---
license: cc-by-4.0
---

# QHMat Dataset Sample (1 GB) Preview


## Dataset Details
It contains a **~1 GB subset** derived from the full QHMat dataset, intended for **quick data quality checks**. The **complete public release of full QHMat (~4.1TB) is planned after peer review**. This subset shares the same indexing conventions and data format as the full release.

> **Hierarchy:** Full QHMat (4.1 TB) → **1 GB subset (this repository)**

### Dataset Description

This dataset is a **small, byte-limited preview** intended for **peer review, sanity checks, and pipeline testing**. It contains **45 crystal/material structures** as serialized graph/tensor records (Hamiltonian and overlap blocks, geometry, and metadata). 

### Files in this release

| File / pattern | Description |
|----------------|-------------|
| `train-000000.tar`, `train-000001.tar`, … | WebDataset-style archives. Each logical sample has two members sharing the same basename. |
| `00000000.pkl`, … | **Pickle blob** of `torch_geometric.data.Data` (same bytes as stored in the source LMDB value). |
| `00000000.json`, … | Small JSON sidecar: `lmdb_key` (integer database index), `value_bytes` (byte length of `.pkl`). |
| `train-manifest.csv` | Table mapping `sample_index` → shard file, member names, `lmdb_key`, `value_bytes`. |

### Statistics (this build)

- **Samples:** 45 (rows in `train-manifest.csv`).
- **Shards:** 2 (`train-000000.tar`, `train-000001.tar`).
- **Approximate on-disk tar footprint:** ~568 MiB + ~490 MiB (see files after upload; exact sizes depend on filesystem).

### Example record fields

After `pickle.loads` on a `.pkl` file, typical `Data` attributes include (names may vary slightly by pipeline version):

- **Geometry / graph:** `pos`, `atoms`, `lattice`, `multi_edge_index`, `multi_edge_vec`, `lattice_translation_vector`, …
- **Hamiltonian / overlap:** `diagonal_hamiltonian`, `off_diagonal_hamiltonian`, optional `diagonal_overlap`, `off_diagonal_overlap`, with associated `*_mask` tensors.
- **Metadata:** `database_idx`, `mp_id`, `raw_basename`, `fermi_level`, `elapsed_time`, …

Use `torch_geometric` introspection or print `data.keys` after loading.


### Minimal load example (single sample)

```python
import io
import json
import pickle
import tarfile

import torch  # noqa: F401 — needed for unpickling tensors inside Data

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)   # {'lmdb_key': ..., 'value_bytes': ...}
print(data)   # torch_geometric.data.Data
```

### Direct Use

- Training or benchmarking **graph neural networks** and related models on **Hamiltonian / overlap tensor blocks** paired with **crystal structure** fields.
- **Dataset loader development** and **integration tests** before downloading larger subsets.
- **Reproducibility checks** during peer review (inspect manifests, decode samples).

### Out-of-Scope Use

- **Not** a guaranteed statistically representative sample of the full corpus (structure count is small).
- **Not** for safety-critical or high-stakes decisions without domain validation.
- **Not** a substitute for the full release when training production-scale models.

### Splits

- **Single split:** all samples are shipped under the `train-*` shard prefix (preview bundle). There is **no** separate val/test split in this artifact.


#### Data Collection and Processing

1. **Upstream:** Structures and electronic-structure-derived tensors originate from computational workflows (OpenMX-class outputs) merged into a large LMDB in the parent project.
2. **Subset:** A **~1 GiB LMDB** subset was built by copying records in **canonical index order** until the byte budget was reached (same convention as documented in the upstream repo).

#### Personal and Sensitive Information

This dataset is **not expected** to contain personal data. Identifiers such as `mp_id` refer to **materials database IDs**, not individuals.

## Bias, Risks, and Limitations

- **Coverage:** The preview contains **very few** structures relative to the full corpus; metrics are **not** indicative of full-dataset diversity.
- **Simulation bias:** Electronic-structure workflows, basis choices, and convergence criteria induce systematic differences versus experiment.
- **Technical:** `.pkl` relies on Python pickling; loading requires compatible **`torch` / `torch_geometric`** versions with the training stack used to build the data.

### Recommendations

- Validate loader behavior on this preview before scaling to multi-TB subsets.
- Pin dependency versions in your training repo.
- Prefer citing the **paper + dataset version + manifest checksum** once available.