Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2024 Dataset
Bibliographic record
Abstract
Updates March 20th, 2025: Added 12089-recommended-screening-list.csv file, which lists unique CR MOFs (ASR, FSR, and ION) from SI, CSD-modified, and CSD-unmodified datasets. While originating from the same source file, the ASR and FSR data differ because ASR structures have the coordinated solvent removed from the open metal sites (OMS), whereas FSR structures retain the solvent coordinated with the OMS. Web interface for the CoRE MOF SI dataset https://mof-db.pusan.ac.kr Full CoRE MOF DB (40,837) = CoRE MOF SI (8,300) + CoRE MOF CSD-modified (20,276) + CoRE MOF CSD-unmodified (12,261) The dataset is the public version of the CoRE MOF database updated in 2024 ("CoRE MOF SI"), which includes 2,664 computation-ready (CR) and 5,636 not computation-ready (NCR) MOF CIF files (total = 8,300 structures) and precomputed material properties. The dataset includes structures reported up to 12/31/2023 (manuscript acceptance date). The dataset, based on the structures obtained from the Cambridge Structural Database (CSD) updated in 2024 ("CoRE MOF CSD"), is split into two datasets (unmodified CIFs and modified CIFs). 1. To obtain modified CIFs from CoRE MOF CSD (9,835 CR and 10,441 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 (4,703 CR and 7,558 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, 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. CoREMOF2024DB_SI_20250204.zip: dataset with computation-ready (CR) and not-computation-ready (NCR) classifications come from supporting information CR dataset: 2,664 ASR (all solvent removed): 1,372 FSR (free solvent removed): 1,192 Ion (with ions): 100 NCR dataset: 5,636 Both Chen_Manz and mofchecker: 3,692 Chen_Manz: 562 mofchecker: 1,073 occupancy of a single atom is less than 1: 309 2. ASR_data_SI.csv, FSR_data_SI.csv and ION_data_SI.csv: the information of CoRE MOF 2024 ASR, FSR and Ion datasets. 3. NCR_ASR_SI.xlsx, NCR_FSR_SI.xlsx and NCR_ION_SI.xlsx: details of all structures by Chen_Manz and mofchecker for each NCR cases. 4. unmodified_check_for_NCR_SI.xlsx: whether the NCR structures are unmodified according to comparing with the original structure 5. mofid-v2.zip: XYZ files of linkers and metal nodes, errors (which is an "unknown" MOFid) 6. water.zip: GEMC water isotherm data of CR dataset 7. TSA.zip: Single isotherms of 35 MOFs used in TSA; TSA results and adsorption data at different feed conditions 8. ASR_FSR_check.csv: duplicated MOFs from ASR & FSR datasets. We recommend that the researcher remove the structures from this list (one of the columns) for high-throughput screening 9. 12089-recommended-screening-list.csv: lists unique CR MOFs from ASR, FSR, and ION datasets.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.159 | 0.126 |
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".