qm9

• Description:

QM9 consists of computed geometric, energetic, electronic, and thermodynamic properties for 134k stable small organic molecules made up of C, H, O, N, and F. As usual, we remove the uncharacterized molecules and provide the remaining 130,831.

• Homepage: https://doi.org/10.6084/m9.figshare.c.978904.v5

• Source code: tfds.datasets.qm9.Builder

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 82.62 MiB

• Dataset size: 177.16 MiB

• Feature structure:

FeaturesDict({
    'A': float32,
    'B': float32,
    'C': float32,
    'Cv': float32,
    'G': float32,
    'G_atomization': float32,
    'H': float32,
    'H_atomization': float32,
    'InChI': string,
    'InChI_relaxed': string,
    'Mulliken_charges': Tensor(shape=(29,), dtype=float32),
    'SMILES': string,
    'SMILES_relaxed': string,
    'U': float32,
    'U0': float32,
    'U0_atomization': float32,
    'U_atomization': float32,
    'alpha': float32,
    'charges': Tensor(shape=(29,), dtype=int64),
    'frequencies': Tensor(shape=(None,), dtype=float32),
    'gap': float32,
    'homo': float32,
    'index': int64,
    'lumo': float32,
    'mu': float32,
    'num_atoms': int64,
    'positions': Tensor(shape=(29, 3), dtype=float32),
    'r2': float32,
    'tag': string,
    'zpve': float32,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
A	Tensor		float32
B	Tensor		float32
C	Tensor		float32
Cv	Tensor		float32
G	Tensor		float32
G_atomization	Tensor		float32
H	Tensor		float32
H_atomization	Tensor		float32
InChI	Tensor		string
InChI_relaxed	Tensor		string
Mulliken_charges	Tensor	(29,)	float32
SMILES	Tensor		string
SMILES_relaxed	Tensor		string
U	Tensor		float32
U0	Tensor		float32
U0_atomization	Tensor		float32
U_atomization	Tensor		float32
alpha	Tensor		float32
charges	Tensor	(29,)	int64
frequencies	Tensor	(None,)	float32
gap	Tensor		float32
homo	Tensor		float32
index	Tensor		int64
lumo	Tensor		float32
mu	Tensor		float32
num_atoms	Tensor		int64
positions	Tensor	(29, 3)	float32
r2	Tensor		float32
tag	Tensor		string
zpve	Tensor		float32

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@article{ramakrishnan2014quantum,
  title={Quantum chemistry structures and properties of 134 kilo molecules},
  author={Ramakrishnan, Raghunathan and Dral, Pavlo O and Rupp, Matthias and von Lilienfeld, O Anatole},
  journal={Scientific Data},
  volume={1},
  year={2014},
  publisher={Nature Publishing Group}
}

qm9/original (default config)

• Config description: QM9 does not define any splits. So this variant puts the full QM9 dataset in the train split, in the original order (no shuffling).

• Auto-cached (documentation): Only when shuffle_files=False (train)

• Splits:

Split	Examples
'train'	130,831

• Examples (tfds.as_dataframe):

qm9/cormorant

• Config description: Dataset split used by Cormorant. 100,000 train, 17,748 validation, and 13,083 test samples. Splitting happens after shuffling with seed 0. Paper: https://arxiv.org/abs/1906.04015 Split: https://github.com/risilab/cormorant/blob/master/src/cormorant/data/prepare/qm9.py

• Auto-cached (documentation): Yes (test, validation), Only when shuffle_files=False (train)

• Splits:

Split	Examples
'test'	13,083
'train'	100,000
'validation'	17,748

• Examples (tfds.as_dataframe):

qm9/dimenet

• Config description: Dataset split used by DimeNet. 110,000 train, 10,000 validation, and 10,831 test samples. Splitting happens after shuffling with seed 42. Paper: https://arxiv.org/abs/2003.03123 Split: https://github.com/gasteigerjo/dimenet/blob/master/dimenet/training/data_provider.py

• Auto-cached (documentation): Yes (test, validation), Only when shuffle_files=False (train)

• Splits:

Split	Examples
'test'	10,831
'train'	110,000
'validation'	10,000

• Examples (tfds.as_dataframe):