File size: 2,977 Bytes
fcd91e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
license: cc0-1.0
task_categories:
- other
tags:
- chemistry
- crystallography
- xrd
- materials-science
pretty_name: COD XRD Patterns
size_categories:
- 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](https://www.crystallography.net/cod/) (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
| File | Patterns | Purpose |
|---|---|---|
| `COD_xrd_patterns_1000.pt` | 1,000 | Quick prototyping |
| `COD_xrd_patterns_10000.pt` | 10,000 | Development / hyperparameter tuning |
| `COD_xrd_patterns_50000.pt` | 50,000 | Development / hyperparameter tuning |
| `COD_xrd_patterns_100000.pt` | 100,000 | Development / hyperparameter tuning |
| `COD_xrd_patterns.pt` | 436,196 | Full dataset |
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:
```python
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
| Parameter | Value |
|---|---|
| Software | pymatgen `XRDCalculator` |
| Radiation | Cu K-alpha (lambda = 1.54184 angstrom) |
| 2-theta range | 0 to 90 degrees |
| Step size | 0.02 degrees |
| Points per pattern | 4,500 |
| Normalisation | Min-max scaled to [0, 1] |
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.
|