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Abstract:Metal-organic framework (MOF) databases have grown rapidly through experimental deposition and large-scale literature extraction, but recent analyses show that nearly half of their entries contain substantial structural errors. These inaccuracies propagate through high-throughput screening and machine-learning workflows, limiting the reliability of data-driven MOF discovery. Correcting such errors is exceptionally difficult because true repairs require integrating crystallographic files, synthesis descriptions, and contextual evidence scattered across the literature. Here we introduce LitMOF, a large language model-driven multi-agent framework that validates crystallographic information directly from the original literature and cross-validates it with database entries to repair structural errors. Applying LitMOF to the experimental MOF database (the CSD MOF Subset), we constructed LitMOF-DB, a curated set of 189,567 computation-ready structures, including the successful repair of 9,227 invalid entries, which accounts for 69.1% of the CSD-derived not-computation-ready MOFs in the latest CoRE MOF DB. Additionally, the system uncovered 8,771 experimentally reported MOFs absent from existing resources, substantially expanding the known experimental design space. Using direct air capture screening as a case study, we demonstrate that structural errors severely distort predicted adsorption energies and CO2/H2O selectivity, leading to systematic misranking of materials, false positives, and the omission of high-performance candidates. This work establishes a scalable pathway toward self-correcting scientific databases and a generalizable approach for LLM-driven curation in materials science.

Submission history

From: Honghui Kim [view email]
[v1] Mon, 1 Dec 2025 13:59:55 UTC (46,356 KB)
[v2] Fri, 3 Apr 2026 01:42:16 UTC (5,089 KB)
[v3] Fri, 26 Jun 2026 05:03:29 UTC (21,478 KB)