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arXiv Machine Learning By Anabel Yong, Matthew Hart, Philippe Schwaller

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

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MOFjson is a new representation for metal‑organic frameworks that separates structural data into a raw atomic layer with explicit bond shift vectors and a derived layer that exposes secondary building units and network topology. The authors benchmarked MOFjson against three baselines across four question categories, achieving superior performance (e.g., 100% vs. 18‑47% for nearest periodic copy detection). Ablation of shift labels reduced accuracy dramatically, demonstrating that models rely on the periodic graph rather than surface format. MOFjson provides language models with a structured, topological view of MOFs, enabling more accurate predictions and facilitating agentic tool workflows and constrained MOF generation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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