In this work we propose LiCOMPGNN, a lightweight yet expressive graph network for materials property, mitigating the need for complex features by attending passed messages on the broader environment they belong in, capturing thus more global information of the graph. The proposed message passing scheme helps enrich the expressivity of the model, while at the same time significantly reduces its complexity allowing it essentially to be trained on consumer-level hardware. We further extend the expressivity of the proposed model by enabling a multimodal representation for solid-state materials through the introduction of a reciprocal space modality in a multiplex graph representation fashion. We empirically test the performance of the proposed model on a variety of materials property prediction tasks and datasets and demonstrate its competitive performance and minimal memory footprint .