Hugging Face's logo

Datasets:
hermanhugging
/
qmof_project

Tasks:
Tabular Regression
Sub-tasks:
tabular-single-column-regression
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
materials
metal-organic-frameworks
MOF
QMOF
hMOF
CoREMOF
License:
Dataset card Files
xet
 Community
qmof_project / README.md
hermanhugging's picture
hermanhugging
Update README.md
86f0295 verified
preview code
|
raw
history blame contribute delete
11.9 kB
metadata 
pretty_name: QMOF / ODAC23 / hMOF / CoREMOF / MOSAEC — MOF Datasets
license: mit
task_categories:
  - tabular-regression
task_ids:
  - tabular-single-column-regression
tags:
  - materials
  - metal-organic-frameworks
  - MOF
  - QMOF
  - hMOF
  - CoREMOF
  - MOSAEC
  - CGCNN
  - MGT
  - PMT
  - chemistry
  - quantum-materials
language:
  - en
size_categories:
  - 100K<n<1M
arxiv: 2307.01547

MOF Property Prediction Datasets

Pre-processed datasets for training graph neural networks on Metal-Organic Framework (MOF) property prediction. Covers five source databases — QMOF, ODAC23, hMOF, CoREMOF, and MOSAEC — prepared for three model architectures: CGCNN, MGT, and PMT.

Each database is available in up to three forms depending on coverage:

  • data/cif_plus_idprop/ — CIF structures bundled with id_prop.csv target files, ready for direct CGCNN/MGT input
  • data/lmdb/ — pre-computed LMDB graph databases, ready for training
  • data/original_data/ — raw upstream source archives (unmodified)

The target variable for training is not fixed at preprocessing time — it is selected when launching a model. All multi-property databases retain the full set of available targets in their id_prop.csv files.

Note: The Hugging Face Dataset Viewer is not available because the archives are not in WebDataset format. Download and extract locally.

Source Databases

Database Structures Source
QMOF ~20,372 MOFs Rosen et al., Matter 2021
ODAC23 IS2R ~160k MOF–adsorbate pairs (CO₂ + H₂O) Sriram et al., arXiv 2307.01547
hMOF ~137k hypothetical MOFs Boyd & Woo, Nature Chemistry 2019 (MOFDB)
CoREMOF ~14k experimental MOFs Chung et al., J. Chem. Eng. Data 2019
MOSAEC TBD TBD

LMDB Coverage by Model

Pre-computed LMDB archives are currently available as follows. All databases are compatible with all three model architectures and can be preprocessed into LMDB format using the provided scripts.

Database CGCNN LMDB MGT LMDB PMT LMDB
QMOF ✅ ready ✅ ready ✅ ready
ODAC23 CO₂ ✅ ready ✅ ready —
ODAC23 H₂O ✅ ready ✅ ready —
hMOF ✅ ready — —
CoREMOF ASR ✅ ready — —
MOSAEC — — —

Databases without a pre-built LMDB can be preprocessed from data/cif_plus_idprop/ using the scripts in data/lmdb/{MODEL}/preprocess/.

Repository Structure 

qmof_project/
│
├── data/
│   │
│   ├── cif_plus_idprop/                     # CIF archives + id_prop.csv — direct CGCNN/MGT input
│   │   ├── QMOF.tar                         # QMOF CIFs + targets
│   │   ├── ODAC_init.tar                    # ODAC23 initial (unrelaxed) MOF structures
│   │   ├── HMOF.tar                         # hMOF CIFs + targets
│   │   ├── CoreMof_SI_NOT_FULL_ASR.tar      # CoREMOF ASR subset (not full)
│   │   ├── CoreMOF_CR_Full.tar              # CoREMOF CR full dataset
│   │   └── MOSAEC_full_and_partial.tar      # MOSAEC full + partial structures
│   │
│   ├── lmdb/                                # Pre-processed LMDB files, ready for training
│   │   │
│   │   ├── CGCNN/                           # Crystal Graph CNN format
│   │   │   ├── qmof_cgcnn_lmdb.tar          # QMOF — 5.69 GB
│   │   │   ├── odac_co2_cgcnn_lmdb.tar      # ODAC CO₂ — 15.1 GB
│   │   │   ├── odac_h2o_cgcnn_lmdb.tar      # ODAC H₂O — 9.39 GB
│   │   │   ├── hmof_cgcnn_lmdb.tar          # hMOF
│   │   │   ├── asr_cgcnn_lmdb.tar           # CoREMOF ASR
│   │   │   └── preprocess/
│   │   │       ├── preprocess_qmof_to_lmdb.py
│   │   │       └── preprocess_is2r_full_to_lmdb.py
│   │   │
│   │   ├── MGT/                             # Materials Graph Transformer format
│   │   │   ├── qmof_mgt_lmdb.tar            # QMOF — 17 GB
│   │   │   ├── odac_co2_mgt_lmdb.tar        # ODAC CO₂ — 38.5 GB
│   │   │   ├── odac_h2o_mgt_lmdb.tar        # ODAC H₂O — 23.7 GB
│   │   │   └── preprocess/
│   │   │       ├── preprocess_qmof_to_lmdb.py
│   │   │       └── preprocess_is2r_full_to_lmdb.py
│   │   │
│   │   └── PMT/                             # PMT model format
│   │       ├── qmof_pmt_lmdb.tar            # QMOF — 1.42 GB
│   │       └── preprocess/
│   │           └── qmof_preprocessor.py
│   │
│   ├── original_data/                       # Raw upstream source files (unmodified)
│   │   ├── QMOF/
│   │   │   └── qmof_database.zip                              # Full QMOF DB, 392 MB
│   │   ├── ODAC23/
│   │   │   ├── odac23_is2r.tar.gz                             # Full IS2R relaxed dataset, 848 MB
│   │   │   ├── pristine_CO2.tar.gz                            # Pristine MOF + CO₂ structures, 93 MB
│   │   │   └── pristine_H2O.tar.gz                            # Pristine MOF + H₂O structures, 72 MB
│   │   ├── hMof/
│   │   │   └── bulk-dl-mofdb-version-dc8a0295db.zip           # hMOF bulk download from MOFDB
│   │   └── CoreMof/
│   │       ├── NOT_FULL_FROM_ZENODO_CoREMOF2024DB_SI_20250204.zip
│   │       └── Core_MOF_CSD-modified.zip
│   │
│   ├── util/                                # Shared utilities and preprocessing scripts
│   │   ├── atom_init.json                   # 92-element atom feature embedding (CGCNN standard)
│   │   ├── prepare_dataset_co2.py           # Dataset prep helper for ODAC CO₂
│   │   ├── prepare_dataset_h2o.py           # Dataset prep helper for ODAC H₂O
│   │   ├── prepare_hmof.py                  # Dataset prep helper for hMOF
│   │   └── prepare_asr.py                   # Dataset prep helper for CoREMOF ASR
│   │
│   └── cif_models/                          # ⚠️ DEPRECATED — see DEPRECATED.md inside
│       ├── DEPRECATED.md
│       ├── cgcnn_mgt/
│       └── mof_transformed/
│
└── README.md

LMDB Graph Format

All LMDB archives contain pre-computed crystal graphs. Each entry is a pickle-serialised dict:

Field Shape Description
atom_fea (N, 92) Per-atom feature vectors from atom_init.json
nbr_fea (N, 12, 41) Gaussian-expanded pairwise distances (8 Å cutoff)
nbr_fea_idx (N, 12) Indices of 12 nearest neighbours per atom
target scalar Property value (selected at model launch via id_prop.csv)
cif_id string Structure identifier

Graph parameters (same across all CGCNN/MGT datasets):

Parameter Value
Cutoff radius 8.0 Å
Max neighbours 12
Atom feature length 92
Edge feature length 41 (Gaussian expansion, step 0.2 Å)

ODAC23 note: Only framework atoms (tags == 0) are included in the graph. Adsorbate atoms (tags == 1) are excluded.

Quick Start

Download a pre-built LMDB (wget) 

# QMOF for CGCNN
wget https://huggingface.co/datasets/hermanhugging/qmof_project/resolve/main/data/lmdb/CGCNN/qmof_cgcnn_lmdb.tar
tar -xf qmof_cgcnn_lmdb.tar

# hMOF for CGCNN
wget https://huggingface.co/datasets/hermanhugging/qmof_project/resolve/main/data/lmdb/CGCNN/hmof_cgcnn_lmdb.tar
tar -xf hmof_cgcnn_lmdb.tar

# QMOF CIF + id_prop (for raw CGCNN/MGT input or re-preprocessing)
wget https://huggingface.co/datasets/hermanhugging/qmof_project/resolve/main/data/cif_plus_idprop/QMOF.tar
tar -xf QMOF.tar

Download via Python 

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="hermanhugging/qmof_project",
    filename="data/lmdb/CGCNN/qmof_cgcnn_lmdb.tar",
    repo_type="dataset",
)

Read an LMDB file 

import lmdb, pickle

env = lmdb.open("path/to/train.lmdb", readonly=True, lock=False, subdir=False)
with env.begin() as txn:
    n = int(txn.get(b"length").decode())
    entry = pickle.loads(txn.get(b"0"))

print(entry.keys())             # ['cif_id', 'target', 'atom_fea', 'nbr_fea', 'nbr_fea_idx']
print(entry["atom_fea"].shape)  # (N_atoms, 92)

Dataset Sizes

Database Archive Size
QMOF CGCNN/qmof_cgcnn_lmdb.tar 5.69 GB
QMOF MGT/qmof_mgt_lmdb.tar 17 GB
QMOF PMT/qmof_pmt_lmdb.tar 1.42 GB
ODAC23 CO₂ CGCNN/odac_co2_cgcnn_lmdb.tar 15.1 GB
ODAC23 CO₂ MGT/odac_co2_mgt_lmdb.tar 38.5 GB
ODAC23 H₂O CGCNN/odac_h2o_cgcnn_lmdb.tar 9.39 GB
ODAC23 H₂O MGT/odac_h2o_mgt_lmdb.tar 23.7 GB
ODAC23 (init structures) cif_plus_idprop/ODAC_init.tar 505 MB
QMOF (raw) original_data/QMOF/qmof_database.zip 392 MB
ODAC23 IS2R (raw) original_data/ODAC23/odac23_is2r.tar.gz 848 MB

Preprocessing Scripts

All scripts to regenerate LMDB files from source data:

Script Input Output
data/lmdb/CGCNN/preprocess/preprocess_qmof_to_lmdb.py original_data/QMOF/ CGCNN/qmof_cgcnn_lmdb.tar
data/lmdb/CGCNN/preprocess/preprocess_is2r_full_to_lmdb.py original_data/ODAC23/ CGCNN/odac_*_cgcnn_lmdb.tar
data/lmdb/MGT/preprocess/preprocess_qmof_to_lmdb.py original_data/QMOF/ MGT/qmof_mgt_lmdb.tar
data/lmdb/MGT/preprocess/preprocess_is2r_full_to_lmdb.py original_data/ODAC23/ MGT/odac_*_mgt_lmdb.tar
data/lmdb/PMT/preprocess/qmof_preprocessor.py QMOF CIF files PMT/qmof_pmt_lmdb.tar
data/util/prepare_dataset_co2.py ODAC23 structures ODAC CO₂ training data
data/util/prepare_dataset_h2o.py ODAC23 structures ODAC H₂O training data
data/util/prepare_hmof.py original_data/hMof/ hMOF training data
data/util/prepare_asr.py original_data/CoreMof/ CoREMOF ASR training data

Deprecated

data/cif_models/ is deprecated and kept only for backward compatibility. Use data/lmdb/ and data/cif_plus_idprop/ for all current workflows. See data/cif_models/DEPRECATED.md for migration notes.

Citations

QMOF Database:

@article{rosen2021machine,
  title   = {Machine learning the quantum-chemical properties of metal-organic frameworks
             for accelerated materials discovery},
  author  = {Rosen, Andrew S. and others},
  journal = {Matter},
  volume  = {4},
  pages   = {1578--1597},
  year    = {2021},
  doi     = {10.1016/j.matt.2021.02.015}
}

ODAC23:

@article{sriram2023open,
  title   = {The Open DAC 2023 Dataset and Challenges for Sorbent Discovery
             in Direct Air Capture},
  author  = {Sriram, Anuroop and others},
  journal = {arXiv preprint arXiv:2307.01547},
  year    = {2023}
}

hMOF (MOFDB):

@article{boyd2019data,
  title   = {Data-driven design of metal-organic frameworks for wet flue gas CO2 capture},
  author  = {Boyd, Peter G. and Woo, Tom K.},
  journal = {Nature Chemistry},
  volume  = {11},
  pages   = {1026--1034},
  year    = {2019},
  doi     = {10.1038/s41557-019-0327-5}
}

CoREMOF:

@article{chung2019advances,
  title   = {Advances, Updates, and Analytics for the Computation-Ready,
             Experimental Metal-Organic Framework Database: CoRE MOF 2019},
  author  = {Chung, Yongchul G. and others},
  journal = {Journal of Chemical \& Engineering Data},
  volume  = {64},
  pages   = {5985--5998},
  year    = {2019},
  doi     = {10.1021/acs.jced.9b00835}
}

License

MIT — see LICENSE for details.