Alarming structural error rates in MOF databases used in data driven workflows identified via a novel metal oxidation state-based method
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are a diverse class of porous materials composed of inorganic nodes joined by organic linkers, currently under investigation for a wide range of applications including gas storage and separation where they have been commercialized. Given the labor-intensive nature of synthesizing and testing individual MOFs, high-throughput computational screening and machine learning (ML) methods are increasingly viewed as essential for facilitating MOF development. However, the structural fidelity of the “computation-ready” MOF databases used in such studies remains largely unquantified. We introduce MOSAEC, an algorithm that detects chemically invalid structures on the basis of metal oxidation states. MOSAEC was manually validated against ~16k MOF structures from the popular CoRE database, and was found to flag erroneous structures with 95% accuracy. Systematic examination of 14 leading experimental and hypothetical MOF databases containing >1.9 million MOFs reveals concerning structural error rates, exceeding 40% in most cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".