--- license: cc-by-4.0 language: - en size_categories: - 100K *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