MOFjson: An Explicit Topological Representation of Metal-Organic Frameworks for Language Models

Language models for metal-organic frameworks (MOFs) rely on formats that separate atomic geometry from structural topology, forcing models to choose between unstructured coordinate lists and non-spatial string identifiers. We present MOFjson, the first representation separating structural data into a raw atomic layer with explicit bond shift vectors and a derived layer exposing secondary building units (metal nodes and organic linkers) and network topology. Additionally, we introduce a benchmark with crystal-derived ground truth across four question categories. Across four models, MOFjson beats all three baselines in every category: 100\% vs.18--47\% on reaching a block's nearest periodic copy, 96\% vs. 71\% on crystal system characterisation, and 60\% vs.48\% on metal node and organic linker coordination. Ablating shift labels drops accuracy to 12.7\%, proving models read the periodic graph rather than the surface format. MOFjson gives language models structure to read rather than reconstruct, establishing a foundation for agentic tool workflows and constrained MOF generation.