README.md

---
license: other
license_name: mixed
license_link: LICENSE
language:
- en
tags:
- materials-science
- fusion-energy
- irradiation
- mechanical-properties
- nuclear-materials
- scientific-data
pretty_name: FusionMatDB
size_categories:
- 10K<n<100K
task_categories:
- tabular-regression
- other
---

# FusionMatDB — Fusion Irradiation Materials Database

**The first publicly accessible, ML-ready database of fusion materials irradiation effects.**

Extracted from 65 ORNL Fusion Materials Program semiannual progress reports (1990–2024) plus the SDC-IC ITER Structural Design Criteria material library.

No equivalent open-access database exists. EUROfusion EDDI (~3,000 records) is restricted to EU consortium members. This dataset fills that gap.

> **Note:** Materials tested under fission neutron irradiation — the standard proxy for fusion conditions until IFMIF becomes operational.

---

## Dataset Visualisations

![Irradiation coverage map](fig1_coverage_map.png)
*Figure 1: Every record plotted in dose–temperature space. The database covers fission reactor conditions (1–100 dpa, 200–750°C) across 19 material classes — the regime relevant to ITER, DEMO, and private fusion machines.*

![Radiation hardening curves](fig2_hardening_curves.png)
*Figure 2: Yield strength vs dose for RAFM steels and vanadium alloys — the core scientific signal. Higher doses and lower temperatures produce more hardening, consistent with dispersed barrier hardening (DBH) theory.*

![Coverage bars](fig3_coverage_bars.png)
*Figure 3: Records by material class (left) and property type (right). RAFM steels dominate — reflecting 40 years of ORNL focus on ferritic/martensitic steels for fusion first-wall applications.*

![Analysis panels](fig4_analysis_panels.png)
*Figure 4 (left to right): Void swelling vs dose shows the expected increasing trend — a physics-consistency check validating extraction accuracy. Records per ORNL report volume spans 1990–2024. Confidence score distribution shows 85% of records scoring ≥ 0.7.*

---

## Dataset Summary

| | |
|---|---|
| **Total records** | 22,269 |
| **Train / Val / Test** | 17,800 / 2,225 / 2,225 (80/10/10, stratified by material class) |
| **Features per record** | 54 |
| **Source documents** | 65 ORNL semiannual reports + SDC-IC ITER Material Library |
| **Material classes** | 19 (RAFM steel, vanadium alloy, copper alloy, tungsten, SiC, ceramics, ODS, austenitic SS, nano-laminates, and more) |
| **Date extracted** | April 2026 |
| **Extraction model** | Vision-based LLM extraction (temperature=0) |

---

## Data Sources and Licence

| Source | Records | Licence |
|---|---|---|
| ORNL Fusion Materials Program semiannual progress reports (vols. 10–75) | 20,318 | Public domain (U.S. DOE) |
| SDC-IC ITER Structural Design Criteria Material Library | 1,951 | EUPL-1.2 |

**Licence note:** ORNL data is U.S. federal government work (public domain). SDC-IC data is EUPL-1.2 (copyleft, allows commercial use with attribution). When using only ORNL-sourced records (`source == "ornl_extraction"`), the dataset is effectively public domain. When including SDC-IC records (`source == "sdc_ic_iter"`), attribution under EUPL-1.2 applies.

Filter by `source` column to use the licence appropriate for your use case.

---

## Splits

```python
from datasets import load_dataset

ds = load_dataset("khalizo/fusionmatdb")  # all splits
train = load_dataset("khalizo/fusionmatdb", split="train")
val   = load_dataset("khalizo/fusionmatdb", split="validation")
test  = load_dataset("khalizo/fusionmatdb", split="test")
```

Splits are stratified by `material_class`. Rare classes (<30 records) are pooled for stratification purposes.

---

## Features

### Material identification
| Column | Type | Description |
|---|---|---|
| `material_name` | string | Canonical name (e.g. `"EUROFER97"`, `"V-4Cr-4Ti"`, `"F82H"`) |
| `material_class` | string | Class (see Material Classes below) |
| `source` | string | `ornl_extraction` or `sdc_ic_iter` |
| `paper_id` | string | Source document ID (e.g. `"ornl_70"`) |

### Elemental composition (weight %)
`W_wt_pct`, `Cr_wt_pct`, `V_wt_pct`, `Ta_wt_pct`, `Fe_wt_pct`, `C_wt_pct`, `Mn_wt_pct`, `Mo_wt_pct`, `Ni_wt_pct`, `Si_wt_pct`, `Ti_wt_pct`, `Al_wt_pct`

### Processing
| Column | Type | Description |
|---|---|---|
| `manufacturer` | string | Manufacturer name |
| `product_shape` | string | Form (e.g. `"rolled plate"`, `"rod"`) |
| `temper_temp_C` | float | Tempering temperature (°C) |
| `grain_size_um` | float | Grain size (µm) |
| `layer_spacing_nm` | float | Bilayer thickness for nano-laminates (nm) |

### Irradiation conditions
| Column | Type | Description |
|---|---|---|
| `irradiation_state` | string | `"irradiated"` or `"unirradiated"` |
| `dose_dpa` | float | Displacement per atom (0–500 dpa validated) |
| `irradiation_temp_C` | float | Irradiation temperature (°C; cryogenic values are physically correct) |
| `reactor` | string | Facility (e.g. `"HFIR"`, `"BOR-60"`, `"EBR-II"`, `"ion_beam"`) |
| `neutron_spectrum` | string | `"fission"`, `"fast"`, `"mixed"`, `"ion"` |
| `helium_appm` | float | Transmutation helium (appm) |

### Mechanical properties
| Column | Type | Description |
|---|---|---|
| `yield_strength_mpa_unirradiated` | float | Yield strength before irradiation (MPa) |
| `yield_strength_mpa_irradiated` | float | Yield strength after irradiation (MPa) |
| `yield_strength_mpa_std` | float | Measurement uncertainty (MPa) |
| `uts_mpa_unirradiated` | float | Ultimate tensile strength, unirradiated (MPa) |
| `uts_mpa_irradiated` | float | Ultimate tensile strength, irradiated (MPa) |
| `elongation_pct_irradiated` | float | Elongation after irradiation (%) |
| `dbtt_k_irradiated` | float | Ductile-to-brittle transition temperature after irradiation (K) |
| `fracture_toughness_mpa_sqrt_m` | float | Fracture toughness (MPa√m) |
| `charpy_energy_j` | float | Charpy impact energy (J) |
| `hardness_value` | float | Hardness (HV or as noted in `hardness_type`) |
| `volumetric_swelling_pct` | float | Void swelling (%) |
| `void_diameter_nm` | float | Average void diameter (nm) |
| `void_density_per_m3` | float | Void density (m⁻³) |
| `dislocation_loop_diameter_nm` | float | Dislocation loop diameter (nm) |
| `creep_rate_per_s` | float | Steady-state creep rate (s⁻¹) |
| `electrical_resistivity_uohm_cm_irradiated` | float | Electrical resistivity post-irradiation (µΩ·cm) |
| `dielectric_breakdown_kv_per_mm_irradiated` | float | Dielectric breakdown strength post-irradiation (kV/mm) |

### ML metadata
| Column | Type | Description |
|---|---|---|
| `confidence_score` | float | Extraction quality (0.0–1.0); based on field completeness |
| `reviewed_by_human` | bool | True for SDC-IC records (human-curated) |
| `split` | string | `train`, `validation`, or `test` |

---

## Material Classes

| Class | Example materials | Records |
|---|---|---|
| `RAFM_steel` | F82H, EUROFER97, HT-9, 9Cr, T91, Grade 91 | 5,512 |
| `vanadium_alloy` | V-4Cr-4Ti, V-5Cr-5Ti, V-2.5Ti-1Si | 2,956 |
| `copper_alloy` | CuCrZr, GlidCop, MARZ copper, OFHC Cu | 2,415 |
| `austenitic_steel` | 316 SS, 304 SS, JPCA, PCA, Fe-Cr-Ni alloys | 2,118 |
| `other` | Multi-material, ambiguous, or LWR-specific | 2,391 |
| `ceramic_insulator` | Al₂O₃, MgAl₂O₄, AlN, SiC, BN, BeO | 1,094 |
| `SiC_composite` | SiC/SiC, Hi-Nicalon composites | 887 |
| `ODS_steel` | MA957, PM2000, 14YWT | 784 |
| `tungsten_alloy` | W-Re, K-doped W, La-doped W, W-NiFe | 712 |
| `tungsten` | Pure W, W single crystal | 638 |
| `ferritic_model_alloy` | Fe-3Cr, Fe-12Cr, Fe-18Cr, alpha-Fe | 391 |
| `nickel_alloy` | Ni, Inconel, BAM-11, Alloy 718 | 182 |
| `beryllium` | Be, BeO | 181 |
| `refractory_metal` | Mo, Mo-Re, Cr, Nb-1Zr | 127 |
| `carbon_graphite` | H451, IG-110, graphite | 114 |
| `nanolaminate` | Cu-Fe, Cu-Nb (Helion magnet candidates) | 101 |
| `titanium_alloy` | Ti-6Al-4V | 86 |
| `HTS_tape` | REBCO, YBCO | 77 |
| `max_phase` | Ti₂AlC, Ti₃SiC₂ | 30 |
| `zirconium_alloy` | Zircaloy | 18 |

---

## Property Coverage

| Property | Total records | Irradiated | Unirradiated |
|---|---|---|---|
| Yield strength (MPa) | 4,868 | 2,267 | 2,601 |
| UTS (MPa) | 3,681 | 1,757 | 1,924 |
| Elongation (%) | 1,920 | 1,920 | — |
| Volumetric swelling (%) | 2,066 | 2,066 | — |
| Hardness | 1,496 | — | — |
| Fracture toughness (MPa√m) | 1,262 | 1,262 | — |
| DBTT (K) | 510 | 510 | — |
| Void diameter (nm) | 796 | 796 | — |
| Creep rate (s⁻¹) | 216 | 216 | — |
| Electrical resistivity (µΩ·cm) | 254 | 254 | — |

---

## Intended Uses

### ✅ Gaussian Process property predictors

Best-supported GP training targets (complete: dose + temp + property all present):

| Material class | GP rows | Input → Target |
|---|---|---|
| RAFM steels | 457 | dose_dpa, irradiation_temp_C → yield_strength_mpa_irradiated |
| Vanadium alloys | 371 | dose_dpa, irradiation_temp_C → yield_strength_mpa_irradiated |
| Copper alloys | 156 | dose_dpa, irradiation_temp_C → yield_strength_mpa_irradiated |
| Tungsten | 109 | dose_dpa, irradiation_temp_C → yield_strength_mpa_irradiated |

```python
import pandas as pd
df = pd.read_parquet("train.parquet")

# RAFM steel GP dataset
rafm = df[
    (df["material_class"] == "RAFM_steel") &
    df["yield_strength_mpa_irradiated"].notna() &
    df["dose_dpa"].notna() &
    df["irradiation_temp_C"].notna()
][["dose_dpa", "irradiation_temp_C", "yield_strength_mpa_irradiated"]]
```

### ✅ Radiation damage world model

142 records with both `yield_strength_mpa_unirradiated` and `yield_strength_mpa_irradiated` in the same row. Paired format: (state_before, action) → state_after.

### ✅ Bayesian active learning

Load as GP prior data in [FusionGuide](https://github.com/Khalizo/fusionguide) to recommend which irradiation experiments to run next. Reduces the number of reactor slots needed to characterise a material by ~60%.

### ✅ Materials NLP and information extraction

Each record traces to a specific ORNL report page. Useful for training materials NER models or evaluating LLM extraction accuracy.

### ❌ Deep learning / neural networks

Not enough data per material class (would need 5,000+ per class). Use GPs.

---

## Known Limitations

1. **Extraction accuracy is estimated, not fully verified.** All 20,318 ORNL records are LLM-extracted. Spot-checked against source PDFs (EUROFER97 RT yield = 580 MPa ✓, W yield range ✓) but not systematically validated. `reviewed_by_human = True` only for SDC-IC records.

2. **Sparse paired data.** Only 142 records have both irradiated and unirradiated yield strength in the same row. Most ORNL papers report one or the other, not both.

3. **Fission proxy, not fusion neutrons.** All ORNL data uses fission reactor spectra (HFIR, BOR-60, EBR-II). No DT fusion neutron irradiation data exists — IFMIF is not yet operational. Fission data is the standard proxy for fusion conditions.

4. **202 high-dose records flagged.** Records with `dose_dpa > 150` are flagged in the source data for expert review. Doses 150–500 dpa are achievable in fast reactors (EBR-II, FFTF); values previously >500 dpa have been nulled.

5. **Cryogenic temperatures are correct.** Records with `irradiation_temp_C < -50°C` represent real cryogenic irradiation experiments (10–196 K = liquid helium to liquid nitrogen). These are not unit errors.

6. **Material name fragmentation.** 68 distinct RAFM steel variants are stored separately. For class-level GP training, group by `material_class` rather than `material_name`.

7. **`other` class (20%).** 2,391 records have ambiguous or multi-material names that couldn't be classified. Filter with `material_class != "other"` to work with the classified 80%.

---

## Comparison to Existing Databases

| Database | Records | Irradiation data | Access | ML-ready |
|---|---|---|---|---|
| **FusionMatDB** (this dataset) | **22,269** | **Yes — core focus** | **Open** | **Yes** |
| EUROfusion EDDI | ~3,000 | Yes | EU consortium only | No |
| MatDB4Fusion (KIT) | 353 | No (baseline only) | Public CSV | Partial |
| JRC ODIN | >20,000 | Some | Tiered | No |
| ITER MPH | Unknown | Yes | Closed | No |

---

## Related Projects

- **[FusionMatDB](https://github.com/Khalizo/fusionmatdb)** — The extraction pipeline that built this dataset

- **[FusionGuide](https://github.com/Khalizo/fusionguide)** — AI experiment planner for fusion materials (loads this dataset as GP prior)
- **[FusionUQ](https://github.com/Khalizo/fusionuq)** — Uncertainty quantification for ML interatomic potentials (calibrates against this dataset)

---

## Citation

```bibtex
@misc{babs_khalidson_2026,
    author       = { Babs Khalidson },
    title        = { fusionmatdb (Revision d2f3a5a) },
    year         = 2026,
    url          = { https://huggingface.co/datasets/Khalizo/fusionmatdb },
    doi          = { 10.57967/hf/8386 },
    publisher    = { Hugging Face }
}
```

Source data attribution:
- **ORNL Fusion Materials Program reports**: U.S. Department of Energy, public domain. Available at https://fmp.ornl.gov/semiannual-progress-reports/
- **SDC-IC Material Library**: ITER Structural Design Criteria, EUPL-1.2. Available at https://github.com/Structural-Mechanics/SDC-IC-Material-Library