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ISO_17

ISO17

Bases: BaseDataset

ISO17 dataset consists of the largest set of isomers from the QM9 dataset that consists of a fixed composition of atoms (C7O2H10) arranged in different chemically valid structures. It consist of 129 molecules, each containing 5,000 conformational geometries, energies and forces with a resolution of 1 fs in the molecular dynamics trajectories. The simulations were carried out using density functional theory (DFT) in the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional and the Tkatchenko-Scheffler (TS) van der Waals correction method.

Usage: 

from openqdc.datasets import ISO17
dataset = ISO17()

References

https://arxiv.org/abs/1706.08566

https://arxiv.org/abs/1609.08259

https://www.nature.com/articles/sdata201422

https://pubmed.ncbi.nlm.nih.gov/10062328/

https://pubmed.ncbi.nlm.nih.gov/19257665/

Source code in openqdc/datasets/potential/iso_17.py 
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class ISO17(BaseDataset):
    """
    ISO17 dataset consists of the largest set of isomers from the QM9 dataset that consists of a fixed composition of
    atoms (C7O2H10) arranged in different chemically valid structures. It consist of 129 molecules, each containing
    5,000 conformational geometries, energies and forces with a resolution of 1 fs in the molecular dynamics
    trajectories. The simulations were carried out using density functional theory (DFT) in the generalized gradient
    approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional and the Tkatchenko-Scheffler (TS) van der
    Waals correction method.

    Usage:
    ```python
    from openqdc.datasets import ISO17
    dataset = ISO17()
    ```

    References:
        https://arxiv.org/abs/1706.08566\n
        https://arxiv.org/abs/1609.08259\n
        https://www.nature.com/articles/sdata201422\n
        https://pubmed.ncbi.nlm.nih.gov/10062328/\n
        https://pubmed.ncbi.nlm.nih.gov/19257665/
    """

    __name__ = "iso_17"

    __energy_methods__ = [
        PotentialMethod.PBE_DEF2_TZVP,  # "pbe/def2-tzvp",
    ]

    energy_target_names = [
        "PBE-TS Energy",
    ]

    __force_mask__ = [True]

    force_target_names = [
        "PBE-TS Gradient",
    ]

    __energy_unit__ = "ev"
    __distance_unit__ = "ang"
    __forces_unit__ = "ev/ang"
    __links__ = {"iso_17.hdf5.gz": "https://zenodo.org/record/3585907/files/216.hdf5.gz"}

    def __smiles_converter__(self, x):
        """util function to convert string to smiles: useful if the smiles is
        encoded in a different format than its display format
        """
        return "-".join(x.decode("ascii").split("_")[:-1])

    def read_raw_entries(self):
        raw_path = p_join(self.root, "iso_17.h5.gz")
        samples = read_qc_archive_h5(raw_path, "iso_17", self.energy_target_names, self.force_target_names)

        return samples

__smiles_converter__(x)

util function to convert string to smiles: useful if the smiles is encoded in a different format than its display format

Source code in openqdc/datasets/potential/iso_17.py 
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def __smiles_converter__(self, x):
    """util function to convert string to smiles: useful if the smiles is
    encoded in a different format than its display format
    """
    return "-".join(x.decode("ascii").split("_")[:-1])