updated docs (934ecc83) · Commits · 钟慕尧 / deepchem

deepchem/feat/material_featurizers/cgcnn_featurizer.py

+6 −2

Original line number	Diff line number	Diff line
		@@ -30,14 +30,18 @@ class CGCNNFeaturizer(MaterialStructureFeaturizer):

		References
		----------
		.. [1] T. Xie and J. C. Grossman, Phys. Rev. Lett. 120, 2018.
		.. [1] T. Xie and J. C. Grossman, "Crystal graph convolutional
		neural networks for an accurate and interpretable prediction
		of material properties", Phys. Rev. Lett. 120, 2018,
		https://arxiv.org/abs/1710.10324

		Examples
		--------
		>>> import deepchem as dc
		>>> import pymatgen as mg
		>>> featurizer = dc.feat.CGCNNFeaturizer()
		>>> lattice = mg.core.Lattice.cubic(4.2)
		>>> structure = mg.core.Structure(lattice, ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]])
		>>> featurizer = CGCNNFeaturizer()
		>>> features = featurizer.featurize([structure])
		>>> feature = features[0]
		>>> print(type(feature))

+6 −1

Original line number	Diff line number	Diff line
		@@ -33,10 +33,15 @@ class ElementPropertyFingerprint(MaterialCompositionFeaturizer):

		Examples
		--------
		>>> import deepchem as dc
		>>> import pymatgen as mg
		>>> comp = mg.core.Composition("Fe2O3")
		>>> featurizer = ElementPropertyFingerprint()
		>>> featurizer = dc.feat.ElementPropertyFingerprint()
		>>> features = featurizer.featurize([comp])
		>>> type(features[0])
		<class 'numpy.ndarray'>
		>>> features[0].shape
		(65,)

		Note
		----

+8 −2

Original line number	Diff line number	Diff line
		@@ -31,10 +31,16 @@ class ElemNetFeaturizer(MaterialCompositionFeaturizer):

		Examples
		--------
		>>> import pymatgen as mg
		>>> import deepchem as dc
		>>> comp = "Fe2O3"
		>>> featurizer = ElemNetFeaturizer()
		>>> featurizer = dc.feat.ElemNetFeaturizer()
		>>> features = featurizer.featurize([comp])
		>>> type(features[0])
		<class 'numpy.ndarray'>
		>>> features[0].shape
		(86,)
		>>> round(sum(features[0]))
		1

		Note
		----

+10 −2

Original line number	Diff line number	Diff line
		@@ -30,15 +30,23 @@ class SineCoulombMatrix(MaterialStructureFeaturizer):

		References
		----------
		.. [1] Faber et al. Inter. J. Quantum Chem. 115, 16, 2015.
		.. [1] Faber et al. "Crystal Structure Representations for Machine
		Learning Models of Formation Energies", Inter. J. Quantum Chem.
		115, 16, 2015. https://arxiv.org/abs/1503.07406

		Examples
		--------
		>>> import deepchem as dc
		>>> import pymatgen as mg
		>>> lattice = mg.core.Lattice.cubic(4.2)
		>>> structure = mg.core.Structure(lattice, ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]])
		>>> featurizer = SineCoulombMatrix(max_atoms=2)
		>>> featurizer = dc.feat.SineCoulombMatrix(max_atoms=2)
		>>> features = featurizer.featurize([structure])
		>>> type(features[0])
		<class 'numpy.ndarray'>
		>>> features[0].shape # (max_atoms,)
		(2,)


		Note
		----