matminer.featurizers package¶
Subpackages¶
Submodules¶
matminer.featurizers.bandstructure module¶
-
class
matminer.featurizers.bandstructure.
BandFeaturizer
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Featurizes a pymatgen band structure object.
-
__init__
()¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(bs)¶ - Args:
- bs (pymatgen BandStructure or BandStructureSymmLine or their dict):
- The band structure to featurize()
- Returns ([float]):
- a list of band structure features. If not bs.structure, the
- features that require the structure will be returned as NaN.
- List of currently supported features:
band_gap (eV): the difference between the CBM and VBM energy is_gap_direct (0.0|1.0): whether the band gap is direct or not direct_gap (eV): the minimum direct distance of the last
valence band and the first conduction band- {n,p}_ex{#}_en (eV): for example p_ex2_en is the absolute value
- of the energy of the second valence (p) band extremum w.r.t. VBM
- {n,p}_ex{#}_norm (float): e.g. n_ex1_norm is norm of the
- fractional coordinates of k-points of the 1st conduction (n) band extremum, i.e., the CBM
-
static
get_bindex_bspin
(extremum, is_cbm)¶ Returns the band index and spin of band extremum
- Args:
- extremum (dict): dictionary containing the CBM/VBM, i.e. output of
- Bandstructure.get_cbm()
is_cbm (bool): whether the extremum is the CBM or not
-
implementors
()¶
-
-
class
matminer.featurizers.bandstructure.
BranchPointEnergy
(n_vb=1, n_cb=1, calculate_band_edges=True)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
-
__init__
(n_vb=1, n_cb=1, calculate_band_edges=True)¶ Calculates the branch point energy and (optionally) an absolute band edge position assuming the branch point energy is the center of the gap
- Args:
n_vb: (int) number of valence bands to include in BPE calc n_cb: (int) number of conduction bands to include in BPE calc calculate_band_edges: (bool) whether to also return band edge
positions
-
citations
()¶
-
feature_labels
()¶
-
featurize
(bs, target_gap=None)¶ - Args:
- bs: (BandStructure)
- Returns:
- (int) branch point energy on same energy scale as BS eigenvalues
-
implementors
()¶
-
matminer.featurizers.base module¶
-
class
matminer.featurizers.base.
BaseFeaturizer
¶ Bases:
object
Abstract class to calculate attributes for compounds
-
citations
()¶ Citation / reference for feature
- Returns:
- array - each element should be str citation, ideally in BibTeX format
-
feature_labels
()¶ Generate attribute names
- Returns:
- list of strings for attribute labels
-
featurize
(*x)¶ Main featurizer function. Only defined in feature subclasses.
- Args:
- x: input data to featurize (type depends on featurizer)
- Returns:
- list of one or more features
-
featurize_dataframe
(df, col_id)¶ Compute features for all entries contained in input dataframe
- Args:
df (Pandas dataframe): Dataframe containing input data col_id (str or list of str): column label containing objects to featurize. Can be multiple labels, if the featurize
function requires multiple inputs- Returns:
- updated Dataframe
-
implementors
()¶ List of implementors of the feature.
- Returns:
- array - each element should either be str with author name (e.g., “Anubhav Jain”) or
- dict with required key “name” and other keys like “email” or “institution” (e.g., {“name”: “Anubhav Jain”, “email”: “ajain@lbl.gov”, “institution”: “LBNL”}).
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matminer.featurizers.composition module¶
-
class
matminer.featurizers.composition.
BandCenter
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ (Rough) estimation of absolution position of band center using geometric mean of electronegativity.
- Args:
- comp: (Composition)
Returns: (float) band center
-
implementors
()¶
-
-
class
matminer.featurizers.composition.
CohesiveEnergy
(mapi_key=None)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
-
__init__
(mapi_key=None)¶ Class to get cohesive energy per atom of a compound by adding known elemental cohesive energies from the formation energy of the compound.
- Parameters:
- mapi_key (str): Materials API key for looking up formation energy
- by composition alone (if you don’t set the formation energy yourself).
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp, formation_energy_per_atom=None)¶ - Args:
comp: (str) compound composition, eg: “NaCl” formation_energy_per_atom: (float) the formation energy per atom of
your compound. If not set, will look up the most stable formation energy from the Materials Project database.
-
implementors
()¶
-
-
class
matminer.featurizers.composition.
ElectronAffinity
(data_source=<matminer.featurizers.data.DemlData object>)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate average electron affinity times formal charge of anion elements
- Parameters:
- data_source (data class): source from which to retrieve element data
Generates average (electron affinity*formal charge) of anions
-
__init__
(data_source=<matminer.featurizers.data.DemlData object>)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ - Args:
- comp: Pymatgen Composition object
- Returns:
- avg_anion_affin (single-element list): average electron affinity*formal charge of anions
-
implementors
()¶
-
class
matminer.featurizers.composition.
ElectronegativityDiff
(data_source=<matminer.featurizers.data.DemlData object>, stats=None)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Calculate electronegativity difference between cations and anions (average, max, range, etc.)
- Parameters:
- data_source (data class): source from which to retrieve element data stats: Property statistics to compute
Generates average electronegativity difference between cations and anions
-
__init__
(data_source=<matminer.featurizers.data.DemlData object>, stats=None)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ - Args:
- comp: Pymatgen Composition object
- Returns:
- en_diff_stats (list of floats): Property stats of electronegativity difference
-
implementors
()¶
-
class
matminer.featurizers.composition.
ElementFraction
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate the atomic fraction of each element in a composition.
Generates: vector where each index represents an element in atomic number order.
-
__init__
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ - Args:
- comp: Pymatgen Composition object
- Returns:
- vector (list of floats): fraction of each element in a composition
-
implementors
()¶
-
-
class
matminer.featurizers.composition.
ElementProperty
(data_source, features, stats)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate elemental property attributes. To initialize quickly, use the from_preset() method.
- Parameters:
- data_source (AbstractData or str): source from which to retrieve
- element property data (or use str for preset: “pymatgen”, “magpie”, or “deml”)
- attributes (list of strings): List of elemental properties to use
- (these must be supported by data_source)
- stats (string): a list of weighted statistics to compute to for each
- property (see PropertyStats for available stats)
-
__init__
(data_source, features, stats)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ Get elemental property attributes
- Args:
- comp: Pymatgen composition object
- Returns:
- all_attributes: Specified property statistics of features
-
static
from_preset
(preset_name)¶ Return ElementProperty from a preset string Args:
preset_name: (str) can be one of “magpie”, “deml”, or “matminer”Returns:
-
implementors
()¶
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class
matminer.featurizers.composition.
FERECorrection
(data_source=<matminer.featurizers.data.DemlData object>, stats=None)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate difference between fitted elemental-phase reference energy (FERE) and GGA+U energy
- Parameters:
- data_source (data class): source from which to retrieve element data stats: Property statistics to compute
Generates: Property statistics of difference between FERE and GGA+U energy
-
__init__
(data_source=<matminer.featurizers.data.DemlData object>, stats=None)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ - Args:
- comp: Pymatgen Composition object
- Returns:
- fere_corr_stats (list of floats): Property stats of FERE correction
-
implementors
()¶
-
class
matminer.featurizers.composition.
IonProperty
(data_source=<matminer.featurizers.data.MagpieData object>)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate ionic property attributes
- Parameters:
- data_source (data class): source from which to retrieve element data
-
__init__
(data_source=<matminer.featurizers.data.MagpieData object>)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ Ionic character attributes
- Args:
- comp: Pymatgen composition object
- Returns:
- cpd_possible (bool): Indicates if a neutral ionic compound is possible max_ionic_char (float): Maximum ionic character between two atoms avg_ionic_char (float): Average ionic character
-
implementors
()¶
-
class
matminer.featurizers.composition.
Stoichiometry
(p_list=[0, 2, 3, 5, 7, 10], num_atoms=False)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate stoichiometric attributes.
- Parameters:
- p_list (list of ints): list of norms to calculate num_atoms (bool): whether to return number of atoms
-
__init__
(p_list=[0, 2, 3, 5, 7, 10], num_atoms=False)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ Get stoichiometric attributes Args:
comp: Pymatgen composition object p_list (list of ints)- Returns:
- p_norm (list of floats): Lp norm-based stoichiometric attributes.
- Returns number of atoms if no p-values specified.
-
implementors
()¶
-
class
matminer.featurizers.composition.
TMetalFraction
¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate fraction of magnetic transition metals in a composition.
- Parameters:
- data_source (data class): source from which to retrieve element data
Generates: Fraction of magnetic transition metal atoms in a compound
-
__init__
()¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ - Args:
- comp: Pymatgen Composition object
- Returns:
- frac_magn_atoms (single-element list): fraction of magnetic transitional metal atoms in a compound
-
implementors
()¶
-
class
matminer.featurizers.composition.
ValenceOrbital
(data_source=<matminer.featurizers.data.MagpieData object>, orbitals=['s', 'p', 'd', 'f'], props=['avg', 'frac'])¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Class to calculate valence orbital attributes
- Parameters:
data_source (data object): source from which to retrieve element data orbitals (list): orbitals to calculate props (list): specifies whether to return average number of electrons in each orbital,
fraction of electrons in each orbital, or both
-
__init__
(data_source=<matminer.featurizers.data.MagpieData object>, orbitals=['s', 'p', 'd', 'f'], props=['avg', 'frac'])¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(comp)¶ Weighted fraction of valence electrons in each orbital
- Args:
- comp: Pymatgen composition object
- Returns:
- valence_attributes (list of floats): Average number and/or fraction of valence electrons in specfied orbitals
-
implementors
()¶
matminer.featurizers.data module¶
-
class
matminer.featurizers.data.
AbstractData
¶ Bases:
object
-
get_property
(comp, property_name, return_per_element=True)¶ Gets data for a composition object.
- Args:
- comp (Composition/str): composition property_name (str): Name of descriptor return_per_element (bool): If true, returns one value per element rather than per atom
- Returns:
- (list): list of values for each atom in comp_obj. Note: the returned values are sorted by the corresponding element’s atomic number. This is done for the sake of consistency.
-
-
class
matminer.featurizers.data.
PymatgenData
¶ Bases:
matminer.featurizers.data.AbstractData
-
static
get_composition_oxidation_state
(formula)¶ Returns the composition and oxidation states from the given formula. Formula examples: “NaCl”, “Na+1Cl-1”, “Fe2+3O3-2” or “Fe2 +3 O3 -2”
- Args:
- formula (str):
- Returns:
- pymatgen.core.composition.Composition, dict of oxidation states as strings
-
get_property
(comp, property_name, return_per_element=True)¶ Get descriptor data for elements in a compound from pymatgen.
- Args:
- comp (str/Composition): Either pymatgen Composition object or string formula,
eg: “NaCl”, “Na+1Cl-1”, “Fe2+3O3-2” or “Fe2 +3 O3 -2” Notes:
- For ‘ionic_radii’ property, the Composition object must be made of oxidation
state decorated Specie objects not the plain Element objects. eg. fe2o3 = Composition({Specie(“Fe”, 3): 2, Specie(“O”, -2): 3})
- For string formula, the oxidation state sign(+ or -) must be specified explicitly.
eg. “Fe2+3O3-2”
- property_name (str): pymatgen element attribute name, as defined in the Element class at
- http://pymatgen.org/_modules/pymatgen/core/periodic_table.html
- Returns:
- (list) of values containing descriptor floats for each atom in the compound(sorted by the
- electronegativity of the contituent atoms)
-
static
matminer.featurizers.site module¶
Features that describe the local environment of a single atom
- The featurize function takes two arguments:
- strc (Structure): Object representing the structure containing the site of interest site (int): Index of the site to be featurized
We have to use two options because the Site object does not hold a pointer back to its structure. To run
featurize_dataframe
, you must pass the column names for both the site and the structure. For example:
-
class
matminer.featurizers.site.
AGNIFingerprints
(directions=(None, 'x', 'y', 'z'), etas=array([ 0.8, 1.22730192, 1.88283751, 2.88851263, 4.43134638, 6.79824993, 10.42938152, 16. ]), cutoff=8)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Integral of the product of the radial distribution function and a Gaussian window function.
Originally used by [Botu et al](http://pubs.acs.org/doi/abs/10.1021/acs.jpcc.6b10908) to fit empiricial potentials, these features come in two forms: atomic fingerprints, and direction-resolved fingerprints.
Atomic fingerprints describe the local environment of an atom and are computed using the function:
:math:`A_i(eta) = sumlimits_{i
e j} e^{-( rac{r_{ij}}{eta})^2} f(r_{ij})`
whereis the index of the atom,
is the index of a neighboring atom,
is a scaling function,
is the distance between atoms
and
, and
is a cutoff function where :math:`f(r) = 0.5[cos(
rac{pi r_{ij}}{R_c}) + 1]` if
and 0 otherwise.
The direction-resolved fingerprints are computed using
:math:`V_i^k(eta) = sumlimits_{i
e j} rac{r_{ij}^k}{r_{ij}} e^{-( rac{r_{ij}}{eta})^2} f(r_{ij})`
where
.is the
component of
- Parameters:
- directions (iterable): List of directions for the fingerprints. Can be none, ‘x’, ‘y’, or ‘z’ etas (iterable of floats): List of which window widths to compute cutoff (float): Cutoff distance
TODO: Differentiate between different atom types (maybe as another class)
-
__init__
(directions=(None, 'x', 'y', 'z'), etas=array([ 0.8, 1.22730192, 1.88283751, 2.88851263, 4.43134638, 6.79824993, 10.42938152, 16. ]), cutoff=8)¶
-
citations
()¶
-
directions
¶
-
feature_labels
()¶
-
featurize
(strc, site)¶
-
implementors
()¶
matminer.featurizers.stats module¶
File containing general methods for computing property statistics
-
class
matminer.featurizers.stats.
PropertyStats
¶ Bases:
object
-
static
avg_dev
(data_lst, weights=None)¶ Average absolute deviation of list of element data Args:
data_lst (list of floats): Value of a property for each atom in a compound weights (list of floats): Atomic fractions- Returns:
- average absolute deviation
-
static
calc_stat
(data_lst, stat, weights=None)¶ Compute a property statistic
- Args:
data_lst (list of floats): list of values stat (str) - Name of property to be compute. If there are arguments to the statistics function, these
should be added after the name and separated by two underscores. For example, the 2nd Holder mean would be “holder_mean__2”weights (list of floats): (Optional) weights for each element in data_lst
- Reteurn:
- float - Desired statistic
-
static
eigenvalues
(data_lst, symm=False, sort=False)¶ Return the eigenvalues of a matrix as a numpy array Args:
data_lst: (matrix-like) of values symm: whether to assume the matrix is symmetric sort: wheter to sort the eigenvaluesReturns: eigenvalues
-
static
flatten
(data_lst)¶ oxi Returns a flattened copy of data_lst-as a numpy array
-
static
gaussian_kernel
(arr0, arr1, SIGMA)¶ Returns a Gaussian kernel of the two arrays for use in KRR or other regressions using the kernel trick.
-
static
holder_mean
(data_lst, weights=None, power=1)¶ Get Holder mean Args:
data_lst: (list/array) of values weights: (list/array) of weights power: (int/float/str) which holder mean to computeReturns: Holder mean
-
static
laplacian_kernel
(arr0, arr1, SIGMA)¶ Returns a Laplacian kernel of the two arrays for use in KRR or other regressions using the kernel trick.
-
static
maximum
(data_lst, weights=None)¶ Maximum value in a list of element data Args:
data_lst (list of floats): Value of a property for each atom in a compound weights (ignored)- Returns:
- maximum value
-
static
mean
(data_lst, weights=None, **kwargs)¶ Mean of list of element data Args:
data_lst (list of floats): Value of a property for each atom or element in a compound weights (list of floats): Weights for each value- Returns:
- mean value
-
static
minimum
(data_lst, weights=None)¶ Minimum value in a list of element data Args:
data_lst (list of floats): Value of a property for each atom in a compound weights (ignored)- Returns:
- minimum value
-
static
mode
(data_lst, weights=None)¶ Mode of a list of element data. If multiple elements occur equally-frequently (or same weight, if weights are provided), this function will return the average of those values Args:
data_lst (list of floats): Value of a property for each atom in a compound weights (list of floats): Atomic fractions- Returns:
- mode
-
static
n_numerical_modes
(data_lst, n, dl=0.1)¶ - Returns the n first modes of a data set that are obtained with
- a finite bin size for the underlying frequency distribution.
- Args:
- data_lst ([float]): data values. n (integer): number of most frequent elements to be determined. dl (float): bin size of underlying (coarsened) distribution.
- Returns:
- ([float]): first n most frequent entries (or nan if not found).
-
static
range
(data_lst, weights=None)¶ Range of a list of element data Args:
data_lst (list of floats): Value of a property for each atom in a compound weights (ignored)- Returns:
- range
-
static
sorted
(data_lst)¶ Returns the sorted data_lst
-
static
std_dev
(data_lst, weights=None)¶ Standard deviation of a list of element data Args:
data_lst (list of floats): Value of a property for each atom in a compound weights (list of floats): Atomic fractions
-
static
matminer.featurizers.structure module¶
-
class
matminer.featurizers.structure.
CoulombMatrix
(diag_elems=True)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Generate the Coulomb matrix, M, of the input structure (or molecule). The Coulomb matrix was put forward by Rupp et al. (Phys. Rev. Lett. 108, 058301, 2012) and is defined by off-diagonal elements M_ij = Z_i*Z_j/|R_i-R_j| and diagonal elements 0.5*Z_i^2.4, where Z_i and R_i denote the nuclear charge and the position of atom i, respectively.
- Args:
- diag_elems: (bool) flag indicating whether (True, default) to use
- the original definition of the diagonal elements; if set to False, the diagonal elements are set to zero.
-
__init__
(diag_elems=True)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ Get Coulomb matrix of input structure.
- Args:
- s: input Structure (or Molecule) object.
- Returns:
- m: (Nsites x Nsites matrix) Coulomb matrix.
-
implementors
()¶
-
class
matminer.featurizers.structure.
DensityFeatures
(desired_features=None)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
-
__init__
(desired_features=None)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶
-
implementors
()¶
-
-
class
matminer.featurizers.structure.
ElectronicRadialDistributionFunction
(cutoff=None, dr=0.05)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Calculate the crystal structure-inherent electronic radial distribution function (ReDF) according to Willighagen et al., Acta Cryst., 2005, B61, 29-36. The ReDF is a structure-integral RDF (i.e., summed over all sites) in which the positions of neighboring sites are weighted by electrostatic interactions inferred from atomic partial charges. Atomic charges are obtained from the ValenceIonicRadiusEvaluator class. Args:
- cutoff: (float) distance up to which the ReDF is to be
- calculated (default: longest diagaonal in primitive cell).
dr: (float) width of bins (“x”-axis) of ReDF (default: 0.05 A).
-
__init__
(cutoff=None, dr=0.05)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ Get ReDF of input structure.
- Args:
- s: input Structure object.
- Returns: (dict) a copy of the electronic radial distribution
- functions (ReDF) as a dictionary. The distance list (“x”-axis values of ReDF) can be accessed via key ‘distances’; the ReDF itself is accessible via key ‘redf’.
-
implementors
()¶
-
class
matminer.featurizers.structure.
MinimumRelativeDistances
(cutoff=10.0)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Determines the relative distance of each site to its closest neighbor. We use the relative distance, f_ij = r_ij / (r^atom_i + r^atom_j), as a measure rather than the absolute distances, r_ij, to account for the fact that different atoms/species have different sizes. The function uses the valence-ionic radius estimator implemented in Pymatgen. Args:
- cutoff: (float) (absolute) distance up to which tentative
- closest neighbors (on the basis of relative distances) are to be determined.
-
__init__
(cutoff=10.0)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s, cutoff=10.0)¶ Get minimum relative distances of all sites of the input structure.
- Args:
- s: Pymatgen Structure object.
- Returns:
- min_rel_dists: (list of floats) list of all minimum relative
- distances (i.e., for all sites).
-
implementors
()¶
-
class
matminer.featurizers.structure.
OrbitalFieldMatrix
(period_tag=False)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
This function generates an orbital field matrix (OFM) as developed by Pham et al (arXiv, May 2017). Each atom is described by a 32-element vector (or 39-element vector, see period tag for details) uniquely representing the valence subshell. A 32x32 (39x39) matrix is formed by multiplying two atomic vectors. An OFM for an atomic environment is the sum of these matrices for each atom the center atom coordinates with multiplied by a distance function (In this case, 1/r times the weight of the coordinating atom in the Voronoi Polyhedra method). The OFM of a structure or molecule is the average of the OFMs for all the sites in the structure.
- Args:
- period_tag (bool): In the original OFM, an element is represented
- by a vector of length 32, where each element is 1 or 0, which represents the valence subshell of the element. With period_tag=True, the vector size is increased to 39, where the 7 extra elements represent the period of the element. Note lanthanides are treated as period 6, actinides as period 7. Default False as in the original paper.
- ...attribute:: size
- Either 32 or 39, the size of the vectors used to describe elements.
-
__init__
(period_tag=False)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ Makes a supercell for structure s (to protect sites from coordinating with themselves), and then finds the mean of the orbital field matrices of each site to characterize a structure
- Args:
- s (Structure): structure to characterize
- Returns:
- mean_ofm (size X size matrix): orbital field matrix
- characterizing s
-
get_atom_ofms
(struct, symm=False)¶ Calls get_single_ofm for every site in struct. If symm=True, get_single_ofm is called for symmetrically distinct sites, and counts is constructed such that ofms[i] occurs counts[i] times in the structure
- Args:
struct (Structure): structure for find ofms for symm (bool): whether to calculate ofm for only symmetrically
distinct sites- Returns:
ofms ([size X size matrix] X len(struct)): ofms for struct if symm:
- ofms ([size X size matrix] X number of symmetrically distinct sites):
- ofms for struct
counts: number of identical sites for each ofm
-
get_mean_ofm
(ofms, counts)¶ Averages a list of ofms, weights by counts
-
get_ohv
(sp, period_tag)¶ Get the “one-hot-vector” for pymatgen Element sp. This 32 or 39-length vector represents the valence shell of the given element. Args:
sp (Element): element whose ohv should be returned period_tag (bool): If true, the vector contains items
corresponding to the period of the element- Returns:
- my_ohv (numpy array length 39 if period_tag, else 32): ohv for sp
-
get_single_ofm
(site, site_dict)¶ Gets the orbital field matrix for a single chemical environment, where site is the center atom whose environment is characterized and site_dict is a dictionary of site : weight, where the weights are the Voronoi Polyhedra weights of the corresponding coordinating sites.
- Args:
- site (Site): center atom site_dict (dict of Site:float): chemical environment
- Returns:
- atom_ofm (size X size numpy matrix): ofm for site
-
get_structure_ofm
(struct)¶ Calls get_mean_ofm on the results of get_atom_ofms to give a size X size matrix characterizing a structure
-
implementors
()¶
-
class
matminer.featurizers.structure.
PartialRadialDistributionFunction
(cutoff=20.0, bin_size=0.1)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Compute the partial radial distribution function (PRDF) of a crystal structure, which is the radial distibution function broken down for each pair of atom types. The PRDF was proposed as a structural descriptor by [Schutt et al.] (https://journals.aps.org/prb/abstract/10.1103/PhysRevB.89.205118) Args:
cutoff: (float) distance up to which to calculate the RDF. bin_size: (float) size of each bin of the (discrete) RDF.-
__init__
(cutoff=20.0, bin_size=0.1)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ Get PRDF of the input structure. Args:
s: Pymatgen Structure object.- Returns:
- prdf, dist: (tuple of arrays) the first element is a
- dictionary where keys are tuples of element names and values are PRDFs.
-
implementors
()¶
-
-
class
matminer.featurizers.structure.
RadialDistributionFunction
(cutoff=20.0, bin_size=0.1)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Calculate the radial distribution function (RDF) of a crystal structure. Args:
cutoff: (float) distance up to which to calculate the RDF. bin_size: (float) size of each bin of the (discrete) RDF.-
__init__
(cutoff=20.0, bin_size=0.1)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ Get RDF of the input structure. Args:
s: Pymatgen Structure object.- Returns:
- rdf, dist: (tuple of arrays) the first element is the
- normalized RDF, whereas the second element is the inner radius of the RDF bin.
-
implementors
()¶
-
-
class
matminer.featurizers.structure.
RadialDistributionFunctionPeaks
(n_peaks=2)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Determine the location of the highest peaks in the radial distribution function (RDF) of a structure. Args:
n_peaks: (int) number of the top peaks to return .-
__init__
(n_peaks=2)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(rdf)¶ Get location of highest peaks in RDF.
- Args:
- rdf: (ndarray) RDF as obtained from the
- RadialDistributionFunction class.
- Returns: (ndarray) distances of highest peaks in descending order
- of the peak height
-
implementors
()¶
-
-
class
matminer.featurizers.structure.
SineCoulombMatrix
(diag_elems=True)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
This function generates a variant of the Coulomb matrix developed for periodic crystals by Faber et al. (Inter. J. Quantum Chem. 115, 16, 2015). It is identical to the Coulomb matrix, except that the inverse distance function is replaced by the inverse of a sin**2 function of the vector between the sites which is periodic in the dimensions of the structure lattice. See paper for details.
- Args:
- diag_elems (bool): flag indication whether (True, default) to use
- the original definition of the diagonal elements; if set to False, the diagonal elements are set to 0
-
__init__
(diag_elems=True)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ - Args:
- s (Structure or Molecule): input structure (or molecule)
- Returns:
- (Nsites x Nsites matrix) Sine matrix.
-
implementors
()¶
-
class
matminer.featurizers.structure.
SitesOrderParameters
(features=None, stats=None, pneighs=None, bond_angles=None)¶ Bases:
matminer.featurizers.base.BaseFeaturizer
Calculates all order parameters (OPs) for all sites in a crystal structure. Args:
features ([str]): list of order parameters supported by OrderParameters stats ([str]): list of weighted statistics to compute for each feature.
If stats is None, for each order parameter, a list is returned that contains the calculated parameter for each site in the structure. *Note for nth mode, stat must be ‘n*_mode’; e.g. stat=‘2nd_mode’- pneighs (dict): specification and parameters of neighbor-finding
- approach (see get_neighbors_of_site_with_index).
- bond_angles ([float]): list of bond angles for which order parameters
- are calculated explicitly (in addition to features)
-
__init__
(features=None, stats=None, pneighs=None, bond_angles=None)¶
-
citations
()¶
-
feature_labels
()¶
-
featurize
(s)¶ Calculate all sites’ local structure order parameters (LSOPs).
- Args:
s: Pymatgen Structure object.
- Returns:
- opvals: (2D array of floats) LSOP values of all sites’ (1st dimension) order parameters (2nd dimension). 46 order parameters are computed per site: q_cn (coordination number), q_lin, 35 x q_bent (starting with a target angle of 5 degrees and, increasing by 5 degrees, until 175 degrees), q_tet, q_oct, q_bcc, q_2, q_4, q_6, q_reg_tri, q_sq, q_sq_pyr.
-
static
from_preset
(preset_name)¶ Returns OrderParameters from a preset string. Args:
preset_name (str): options are ‘matminer’,Returns:
-
implementors
()¶
-
matminer.featurizers.structure.
get_neighbors_of_site_with_index
(struct, n, p=None)¶ Determine the neighbors around the site that has index n in the input Structure object struct, given the approach defined by parameters p. All supported neighbor-finding approaches and listed and explained in the following. All approaches start by creating a tentative list of neighbors using a large cutoff radius defined in parameter dictionary p via key “cutoff”. “min_dist”: find nearest neighbor and its distance d_nn; consider all
neighbors which are within a distance of d_nn * (1 + delta), where delta is an additional parameter provided in the dictionary p via key “delta”.- “scaled_VIRE”: compute the radii, r_i, of all sites on the basis of
- the valence-ionic radius evaluator (VIRE); consider all neighbors for which the distance to the central site is less than the sum of the radii multiplied by an a priori chosen parameter, delta, (i.e., dist < delta * (r_central + r_neighbor)).
- “min_relative_VIRE”: same approach as “min_dist”, except that we
- use relative distances (i.e., distances divided by the sum of the atom radii from VIRE).
- “min_relative_OKeeffe”: same approach as “min_relative_VIRE”, except
- that we use the bond valence parameters from O’Keeffe’s bond valence method (J. Am. Chem. Soc. 1991, 3226-3229) to calculate relative distances.
- Args:
struct (Structure): input structure. n (int): index of site in Structure object for which
neighbors are to be determined.- p (dict): specification (via “approach” key; default is “min_dist”)
- and parameters of neighbor-finding approach. Default cutoff radius is 6 Angstrom (key: “cutoff”). Other default parameters are as follows. min_dist: “delta”: 0.15; min_relative_OKeeffe: “delta”: 0.05; min_relative_VIRE: “delta”: 0.05; scaled_VIRE: “delta”: 2.
- Returns: ([site]) list of sites that are considered to be nearest
- neighbors to site with index n in Structure object struct.
-
matminer.featurizers.structure.
get_neighbors_of_site_with_index_future
(struct, n, approach=u'min_dist', delta=0.1, cutoff=10.0)¶ Returns the neighbors of a given site using a specific neighbor-finding method.
- Args:
struct (Structure): input structure. n (int): index of site in Structure object for which motif type
is to be determined.- approach (str): type of neighbor-finding approach, where
- “min_dist” will use the MinimumDistanceNN class, “voronoi” the VoronoiNN class, “min_OKeeffe” the MinimumOKeeffe class, and “min_VIRE” the MinimumVIRENN class.
delta (float): tolerance involved in neighbor finding. cutoff (float): (large) radius to find tentative neighbors.
Returns: neighbor sites.
-
matminer.featurizers.structure.
get_order_parameter_feature_vectors_difference
(struct1, struct2, pneighs=None, convert_none_to_zero=True, delta_op=0.01, ignore_op_types=None)¶ Determine the difference vector between two order parameter-statistics feature vector resulting from two input structures.
- Args:
struct1 (Structure): first input structure. struct2 (Structure): second input structure. pneighs (dict): specification and parameters of
neighbor-finding approach (see get_neighbors_of_site_with_index function for more details).- convert_none_to_zero (bool): flag indicating whether or not
- to convert None values in OPs to zero (cf., get_order_parameters function).
- delta_op (float): bin size of histogram that is computed
- in order to identify peak locations (cf., get_order_parameters_stats function).
- ignore_op_types ([str]): list of OP types to be ignored in
- output dictionary (cf., get_order_parameters_stats function).
- Returns: ([float]) difference vector between order
- parameter-statistics feature vectors obtained from the two input structures (structure 1 - structure 2).
-
matminer.featurizers.structure.
get_order_parameter_stats
(struct, pneighs=None, convert_none_to_zero=True, delta_op=0.01, ignore_op_types=None, bond_angles=None)¶ Determine the order parameter statistics accumulated across all sites in Structure object struct using the get_order_parameters function.
- Args:
struct (Structure): input structure. pneighs (dict): specification and parameters of
neighbor-finding approach (see get_neighbors_of_site_with_index function for more details).- convert_none_to_zero (bool): flag indicating whether or not
- to convert None values in LSOPs to zero (cf., get_order_parameters function).
- delta_op (float): bin size of histogram that is computed
- in order to identify peak locations.
- ignore_op_types ([str]): list of OP types to be ignored in
- output dictionary (e.g., [“cn”, “bent”]). Default (None) will consider all OPs.
- Returns: ({}) dictionary, the keys of which represent
- the order parameter type (e.g., “bent5”, “tet”, “sq_pyr”) and the values of which are dictionaries carring the statistics (“min”, “max”, “mean”, “std”, “peak1”, “peak2”).