schrodinger.application.matsci.mlearn.features module

Classes and functions to deal with ML features.

Copyright Schrodinger, LLC. All rights reserved.

schrodinger.application.matsci.mlearn.features.avg_atomic_vol(struct)

Get average atomic volume.

Parameters:struct (schrodinger.structure.Structure) – Structure to be used for feature calculation
Return type:float
Returns:Average atomic volume (A^3)
schrodinger.application.matsci.mlearn.features.get_distance_cell(struct, cutoff, cell, pbc)

Create an infrastructure Distance Cell. Struct MUST have the Chorus box properties.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

schrodinger.structure.Structure, , schrodinger.infra.structure.DistanceCell, schrodinger.infra.structure.PBC
Returns:

Supercell, an infrastructure Distance Cell that accounts for the PBC, and the pbc used to create it.
Raise:

ValueError if struct is missing PBCs
schrodinger.application.matsci.mlearn.features.elemental_generator(struct, element, is_equal=True)
schrodinger.application.matsci.mlearn.features.avg_neighbor_count(struct, element, cutoff, cell=None, pbc=None)

Get average neighbor count.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the count
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average neighbor count
schrodinger.application.matsci.mlearn.features.std_neighbor_count(struct, element, cutoff, cell=None, pbc=None)

Get standard deviation of neighbor count.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the count
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average neighbor count
schrodinger.application.matsci.mlearn.features.avg_element_neighbor_count(struct, cutoff, element, cell=None, pbc=None)

Get average element neighbor count.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • element (str) – Element for which to compute the average
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average number of bonds per element
schrodinger.application.matsci.mlearn.features.avg_sublattice_neighbor_count(struct, element, cutoff, cell=None, pbc=None)

Get average sublattice neighbor count.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the average
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average sublattice neighbor count
schrodinger.application.matsci.mlearn.features.avg_neighbor_ion(struct, element, cutoff, cell=None, pbc=None)

Get average neighbor ionicity.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the average
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average neighbor ionicity
schrodinger.application.matsci.mlearn.features.std_neighbor_ion(struct, element, cutoff, cell=None, pbc=None)

Get standard deviation of neighbor ionicity.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the average
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average neighbor ionicity
schrodinger.application.matsci.mlearn.features.avg_sublattice_neighbor_ion(struct, element, cutoff, cell=None, pbc=None)

Get average sublattice neighbor ionicity.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the average
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Average sublattice neighbor count
schrodinger.application.matsci.mlearn.features.get_anion(struct)

Get the most electronegative element in the structure (anion).

Parameters:struct (schrodinger.structure.Structure) – Input structure
Return type:str, float, int
Returns:Element, it’s electronegativity, number of anions in the cell
schrodinger.application.matsci.mlearn.features.vol_per_anion(struct)

Get volume per anion.

Parameters:struct (schrodinger.structure.Structure) – Input structure
Return type:float
Returns:Volume per anion
schrodinger.application.matsci.mlearn.features.avg_anion_anion_short_distance(struct, supercell)

Get average anion anion shortest distance.

Parameters:
Return type:

float
Returns:

Average anion anion shortest distance
schrodinger.application.matsci.mlearn.features.avg_element_anion_short_distance(struct, element, supercell)

Get average element anion shortest distance.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the average
  • struct – Supercell input structure
Return type:

float
Returns:

Average element anion shortest distance
schrodinger.application.matsci.mlearn.features.avg_element_element_short_distance(struct, element, supercell)

Get average element element shortest distance.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • element (str) – Element for which to compute the average
  • struct – Supercell input structure
Return type:

float
Returns:

Average element element shortest distance
schrodinger.application.matsci.mlearn.features.anion_frame_coordination(struct, supercell)

Get anion framework coordination.

Parameters:
Return type:

float
Returns:

Anion framework coordination
schrodinger.application.matsci.mlearn.features.avg_sublattice_eneg(struct, element)

Get average sublattice electronegativity.

Parameters:
Return type:

float
Returns:

Average sublattice electronegativity
schrodinger.application.matsci.mlearn.features.packing_fraction(struct, cutoff, cell=None, pbc=None)

Get packing fraction of the crystal.

Parameters:
  • struct (schrodinger.structure.Structure) – Input structure
  • cutoff (float) – The cutoff for finding nearest neighbor atoms
  • cell (schrodinger.infra.structure.DistanceCell) – Distance cell object for the struct
  • pbc (schrodinger.infra.structure.PBC) – PBC object for the struct
Return type:

float
Returns:

Packing fraction
class schrodinger.application.matsci.mlearn.features.LatticeFeatures(element='Li', cutoff=4.0)

Bases: schrodinger.application.matsci.mlearn.base.BaseFeaturizer

Class to generate lattice-based features.

__init__(element='Li', cutoff=4.0)

Initialize the object.

transform(structs)

Get numerical features from structures. Also sets features names in self.labels. See parent class for more documentation.

Parameters:structs (list(schrodinger.structure.Structure)) – List of structures to be featurized
Return type:numpy array of shape [n_samples, n_features]
Returns:Transformed array
__class__

alias of builtins.type

__delattr__

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'schrodinger.application.matsci.mlearn.features', '__doc__': '\n Class to generate lattice-based features.\n ', '__init__': <function LatticeFeatures.__init__>, 'transform': <function LatticeFeatures.transform>})
__dir__() → list

default dir() implementation

__eq__

Return self==value.

__format__()

default object formatter

__ge__

Return self>=value.

__getattribute__

Return getattr(self, name).

__getstate__()
__gt__

Return self>value.

__hash__

Return hash(self).

__init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__

Return self<=value.

__lt__

Return self<value.

__module__ = 'schrodinger.application.matsci.mlearn.features'
__ne__

Return self!=value.

__new__()

Create and return a new object. See help(type) for accurate signature.

__reduce__()

helper for pickle

__reduce_ex__()

helper for pickle

__repr__()

Return repr(self).

__setattr__

Implement setattr(self, name, value).

__setstate__(state)
__sizeof__() → int

size of object in memory, in bytes

__str__

Return str(self).

__subclasshook__()

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object (if defined)

fit(data, data_y=None)

Fit and return self. Anything that evaluates properties related to the passed data should go here. For example, compute physical properties of a stucture and save them as class property, to be used in the transform method.

Parameters:
  • data (numpy array of shape [n_samples, n_features]) – Training set
  • data_y (numpy array of shape [n_samples]) – Target values
Return type:

BaseFeaturizer
Returns:

self object with fitted data
fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
get_params(deep=True)

Get parameters for this estimator.

deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
params : mapping of string to any
Parameter names mapped to their values.
set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

self