MET-dateset

LMDB dataset release for the MET project:

Integrating equivariant architectures and charge supervision for data-efficient molecular property prediction

Links

• Paper DOI: https://doi.org/10.1039/D5ME00173K
• GitHub repository: https://github.com/mint258/MET
• Intended Hugging Face dataset page: https://huggingface.co/datasets/Mint258/MET-dateset

Contents

This upload contains single-file LMDB datasets prepared for the MET codebase.

QM9

• QM9/full_database.lmdb
• QM9/train_valid_database.lmdb
• QM9/test_database.lmdb

QM7

• QM7/full_database.lmdb
• QM7/train_database_300.lmdb
• QM7/test_database.lmdb

Record Format

Each LMDB record stores the molecular graph and associated labels used by MET. The per-record payload includes:

• z: atomic numbers
• pos: 3D atomic coordinates
• y: main supervised target used by the corresponding pipeline
• scalar_props: graph-level scalar properties when available
• filename: source molecule identifier
• chiral_inchi: stored molecule identifier string when available

Metadata entries are also included, such as __meta__/property_names.

QM9 Graph-Level Properties

rot_A, rot_B, rot_C, dipole, polarizability, HOMO_energy, LUMO_energy, gap, R2, zpve, U0, U298, H298, G298, Cv

QM7 Graph-Level Properties

atomization_energy

Intended Local Layout

After download, place the files into the MET repository like this:

data/
  QM7/
    full_database.lmdb
    train_database_300.lmdb
    test_database.lmdb
  QM9/
    full_database.lmdb
    train_valid_database.lmdb
    test_database.lmdb

Usage with MET

Example pretraining command:

python pretrain/training_charge_model.py \
  --data_root data/QM9/train_valid_database.lmdb \
  --save_path pretrained_ckpt/best_model_dim128_reproduced.pth

Example evaluation command:

python pretrain/charge_predict.py \
  --checkpoint_path pretrained_ckpt/best_model_dim128.pth \
  --test_data_root data/QM9/test_database.lmdb

Notes

• These LMDB files are derived from the local QM7 and QM9 inputs used by the MET repository.
• Downstream fine-tuning in MET still supports plain-file inputs such as xyz directories, manifests, and CSV files with SMILES.
• This dataset card is prepared for manual upload to Hugging Face.