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QMX

QM7

Bases: QMX

QM7 is a dataset constructed from subsets of the GDB-13 database ( stable and synthetically accessible organic molecules), containing up to seven “heavy” atoms. The molecules conformation are optimized using DFT at the PBE0/def2-TZVP level of theory.

Chemical species

[C, N, O, S, H]

Usage: 

from openqdc.datasets import QM7
dataset = QM7()

References

https://arxiv.org/pdf/1703.00564

Source code in openqdc/datasets/potential/qmx.py 
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class QM7(QMX):
    """
    QM7 is a dataset constructed from subsets of the GDB-13 database (
    stable and synthetically accessible organic molecules),
    containing up to seven “heavy” atoms.
    The molecules conformation are optimized using DFT at the
    PBE0/def2-TZVP level of theory.

    Chemical species:
        [C, N, O, S, H]

    Usage:
    ```python
    from openqdc.datasets import QM7
    dataset = QM7()
    ```

    References:
        https://arxiv.org/pdf/1703.00564
    """

    __links__ = {"qm7.hdf5.gz": "https://zenodo.org/record/3588337/files/150.hdf5.gz?download=1"}
    __name__ = "qm7"

    energy_target_names = [
        "B2PLYP-D3(BJ):aug-cc-pvdz",
        "B2PLYP-D3(BJ):aug-cc-pvtz",
        "B2PLYP-D3(BJ):def2-svp",
        "B2PLYP-D3(BJ):def2-tzvp",
        "B2PLYP-D3(BJ):sto-3g",
        "B2PLYP-D3:aug-cc-pvdz",
        "B2PLYP-D3:aug-cc-pvtz",
        "B2PLYP-D3:def2-svp",
        "B2PLYP-D3:def2-tzvp",
        "B2PLYP-D3:sto-3g",
        "B2PLYP-D3M(BJ):aug-cc-pvdz",
        "B2PLYP-D3M(BJ):aug-cc-pvtz",
        "B2PLYP-D3M(BJ):def2-svp",
        "B2PLYP-D3M(BJ):def2-tzvp",
        "B2PLYP-D3M(BJ):sto-3g",
        "B2PLYP-D3M:aug-cc-pvdz",
        "B2PLYP-D3M:aug-cc-pvtz",
        "B2PLYP-D3M:def2-svp",
        "B2PLYP-D3M:def2-tzvp",
        "B2PLYP-D3M:sto-3g",
        "B2PLYP:aug-cc-pvdz",
        "B2PLYP:aug-cc-pvtz",
        "B2PLYP:def2-svp",
        "B2PLYP:def2-tzvp",
        "B2PLYP:sto-3g",
        "B3LYP-D3(BJ):aug-cc-pvdz",
        "B3LYP-D3(BJ):aug-cc-pvtz",
        "B3LYP-D3(BJ):def2-svp",
        "B3LYP-D3(BJ):def2-tzvp",
        "B3LYP-D3(BJ):sto-3g",
        "B3LYP-D3:aug-cc-pvdz",
        "B3LYP-D3:aug-cc-pvtz",
        "B3LYP-D3:def2-svp",
        "B3LYP-D3:def2-tzvp",
        "B3LYP-D3:sto-3g",
        "B3LYP-D3M(BJ):aug-cc-pvdz",
        "B3LYP-D3M(BJ):aug-cc-pvtz",
        "B3LYP-D3M(BJ):def2-svp",
        "B3LYP-D3M(BJ):def2-tzvp",
        "B3LYP-D3M(BJ):sto-3g",
        "B3LYP-D3M:aug-cc-pvdz",
        "B3LYP-D3M:aug-cc-pvtz",
        "B3LYP-D3M:def2-svp",
        "B3LYP-D3M:def2-tzvp",
        "B3LYP-D3M:sto-3g",
        "B3LYP:aug-cc-pvdz",
        "B3LYP:aug-cc-pvtz",
        "B3LYP:def2-svp",
        "B3LYP:def2-tzvp",
        "B3LYP:sto-3g",
        "HF:aug-cc-pvdz",
        "HF:aug-cc-pvtz",
        "HF:def2-svp",
        "HF:def2-tzvp",
        "HF:sto-3g",
        "MP2:aug-cc-pvdz",
        "MP2:aug-cc-pvtz",
        "MP2:def2-svp",
        "MP2:def2-tzvp",
        "MP2:sto-3g",
        "PBE0:aug-cc-pvdz",
        "PBE0:aug-cc-pvtz",
        "PBE0:def2-svp",
        "PBE0:def2-tzvp",
        "PBE0:sto-3g",
        "PBE:aug-cc-pvdz",
        "PBE:aug-cc-pvtz",
        "PBE:def2-svp",
        "PBE:def2-tzvp",
        "PBE:sto-3g",
        "WB97M-V:aug-cc-pvdz",
        "WB97M-V:aug-cc-pvtz",
        "WB97M-V:def2-svp",
        "WB97M-V:def2-tzvp",
        "WB97M-V:sto-3g",
        "WB97X-D:aug-cc-pvdz",
        "WB97X-D:aug-cc-pvtz",
        "WB97X-D:def2-svp",
        "WB97X-D:def2-tzvp",
        "WB97X-D:sto-3g",
    ]

    __energy_methods__ = [PotentialMethod.NONE for _ in range(len(energy_target_names))]  # "wb97x/6-31g(d)"

QM7b

Bases: QMX

QM7b is a dataset constructed from subsets of the GDB-13 database ( stable and synthetically accessible organic molecules), containing up to seven “heavy” atoms. The molecules conformation are optimized using DFT at the PBE0/def2-TZVP level of theory.

Chemical species

[C, N, O, S, Cl, H]

Usage: 

from openqdc.datasets import QM7b
dataset = QM7b()

References

https://arxiv.org/pdf/1703.00564

Source code in openqdc/datasets/potential/qmx.py 
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class QM7b(QMX):
    """
    QM7b is a dataset constructed from subsets of the GDB-13 database (
    stable and synthetically accessible organic molecules),
    containing up to seven “heavy” atoms.
    The molecules conformation are optimized using DFT at the
    PBE0/def2-TZVP level of theory.

    Chemical species:
        [C, N, O, S, Cl, H]

    Usage:
    ```python
    from openqdc.datasets import QM7b
    dataset = QM7b()
    ```

    References:
        https://arxiv.org/pdf/1703.00564
    """

    __links__ = {"qm7b.hdf5.gz": "https://zenodo.org/record/3588335/files/200.hdf5.gz?download=1"}
    __name__ = "qm7b"
    energy_target_names = [
        "CCSD(T0):cc-pVDZ",
        "HF:cc-pVDZ",
        "HF:cc-pVTZ",
        "MP2:cc-pVTZ",
        "B2PLYP-D3:aug-cc-pvdz",
        "B2PLYP-D3:aug-cc-pvtz",
        "B2PLYP-D3:def2-svp",
        "B2PLYP-D3:def2-tzvp",
        "B2PLYP-D3:sto-3g",
        "B2PLYP-D3M(BJ):aug-cc-pvdz",
        "B2PLYP-D3M(BJ):aug-cc-pvtz",
        "B2PLYP-D3M(BJ):def2-svp",
        "B2PLYP-D3M(BJ):def2-tzvp",
        "B2PLYP-D3M(BJ):sto-3g",
        "B2PLYP-D3M:aug-cc-pvdz",
        "B2PLYP-D3M:aug-cc-pvtz",
        "B2PLYP-D3M:def2-svp",
        "B2PLYP-D3M:def2-tzvp",
        "B2PLYP-D3M:sto-3g",
        "B2PLYP:aug-cc-pvdz",
        "B2PLYP:aug-cc-pvtz",
        "B2PLYP:def2-svp",
        "B2PLYP:def2-tzvp",
        "B2PLYP:sto-3g",
        "B3LYP-D3(BJ):aug-cc-pvdz",
        "B3LYP-D3(BJ):aug-cc-pvtz",
        "B3LYP-D3(BJ):def2-svp",
        "B3LYP-D3(BJ):def2-tzvp",
        "B3LYP-D3(BJ):sto-3g",
        "B3LYP-D3:aug-cc-pvdz",
        "B3LYP-D3:aug-cc-pvtz",
        "B3LYP-D3:def2-svp",
        "B3LYP-D3:def2-tzvp",
        "B3LYP-D3:sto-3g",
        "B3LYP-D3M(BJ):aug-cc-pvdz",
        "B3LYP-D3M(BJ):aug-cc-pvtz",
        "B3LYP-D3M(BJ):def2-svp",
        "B3LYP-D3M(BJ):def2-tzvp",
        "B3LYP-D3M(BJ):sto-3g",
        "B3LYP-D3M:aug-cc-pvdz",
        "B3LYP-D3M:aug-cc-pvtz",
        "B3LYP-D3M:def2-svp",
        "B3LYP-D3M:def2-tzvp",
        "B3LYP-D3M:sto-3g",
        "B3LYP:aug-cc-pvdz",
        "B3LYP:aug-cc-pvtz",
        "B3LYP:def2-svp",
        "B3LYP:def2-tzvp",
        "B3LYP:sto-3g",
        "HF:aug-cc-pvdz",
        "HF:aug-cc-pvtz",
        "HF:cc-pvtz",
        "HF:def2-svp",
        "HF:def2-tzvp",
        "HF:sto-3g",
        "PBE0:aug-cc-pvdz",
        "PBE0:aug-cc-pvtz",
        "PBE0:def2-svp",
        "PBE0:def2-tzvp",
        "PBE0:sto-3g",
        "PBE:aug-cc-pvdz",
        "PBE:aug-cc-pvtz",
        "PBE:def2-svp",
        "PBE:def2-tzvp",
        "PBE:sto-3g",
        "SVWN:sto-3g",
        "WB97M-V:aug-cc-pvdz",
        "WB97M-V:aug-cc-pvtz",
        "WB97M-V:def2-svp",
        "WB97M-V:def2-tzvp",
        "WB97M-V:sto-3g",
        "WB97X-D:aug-cc-pvdz",
        "WB97X-D:aug-cc-pvtz",
        "WB97X-D:def2-svp",
        "WB97X-D:def2-tzvp",
        "WB97X-D:sto-3g",
    ]
    __energy_methods__ = [PotentialMethod.NONE for _ in range(len(energy_target_names))]  # "wb97x/6-31g(d)"]

QM8

Bases: QMX

QM8 is the subset of QM9 used in a study on modeling quantum mechanical calculations of electronic spectra and excited state energy (a increase of energy from the ground states) of small molecules up to eight heavy atoms. Multiple methods were used, including time-dependent density functional theories (TDDFT) and second-order approximate coupled-cluster (CC2). The molecules conformations are relaxed geometries computed using the DFT B3LYP with basis set 6-31G(2df,p). For more information about the sampling, check QM9 dataset.

Usage: 

from openqdc.datasets import QM8
dataset = QM8()

References

https://arxiv.org/pdf/1504.01966

Source code in openqdc/datasets/potential/qmx.py 
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class QM8(QMX):
    """QM8 is the subset of QM9 used in a study on modeling quantum
    mechanical calculations of electronic spectra and excited
    state energy (a increase of energy from the ground states) of small molecules
    up to eight heavy atoms.
    Multiple methods were used, including
    time-dependent density functional theories (TDDFT) and
    second-order approximate coupled-cluster (CC2).
    The molecules conformations are relaxed geometries computed using
    the DFT B3LYP with basis set 6-31G(2df,p).
    For more information about the sampling, check QM9 dataset.

    Usage:
    ```python
    from openqdc.datasets import QM8
    dataset = QM8()
    ```

    References:
        https://arxiv.org/pdf/1504.01966
    """

    __name__ = "qm8"

    __energy_methods__ = [
        PotentialMethod.NONE,  # "wb97x/6-31g(d)"
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
    ]

    __links__ = {
        "qm8.csv": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv",
        "qm8.tar.gz": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/gdb8.tar.gz",
    }

    def read_raw_entries(self):
        df = pd.read_csv(p_join(self.root, "qm8.csv"))
        mols = dm.read_sdf(p_join(self.root, "qm8.sdf"), sanitize=False, remove_hs=False)
        samples = []
        for idx_row, mol in zip(df.iterrows(), mols):
            _, row = idx_row
            positions = mol.GetConformer().GetPositions()
            x = get_atomic_number_and_charge(mol)
            n_atoms = positions.shape[0]
            samples.append(
                dict(
                    atomic_inputs=np.concatenate((x, positions), axis=-1, dtype=np.float32).reshape(-1, 5),
                    name=np.array([row["smiles"]]),
                    energies=np.array(
                        [
                            row[
                                ["E1-CC2", "E2-CC2", "E1-PBE0", "E2-PBE0", "E1-PBE0.1", "E2-PBE0.1", "E1-CAM", "E2-CAM"]
                            ].tolist()
                        ],
                        dtype=np.float64,
                    ).reshape(1, -1),
                    n_atoms=np.array([n_atoms], dtype=np.int32),
                    subset=np.array([f"{self.__name__}"]),
                )
            )
        return samples

QM9

Bases: QMX

QM7b is a dataset constructed containing 134k molecules from subsets of the GDB-17 database, containing up to 9 “heavy” atoms. All molecular properties are calculated at B3LUP/6-31G(2df,p) level of quantum chemistry. For each of the 134k molecules, equilibrium geometries are computed by relaxing geometries with quantum mechanical method B3LYP.

Usage: 

from openqdc.datasets import QM9
dataset = QM9()

Reference

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

Source code in openqdc/datasets/potential/qmx.py 
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class QM9(QMX):
    """
    QM7b is a dataset constructed containing 134k molecules from subsets of the GDB-17 database,
    containing up to 9 “heavy” atoms. All molecular properties are calculated at B3LUP/6-31G(2df,p)
    level of quantum chemistry. For each of the 134k molecules, equilibrium geometries are computed
    by relaxing geometries with quantum mechanical method B3LYP.

    Usage:
    ```python
    from openqdc.datasets import QM9
    dataset = QM9()
    ```

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

    __links__ = {"qm9.hdf5.gz": "https://zenodo.org/record/3588339/files/155.hdf5.gz?download=1"}
    __name__ = "qm9"
    energy_target_names = [
        "Internal energy at 0 K",
        "B3LYP:def2-svp",
        "HF:cc-pvtz",
        "HF:sto-3g",
        "PBE:sto-3g",
        "SVWN:sto-3g",
        "WB97X-D:aug-cc-pvtz",
        "WB97X-D:def2-svp",
        "WB97X-D:def2-tzvp",
    ]

    __energy_methods__ = [
        PotentialMethod.NONE,  # "wb97x/6-31g(d)"
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
        PotentialMethod.NONE,
    ]

QMX

Bases: ABC, BaseDataset

QMX dataset base abstract class

Source code in openqdc/datasets/potential/qmx.py 
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class QMX(ABC, BaseDataset):
    """
    QMX dataset base abstract class
    """

    __name__ = "qm9"

    __energy_methods__ = [
        PotentialMethod.WB97X_6_31G_D,  # "wb97x/6-31g(d)"
    ]

    energy_target_names = [
        "ωB97x:6-31G(d) Energy",
    ]

    __energy_unit__ = "hartree"
    __distance_unit__ = "bohr"
    __forces_unit__ = "hartree/bohr"
    __links__ = {}

    @property
    def root(self):
        return p_join(get_local_cache(), "qmx")

    @property
    def preprocess_path(self):
        path = p_join(self.root, "preprocessed", self.__name__)
        os.makedirs(path, exist_ok=True)
        return path

    @property
    def config(self):
        assert len(self.__links__) > 0, "No links provided for fetching"
        return dict(dataset_name="qmx", links=self.__links__)

    def read_raw_entries(self):
        raw_path = p_join(self.root, f"{self.__name__}.h5.gz")
        samples = read_qc_archive_h5(raw_path, self.__name__, self.energy_target_names, None)
        return samples