xiaodu-ali/CrystalXRD-Bench

LLM4Mat-Bench: XRD Max-Peak HKL Identification Benchmark

Dataset Description

LLM4Mat-Bench is a multimodal benchmark for evaluating Vision-Language Models (VLMs) on crystallographic reasoning tasks. Given a theoretical X-ray Diffraction (XRD) pattern image and the corresponding crystal structure (CIF), the model must identify the Miller indices (HKL) of the crystallographic planes contributing to the highest-intensity peak.

This benchmark tests the intersection of visual pattern recognition and materials science domain knowledge — a challenging task that requires understanding both XRD physics and crystallographic indexing.

Task Definition

Input:

• XRD pattern image (theoretical, Cu Kα radiation)
• Chemical formula
• Full CIF crystal structure data

Output:

• Miller indices [h, k, l] (or [h, k, i, l] for hexagonal/trigonal systems) of all crystallographic planes contributing to the highest peak

Key challenge: A single visual peak may be an overlap of multiple Bragg reflections due to lattice symmetry or accidental degeneracy. The model must identify ALL contributing planes.

Dataset Statistics

Dataset	Samples	Material Type	Description
cantor_hea	195	HEA	Cantor high-entropy alloys
gnome	183	Inorganic	GNoME predicted structures
hmof	199	MOF	Hypothetical metal-organic frameworks
jarvis_dft	172	Inorganic	JARVIS-DFT materials
jarvis_qetb	200	Inorganic	JARVIS quantum espresso tight-binding
mp	199	Inorganic	Materials Project
omdb	201	Organic	Organic Materials Database
oqmd	200	Inorganic	Open Quantum Materials Database
qmof	199	MOF	Quantum MOF database
snumat	188	Inorganic	SNU Materials database
Total	1936

Material Type Distribution

Type	Count	Fraction
Inorganic	1142	59.0%
MOF	398	20.6%
Organic	201	10.4%
HEA	195	10.1%

HKL Convention

Convention	Count	Description
3-index	1764	Standard Miller indices [h, k, l]
4-index	172	Miller-Bravais indices [h, k, i, l] for hexagonal/trigonal

Field Schema

Field	Type	Description
file_name	string	Relative path to XRD pattern image
sample_id	string	Unique material identifier
dataset	string	Source dataset name
material_type	string	Material category: Inorganic, MOF, Organic, HEA
formula	string	Chemical formula
cif_content	string	Full CIF crystal structure text
task_name	string	Always xrd_max_peak_hkl
hkl_convention	string	3-index or 4-index
prompt	string	Complete VLM inference prompt
gt_theta_argmax	float	2θ angle of the highest peak (degrees)
gt_hkls	string (JSON)	Ground truth HKL set (JSON array of arrays)
gt_union_size	int	Number of contributing HKL planes
gt_nearest_hkl	string (JSON)	Nearest theoretical peak's HKL
gt_nearest_distance_deg	float	Distance to nearest theoretical peak (degrees)
gt_method	string	GT generation method
properties	string (JSON)	Material properties from source dataset

XRD Pattern Generation Parameters

All XRD patterns are theoretical (computed from CIF, not experimental), generated with:

Parameter	Value
Radiation	Cu Kα (λ₁=1.54056 Å, λ₂=1.54439 Å, I₂/I₁=0.5)
2θ range	2°–90°
Step size	0.01°
Peak profile	Pseudo-Voigt (η=0.4)
FWHM	0.15°
Union tolerance	±0.30° (2×FWHM)
Intensity threshold	10% (reflections < 10% relative intensity within window are excluded)

Ground Truth Method

The ground truth is generated using the "contributors union within tolerance" method:

1. Compute all theoretical Bragg reflections from CIF using pymatgen
2. Locate the highest-intensity peak position (θ_argmax) in the simulated pattern
3. Find ALL reflections within ±0.30° of θ_argmax
4. Exclude reflections with relative intensity < 10% of the strongest in the window
5. The union of remaining HKL planes forms the ground truth set

Usage

from datasets import load_dataset

# Load the dataset
ds = load_dataset("<your-org>/LLM4Mat-Bench", split="test")

print(f"Total samples: {len(ds)}")
print(ds[0].keys())

# Access a sample
sample = ds[0]
print(f"Sample ID: {sample['sample_id']}")
print(f"Formula: {sample['formula']}")
print(f"GT HKLs: {sample['gt_hkls']}")
print(f"Image: {sample['image']}")  # PIL Image object

# Filter by dataset
import json
mp_samples = ds.filter(lambda x: x["dataset"] == "mp")
print(f"MP samples: {len(mp_samples)}")

# Parse ground truth
gt_hkls = json.loads(sample["gt_hkls"])
print(f"Ground truth HKL planes: {gt_hkls}")

Evaluation Metrics

We use set-based metrics since the task output is a set of HKL tuples:

• Jaccard Similarity: |Pred ∩ GT| / |Pred ∪ GT|
• Precision: |Pred ∩ GT| / |Pred|
• Recall: |Pred ∩ GT| / |GT|
• F1 Score: Harmonic mean of precision and recall

An evaluation script is provided: evaluate.py

python evaluate.py --predictions predictions.jsonl --ground_truth metadata.jsonl

Important Notes

• [0,0,0] HKL tuples are treated as invalid and excluded from both predictions and ground truth
• HKL equivalence is NOT applied: exact tuple matching only (e.g., [1,0,0] ≠ [-1,0,0])
• XRD images show clean curves only — no peak annotations or markers
• The properties field contains dataset-specific material properties as a JSON string

Citation

@misc{llm4matbench2025,
  title={LLM4Mat-Bench: A Multimodal Benchmark for Evaluating Vision-Language Models on Crystallographic XRD Analysis},
  year={2025},
  url={https://huggingface.co/datasets/<your-org>/LLM4Mat-Bench}
}

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0).

The source crystallographic data originates from publicly available materials databases (Materials Project, JARVIS, OQMD, GNoME, etc.) under their respective licenses.