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

SiOx-ACE-24
===========

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    .. grid-item::

        .. raw:: html
            :file: ../_static/visualisations/SiOx-ACE-24.html

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        :class: info-card

        The training database used to fit the `SiOx-ACE-24 potential <https://zenodo.org/records/10419194>`_ in: 
        `Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine-learning <https://www.nature.com/articles/s41467-024-45840-9>`_.
        The dataset comprises structures taken from the `Si-GAP-18 <https://jla-gardner.github.io/load-atoms/datasets/Si-GAP-18.html>`__ 
        and `SiO2-GAP-22 <https://jla-gardner.github.io/load-atoms/datasets/SiO2-GAP-22.html>`__ datasets, together
        with new structures generated using an active-learning approach.
        


.. code-block:: pycon

    >>> from load_atoms import load_dataset
    >>> load_dataset("SiOx-ACE-24")
    SiOx-ACE-24:
        structures: 11,428
        atoms: 1,258,198
        species:
            O: 55.82%
            Si: 44.18%
        properties:
            per atom: (charge_bader, forces)
            per structure: (config_type, energy, free_energy, stress, 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-24-03,
        title = {
            Modelling Atomic and Nanoscale Structure in the 
            Silicon--Oxygen System through Active Machine Learning
        },
        author = {
            Erhard, Linus C. and Rohrer, Jochen 
            and Albe, Karsten and Deringer, Volker L.
        },
        year = {2024},
        journal = {Nature Communications},
        volume = {15},
        number = {1},
        pages = {1927},
        doi = {10.1038/s41467-024-45840-9},
    }


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:`free_energy`
      - eV
      - :class:`~float64`
      - total structure free energy (DFT)

    * - :code:`virial`
      - eV
      - :class:`ndarray(3, 3) <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
-------------------------

``SiOx-ACE-24`` is imported as an 
:class:`~load_atoms.atoms_dataset.InMemoryAtomsDataset`:

.. dropdown:: Importer script for :code:`SiOx-ACE-24`

    .. literalinclude:: ../../../src/load_atoms/database/importers/siox_ace_24.py
       :language: python



.. dropdown:: :class:`~load_atoms.database.DatabaseEntry` for :code:`SiOx-ACE-24`

    .. code-block:: yaml

        name: SiOx-ACE-24
        year: 2024
        description: |
            The training database used to fit the `SiOx-ACE-24 potential <https://zenodo.org/records/10419194>`_ in: 
            `Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine-learning <https://www.nature.com/articles/s41467-024-45840-9>`_.
            The dataset comprises structures taken from the `Si-GAP-18 <https://jla-gardner.github.io/load-atoms/datasets/Si-GAP-18.html>`__ 
            and `SiO2-GAP-22 <https://jla-gardner.github.io/load-atoms/datasets/SiO2-GAP-22.html>`__ datasets, together
            with new structures generated using an active-learning approach.
        category: Potential Fitting
        minimum_load_atoms_version: 0.2
        license: CC BY 4.0
        citation: |
            @article{Erhard-24-03,
                title = {
                    Modelling Atomic and Nanoscale Structure in the 
                    Silicon--Oxygen System through Active Machine Learning
                },
                author = {
                    Erhard, Linus C. and Rohrer, Jochen 
                    and Albe, Karsten and Deringer, Volker L.
                },
                year = {2024},
                journal = {Nature Communications},
                volume = {15},
                number = {1},
                pages = {1927},
                doi = {10.1038/s41467-024-45840-9},
            }
        representative_structure: 7390
        per_atom_properties:
            forces:
                desc: force vectors (DFT)
                units: eV/Å
        per_structure_properties:
            energy:
                desc: total structure energy (DFT)
                units: eV
            free_energy:
                desc: total structure free 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/10419194/files/database.zip
              hash: 42eb5808b0aa
        processing:
            - UnZip
            - SelectFile:
                file: database/training.general_purpose.SiOx.xyz
            - ReadASE
            - Rename:
                dft_forces: forces
                dft_energy: energy
                dft_free_energy: free_energy
                dft_stress: stress
                dft_virials: virial