---
pretty_name: MET-dateset
tags:
  - chemistry
  - molecular-property-prediction
  - 3d-molecules
  - lmdb
---

# 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:

```text
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:

```bash
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:

```bash
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.