.. This file is autogenerated by dev/scripts/generate_page.py

SiO2-GAP-22
===========

.. grid:: 1 1 2 2
    
    .. grid-item::

        .. raw:: html
            :file: ../_static/visualisations/SiO2-GAP-22.html

    .. grid-item::
        :class: info-card

        The training database used to fit the `GAP-22 potential for silica <https://zenodo.org/records/6353684>`_ in: 
        `A Machine-Learned Interatomic Potential for Silica and Its Relation to Empirical Models <https://doi.org/10.1038/s41524-022-00768-w>`_.
        The dataset was generated using an iterative approach, in some cases driven by an emprical potential. More details are available in the 
        `supplementary information <https://static-content.springer.com/esm/art%3A10.1038%2Fs41524-022-00768-w/MediaObjects/41524_2022_768_MOESM1_ESM.pdf>`_.
        


.. code-block:: pycon

    >>> from load_atoms import load_dataset
    >>> load_dataset("SiO2-GAP-22")
    SiO2-GAP-22:
        structures: 3,074
        atoms: 268,118
        species:
            O: 66.47%
            Si: 33.53%
        properties:
            per atom: (forces)
            per structure: (config_type, energy, free_energy, virial)
    


License
-------

This dataset is licensed under the `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/deed.en>`_ license.


Citation
--------

If you use this dataset in your work, please cite the following:

.. code-block:: latex
    
    @article{Erhard-22-04,
      title = {
        A Machine-Learned Interatomic Potential for Silica 
        and Its Relation to Empirical Models
      },
      author = {
        Erhard, Linus C. and Rohrer, Jochen 
        and Albe, Karsten and Deringer, Volker L.
      },
      year = {2022},
      journal = {npj Computational Materials},
      volume = {8},
      number = {1},
      pages = {1--12},
    }


Properties
----------

**Per-atom**:

.. list-table::
    :header-rows: 1

    * - Property
      - Units
      - Type
      - Description
    * - :code:`forces`
      - eV/Å
      - :class:`ndarray(N, 3) <numpy.ndarray>`
      - force vectors (DFT)


**Per-structure**:
    
.. list-table::
    :header-rows: 1

    * - Property
      - Units
      - Type
      - Description
    * - :code:`energy`
      - eV
      - :class:`~float64`
      - total structure energy (DFT)

    * - :code:`virial`
      - eV
      - :class:`ndarray(9,) <numpy.ndarray>`
      - virial stress tensor (DFT)

    * - :code:`stress`
      - eV Å\ :math:`{}^{-3}`
      - :class:`ndarray(3, 3) <numpy.ndarray>`
      - | stress tensor (DFT)
        | (:code:`- virial / cell.volume`)
        

    * - :code:`config_type`
      - 
      - :class:`~str`
      - category of structure



Miscellaneous information
-------------------------

``SiO2-GAP-22`` is imported as an 
:class:`~load_atoms.atoms_dataset.InMemoryAtomsDataset`:

.. dropdown:: Importer script for :code:`SiO2-GAP-22`

    .. literalinclude:: ../../../src/load_atoms/database/importers/sio2_gap_22.py
       :language: python



.. dropdown:: :class:`~load_atoms.database.DatabaseEntry` for :code:`SiO2-GAP-22`

    .. code-block:: yaml

        name: SiO2-GAP-22
        year: 2022
        description: |
            The training database used to fit the `GAP-22 potential for silica <https://zenodo.org/records/6353684>`_ in: 
            `A Machine-Learned Interatomic Potential for Silica and Its Relation to Empirical Models <https://doi.org/10.1038/s41524-022-00768-w>`_.
            The dataset was generated using an iterative approach, in some cases driven by an emprical potential. More details are available in the 
            `supplementary information <https://static-content.springer.com/esm/art%3A10.1038%2Fs41524-022-00768-w/MediaObjects/41524_2022_768_MOESM1_ESM.pdf>`_.
        category: Potential Fitting
        minimum_load_atoms_version: 0.2
        license: CC BY 4.0
        citation: |
            @article{Erhard-22-04,
              title = {
                A Machine-Learned Interatomic Potential for Silica 
                and Its Relation to Empirical Models
              },
              author = {
                Erhard, Linus C. and Rohrer, Jochen 
                and Albe, Karsten and Deringer, Volker L.
              },
              year = {2022},
              journal = {npj Computational Materials},
              volume = {8},
              number = {1},
              pages = {1--12},
            }
        per_atom_properties:
            forces:
                desc: force vectors (DFT)
                units: eV/Å
        per_structure_properties:
            energy:
                desc: total structure energy (DFT)
                units: eV
            virial:
                desc: virial stress tensor (DFT)
                units: eV
            stress:
                desc: |
                    | stress tensor (DFT)
                    | (:code:`- virial / cell.volume`)
                units: eV Å\ :math:`{}^{-3}`
            config_type:
                desc: category of structure
        
        
        # TODO: remove after Dec 2024
        # backwards compatability: unused as of 0.3.0
        files:
            - url: https://zenodo.org/records/6353684/files/sio2_potential_data.zip
              hash: 98ea6e58f6d9
        processing:
            - UnZip
            - SelectFile:
                file: sio2_potential_data/database/dataset.scan.2.xyz
            - ReadASE
            - Rename:
                virials: virial