sf.apps.data.feature.QM9Exact¶
-
class
QM9Exact
[source]¶ Bases:
strawberryfields.apps.data.feature.FeatureDataset
Exactly-calculated feature vectors of 1100 randomly-chosen molecules from the QM9 dataset.
The QM9 dataset is widely used in benchmarking performance of machine learning models in estimating molecular properties [7], [8].
Coulomb matrices were used as adjacency matrices to represent molecules in this case.
The Monte-Carlo estimated feature vectors of certain events of these 1100 molecules are also available in the
QM9MC
class.-
method = "exact"
-
n_mean = 6
-
unit = "orbits"
-
unit_data = [[1, 1], [2], [1, 1, 1, 1], [2, 1, 1], [2, 2], [1, 1, 1, 1, 1, 1], [2, 1, 1, 1, 1], [2, 2, 1, 1], [2, 2, 2]]
Attributes
-
code/api/api/strawberryfields.apps.data.feature.QM9Exact
Download Python script
Download Notebook
View on GitHub