the proposed model by examining whether LiCOMPGNN and its variants can successfully extrapolate static properties of crystals, such as formation energy and band gap, subject to supercell transformation. In solid-state applications it is crucial that representations are supercell-invariant, i.e., static properties do not change under supercell transformations. For deep graph representations for example, the radial or k-NN cut-offs are only coordinate-wise invariant and a (2,1,1) supercell transformation will naturally contain twice as many atoms (graph nodes), as illustrated in Figure and previous self-loops to self-images may become edges to these new explicit "image" nodes, treated as separate, unique entities. This is also observed in reciprocal space due to it being a function of the real space lattice. Here we test our model's power to generalise to supercell-invariant predictions driven by the inherent contextual message passing scheme that is able to better capture global information and allows an invariant interpolation of supercell target properties. We compare prediction performance against GEOCGNN which uses a plane-wave mask function to specifically learn supercell-invariant representations. We experiment on 60,000 datapoints of Materials Project 2018 (MP18) dataset in a leave-k-out setting allowing for seen versus unseen prediction performance, differently from GEOCGNN which uses the complete database leaving only mp -2657 111 , mp -5827 111 and mp -5229 111 out.