README.md

---
license: cc-by-4.0
task_categories:
- graph-ml
language:
- en
tags:
- Quantum Materials, Hamiltonian Matrix Prediction, Density Functional Theory,
  Equivariant Graph Networks
pretty_name: QHMat
size_categories:
- n<1K
---

# QHMat Dataset
**QHMat** is a large-scale quantum Hamiltonian dataset for materials comprising **105,260 crystal structures** spanning **75** elements. It contains crystal structure features together with Hamiltonian/overlap tensor blocks, serialized as `torch_geometric.data.Data` objects.

The current release provides a **~50 GiB subset** suitable for model development and integration tests. **The full dataset (~4.1 TB) is planned to be uploaded after peer review.**


## Dataset Structure

### Files in this release

| File / pattern | Description |
|----------------|-------------|
| `train-000000.tar`, `train-000001.tar`, ... | WebDataset tar shards containing sample payloads. |
| `XXXXXXXX.pkl` | Pickled `torch_geometric.data.Data` object (payload from LMDB value). |
| `XXXXXXXX.json` | Sidecar JSON with `lmdb_key` and `value_bytes`. |
| `train-manifest.csv` | Sample index table: shard path + member names + source LMDB key. |

### Splits

- Single split under `train-*` shards.

### Example record fields

Typical decoded `Data` keys include (exact keys can vary by record):

- Geometry/graph: `pos`, `atoms`, `lattice`, `multi_edge_index`, `multi_edge_vec`, `lattice_translation_vector`
- Hamiltonian/overlap blocks: `diagonal_hamiltonian`, `off_diagonal_hamiltonian`, optional overlap tensors and masks
- Metadata: `database_idx`, `mp_id`, `raw_basename`, `fermi_level`, `elapsed_time`

### Minimal load example (single sample)

Use the same Python environment as training (any env where `torch` and `torch_geometric` import correctly).

```python
import json
import pickle
import tarfile

import torch  # noqa: F401

tar_path = "train-000000.tar"
with tarfile.open(tar_path, "r") as tar:
    pkl_info = tar.getmember("00000000.pkl")
    pkl_bytes = tar.extractfile(pkl_info).read()
    meta = json.loads(tar.extractfile(tar.getmember("00000000.json")).read())

data = pickle.loads(pkl_bytes)
print(meta)
print(data)
```


## Uses

### Direct Use

- Training and evaluating graph neural networks for Hamiltonian / electronic-structure prediction.
- Data loader validation and distributed training pipeline setup.
- Reproducibility checks against LMDB-originated records.

### Out-of-Scope Use

- Safety-critical or high-stakes decision workflows without domain validation.
- Claims of complete physical/materials coverage from this subset alone.
## Dataset Creation

#### Data Collection and Processing

1. A large LMDB (~4.1 TB) is produced from electronic-structure workflows of OpenMX.
2. A ~50 GiB LMDB subset is constructed by ID-list-driven extraction.
3. `train-manifest.csv` is emitted for deterministic mapping and audit.


#### Personal and Sensitive Information

No personal data is expected. IDs such as `mp_id` represent materials-system identifiers.

## Bias, Risks, and Limitations

- This is a subset release, not the full corpus yet.
- Data reflects simulation settings and source pipeline assumptions.
- Pickle-based payloads require environment compatibility (`torch`, `torch_geometric`).

### Recommendations

- Use the same runtime stack as training/preprocessing.
- Pin package versions for reproducibility.
- Validate model behavior on broader/full releases once available.