Initial upload: extxyz splits

.gitattributes

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CITATION.cff

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+cff-version: 1.2.0
+message: "If you use this dataset, please cite it as below."
+title: "My-MLIAP-Dataset: Train/Val/Test extxyz for <system/domain>"
+authors:
+  - family-names: "<Surname>"
+    given-names: "<Given name>"
+    affiliation: "<Affiliation>"
+    orcid: "<ORCID>"
+version: "1.0.0"
+doi: "<optional DOI>"
+date-released: "<YYYY-MM-DD>"
+repository-code: "https://huggingface.co/datasets/<user>/my-mliap-dataset"
+license: "CC-BY-4.0"
+preferred-citation:
+  type: article
+  authors:
+    - family-names: "<Surname>"
+      given-names: "<Given name>"
+  title: "<Related paper title>"
+  journal: "<Journal/Preprint>"
+  year: <YYYY>
+  doi: "<paper DOI>"

LICENSE

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+License will be here

README.md

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+# My-MLIAP-Dataset: Train/Val/Test extxyz for <system/domain>
+
+**Pretty name:** My MLIAP Dataset  
+**Tags:** materials-science, atomistic-simulations, extxyz, ase, mliap  
+**License:** CC-BY-4.0  
+**Dataset type:** splits (train/val/test)
+
+## Summary
+This dataset provides three `.extxyz` splits (train/val/test) for training and benchmarking machine-learning interatomic potentials (MLIAPs).
+
+- **Format:** ASE/EXTXYZ with keys: `REF_energy`, `REF_forces`, `REF_stress`
+- **Units:** energy (eV), forces (eV/Å), stress (eV/ų)
+- **Systems:** <brief description, e.g., perovskites ABO3, Li-containing oxides, ...>
+- **Provenance:** <DFT/MD details: functional, code, cutoffs, k-points, thermostat/barostat, etc.>
+
+## Files
+- `data/train.extxyz` — <N_train> frames
+- `data/val.extxyz` — <N_val> frames
+- `data/test.extxyz` — <N_test> frames
+- `index.json` — split counts, checksums, and schema
+
+### Schema (per-frame)
+- Energies stored in `info["REF_energy"]` (float, eV)
+- Forces stored in `arrays["REF_forces"]` (shape: (N,3), eV/Å)
+- Stress stored in `info["REF_stress"]` (Voigt 6 or 3x3; eV/ų). Specify exact convention below.
+
+**Stress convention:** <Voigt order and sign convention; e.g., [σ_xx, σ_yy, σ_zz, σ_yz, σ_xz, σ_xy]>
+
+## Quickstart
+
+### Python: Download entire repository
+```python
+from huggingface_hub import snapshot_download
+local_dir = snapshot_download(
+    repo_id="<your-username>/my-mliap-dataset",
+    repo_type="dataset",
+    local_dir="my-mliap-dataset",
+    local_dir_use_symlinks=False
+)
+```
+
+### Python: Load a split with ASE
+```python
+from ase.io import read
+train = read('hf://datasets/<your-username>/my-mliap-dataset/data/train.extxyz', ':')
+```
+
+### Python: Fetch a single file
+```python
+from huggingface_hub import hf_hub_download
+path = hf_hub_download(
+    repo_id="<your-username>/my-mliap-dataset",
+    repo_type="dataset",
+    filename="data/train.extxyz"
+)
+from ase.io import read
+train = read(path, ':')
+```
+
+## Benchmarks / Intended use
+- Primary task: supervised learning of energies/forces/stresses for MLIAPs.
+- Suggested splits: train/val/test as provided.
+- Recommended metrics: energy MAE (meV/atom), force MAE (meV/Å), virial/stress MAE (GPa or eV/ų).
+
+## Provenance & generation
+- **Code:** <e.g., VASP/PBE or CP2K/R2SCAN, etc.>
+- **Settings:** <ENCUT, K-mesh, smearing, thresholds, etc.>
+- **MD Sampling:** <NVT/NPT, T/P ranges, timesteps>
+- **Post-processing:** <relaxations, filters, deduplication, unit conversions>
+
+## License
+This dataset is released under **CC-BY-4.0**. See `LICENSE`.
+
+## How to cite
+Please cite:
+1. <Your article / preprint / DOI>
+2. This dataset (see `CITATION.cff`)
+
+## Changelog
+- v1.0.0 — initial release.
+
+---
+**Contact:** <name, affiliation, email or GitHub handle>

data/test.extxyz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f592658225bbd57ac361d924c64fffc62c092085eeb8b71fc11c2ea512fd530
+size 87246759

data/train.extxyz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:02bfa5d7e6c72762de02055873adad2feec4e35b8c286813dc1c97cca17eca07
+size 610602201

data/val.extxyz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b705480a15a23844621fcea89ab5edfbded91650f4e722540f9a038ab7c022f
+size 99866454

index.json

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+{
+  "format": "extxyz",
+  "properties": {
+    "energy_key": "REF_energy",
+    "forces_key": "REF_forces",
+    "stress_key": "REF_stress",
+    "units": {
+      "energy": "eV",
+      "forces": "eV/Å",
+      "stress": "eV/ų"
+    },
+    "stress_convention": "Voigt: [xx, yy, zz, yz, xz, xy]; sign: +tension"
+  },
+  "splits": [
+    {
+      "name": "train",
+      "file": "data/train.extxyz",
+      "num_frames": 53426,
+      "sha256": "02bfa5d7e6c72762de02055873adad2feec4e35b8c286813dc1c97cca17eca07"
+    },
+    {
+      "name": "val",
+      "file": "data/val.extxyz",
+      "num_frames": 8670,
+      "sha256": "1b705480a15a23844621fcea89ab5edfbded91650f4e722540f9a038ab7c022f"
+    },
+    {
+      "name": "test",
+      "file": "data/test.extxyz",
+      "num_frames": 7656,
+      "sha256": "3f592658225bbd57ac361d924c64fffc62c092085eeb8b71fc11c2ea512fd530"
+    }
+  ]
+}

index_gen.py

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+#!/usr/bin/env python3
+import argparse, json, hashlib, os
+from ase.io import iread
+
+def sha256(path, chunk=1024*1024):
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        while True:
+            b = f.read(chunk)
+            if not b:
+                break
+            h.update(b)
+    return h.hexdigest()
+
+def count_frames(path):
+    n = 0
+    for _ in iread(path, ":"):
+        n += 1
+    return n
+
+def main():
+    ap = argparse.ArgumentParser(description="Generate index.json for MLIAP extxyz splits")
+    ap.add_argument("--root", default=".", help="Dataset root containing data/train.extxyz, data/val.extxyz, data/test.extxyz")
+    ap.add_argument("--energy-key", default="REF_energy")
+    ap.add_argument("--forces-key", default="REF_forces")
+    ap.add_argument("--stress-key", default="REF_stress")
+    ap.add_argument("--energy-unit", default="eV")
+    ap.add_argument("--forces-unit", default="eV/Å")
+    ap.add_argument("--stress-unit", default="eV/ų")
+    ap.add_argument("--stress-convention", default="Voigt: [xx, yy, zz, yz, xz, xy]; sign: +tension")
+    args = ap.parse_args()
+
+    root = args.root
+    splits = ["train", "val", "test"]
+    entries = []
+    for s in splits:
+        rel = os.path.join("data", f"{s}.extxyz")
+        path = os.path.join(root, rel)
+        if not os.path.exists(path):
+            print(f"WARNING: missing {path}; skipping")
+            continue
+        entries.append({
+            "name": s,
+            "file": rel,
+            "num_frames": count_frames(path),
+            "sha256": sha256(path)
+        })
+    meta = {
+        "format": "extxyz",
+        "properties": {
+            "energy_key": args.energy_key,
+            "forces_key": args.forces_key,
+            "stress_key": args.stress_key,
+            "units": {
+                "energy": args.energy_unit,
+                "forces": args.forces_unit,
+                "stress": args.stress_unit
+            },
+            "stress_convention": args.stress_convention
+        },
+        "splits": entries
+    }
+    out = os.path.join(root, "index.json")
+    with open(out, "w", encoding="utf-8") as f:
+        json.dump(meta, f, indent=2, ensure_ascii=False)
+    print(f"Wrote {out}")
+
+if __name__ == "__main__":
+    main()