Datasets:

Scandium-Labs
/

solid-state-electrolyte-conductivity

@@ -1,234 +0,0 @@
----
-license: cc-by-4.0
-language:
-- en
-size_categories:
-- 100K<n<1M
-task_categories:
-- other
-tags:
-- dft
-- materials-project
-- oqmd
-- jarvis
-- materials-screening
-- dft-stability
-- parquet
-- materials-science
-- battery-materials
-- inorganic-materials
-pretty_name: Scandium-Dataset
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: dataset/entries_v4_typed.parquet
----
-# Dataset Card — Scandium-Dataset v1.0.0
-## Summary
-Scandium-Dataset provides a harmonized, quality-scored foundation of DFT-computed structural and thermodynamic properties across 267,230 materials from Materials Project, OQMD, and JARVIS-DFT. It supports the **early screening stage** of battery materials discovery — filtering by phase stability, electronic structure, and structural family — before downstream property prediction (ionic conductivity, mechanical stability, electrochemical window) via AIMD, NEB, or targeted DFT.
-*Previous versions positioned this as a solid-state battery discovery dataset. v1.0.0 corrects that framing: the data covers thermodynamic screening, not ionic transport. See [CHANGELOG](CHANGELOG.md) for details.*
-- **Version:** v1.0.0
-- **Total entries:** 267,230 (Gold: 96,242 | Validated: 140,382 | Raw: 30,108 | Experimental: 498)
-- **Strict Gold:** 56,966 entries (subset of Gold with all gates passed)
-- **Battery-relevant subset:** 82,925 entries | **Electrolyte candidate subset:** 41,665 entries (strict Gold)
-  > *File names on disk (`battery_candidate_subset_v1.json`, `solid_electrolyte_candidate_subset_v1.json`) — the v3 suffix was retired in v1.0.0-rc2 when the scope was clarified.*
-- **Storage:** Typed Parquet (`dataset/entries_v4_typed.parquet`, 47 columns) with indexed lookup — 178 MB with proper int/float/bool types for HF Dataset viewer compat.
-- **Schema:** Full column dictionary at [`SCHEMA.md`](SCHEMA.md) — names, types, units, null semantics, source mappings.
-- **Preview:** [`dataset/entries_v4_typed_sample.parquet`](dataset/entries_v4_typed_sample.parquet) — 1,000-row stratified sample (1.25 MB) for schema inspection without downloading the full dataset.
-- **Experimental data:** 498 OBELiX entries integrated (Therrien et al. 2025) as `experimental_gold` tier with measured Li-ion conductivity.
-- **Transport proxy draft:** 24,873 BVSE migration barriers computed (bvlain engine) at 23% Li/Na coverage — see "Roadmap" below for the gap this addresses.
-## Quick Start
-```python
-# Inspect schema (no download needed)
-import pyarrow.parquet as pq
-schema = pq.read_schema("dataset/entries_v4_typed_sample.parquet")
-for f in schema:
-    print(f"{f.name}: {f.type}")
-# Load full dataset
-import pandas as pd
-df = pd.read_parquet("dataset/entries_v4_typed.parquet")
-print(df.shape, list(df.columns))
-# Stream in batches (low memory)
-pf = pq.ParquetFile("dataset/entries_v4_typed.parquet")
-for batch in pf.iter_batches(batch_size=1000, columns=["formula","band_gap"]):
-    df_chunk = batch.to_pandas()
-    print(f"Loaded {len(df_chunk)} rows")
-# Filter by source before loading
-df_mp = pd.read_parquet("dataset/entries_v4_typed.parquet", filters=[("source","=","mp")])
-# Load partitioned source
-df_mp2 = pd.read_parquet("dataset/partitioned/source=mp/data.parquet")
-# Load from chunk
-df_chunk = pd.read_parquet("dataset/chunks/entries_v4_chunk_00.parquet")
-```
-See [`SCHEMA.md`](SCHEMA.md) for full column dictionary with units and null semantics.
-## Included vs. Not Included
-| Included | Not Included (requires augmentation) |
-|---|---|
-| Formation energy (eV/atom) — 100% | Ionic conductivity (S/cm) |
-| Energy above hull (eV/atom) — 90.4% | Migration/activation energy (NEB) |
-| Band gap (eV) — 99.9% | Electrochemical stability window |
-| Space group, volume, density — 99.9%+ | Elastic/shear moduli (full DFT) |
-| Crystal structure (pymatgen `Structure` JSON) — 100% | Experimental validation beyond pilot OBELiX integration |
-| Quality tier (Gold/Validated/Raw) — 100% | |
-| Provenance tracking (source, source_id, checksum) — 99.8% | |
-| SSE family classification (composition-based) — 100% | |
-| BVSE migration barrier proxy — 23% of Li/Na entries | |
-## Suitable For / Not Suitable For
-**Suitable for:** phase stability screening, structural family classification, band gap prediction, materials-informatics benchmarking, pretraining general property predictors on inorganic crystal structures, cross-source DFT property harmonization studies.
-**Not suitable for (without augmentation):** direct ionic conductivity prediction, SSE performance ranking, electrochemical stability assessment. The dataset contains migration barrier proxies for 23% of Li/Na entries, but at ~75% skip rate due to bond-valence parameter coverage, these do not constitute a complete transport-property layer.
-## What the Data Actually Supports
-### Property coverage (verified against Parquet store)
-| Property | Coverage | Notes |
-|----------|----------|-------|
-| Formation energy | 267,230 (100%) | From MP, OQMD, JARVIS-DFT |
-| Energy above hull | 257,799 (96.5%) | **14,669 JARVIS entries newly computed** in v1.0.0-rc2 via internal convex hull; 8,933 JARVIS entries lack EaH due to sparse chemical systems |
-| Band gap | 267,079 (99.9%) | 151 OQMD entries with non-converged band gap |
-| Space group | 267,214 (100%) | spglib symmetry analysis |
-| Volume | 267,230 (100%) | From structure |
-| Density | 267,230 (100%) | From elements + volume |
-| Structure JSON | 267,230 (100%) | pymatgen Structure serialization |
-| Quality score | 267,230 (100%) | Composite score (0–88) |
-| Provenance | 267,230 (100%) | Source, source_id, checksum |
-| Duplicate group | 21,140 (7.9%) | Entries in dedup groups (31,997 total removed upstream) |
-### SSE screening fields (stored in `ssb_screening` block)
-| Field | Coverage | Notes |
-|-------|----------|-------|
-| SSE family | 267,230 (100%) | Composition-based heuristic (not structure-based) |
-| Mobile ion | 267,230 (100%) | Li/Na/Mg presence-based |
-| CAVD channel dimensionality | **0%** | Algorithm was not re-run against Parquet store |
-| SSE candidate score | 100% | 5-gate system (thermo + electronic + mobility + window + mechanical) |
-| Thermo stability flag | 99.8% | E_hull < 0.025 eV/atom |
-| Bulk/shear modulus | 100% | Geometric density-based proxy (not DFT elastic tensors) |
-| Stability window | 1,814 (0.7%) | Grand-potential phase diagrams — computed for subset with low EaH |
-| Interfacial reaction energy | 39,706 (14.9%) | Decomposition energy vs Li |
-| BVSE migration barrier | 24,873 (23% of Li/Na) | bvlain v0.25.1, softBV percolation. **74.8% skip rate** on attempted entries (98,773) due to bond-valence parameter coverage gaps |
-## Sources
-| Source | Entries | License | Download Date |
-|--------|---------|---------|---------------|
-| Materials Project | 69,279 | CC BY 4.0 | 2026-07-20 |
-| OQMD | 171,780 | Non-commercial + attribution | 2026-07-20 |
-| JARVIS-DFT | 25,673 | CC0 | 2026-07-20 |
-| OBELiX (experimental) | 498 | Per-article terms | 2026-07-24 |
-## License Warning
-⚠️ **This dataset is NOT uniformly licensed.** Each entry carries its own license.
-- `license: "CC-BY-4.0"` → MP entries (commercial safe, 26.1%)
-- `license: "CC0-1.0"` → JARVIS entries (commercial safe, 9.6%)
-- `license: "OQMD-noncommercial"` → OQMD entries (non-commercial only, 64.3%)
-See [`LICENSE_BREAKDOWN.md`](LICENSE_BREAKDOWN.md). A **Commercial-Safe edition** (MP+JARVIS, ~94,952 entries) is extractable via `scripts/extract_commercial_safe_edition.py`.
-## Tier System
-| Tier | Count | Criteria |
-|------|-------|----------|
-| **Strict Gold** ⭐ | **56,966** | 11 gates: base Gold + quality ≥ 80 + no defects + provenance |
-| **Gold** | 96,242 | 8 gates: validated + unique + stable + complete metadata |
-| **Validated** | 140,382 | 5 gates: valid structure + targets + no critical issues |
-| **Raw** | 30,108 | Source + formula present (may have quality issues) |
-| **Experimental Gold** | 498 | OBELiX entries with measured conductivity |
-## Family Imbalance
-The dataset skews heavily toward intermetallics (62.5%) and layered oxides (15.7%). Solid-electrolyte-relevant composition families are a small fraction:
-| Family | Total | Gold | Validated | Raw |
-|--------|-------|------|-----------|-----|
-| Intermetallic | 166,930 | 36,520 | 109,056 | 21,354 |
-| Layered oxide | 42,015 | 26,295 | 12,756 | 2,964 |
-| Halide SSE | 18,803 | 12,560 | 5,211 | 1,032 |
-| Sulfide SSE | 16,359 | 9,458 | 5,591 | 1,310 |
-| NASICON | 560 | 488 | 70 | 2 |
-| Garnet | 23 + 113 experimental | 19 | 4 | 0 |
-If your target chemistry is garnets or sulfides specifically, usable Gold-tier entries number in the tens to low thousands — consider targeted acquisition (ICSD, structured literature extraction) before expecting ML models to generalize within these families.
-## BVSE Migration Barriers (Draft Quality)
-| Metric | Value |
-|--------|-------|
-| Total Li/Na entries | 108,015 |
-| Excluded (>60 sites) | 8,744 |
-| No structure (experimental) | 498 |
-| **Attempted** | **98,773** |
-| Barriers computed | 24,873 |
-| Superionic (≤0.25 eV) | 1,501 |
-| Good (0.25–0.40 eV) | 4,705 |
-| Moderate (0.40–0.55 eV) | 5,009 |
-| Poor (>0.55 eV) | 13,658 |
-| Skipped (no BV params) | 73,900 |
-| Errors | 0 |
-| **Coverage of attempted** | **25.2%** |
-| **Coverage of all Li/Na** | **23.0%** |
-**Caveat:** 73,900 skipped entries (~75% of attempted) reflect bond-valence parameter coverage, not a sampling gap. Entries are flagged with `bvse_skip_reason`. Engine: bvlain v0.25.1, validated against 7 known SSEs (5/7 pass within literature, 2 known-marginal outliers documented in KNOWN_ISSUES.md).
-## Roadmap to True SSE-Property Coverage
-v1.0.0 provides thermodynamic and structural screening data. Planned extensions:
-1. **CAVD re-computation** against the Parquet store (currently 0% coverage; algorithm exists in `scripts/compute_cavd_channel_dimensionality.py`)
-2. **JARVIS EaH** re-computation against the Parquet store (currently 0% for 25,673 JARVIS entries; script exists in `scripts/compute_jarvis_hull_energy.py`)
-3. **BVSE barrier expansion** — re-run with lower `--max-sites` threshold and extended parameter table to reduce the 75% skip rate
-4. **MLIP-NEB migration barriers** for top-tier stable candidates (active development)
-5. **DFT NEB validation** for a curated set of halide/sulfide/garnet candidates
-6. **Elastic tensor data** from MP API for mechanical property validation (scaffold exists in `scripts/compute_mechanical_properties.py`)
-7. **Experimental conductivity cross-references** beyond OBELiX pilot
-## Benchmark
-Frozen train/val/test splits at [`dataset/splits/`](dataset/splits/). Four split types with RF+Ridge baselines in [`MODEL_LEADERBOARD.md`](MODEL_LEADERBOARD.md):
-| Split | Train | Val | Test | Purpose |
-|-------|-------|-----|------|---------|
-| Random 80/10/10 | 200,122 | 26,670 | 39,940 | Basic generalization |
-| Composition held-out | 213,383 | 27,011 | 26,338 | No formula overlap |
-| Family held-out | 260,984 | 5,165 | 583 | Cross-family |
-| Chemistry held-out | 227,384 | 26,673 | 12,675 | OOD (halides) |
-GNN baselines (CGCNN, MEGNet, ALIGNN) are in progress.
-## Intended Use
-- **Primary:** Upstream materials screening — filtering by phase stability, electronic structure, and structural family
-- **Secondary:** Cross-source DFT property harmonization, materials-informatics benchmarking, pretraining structure-based property predictors
-- **Not recommended for:** Quantitative phase diagram construction (use MP/OQMD directly), SSE conductivity ranking (requires transport-property labels not in this dataset)
-## Related Datasets
-- [**Scandium-Labs/solid-state-electrolyte-conductivity**](https://huggingface.co/datasets/Scandium-Labs/solid-state-electrolyte-conductivity) — the Scandium Labs **SSB electrolyte transport dataset**: literature-verified ionic conductivity & activation energy labels (183 verified) across 11 electrolyte families, with cross-paper consensus and a gold benchmark. Use it *downstream* of this dataset, where the screening filter has already narrowed candidates to those needing transport-property prediction.
-## Maintenance
-- Version: v1.0.0
-- DOI: pending (Zenodo archival in progress)
-- Issue tracking: GitHub Issues
-- Contact: Scandium Labs