Here we employ a radial-cutoff approach in a multimodal setting where apart from real space bonding information we also enrich it with reciprocal space information through a multiplex graph framework. Multiplex graphs are an extension to basic graphs containing multiple types of edges and can be considered as having multiple graphs, or layers within it . They have shown to improve predictive performance on molecular property datasets by constructing both local and global edges to perform different types of message passing . Specifically, we enrich our model's representation power with reciprocal space information by multiplexing the pairwise-distances of each graph in reciprocal space as additional bonds. We consider the reciprocal space as separate modality graph of the real space where the contextual message passing is applied independently and pooled together with the real space embedding in the end. The following section details our experimental setup and performance results for the proposed architecture.