Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2025 Dataset
Bibliographic record
Abstract
Updates June 13th, 2025: Update CoRE MOF DB by adding new structures reported from 2024/01/01 to 2025/02/01 according to @MOF_papers (X or Bluesky). We combined a new NCR classifier (MOSAEC) and other two checker ("Chen-Manz" and MOFChecker) to check all structures and trained a PU-CGCNN model (MOFClassifier) for Validating Computation-Ready Metal-Organic Frameworks Web interface for the CoRE MOF SI dataset https://mof-db.pusan.ac.kr Full CoRE MOF DB (43,439) = CoRE MOF SI (9,256) + CoRE MOF CSD-modified (21,009) + CoRE MOF CSD-unmodified (13,174) The dataset is the public version of the CoRE MOF database updated in 2025 ("CoRE MOF SI"), which includes 2,737 computation-ready (CR) and 6,519 not computation-ready (NCR) MOF CIF files (total = 9,256 structures) and precomputed material properties. The dataset includes structures reported up to 02/01/2025. The dataset, based on the structures obtained from the Cambridge Structural Database (CSD) updated in 2025 ("CoRE MOF CSD"), is split into two datasets (unmodified CIFs and modified CIFs). We plan to submit it to CCDC and update it on their website and GitHub. CoRE MOF 2025 CSD (not updated): 1. To obtain modified CIFs from CoRE MOF CSD (8,985 CR and 12,024 NCR), please go: https://www.ccdc.cam.ac.uk/support-and-resources/downloads/ You will need a valid email to log in to the CCDC website to download the dataset for free. 2. To obtain unmodified CIFs from CoRE MOF CSD (4,314 CR and 8,860 NCR), please go: https://www.ccdc.cam.ac.uk/support-and-resources/downloads/ You will need a CCDC license to obtain the unmodified CIFs. Precomputed properties: pore limiting diameter (PLD), largest cavity diameter (LCD), pore volume (PV), framework dimensions, accessible surface area, crystal density, topology, open metal site, MOFidv1, MOFidv2, RACs, DDEC06 partial atomic charges from PACMAN model, heat capacity, decomposition temperature, probability of solvent removal stability, probability of water stability, hydrophobic classification based on GEMC Dataset Directory Organization 1. CoREMOFDBSI_0613.zip: dataset with computation-ready (CR) and not-computation-ready (NCR) classifications come from supporting information 2. CR_meta_data_SI.json: the information of the CoRE MOF SI CR dataset. 3. NCR_meta_data_SI.json: the information of the CoRE MOF SI NCR dataset. 4. 8806-recommended-screening-list.txt: We recommend that the researcher use this list for high-throughput screening (we removed the duplicated MOFs from the ASR and FSR datasets). 5. error_flag_by_Chen-Manz_MOFChecker_MOSAEC.txt: the list of structures that can not pass Chen-Manz, MOFChecker and MOSAEC of CR dataset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.009 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.058 |
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".