ohlstrom/Crystallography-Open-Database-XRD-Patterns

license: cc0-1.0 task_categories:

• —other tags:
• —chemistry
• —crystallography
• —xrd
• —materials-science prettyname: COD XRD Patterns sizecategories:
• —100K<n<1M ---

Crystallography Open Database — Synthetic XRD Patterns

436,196 synthetic powder X-ray diffraction (XRD) patterns generated from every CIF file in the Crystallography Open Database (COD).

What's in the dataset

Each pattern is a simulated powder diffractogram computed from a published crystal structure. The dataset covers the full chemical and structural diversity of COD: metals, minerals, organics, MOFs, and everything in between. No measured (experimental) patterns are included — these are purely computational.

Files and subsets

The smaller subsets are random samples drawn from the full set.

Data format

Each .pt file is a Python dictionary saved with torch.save(). Load it with:

import torch

data = torch.load("COD_xrd_patterns.pt")

patterns = data["patterns"]    # torch.Tensor, shape (N, 4500)
filenames = data["filenames"]  # list[str], length N

• —`patterns`: A float tensor where each row is a 4,500-point XRD intensity vector.
• —`filenames`: The corresponding COD CIF filenames (e.g. "4326570.cif").

Generation parameters

Patterns were generated by reading each CIF file, computing the theoretical diffraction peak positions and intensities, and interpolating onto the uniform 2-theta grid. Regions with no diffraction peaks are zero-padded.

Finding the original CIF files

Every filename maps directly to a COD entry. Strip the .cif extension to get the COD ID, then download the structure file:

Filename:  4326570.cif
COD ID:    4326570
URL:       https://www.crystallography.net/cod/4326570.cif

License

CC0 1.0 Universal — same as the Crystallography Open Database itself.

Citation

If you use this dataset, please cite:

COD: Grazulis, S. et al. "Crystallography Open Database — an open-access collection of crystal structures." Journal of Applied Crystallography, 42(4), 726–729, 2009.

pymatgen: Ong, S. P. et al. "Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis." Computational Materials Science, 68, 314–319, 2013.