In this section we detail the experimental setup used to test the performance of the proposed contextualised message passing scheme on various property prediction tasks with different datasets and settings. We further introduce several ablations on the proposed scheme resulting into another two distinct model variants including a reciprocal-space multiplex representation model (COMPGNN Light+k ) as introduced in Section 2.2 and a simple expansion of the model's width to 256 (COMPGNN Large). We compare against stateof-the-art architectures such as ALIGNN , SchNET , MEGNET , CGCNN , Matformer , coGN and GEOCGNN , and demonstrate the competitive performance of our methodology in Section 2.4 where all finding are reported. We finally consider the representation power of the proposed models in terms of supercell invariance for solid-state materials in Section 2.5.