class_docstr: A variety of ab-initio molecular dynamics trajectories from the authors
  of `sGDML <http://quantum-machine.org/gdml>`_.
  This class provides access to the original MD17 datasets, their revised
  versions, and the CCSD(T) trajectories.
  
  For every trajectory, the dataset contains the Cartesian positions of atoms
  (in Angstrom), their atomic numbers, as well as the total energy
  (in kcal/mol) and forces (kcal/mol/Angstrom) on each atom.
  The latter two are the regression targets for this collection.
  
  .. note::
  
      Data objects contain no edge indices as these are most commonly
      constructed via the :obj:`torch_geometric.transforms.RadiusGraph`
      transform, with its cut-off being a hyperparameter.
  
  The `original MD17 dataset <https://arxiv.org/abs/1611.04678>`_ contains
  ten molecule trajectories.
  This version of the dataset was found to suffer from high numerical noise.
  The `revised MD17 dataset <https://arxiv.org/abs/2007.09593>`_ contains the
  same molecules, but the energies and forces were recalculated at the
  PBE/def2-SVP level of theory using very tight SCF convergence and very
  dense DFT integration grid.
  The third version of the dataset contains fewer molecules, computed at the
  CCSD(T) level of theory.
  The benzene molecule at the DFT FHI-aims level of theory was
  `released separately <https://arxiv.org/abs/1802.09238>`_.
  
  Check the table below for detailed information on the molecule, level of
  theory and number of data points contained in each dataset.
  Which trajectory is loaded is determined by the :attr:`name` argument.
  For the coupled cluster trajectories, the dataset comes with pre-defined
  training and testing splits which are loaded separately via the
  :attr:`train` argument.
  
  +--------------------+--------------------+-------------------------------+-----------+
  | Molecule           | Level of Theory    | Name                          | #Examples |
  +====================+====================+===============================+===========+
  | Benzene            | DFT                | :obj:`benzene`                | 627,983   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Uracil             | DFT                | :obj:`uracil`                 | 133,770   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Naphthalene        | DFT                | :obj:`napthalene`             | 326,250   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Aspirin            | DFT                | :obj:`aspirin`                | 211,762   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Salicylic acid     | DFT                | :obj:`salicylic acid`         | 320,231   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Malonaldehyde      | DFT                | :obj:`malonaldehyde`          | 993,237   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Ethanol            | DFT                | :obj:`ethanol`                | 555,092   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Toluene            | DFT                | :obj:`toluene`                | 442,790   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Paracetamol        | DFT                | :obj:`paracetamol`            | 106,490   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Azobenzene         | DFT                | :obj:`azobenzene`             | 99,999    |
  +--------------------+--------------------+-------------------------------+-----------+
  | Benzene (R)        | DFT (PBE/def2-SVP) | :obj:`revised benzene`        | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Uracil (R)         | DFT (PBE/def2-SVP) | :obj:`revised uracil`         | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Naphthalene (R)    | DFT (PBE/def2-SVP) | :obj:`revised napthalene`     | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Aspirin (R)        | DFT (PBE/def2-SVP) | :obj:`revised aspirin`        | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Salicylic acid (R) | DFT (PBE/def2-SVP) | :obj:`revised salicylic acid` | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Malonaldehyde (R)  | DFT (PBE/def2-SVP) | :obj:`revised malonaldehyde`  | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Ethanol (R)        | DFT (PBE/def2-SVP) | :obj:`revised ethanol`        | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Toluene (R)        | DFT (PBE/def2-SVP) | :obj:`revised toluene`        | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Paracetamol (R)    | DFT (PBE/def2-SVP) | :obj:`revised paracetamol`    | 100,000   |
  +--------------------+--------------------+-------------------------------+-----------+
  | Azobenzene (R)     | DFT (PBE/def2-SVP) | :obj:`revised azobenzene`     | 99,988    |
  +--------------------+--------------------+-------------------------------+-----------+
  | Benzene            | CCSD(T)            | :obj:`benzene CCSD(T)`        | 1,500     |
  +--------------------+--------------------+-------------------------------+-----------+
  | Aspirin            | CCSD               | :obj:`aspirin CCSD`           | 1,500     |
  +--------------------+--------------------+-------------------------------+-----------+
  | Malonaldehyde      | CCSD(T)            | :obj:`malonaldehyde CCSD(T)`  | 1,500     |
  +--------------------+--------------------+-------------------------------+-----------+
  | Ethanol            | CCSD(T)            | :obj:`ethanol CCSD(T)`        | 2,000     |
  +--------------------+--------------------+-------------------------------+-----------+
  | Toluene            | CCSD(T)            | :obj:`toluene CCSD(T)`        | 1,501     |
  +--------------------+--------------------+-------------------------------+-----------+
  | Benzene            | DFT FHI-aims       | :obj:`benzene FHI-aims`       | 49,863    |
  +--------------------+--------------------+-------------------------------+-----------+
  
  .. warning::
  
      It is advised to not train a model on more than 1,000 samples from the
      original or revised MD17 dataset.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): Keyword of the trajectory that should be loaded.
      train (bool, optional): Determines whether the train or test split
          gets loaded for the coupled cluster trajectories.
          (default: :obj:`None`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 20 10 10 10 10 10
      :header-rows: 1
  
      * - Name
        - #graphs
        - #nodes
        - #edges
        - #features
        - #tasks
      * - Benzene
        - 627,983
        - 12
        - 0
        - 1
        - 2
      * - Uracil
        - 133,770
        - 12
        - 0
        - 1
        - 2
      * - Naphthalene
        - 326,250
        - 10
        - 0
        - 1
        - 2
      * - Aspirin
        - 211,762
        - 21
        - 0
        - 1
        - 2
      * - Salicylic acid
        - 320,231
        - 16
        - 0
        - 1
        - 2
      * - Malonaldehyde
        - 993,237
        - 9
        - 0
        - 1
        - 2
      * - Ethanol
        - 555,092
        - 9
        - 0
        - 1
        - 2
      * - Toluene
        - 442,790
        - 15
        - 0
        - 1
        - 2
      * - Paracetamol
        - 106,490
        - 20
        - 0
        - 1
        - 2
      * - Azobenzene
        - 99,999
        - 24
        - 0
        - 1
        - 2
      * - Benzene (R)
        - 100,000
        - 12
        - 0
        - 1
        - 2
      * - Uracil (R)
        - 100,000
        - 12
        - 0
        - 1
        - 2
      * - Naphthalene (R)
        - 100,000
        - 10
        - 0
        - 1
        - 2
      * - Aspirin (R)
        - 100,000
        - 21
        - 0
        - 1
        - 2
      * - Salicylic acid (R)
        - 100,000
        - 16
        - 0
        - 1
        - 2
      * - Malonaldehyde (R)
        - 100,000
        - 9
        - 0
        - 1
        - 2
      * - Ethanol (R)
        - 100,000
        - 9
        - 0
        - 1
        - 2
      * - Toluene (R)
        - 100,000
        - 15
        - 0
        - 1
        - 2
      * - Paracetamol (R)
        - 100,000
        - 20
        - 0
        - 1
        - 2
      * - Azobenzene (R)
        - 99,988
        - 24
        - 0
        - 1
        - 2
      * - Benzene CCSD-T
        - 1,500
        - 12
        - 0
        - 1
        - 2
      * - Aspirin CCSD-T
        - 1,500
        - 21
        - 0
        - 1
        - 2
      * - Malonaldehyde CCSD-T
        - 1,500
        - 9
        - 0
        - 1
        - 2
      * - Ethanol CCSD-T
        - 2000
        - 9
        - 0
        - 1
        - 2
      * - Toluene CCSD-T
        - 1,501
        - 15
        - 0
        - 1
        - 2
      * - Benzene FHI-aims
        - 49,863
        - 12
        - 0
        - 1
        - 2
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code: def __init__(
      self,
      root: str,
      name: str,
      train: Optional[bool] = None,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      pre_filter: Optional[Callable] = None,
      force_reload: bool = False,
  ) -> None:
      if name not in self.file_names:
          raise ValueError(f"Unknown dataset name '{name}'")
  
      self.name = name
      self.revised = "revised" in name
      self.ccsd = "CCSD" in self.name
  
      super().__init__(
          root,
          transform,
          pre_transform,
          pre_filter,
          force_reload=force_reload,
      )
  
      if len(self.processed_file_names) == 1 and train is not None:
          raise ValueError(
              f"'{self.name}' dataset does not provide pre-defined splits "
              f"but the 'train' argument is set to '{train}'"
          )
      elif len(self.processed_file_names) == 2 and train is None:
          raise ValueError(
              f"'{self.name}' dataset does provide pre-defined splits but "
              f"the 'train' argument was not specified"
          )
  
      idx = 0 if train is None or train else 1
      self.load(self.processed_paths[idx])
  
file_path: pytorch_geometric/torch_geometric/datasets/md17.py
imports: import os
  import os.path
  from typing import Callable, List, Optional, Union
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_tar, extract_zip
label_desc: Use image node_id 1 to create a new MD17 object from inherited base classes: InMemoryDataset with example: obj = MD17(root, name, train, transform, pre_transform, pre_filter, force_reload)