Tools¶
Mapping structures¶
- icet.tools.map_structure_to_reference(structure: ase.atoms.Atoms, reference: ase.atoms.Atoms, inert_species: Optional[List[str]] = None, tol_positions: float = 0.0001, suppress_warnings: bool = False, assume_no_cell_relaxation: bool = False) → Tuple[ase.atoms.Atoms, dict][source]¶
Maps a structure onto a reference structure. This is often desirable when, for example, a structure has been relaxed using DFT, and one wants to use it as a training structure in a cluster expansion.
The function returns a tuple comprising the ideal supercell most closely matching the input structure and a dictionary with supplementary information concerning the mapping. The latter includes for example the largest deviation of any position in the input structure from its reference position (drmax), the average deviation of the positions in the input structure from the reference positions (dravg), and the strain tensor for the input structure relative to the reference structure (strain_tensor).
The Atoms object that provide further supplemental information via custom per-atom arrays including the atomic displacements (Displacement, Displacement_Magnitude) as well as the distances to the three closest sites (Minimum_Distances).
- Parameters
structure – input structure, typically a relaxed structure
reference – reference structure, which can but need not be the primitive structure
inert_species – list of chemical symbols (e.g.,
['Au', 'Pd']
) that are never substituted for a vacancy; the number of inert sites is used to rescale the volume of the input structure to match the reference structure.tol_positions – tolerance factor applied when scanning for overlapping positions in Angstrom (forwarded to
ase.build.make_supercell()
)suppress_warnings – if True, print no warnings of large strain or relaxation distances
assume_no_cell_relaxation –
if False volume and cell metric of the input structure are rescaled to match the reference structure; this can be unnecessary (and counterproductive) for some structures, e.g., with many vacancies
Note: When setting this parameter to False the reference cell metric must be obtainable via an integer transformation matrix from the reference cell metric. In other words the input structure should not involve relaxations of the volume or the cell metric.
Example
The following code snippet illustrates the general usage. It first creates a primitive FCC cell, which is latter used as reference structure. To emulate a relaxed structure obtained from, e.g., a density functional theory calculation, the code then creates a 4x4x4 conventional FCC supercell, which is populated with two different atom types, has distorted cell vectors, and random displacements to the atoms. Finally, the present function is used to map the structure back the ideal lattice:
>>> from ase.build import bulk >>> reference = bulk('Au', a=4.09) >>> structure = bulk('Au', cubic=True, a=4.09).repeat(4) >>> structure.set_chemical_symbols(10 * ['Ag'] + (len(structure) - 10) * ['Au']) >>> structure.set_cell(structure.cell * 1.02, scale_atoms=True) >>> structure.rattle(0.1) >>> mapped_structure, info = map_structure_to_reference(structure, reference)
Structure enumeration¶
- icet.tools.enumerate_structures(structure: ase.atoms.Atoms, sizes: Union[List[int], range], chemical_symbols: list, concentration_restrictions: Optional[dict] = None, niggli_reduce: Optional[bool] = None, symprec: float = 1e-05, position_tolerance: Optional[float] = None) → ase.atoms.Atoms[source]¶
Yields a sequence of enumerated structures. The function generates all inequivalent structures that are permissible given a certain lattice. Using the
chemical_symbols
andconcentration_restrictions
keyword arguments it is possible to specify which chemical_symbols are to be included on which site and in which concentration range.The function is sensitive to the boundary conditions of the input structure. An enumeration of, for example, a surface can thus be performed by setting
structure.pbc = [True, True, False]
.The algorithm implemented here was developed by Gus L. W. Hart and Rodney W. Forcade in Phys. Rev. B 77, 224115 (2008) [HarFor08] and Phys. Rev. B 80, 014120 (2009) [HarFor09].
- Parameters
structure – primitive structure from which derivative superstructures should be generated
sizes – number of sites (included in enumeration)
chemical_symbols – chemical species with which to decorate the structure, e.g.,
['Au', 'Ag']
; see below for more examplesconcentration_restrictions – allowed concentration range for one or more element in chemical_symbols, e.g.,
{'Au': (0, 0.2)}
will only enumerate structures in which the Au content is between 0 and 20 %; here, concentration is always defined as the number of atoms of the specified kind divided by the number of all atoms.niggli_reduction – if True perform a Niggli reduction with spglib for each structure; the default is
True
ifstructure
is periodic in all directions,False
otherwise.symprec – tolerance imposed when analyzing the symmetry using spglib
position_tolerance – tolerance applied when comparing positions in Cartesian coordinates; by default this value is set equal to symprec
Examples
The following code snippet illustrates how to enumerate structures with up to 6 atoms in the unit cell for a binary alloy without any constraints:
>>> from ase.build import bulk >>> prim = bulk('Ag') >>> for structure in enumerate_structures(structure=prim, ... sizes=range(1, 5), ... chemical_symbols=['Ag', 'Au']): ... pass # Do something with the structure
To limit the concentration range to 10 to 40% Au the code should be modified as follows:
>>> conc_restr = {'Au': (0.1, 0.4)} >>> for structure in enumerate_structures(structure=prim, ... sizes=range(1, 5), ... chemical_symbols=['Ag', 'Au'], ... concentration_restrictions=conc_restr): ... pass # Do something with the structure
Often one would like to consider mixing on only one sublattice. This can be achieved as illustrated for a Ga(1-x)Al(x)As alloy as follows:
>>> prim = bulk('GaAs', crystalstructure='zincblende', a=5.65) >>> for structure in enumerate_structures(structure=prim, ... sizes=range(1, 9), ... chemical_symbols=[['Ga', 'Al'], ['As']]): ... pass # Do something with the structure
- icet.tools.enumerate_supercells(structure: ase.atoms.Atoms, sizes: Union[List[int], range], niggli_reduce: Optional[bool] = None, symprec: float = 1e-05, position_tolerance: Optional[float] = None) → ase.atoms.Atoms[source]¶
Yields a sequence of enumerated supercells. The function generates all inequivalent supercells that are permissible given a certain lattice. Any supercell can be reduced to one of the supercells generated.
The function is sensitive to the boundary conditions of the input structure. An enumeration of, for example, a surface can thus be performed by setting
structure.pbc = [True, True, False]
.The algorithm is based on Gus L. W. Hart and Rodney W. Forcade in Phys. Rev. B 77, 224115 (2008) [HarFor08] and Phys. Rev. B 80, 014120 (2009) [HarFor09].
- Parameters
structure – primitive structure from which supercells should be generated
sizes – number of sites (included in enumeration)
niggli_reduction – if True perform a Niggli reduction with spglib for each supercell; the default is
True
ifstructure
is periodic in all directions,False
otherwise.symprec – tolerance imposed when analyzing the symmetry using spglib
position_tolerance – tolerance applied when comparing positions in Cartesian coordinates; by default this value is set equal to symprec
Examples
The following code snippet illustrates how to enumerate supercells with up to 6 atoms in the unit cell:
>>> from ase.build import bulk >>> prim = bulk('Ag') >>> for supercell in enumerate_supercells(structure=prim, sizes=range(1, 7)): ... pass # Do something with the supercell
Generation of special structures¶
- icet.tools.structure_generation.generate_sqs(cluster_space: icet.core.cluster_space.ClusterSpace, max_size: int, target_concentrations: dict, include_smaller_cells: bool = True, pbc: Optional[Union[Tuple[bool, bool, bool], Tuple[int, int, int]]] = None, T_start: float = 5.0, T_stop: float = 0.001, n_steps: Optional[int] = None, optimality_weight: float = 1.0, random_seed: Optional[int] = None, tol: float = 1e-05) → ase.atoms.Atoms[source]¶
Given a
cluster_space
, generate a special quasirandom structure (SQS), i.e., a structure that for a given supercell size provides the best possible approximation to a random alloy [ZunWeiFer90].In the present case, this means that the generated structure will have a cluster vector that as closely as possible matches the cluster vector of an infintely large randomly occupated supercell. Internally the function uses a simulated annealing algorithm and the difference between two cluster vectors is calculated with the measure suggested by A. van de Walle et al. in Calphad 42, 13-18 (2013) [WalTiwJon13] (for more information, see
mchammer.calculators.TargetVectorCalculator
).- Parameters
cluster_space – a cluster space defining the lattice to be occupated
max_size – maximum supercell size
target_concentrations – concentration of each species in the target structure, per sublattice (for example
{'Au': 0.5, 'Pd': 0.5}
for a single sublattice Au-Pd structure, or{'A': {'Au': 0.5, 'Pd': 0.5}, 'B': {'H': 0.25, 'X': 0.75}}
for a system with two sublattices. The symbols defining sublattices (‘A’, ‘B’ etc) can be found by printing the cluster_spaceinclude_smaller_cells – if True, search among all supercell sizes including
max_size
, else search only among those exactly matchingmax_size
pbc – Periodic boundary conditions for each direction, e.g.,
(True, True, False)
. The axes are defined by the cell ofcluster_space.primitive_structure
. Default is periodic boundary in all directions.T_start – artificial temperature at which the simulated annealing starts
T_stop – artifical temperature at which the simulated annealing stops
n_steps – total number of Monte Carlo steps in the simulation
optimality_weight – controls weighting \(L\) of perfect correlations, see
mchammer.calculators.TargetVectorCalculator
random_seed – seed for the random number generator used in the Monte Carlo simulation
tol – Numerical tolerance
- icet.tools.structure_generation.generate_sqs_by_enumeration(cluster_space: icet.core.cluster_space.ClusterSpace, max_size: int, target_concentrations: dict, include_smaller_cells: bool = True, pbc: Optional[Union[Tuple[bool, bool, bool], Tuple[int, int, int]]] = None, optimality_weight: float = 1.0, tol: float = 1e-05) → ase.atoms.Atoms[source]¶
Given a
cluster_space
, generate a special quasirandom structure (SQS), i.e., a structure that for a given supercell size provides the best possible approximation to a random alloy [ZunWeiFer90].In the present case, this means that the generated structure will have a cluster vector that as closely as possible matches the cluster vector of an infintely large randomly occupied supercell. Internally the function uses a simulated annealing algorithm and the difference between two cluster vectors is calculated with the measure suggested by A. van de Walle et al. in Calphad 42, 13-18 (2013) [WalTiwJon13] (for more information, see
mchammer.calculators.TargetVectorCalculator
).This functions generates SQS cells by exhaustive enumeration, which means that the generated SQS cell is guaranteed to be optimal with regard to the specified measure and cell size.
- Parameters
cluster_space – a cluster space defining the lattice to be occupied
max_size – maximum supercell size
target_concentrations – concentration of each species in the target structure, per sublattice (for example
{'Au': 0.5, 'Pd': 0.5}
for a single sublattice Au-Pd structure, or{'A': {'Au': 0.5, 'Pd': 0.5}, 'B': {'H': 0.25, 'X': 0.75}}
for a system with two sublattices. The symbols defining sublattices (‘A’, ‘B’ etc) can be found by printing the cluster_spaceinclude_smaller_cells – if True, search among all supercell sizes including
max_size
, else search only among those exactly matchingmax_size
pbc – Periodic boundary conditions for each direction, e.g.,
(True, True, False)
. The axes are defined by the cell ofcluster_space.primitive_structure
. Default is periodic boundary in all directions.optimality_weight – controls weighting \(L\) of perfect correlations, see
mchammer.calculators.TargetVectorCalculator
tol – Numerical tolerance
- icet.tools.structure_generation.generate_sqs_from_supercells(cluster_space: icet.core.cluster_space.ClusterSpace, supercells: List[ase.atoms.Atoms], target_concentrations: dict, T_start: float = 5.0, T_stop: float = 0.001, n_steps: Optional[int] = None, optimality_weight: float = 1.0, random_seed: Optional[int] = None, tol: float = 1e-05) → ase.atoms.Atoms[source]¶
Given a
cluster_space
and one or moresupercells
, generate a special quasirandom structure (SQS), i.e., a structure that for the provided supercells size provides the best possible approximation to a random alloy [ZunWeiFer90].In the present case, this means that the generated structure will have a cluster vector that as closely as possible matches the cluster vector of an infintely large randomly occupated supercell. Internally the function uses a simulated annealing algorithm and the difference between two cluster vectors is calculated with the measure suggested by A. van de Walle et al. in Calphad 42, 13-18 (2013) [WalTiwJon13] (for more information, see
mchammer.calculators.TargetVectorCalculator
).- Parameters
cluster_space – a cluster space defining the lattice to be occupated
supercells – list of one or more supercells among which an optimal structure will be searched for
target_concentrations – concentration of each species in the target structure, per sublattice (for example
{'Au': 0.5, 'Pd': 0.5}
for a single sublattice Au-Pd structure, or{'A': {'Au': 0.5, 'Pd': 0.5}, 'B': {'H': 0.25, 'X': 0.75}}
for a system with two sublattices. The symbols defining sublattices (‘A’, ‘B’ etc) can be found by printing the cluster_spaceT_start – artificial temperature at which the simulated annealing starts
T_stop – artifical temperature at which the simulated annealing stops
n_steps – total number of Monte Carlo steps in the simulation
optimality_weight – controls weighting \(L\) of perfect correlations, see
mchammer.calculators.TargetVectorCalculator
random_seed – seed for the random number generator used in the Monte Carlo simulation
tol – Numerical tolerance
- icet.tools.structure_generation.generate_target_structure(cluster_space: icet.core.cluster_space.ClusterSpace, max_size: int, target_concentrations: dict, target_cluster_vector: List[float], include_smaller_cells: bool = True, pbc: Optional[Union[Tuple[bool, bool, bool], Tuple[int, int, int]]] = None, T_start: float = 5.0, T_stop: float = 0.001, n_steps: Optional[int] = None, optimality_weight: float = 1.0, random_seed: Optional[int] = None, tol: float = 1e-05) → ase.atoms.Atoms[source]¶
Given a
cluster_space
and atarget_cluster_vector
, generate a structure that as closely as possible matches that cluster vector. The search is performed among all inequivalent supercells shapes up to a certain size.Internally the function uses a simulated annealing algorithm and the difference between two cluster vectors is calculated with the measure suggested by A. van de Walle et al. in Calphad 42, 13-18 (2013) [WalTiwJon13] (for more information, see
mchammer.calculators.TargetVectorCalculator
).- Parameters
cluster_space – a cluster space defining the lattice to be occupied
max_size – maximum supercell size
target_concentrations – concentration of each species in the target structure, per sublattice (for example
{'Au': 0.5, 'Pd': 0.5}
for a single sublattice Au-Pd structure, or{'A': {'Au': 0.5, 'Pd': 0.5}, 'B': {'H': 0.25, 'X': 0.75}}
for a system with two sublattices. The symbols defining sublattices (‘A’, ‘B’ etc) can be found by printing the cluster_spacetarget_cluster_vector – cluster vector that the generated structure should match as closely as possible
include_smaller_cells – if True, search among all supercell sizes including
max_size
, else search only among those exactly matchingmax_size
pbc – Periodic boundary conditions for each direction, e.g.,
(True, True, False)
. The axes are defined by the cell ofcluster_space.primitive_structure
. Default is periodic boundary in all directions.T_start – artificial temperature at which the simulated annealing starts
T_stop – artifical temperature at which the simulated annealing stops
n_steps – total number of Monte Carlo steps in the simulation
optimality_weight – controls weighting \(L\) of perfect correlations, see
mchammer.calculators.TargetVectorCalculator
random_seed – seed for the random number generator used in the Monte Carlo simulation
tol – Numerical tolerance
- icet.tools.structure_generation.generate_target_structure_from_supercells(cluster_space: icet.core.cluster_space.ClusterSpace, supercells: List[ase.atoms.Atoms], target_concentrations: dict, target_cluster_vector: List[float], T_start: float = 5.0, T_stop: float = 0.001, n_steps: Optional[int] = None, optimality_weight: float = 1.0, random_seed: Optional[int] = None, tol: float = 1e-05) → ase.atoms.Atoms[source]¶
Given a
cluster_space
and atarget_cluster_vector
and one or moresupercells
, generate a structure that as closely as possible matches that cluster vector.Internally the function uses a simulated annealing algorithm and the difference between two cluster vectors is calculated with the measure suggested by A. van de Walle et al. in Calphad 42, 13-18 (2013) [WalTiwJon13] (for more information, see
mchammer.calculators.TargetVectorCalculator
).- Parameters
cluster_space – a cluster space defining the lattice to be occupied
supercells – list of one or more supercells among which an optimal structure will be searched for
target_concentrations – concentration of each species in the target structure, per sublattice (for example
{'Au': 0.5, 'Pd': 0.5}
for a single sublattice Au-Pd structure, or{'A': {'Au': 0.5, 'Pd': 0.5}, 'B': {'H': 0.25, 'X': 0.75}}
for a system with two sublattices. The symbols defining sublattices (‘A’, ‘B’ etc) can be found by printing the cluster_spacetarget_cluster_vector – cluster vector that the generated structure should match as closely as possible
T_start – artificial temperature at which the simulated annealing starts
T_stop – artifical temperature at which the simulated annealing stops
n_steps – total number of Monte Carlo steps in the simulation
optimality_weight – controls weighting \(L\) of perfect correlations, see
mchammer.calculators.TargetVectorCalculator
random_seed – seed for the random number generator used in the Monte Carlo simulation
tol – Numerical tolerance
- icet.tools.structure_generation.occupy_structure_randomly(structure: ase.atoms.Atoms, cluster_space: icet.core.cluster_space.ClusterSpace, target_concentrations: dict) → None[source]¶
Occupy a structure with quasirandom order but fulfilling
target_concentrations
.- Parameters
structure – ASE Atoms object that will be occupied randomly
cluster_space – cluster space (needed as it carries information about sublattices)
target_concentrations – concentration of each species in the target structure, per sublattice (for example
{'Au': 0.5, 'Pd': 0.5}
for a single sublattice Au-Pd structure, or{'A': {'Au': 0.5, 'Pd': 0.5}, 'B': {'H': 0.25, 'X': 0.75}}
for a system with two sublattices. The symbols defining sublattices (‘A’, ‘B’ etc) can be found by printing the cluster_space
Ground state finder¶
- class icet.tools.ground_state_finder.GroundStateFinder(cluster_expansion: icet.core.cluster_expansion.ClusterExpansion, structure: ase.atoms.Atoms, solver_name: Optional[str] = None, verbose: bool = True)[source]¶
This class provides functionality for determining the ground states using a binary cluster expansion. This is efficiently achieved through the use of mixed integer programming (MIP) as developed by Larsen et al. in Phys. Rev. Lett. 120, 256101 (2018).
This class relies on the Python-MIP package. Python-MIP can be used together with Gurobi, which is not open source but issues academic licenses free of charge. Pleaase note that Gurobi needs to be installed separately. The GroundStateFinder works also without Gurobi, but if performance is critical, Gurobi is highly recommended.
Warning
In order to be able to use Gurobi with python-mip one must ensure that GUROBI_HOME should point to the installation directory (
<installdir>
):export GUROBI_HOME=<installdir>
Note
The current implementation only works for binary systems.
- Parameters
cluster_expansion (ClusterExpansion) – cluster expansion for which to find ground states
structure (Atoms) – atomic configuration
solver_name (str, optional) – ‘gurobi’, alternatively ‘grb’, or ‘cbc’, searches for available solvers if not informed
verbose (bool, optional) – whether to display solver messages on the screen (default: True)
Example
The following snippet illustrates how to determine the ground state for a Au-Ag alloy. Here, the parameters of the cluster expansion are set to emulate a simple Ising model in order to obtain an example that can be run without modification. In practice, one should of course use a proper cluster expansion:
>>> from ase.build import bulk >>> from icet import ClusterExpansion, ClusterSpace >>> # prepare cluster expansion >>> # the setup emulates a second nearest-neighbor (NN) Ising model >>> # (zerolet and singlet parameters are zero; only first and second neighbor >>> # pairs are included) >>> prim = bulk('Au') >>> chemical_symbols = ['Ag', 'Au'] >>> cs = ClusterSpace(prim, cutoffs=[4.3], chemical_symbols=chemical_symbols) >>> ce = ClusterExpansion(cs, [0, 0, 0.1, -0.02]) >>> # prepare initial configuration >>> structure = prim.repeat(3) >>> # set up the ground state finder and calculate the ground state energy >>> gsf = GroundStateFinder(ce, structure) >>> ground_state = gsf.get_ground_state({'Ag': 5}) >>> print('Ground state energy:', ce.predict(ground_state))
- get_ground_state(species_count: Optional[Dict[str, int]] = None, max_seconds: float = inf, threads: int = 0) → ase.atoms.Atoms[source]¶
Finds the ground state for a given structure and species count, which refers to the count_species, if provided when initializing the instance of this class, or the first species in the list of chemical symbols for the active sublattice.
- Parameters
species_count – dictionary with count for one of the species on each active sublattice. If no count is provided for a sublattice, the concentration is allowed to vary.
max_seconds – maximum runtime in seconds (default: inf)
threads – number of threads to be used when solving the problem, given that a positive integer has been provided. If set to 0 the solver default configuration is used while -1 corresponds to all available processing cores.
- property model: mip.model.Model¶
Python-MIP model
- property optimization_status: mip.constants.OptimizationStatus¶
Optimization status
Convex hull construction¶
This module collects a number of different tools, e.g., for structure generation and analysis.
- class icet.tools.ConvexHull(concentrations: Union[List[float], List[List[float]]], energies: List[float])[source]¶
This class provides functionality for extracting the convex hull of the (free) energy of mixing. It is based on the convex hull calculator in SciPy.
- Parameters
concentrations (list(float) or list(list(float))) – concentrations for each structure listed as
[[c1, c2], [c1, c2], ...]
; for binaries, in which case there is only one independent concentration, the format[c1, c2, c3, ...]
works as well.energies (list(float)) – energy (or energy of mixing) for each structure
- concentrations¶
concentrations of the N structures on the convex hull
- Type
np.ndarray
- energies¶
energies of the N structures on the convex hull
- Type
np.ndarray
- dimensions¶
number of independent concentrations needed to specify a point in concentration space (1 for binaries, 2 for ternaries etc.)
- Type
int
- structures¶
indices of structures that constitute the convex hull (indices are defined by the order of their concentrations and energies are fed when initializing the ConvexHull object)
- Type
list(int)
Examples
A ConvexHull object is easily initialized by providing lists of concentrations and energies:
>>> data = {'concentration': [0, 0.2, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0], ... 'mixing_energy': [0.1, -0.2, -0.1, -0.2, 0.2, -0.4, -0.2, -0.1]} >>> hull = ConvexHull(data['concentration'], data['mixing_energy'])
Now one can for example access the points along the convex hull directly:
>>> for c, e in zip(hull.concentrations, hull.energies): ... print(c, e) 0.0 0.1 0.2 -0.2 0.5 -0.4 1.0 -0.1
or plot the convex hull along with the original data using e.g., matplotlib:
>>> import matplotlib.pyplot as plt >>> plt.scatter(data['concentration'], data['mixing_energy'], color='darkred') >>> plt.plot(hull.concentrations, hull.energies) >>> plt.show(block=False)
It is also possible to extract structures at or close to the convex hull:
>>> low_energy_structures = hull.extract_low_energy_structures( ... data['concentration'], data['mixing_energy'], ... energy_tolerance=0.005)
A complete example can be found in the basic tutorial.
- extract_low_energy_structures(concentrations: Union[List[float], List[List[float]]], energies: List[float], energy_tolerance: float) → List[int][source]¶
Returns the indices of energies that lie within a certain tolerance of the convex hull.
- Parameters
concentrations –
concentrations of candidate structures
If there is one independent concentration, a list of floats is sufficient. Otherwise, the concentrations must be provided as a list of lists, such as
[[0.1, 0.2], [0.3, 0.1], ...]
.energies – energies of candidate structures
energy_tolerance – include structures with an energy that is at most this far from the convex hull
- get_energy_at_convex_hull(target_concentrations: Union[List[float], List[List[float]]]) → numpy.ndarray[source]¶
Returns the energy of the convex hull at specified concentrations. If any concentration is outside the allowed range, NaN is returned.
- Parameters
target_concentrations –
concentrations at target points
If there is one independent concentration, a list of floats is sufficient. Otherwise, the concentrations ought to be provided as a list of lists, such as
[[0.1, 0.2], [0.3, 0.1], ...]
.
Fitting with constraints¶
- class icet.tools.constraints.Constraints(n_params: int)[source]¶
Class for handling linear constraints with right hand side equal to zero.
- Parameters
n_params – number of parameters in model
Example
The following example demonstrates fitting of a cluster expansion under the constraint that parameter 2 and parameter 4 should be equal:
>>> from icet.tools import Constraints >>> from icet.fitting import Optimizer >>> import numpy as np >>> # Set up random sensing matrix and target "energies" >>> n_params = 10 >>> A = np.random.random((10, n_params)) >>> y = np.random.random(10) >>> # Define constraints >>> c = Constraints(n_params=n_params) >>> M = np.zeros((1, n_params)) >>> M[0, [2, 4]] = 1 >>> c.add_constraint(M) >>> # Do the actual fit and finally extract parameters >>> A_constrained = c.transform(A) >>> opt = Optimizer((A_constrained, y), fit_method='ridge') >>> opt.train() >>> parameters = c.inverse_transform(opt.parameters)
- add_constraint(M: numpy.ndarray) → None[source]¶
Add a constraint matrix and resolve for the constraint space
- Parameters
M – Constraint matrix with each constraint as a row. Can (but need not be) cluster vectors.
- inverse_transform(A: numpy.ndarray) → numpy.ndarray[source]¶
Inverse transform array from constrained parameter space to unconstrained space
- Parameters
A – array to be inversed transformed
- transform(A: numpy.ndarray) → numpy.ndarray[source]¶
Transform array to constrained parameter space
- Parameters
A – array to be transformed
- icet.tools.constraints.get_mixing_energy_constraints(cluster_space) → icet.tools.constraints.Constraints[source]¶
A cluster expansion of mixing energy should ideally predict zero energy for concentration 0 and 1. This function constructs a
Constraints
object that enforces that condition during fitting.- Parameters
cluster_space (ClusterSpace) – Cluster space corresponding to cluster expansion for which constraints should be imposed
Example
This example demonstrates how to constrain the mixing energy to zero at the pure phases in a toy example with random cluster vectors and random target energies:
>>> from icet.tools import get_mixing_energy_constraints >>> from icet.fitting import Optimizer >>> from icet import ClusterSpace >>> from ase.build import bulk >>> import numpy as np >>> # Set up cluster space along with random sensing matrix and target "energies" >>> prim = bulk('Au') >>> cs = ClusterSpace(prim, cutoffs=[6.0, 5.0], chemical_symbols=['Au', 'Ag']) >>> n_params = len(cs) >>> A = np.random.random((10, len(cs))) >>> y = np.random.random(10) >>> # Define constraints >>> c = get_mixing_energy_constraints(cs) >>> # Do the actual fit and finally extract parameters >>> A_constrained = c.transform(A) >>> opt = Optimizer((A_constrained, y), fit_method='ridge') >>> opt.train() >>> parameters = c.inverse_transform(opt.parameters)
Warning
Constraining the energy of one structure is always done at the expense of the fit quality of the others. Always expect that your CV scores will increase somewhat when using this function.
Other structure tools¶
- icet.tools.get_primitive_structure(structure: ase.atoms.Atoms, no_idealize: bool = True, to_primitive: bool = True, symprec: float = 1e-05) → ase.atoms.Atoms[source]¶
Returns the primitive structure using spglib.
- Parameters
structure – input atomic structure
no_idealize – if True lengths and angles are not idealized
to_primitive – convert to primitive structure
symprec – tolerance imposed when analyzing the symmetry using spglib
- icet.tools.get_wyckoff_sites(structure: ase.atoms.Atoms, map_occupations: Optional[List[List[str]]] = None, symprec: float = 1e-05) → List[str][source]¶
Returns the Wyckoff symbols of the input structure. The Wyckoff sites are of general interest for symmetry analysis but can be especially useful when setting up, e.g., a
SiteOccupancyObserver
. The Wyckoff labels can be conveniently attached as an array to the structure object as demonstrated in the examples section below.By default the occupation of the sites is part of the symmetry analysis. If a chemically disordered structure is provided this will usually reduce the symmetry substantially. If one is interested in the symmetry of the underlying structure one can control how occupations are handled. To this end, one can provide the
map_occupations
keyword argument. The latter must be a list, each entry of which is a list of species that should be treated as indistinguishable. As a shortcut, if all species should be treated as indistinguishable one can provide an empty list. Examples that illustrate the usage of the keyword are given below.- Parameters
structure – input structure, note that the occupation of the sites is included in the symmetry analysis
map_occupations – each sublist in this list specifies a group of chemical species that shall be treated as indistinguishable for the purpose of the symmetry analysis
symprec – tolerance imposed when analyzing the symmetry using spglib
Examples
Wyckoff sites of a hexagonal-close packed structure:
>>> from ase.build import bulk >>> structure = bulk('Ti') >>> wyckoff_sites = get_wyckoff_sites(structure) >>> print(wyckoff_sites) ['2d', '2d']
The Wyckoff labels can also be attached as an array to the structure, in which case the information is also included when storing the Atoms object:
>>> from ase.io import write >>> structure.new_array('wyckoff_sites', wyckoff_sites, str) >>> write('structure.xyz', structure)
The function can also be applied to supercells:
>>> structure = bulk('GaAs', crystalstructure='zincblende', a=3.0).repeat(2) >>> wyckoff_sites = get_wyckoff_sites(structure) >>> print(wyckoff_sites) ['4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c']
Now assume that one is given a supercell of a (Ga,Al)As alloy. Applying the function directly yields much lower symmetry since the symmetry of the original structure is broken:
>>> structure.set_chemical_symbols( ... ['Ga', 'As', 'Al', 'As', 'Ga', 'As', 'Al', 'As', ... 'Ga', 'As', 'Ga', 'As', 'Al', 'As', 'Ga', 'As']) >>> print(get_wyckoff_sites(structure)) ['8g', '8i', '4e', '8i', '8g', '8i', '2c', '8i', '2d', '8i', '8g', '8i', '4e', '8i', '8g', '8i']
Since Ga and Al occupy the same sublattice, they should, however, be treated as indistinguishable for the purpose of the symmetry analysis, which can be achieved via the
map_occupations
keyword:>>> print(get_wyckoff_sites(structure, map_occupations=[['Ga', 'Al'], ['As']])) ['4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c', '4a', '4c']
If occupations are to ignored entirely, one can simply provide an empty list. In the present case, this turns the zincblende lattice into a diamond lattice, on which case there is only one Wyckoff site:
>>> print(get_wyckoff_sites(structure, map_occupations=[])) ['8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a', '8a']