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.

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Dataset Visualisations

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.

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.

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.

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.

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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)

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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.

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Splits

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.

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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

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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

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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	—

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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

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 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.

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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%.

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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

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Related Projects

• FusionMatDB — The extraction pipeline that built this dataset

• FusionGuide — AI experiment planner for fusion materials (loads this dataset as GP prior)

• FusionUQ — Uncertainty quantification for ML interatomic potentials (calibrates against this dataset)

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Citation

@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