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# nomad-examples [![DOI](https://img.shields.io/badge/doi-10.26180/5c6e1160b8d8a-blue.svg?style=flat&labelColor=whitesmoke&logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAB8AAAAfCAYAAAAfrhY5AAAJsklEQVR42qWXd1DTaRrHf%2BiB2Hdt5zhrAUKz4IKEYu9IGiGFFJJQ0gkJCAKiWFDWBRdFhCQUF3UVdeVcRQEBxUI3yY9iEnQHb3bdW1fPubnyz%2F11M7lvEHfOQee2ZOYzPyDv%2B3yf9%2Fk95YX4fx%2BltfUt08GcFEuPR4U9hDDZ%2FVngIlhb%2FSiI6InkTgLzgDcgfvtnovhH4BzoVlrbwr55QnhCtBW4QHXnFrZbPBaQoBh4%2FSYH2EnpBEtqcDMVzB93wA%2F8AFwa23XFGcc8CkT3mxz%2BfXWtq9T9IQlLIXYEuHojudb%2BCM7Hgdq8ydi%2FAHiBXyY%2BLjwFlAEnS6Jnar%2FvnQVhvdzasad0eKvWZKe8hvDB2ofLZ%2FZEcWsh%2BhyIuyO5Bxs2iZIE4nRv7NWAb0EO8AC%2FWPxjYAWuOEX2MSXZVgPxzmRL3xKz3ScGpx6p6QnOx4mDIFqO0w6Q4fEhO5IzwxlSwyD2FYHzwAW%2BAZ4fEsf74gCumykwNHskLM7taQxLYjjIyy8MUtraGhTWdkfhkFJqtvuVl%2F9l2ZquDfEyrH8B0W06nnpH3JtIyRGpH1iJ6SfxDIHjRXHJmdQjLpfHeN54gnfFx4W9QRnovx%2FN20aXZeTD2J84hn3%2BqoF2Tqr14VqTPUCIcP%2B5%2Fly4qC%2BUL3sYxSvNj1NwsVYPsWdMUfomsdkYm3Tj0nbV0N1wRKwFe1MgKACDIBdMAhPE%2FwicwNWxll8Ag40w%2BFfhibJkGHmutjYeQ8gVlaN%2BjO51nDysa9TwNUFMqaGbKdRJZFfOJSp6mkRKsv0rRIpEVWjAvyFkxNOEpwvcAVPfEe%2Bl8ojeNTx3nXLBcWRrYGxSRjDEk0VlpxYrbe1ZmaQ5xuT0u3r%2B2qe5j0J5uytiZPGsRL2Jm32AldpxPUNJ3jmmsN4x62z1cXrbedXBQf2yvIFCeZrtyicZZG2U2nrrBJzYorI2EXLrvTfCSB43s41PKEvbZDEfQby6L4JTj%2FfIwam%2B4%2BwucBu%2BDgNK05Nle1rSt9HvR%2FKPC4U6LTfvUIaip1mjIa8fPzykii23h2eanT57zQ7fsyYH5QjywwlooAUcAdOh5QumgTHx6aAO7%2FL52eaQNEShrxfhL6albEDmfhGflrsT4tps8gTHNOJbeDeBlt0WJWDHSgxs6cW6lQqyg1FpD5ZVDfhn1HYFF1y4Eiaqa18pQf3zzYMBhcanlBjYfgWNayAf%2FASOgklu8bmgD7hADrk4cRlOL7NSOewEcbqSmaivT33QuFdHXj5sdvjlN5yMDrAECmdgDWG2L8P%2BAKLs9ZLZ7dJda%2BB4Xl84t7QvnKfvpXJv9obz2KgK8dXyqISyV0sXGZ0U47hOA%2FAiigbEMECJxC9aoKp86re5O5prxOlHkcksutSQJzxZRlPZmrOKhsQBF5zEZKybUC0vVjG8PqOnhOq46qyDTDnj5gZBriWCk4DvXrudQnXQmnXblebhAC2cCB6zIbM4PYgGl0elPSgIf3iFEA21aLdHYLHUQuVkpgi02SxFdrG862Y8ymYGMvXDzUmiX8DS5vKZyZlGmsSgQqfLub5RyLNS4zfDiZc9Edzh%2FtCE%2BX8j9k%2FqWB071rcZyMImne1SLkL4GRw4UPHMV3jjwEYpPG5uW5fAEot0aTSJnsGAwHJi2nvF1Y5OIqWziVCQd5NT7t6Q8guOSpgS%2Fa1dSRn8JGGaCD3BPXDyQRG4Bqhu8XrgAp0yy8DMSvvyVXDgJcJTcr1wQ2BvFKf65jqhvmxXUuDpGBlRvV36XvGjQzLi8KAKT2lYOnmxQPGorURSV0NhyTIuIyqOmKTMhQ%2BieEsgOgpc4KBbfDM4B3SIgFljvfHF6cef7qpyLBXAiQcXvg5l3Iunp%2FWv4dH6qFziO%2BL9PbrimQ9RY6MQphEfGUpOmma7KkGzuS8sPUFnCtIYcKCaI9EXo4HlQLgGrBjbiK5EqMj2AKWt9QWcIFMtnVvQVDQV9lXJJqdPVtUQpbh6gCI2Ov1nvZts7yYdsnvRgxiWFOtNJcOMVLn1vgptVi6qrNiFOfEjHCDB3J%2BHDLqUB77YgQGwX%2Fb1eYna3hGKdlqJKIyiE4nSbV8VFgxmxR4b5mVkkeUhMgs5YTi4ja2XZ009xJRHdkfwMi%2BfocaancuO7h%2FMlcLOa0V%2FSw6Dq47CumRQAKhgbOP8t%2BMTjuxjJGhXCY6XpmDDFqWlVYbQ1aDJ5Cptdw4oLbf3Ck%2BdWkVP0LpH7s9XLPXI%2FQX8ws%2Bj2In63IcRvOOo%2BTTjiN%2BlssfRsanW%2B3REVKoavBOAPTXABW4AL7e4NygHdpAKBscmlDh9Jysp4wxbnUNna3L3xBvyE1jyrGIkUHaqQMuxhHElV6oj1picvgL1QEuS5PyZTEaivqh5vUCKJqOuIgPFGESns8kyFk7%2FDxyima3cYxi%2FYOQCj%2F%2B9Ms2Ll%2Bhn4FmKnl7JkGXQGDKDAz9rUGL1TIlBpuJr9Be2JjK6qPzyDg495UxXYF7JY1qKimw9jWjF0iV6DRIqE%2B%2FeWG0J2ofmZTk0mLYVd4GLiFCOoKR0Cg727tWq981InYynvCuKW43aXgEjofVbxIqrm0VL76zlH3gQzWP3R3Bv9oXxclrlO7VVtgBRpSP4hMFWJ8BrUSBCJXC07l40X4jWuvtc42ofNCxtlX2JH6bdeojXgTh5TxOBKEyY5wvBE%2BACh8BtOPNPkApjoxi5h%2B%2FFMQQNpWvZaMH7MKFu5Ax8HoCQdmGkJrtnOiLHwD3uS5y8%2F2xTSDrE%2F4PT1yqtt6vGe8ldMBVMEPd6KwqiYECHDlfbvzphcWP%2BJiZuL5swoWQYlS%2Br7Yu5mNUiGD2retxBi9fl6RDGn4Ti9B1oyYy%2BMP5G87D%2FCpRlvdnuy0PY6RC8BzTA40NXqckQ9TaOUDywkYsudxJzPgyDoAWn%2BB6nEFbaVxxC6UXjJiuDkW9TWq7uRBOJocky9iMfUhGpv%2FdQuVVIuGjYqACbXf8aa%2BPeYNIHZsM7l4s5gAQuUAzRUoT51hnH3EWofXf2vkD5HJJ33vwE%2FaEWp36GHr6GpMaH4AAPuqM5eabH%2FhfG9zcCz4nN6cPinuAw6IHwtvyB%2FdO1toZciBaPh25U0ducR2PI3Zl7mokyLWKkSnEDOg1x5fCsJE9EKhH7HwFNhWMGMS7%2BqxyYsbHHRUDUH4I%2FAheQY7wujJNnFUH4KdCju83riuQeHU9WEqNzjsJFuF%2FdTDAZ%2FK7%2F1WaAU%2BAWymT59pVMT4g2AxcwNa0XEBDdBDpAPvgDIH73R25teeuAF5ime2Ul0OUIiG4GpSAEJeYW9wDTf43wfwHgHLKJoPznkwAAAABJRU5ErkJggg%3D%3D)](https://doi.org/10.6084/m9.figshare.19319783.v2)
# nomad-examples [![DOI](https://img.shields.io/badge/doi-10.26180/5c6e1160b8d8a-blue.svg?style=flat&labelColor=whitesmoke&logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAB8AAAAfCAYAAAAfrhY5AAAJsklEQVR42qWXd1DTaRrHf%2BiB2Hdt5zhrAUKz4IKEYu9IGiGFFJJQ0gkJCAKiWFDWBRdFhCQUF3UVdeVcRQEBxUI3yY9iEnQHb3bdW1fPubnyz%2F11M7lvEHfOQee2ZOYzPyDv%2B3yf9%2Fk95YX4fx%2BltfUt08GcFEuPR4U9hDDZ%2FVngIlhb%2FSiI6InkTgLzgDcgfvtnovhH4BzoVlrbwr55QnhCtBW4QHXnFrZbPBaQoBh4%2FSYH2EnpBEtqcDMVzB93wA%2F8AFwa23XFGcc8CkT3mxz%2BfXWtq9T9IQlLIXYEuHojudb%2BCM7Hgdq8ydi%2FAHiBXyY%2BLjwFlAEnS6Jnar%2FvnQVhvdzasad0eKvWZKe8hvDB2ofLZ%2FZEcWsh%2BhyIuyO5Bxs2iZIE4nRv7NWAb0EO8AC%2FWPxjYAWuOEX2MSXZVgPxzmRL3xKz3ScGpx6p6QnOx4mDIFqO0w6Q4fEhO5IzwxlSwyD2FYHzwAW%2BAZ4fEsf74gCumykwNHskLM7taQxLYjjIyy8MUtraGhTWdkfhkFJqtvuVl%2F9l2ZquDfEyrH8B0W06nnpH3JtIyRGpH1iJ6SfxDIHjRXHJmdQjLpfHeN54gnfFx4W9QRnovx%2FN20aXZeTD2J84hn3%2BqoF2Tqr14VqTPUCIcP%2B5%2Fly4qC%2BUL3sYxSvNj1NwsVYPsWdMUfomsdkYm3Tj0nbV0N1wRKwFe1MgKACDIBdMAhPE%2FwicwNWxll8Ag40w%2BFfhibJkGHmutjYeQ8gVlaN%2BjO51nDysa9TwNUFMqaGbKdRJZFfOJSp6mkRKsv0rRIpEVWjAvyFkxNOEpwvcAVPfEe%2Bl8ojeNTx3nXLBcWRrYGxSRjDEk0VlpxYrbe1ZmaQ5xuT0u3r%2B2qe5j0J5uytiZPGsRL2Jm32AldpxPUNJ3jmmsN4x62z1cXrbedXBQf2yvIFCeZrtyicZZG2U2nrrBJzYorI2EXLrvTfCSB43s41PKEvbZDEfQby6L4JTj%2FfIwam%2B4%2BwucBu%2BDgNK05Nle1rSt9HvR%2FKPC4U6LTfvUIaip1mjIa8fPzykii23h2eanT57zQ7fsyYH5QjywwlooAUcAdOh5QumgTHx6aAO7%2FL52eaQNEShrxfhL6albEDmfhGflrsT4tps8gTHNOJbeDeBlt0WJWDHSgxs6cW6lQqyg1FpD5ZVDfhn1HYFF1y4Eiaqa18pQf3zzYMBhcanlBjYfgWNayAf%2FASOgklu8bmgD7hADrk4cRlOL7NSOewEcbqSmaivT33QuFdHXj5sdvjlN5yMDrAECmdgDWG2L8P%2BAKLs9ZLZ7dJda%2BB4Xl84t7QvnKfvpXJv9obz2KgK8dXyqISyV0sXGZ0U47hOA%2FAiigbEMECJxC9aoKp86re5O5prxOlHkcksutSQJzxZRlPZmrOKhsQBF5zEZKybUC0vVjG8PqOnhOq46qyDTDnj5gZBriWCk4DvXrudQnXQmnXblebhAC2cCB6zIbM4PYgGl0elPSgIf3iFEA21aLdHYLHUQuVkpgi02SxFdrG862Y8ymYGMvXDzUmiX8DS5vKZyZlGmsSgQqfLub5RyLNS4zfDiZc9Edzh%2FtCE%2BX8j9k%2FqWB071rcZyMImne1SLkL4GRw4UPHMV3jjwEYpPG5uW5fAEot0aTSJnsGAwHJi2nvF1Y5OIqWziVCQd5NT7t6Q8guOSpgS%2Fa1dSRn8JGGaCD3BPXDyQRG4Bqhu8XrgAp0yy8DMSvvyVXDgJcJTcr1wQ2BvFKf65jqhvmxXUuDpGBlRvV36XvGjQzLi8KAKT2lYOnmxQPGorURSV0NhyTIuIyqOmKTMhQ%2BieEsgOgpc4KBbfDM4B3SIgFljvfHF6cef7qpyLBXAiQcXvg5l3Iunp%2FWv4dH6qFziO%2BL9PbrimQ9RY6MQphEfGUpOmma7KkGzuS8sPUFnCtIYcKCaI9EXo4HlQLgGrBjbiK5EqMj2AKWt9QWcIFMtnVvQVDQV9lXJJqdPVtUQpbh6gCI2Ov1nvZts7yYdsnvRgxiWFOtNJcOMVLn1vgptVi6qrNiFOfEjHCDB3J%2BHDLqUB77YgQGwX%2Fb1eYna3hGKdlqJKIyiE4nSbV8VFgxmxR4b5mVkkeUhMgs5YTi4ja2XZ009xJRHdkfwMi%2BfocaancuO7h%2FMlcLOa0V%2FSw6Dq47CumRQAKhgbOP8t%2BMTjuxjJGhXCY6XpmDDFqWlVYbQ1aDJ5Cptdw4oLbf3Ck%2BdWkVP0LpH7s9XLPXI%2FQX8ws%2Bj2In63IcRvOOo%2BTTjiN%2BlssfRsanW%2B3REVKoavBOAPTXABW4AL7e4NygHdpAKBscmlDh9Jysp4wxbnUNna3L3xBvyE1jyrGIkUHaqQMuxhHElV6oj1picvgL1QEuS5PyZTEaivqh5vUCKJqOuIgPFGESns8kyFk7%2FDxyima3cYxi%2FYOQCj%2F%2B9Ms2Ll%2Bhn4FmKnl7JkGXQGDKDAz9rUGL1TIlBpuJr9Be2JjK6qPzyDg495UxXYF7JY1qKimw9jWjF0iV6DRIqE%2B%2FeWG0J2ofmZTk0mLYVd4GLiFCOoKR0Cg727tWq981InYynvCuKW43aXgEjofVbxIqrm0VL76zlH3gQzWP3R3Bv9oXxclrlO7VVtgBRpSP4hMFWJ8BrUSBCJXC07l40X4jWuvtc42ofNCxtlX2JH6bdeojXgTh5TxOBKEyY5wvBE%2BACh8BtOPNPkApjoxi5h%2B%2FFMQQNpWvZaMH7MKFu5Ax8HoCQdmGkJrtnOiLHwD3uS5y8%2F2xTSDrE%2F4PT1yqtt6vGe8ldMBVMEPd6KwqiYECHDlfbvzphcWP%2BJiZuL5swoWQYlS%2Br7Yu5mNUiGD2retxBi9fl6RDGn4Ti9B1oyYy%2BMP5G87D%2FCpRlvdnuy0PY6RC8BzTA40NXqckQ9TaOUDywkYsudxJzPgyDoAWn%2BB6nEFbaVxxC6UXjJiuDkW9TWq7uRBOJocky9iMfUhGpv%2FdQuVVIuGjYqACbXf8aa%2BPeYNIHZsM7l4s5gAQuUAzRUoT51hnH3EWofXf2vkD5HJJ33vwE%2FaEWp36GHr6GpMaH4AAPuqM5eabH%2FhfG9zcCz4nN6cPinuAw6IHwtvyB%2FdO1toZciBaPh25U0ducR2PI3Zl7mokyLWKkSnEDOg1x5fCsJE9EKhH7HwFNhWMGMS7%2BqxyYsbHHRUDUH4I%2FAheQY7wujJNnFUH4KdCju83riuQeHU9WEqNzjsJFuF%2FdTDAZ%2FK7%2F1WaAU%2BAWymT59pVMT4g2AxcwNa0XEBDdBDpAPvgDIH73R25teeuAF5ime2Ul0OUIiG4GpSAEJeYW9wDTf43wfwHgHLKJoPznkwAAAABJRU5ErkJggg%3D%3D)](https://doi.org/10.6084/m9.figshare.19319783.v3)

Examples of using the Novel Materials Discovery (NOMAD) database, especially downloading all chemical formulas.

### Installation
Clone or download the repository. To clone:
```bash
git clone https://github.com/sparks-baird/nomad-examples.git
cd nomad-examples
```

Install the dependencies, e.g. via:
```bash
pip install -r requirements.txt
```

### Reproducer
Use [all_formula_basic_metadata.py](all_formula_basic_metadata.py) to download the data from NOMAD and to do some basic processing. This might take somewhere around an hour.

```bash
python -m all_formula_basic_metadata.py
```

Use [remove_duplicate_compositions.py](remove_duplicate_compositions.py) to process the chemical formulas down to a list of unique chemical compositions (represented as reduced formulas). This also might take around an hour.

```bash
python -m remove_duplicate_compositions.py
```

### Data Descriptions
The data is available via figshare [DOI: 10.6084/m9.figshare.19319783.v3](https://doi.org/10.6084/m9.figshare.19319783.v3) and was downloaded on 2022-03-07. There are four files available: `all-formula.csv`, `unique-formula.csv`, `unique-reduced-formula.csv`, and `bad-formula.csv`. There are `11680557`, `764431`, `695612`, and `15` rows for each of these files, respectively. Descriptions are given below.

#### `all-formula.csv`
[`all-formula.csv`](https://figshare.com/ndownloader/files/34313849) contains two columns: `calc_id` (Calculation ID) and `formula` (Chemical Formula). These were restricted to VASP DFT calculations, and do not include noble gases nor radioactive elements. Some calculation IDs have missing chemical formulas.

#### `unique-formula.csv`
The list has also been filtered down to unique (non-reduced) chemical formulas in [`unique-formula.csv`](https://figshare.com/ndownloader/files/34313978) along with the `calc_id` for each unique formula. No structural information is included directly in this data.

#### `unique-reduced-formula.csv`
REALLY, what you're probably most interested in is [`unique-reduced-formula.csv`](https://figshare.com/ndownloader/files/34439036) because it is the most curated and is directly usable with e.g. `pymatgen`. This contains three columns: calc_id, reduced_formula, and factor which correspond to the Calculation ID, the reduced formula (e.g. Si2O4 --> SiO2), and the factor (e.g. for Si2O4 --> SiO2 the factor is 2). The formulas were first parsed via the [`pymatgen.core.Composition`](https://pymatgen.org/pymatgen.core.composition.html#pymatgen.core.composition.Composition) class.

#### `bad-formula.csv`
Finally, [`bad-formula.csv`](https://figshare.com/ndownloader/files/34439039) contains the formulas that were skipped during processing (i.e. not successfully processed with [`pymatgen.core.Composition`](https://pymatgen.org/pymatgen.core.composition.html#pymatgen.core.composition.Composition) for various reasons comprising 15 in total).

### Future Work
Downloading all of the crystal structures and reducing this to a list of unique _phases_ each with a CIF file.

### Issues
See something missing? Please don't hesitate to [drop me a note in issues](https://github.com/sparks-baird/nomad-examples/issues/new/choose).

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