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

rMD17
=====

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

        .. raw:: html
            :file: ../_static/visualisations/rMD17.html

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

        A dataset composed of a single MD trajectory for each of 10 molecules.
        Original structures are taken from Chmiela et al., with energy and force
        labels recalculated by Christensen and von Lilienfeld using "the PBE/def2-SVP
        level of theory [with] very tight SCF convergence and [a] very dense DFT
        integration grid". The MD trajectories are presented one at a time, with
        structures within each trajectory in chronological order.
        


.. code-block:: pycon

    >>> from load_atoms import load_dataset
    >>> load_dataset("rMD17")
    rMD17:
        structures: 999,988
        atoms: 15,599,712
        species:
            H: 44.23%
            C: 43.59%
            O: 8.97%
            N: 3.21%
        properties:
            per atom: (forces)
            per structure: (energy, name)
    




Citation
--------

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

.. code-block:: latex
    
    @article{Christensen-20-10,
        title = {
            On the Role of Gradients for Machine 
            Learning of Molecular Energies and Forces
        },
        author = {Christensen, Anders S. and von Lilienfeld, O. Anatole},
        year = {2020},
        journal = {Machine Learning: Science and Technology},
        volume = {1},
        number = {4},
        pages = {045018},
        doi = {10.1088/2632-2153/abba6f},
    }
    
    @article{Chmiela-17-05,
        title = {
            Machine Learning of Accurate Energy-Conserving Molecular Force Fields
        },
        author = {
            Chmiela, Stefan and Tkatchenko, Alexandre 
            and Sauceda, Huziel E. and Poltavsky, Igor 
            and Sch{\"u}tt, Kristof T. and M{\"u}ller, Klaus-Robert
        },
        year = {2017},
        journal = {Science Advances},
        volume = {3},
        number = {5},
        pages = {e1603015},
        doi = {10.1126/sciadv.1603015},
    }


Properties
----------

**Per-atom**:

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

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


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

    * - Property
      - Units
      - Type
      - Description
    * - :code:`energy`
      - eV
      - :class:`~float64`
      - energy

    * - :code:`name`
      - str
      - :class:`~str`
      - name of the molecule



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

``rMD17`` is imported as an 
:class:`~load_atoms.atoms_dataset.InMemoryAtomsDataset`:

.. dropdown:: Importer script for :code:`rMD17`

    .. literalinclude:: ../../../src/load_atoms/database/importers/rmd17.py
       :language: python



.. dropdown:: :class:`~load_atoms.database.DatabaseEntry` for :code:`rMD17`

    .. code-block:: yaml

        name: rMD17
        year: 2020
        description: |
            A dataset composed of a single MD trajectory for each of 10 molecules.
            Original structures are taken from Chmiela et al., with energy and force
            labels recalculated by Christensen and von Lilienfeld using "the PBE/def2-SVP
            level of theory [with] very tight SCF convergence and [a] very dense DFT
            integration grid". The MD trajectories are presented one at a time, with
            structures within each trajectory in chronological order.
        category: Benchmarks
        minimum_load_atoms_version: 0.2
        per_structure_properties:
            energy:
                desc: energy
                units: eV
            name:
                desc: name of the molecule
                units: str
        per_atom_properties:
            forces:
                desc: forces
                units: eV/Å
        representative_structure: 0
        citation: |
            @article{Christensen-20-10,
                title = {
                    On the Role of Gradients for Machine 
                    Learning of Molecular Energies and Forces
                },
                author = {Christensen, Anders S. and von Lilienfeld, O. Anatole},
                year = {2020},
                journal = {Machine Learning: Science and Technology},
                volume = {1},
                number = {4},
                pages = {045018},
                doi = {10.1088/2632-2153/abba6f},
            }
        
            @article{Chmiela-17-05,
                title = {
                    Machine Learning of Accurate Energy-Conserving Molecular Force Fields
                },
                author = {
                    Chmiela, Stefan and Tkatchenko, Alexandre 
                    and Sauceda, Huziel E. and Poltavsky, Igor 
                    and Sch{\"u}tt, Kristof T. and M{\"u}ller, Klaus-Robert
                },
                year = {2017},
                journal = {Science Advances},
                volume = {3},
                number = {5},
                pages = {e1603015},
                doi = {10.1126/sciadv.1603015},
            }