The HEALED SBU library of chemically realistic building blocks for construction of hypothetical metal-organic frameworks
Bibliographic record
Abstract
Advancements in hypothetical metal-organic framework (hMOF) databases and construction tools have resulted in a rapidly expanding chemical design space for nanoporous materials. The bulk of these hypothetical structures are constructed using structural building units (SBUs) derived from experimental MOF structures, often collected from the CoRE-MOF database. Recent investigations into the state of these deposited experimental structures’ chemical accuracy identified an array of common structural errors—including omitted protons, missing counterions, and disordered structures. These structural errors propagate into the SBUs mined from experimental MOFs, culminating in inaccurate hMOF structures possessing net charges or missing atoms which were not accounted for previously. This work demonstrates how manual investigation was applied to diagnose structural errors in SBUs obtained from several popular hMOF construction tools and databases. An analysis of the prevailing errors discovered during the examination process is provided along with representative cases to aid with error detection in future studies involving SBU extraction and hMOF construction. A novel repair protocol was established and employed to generate a library of SBUs that are hand-examined and labeled with enhanced detail (HEALED). This repaired library of SBUs contains 952 inorganic SBUs and 568 organic SBUs ideally suited for the generation of hypothetical frameworks that are chemically accurate and properly charge labelled. Additionally, case studies following the effects of SBU errors on electrostatic potential-fitted charges and GCMC-simulated gas adsorption predictions are presented to highlight the significance of using chemically accurate hMOF structures exclusively in all screening efforts going forward.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".