| |
|
| | --- |
| | 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. |
| |
|
