Alarming structural error rates in MOF databases used in data driven workflows identified via a novel metal oxidation state-based method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it