Force constant models

ForceConstantPotential

class hiphive.ForceConstantPotential(cs, parameters, metadata=None)[source]

A finalized force constant model. Can produce force constants for any structure compatible with the structure for which the model was set up.

Parameters:

cs (ClusterSpace) – The cluster space the model is based upon
parameters (numpy.ndarray) – The fitted paramteres
metadata (dict) – metadata dictionary, will be pickled when object is written to file

get_force_constants(atoms)[source]

Return the force constants of a compatible structure.

Parameters:: atoms (ase.Atoms) – input structure
Returns:: force constants
Return type:: ForceConstants

property metadata

metadata associated with force constant potential

Type:: dict

property orbit_data: List[Dict[str, Any]]: list of dictionaries containing detailed information for each orbit, e.g. cluster radius and force constant

property primitive_structure

atomic structure

Type:: ase.Atoms

print_tables()[source]: Prints information concerning the underlying cluster space to stdout,including, e.g., the number of cluster, orbits, and parameters by order and number of bodies.

static read(filename)[source]

Reads a ForceConstantPotentialfrom file.

Parameters:: filename (str) – name of input file to load ForceConstantPotential from
Returns:: the original object as stored in the file
Return type:: ForceConstantPotential

property symprec

write(filename)[source]

Writes a ForceConstantPotential to file.

Parameters:: filename (str) – name of file to write ForceConstantPotential to

ForceConstantCalculator

class hiphive.calculators.ForceConstantCalculator(fcs, max_disp=3.0)[source]

This class provides an ASE calculator that can be used in conjunction with integrators and optimizers with the atomic simulation environment (ASE). To initialize an object of this class one must provide the ideal atomic configuration along with a compatible force constant model.

Parameters:

fcs (ForceConstants) – the force constants instance must include the atomic configuration
max_disp (float) – maximum allowed displacement before calculator raises ValueError

calculate(atoms=None, properties=['energy'], system_changes=['positions', 'numbers', 'cell', 'pbc', 'initial_charges', 'initial_magmoms'])[source]

Do the calculation.

Return type:: None

properties: list of str: List of what needs to be calculated. Can be any combination of ‘energy’, ‘forces’, ‘stress’, ‘dipole’, ‘charges’, ‘magmom’ and ‘magmoms’.
system_changes: list of str: List of what has changed since last calculation. Can be any combination of these six: ‘positions’, ‘numbers’, ‘cell’, ‘pbc’, ‘initial_charges’ and ‘initial_magmoms’.

Subclasses need to implement this, but can ignore properties and system_changes if they want. Calculated properties should be inserted into results dictionary like shown in this dummy example:

self.results = {'energy': 0.0,
                'forces': np.zeros((len(atoms), 3)),
                'stress': np.zeros(6),
                'dipole': np.zeros(3),
                'charges': np.zeros(len(atoms)),
                'magmom': 0.0,
                'magmoms': np.zeros(len(atoms))}

The subclass implementation should first call this implementation to set the atoms attribute and create any missing directories.

compute_energy_and_forces()[source]

Computes energy and forces.

Returns:: energy and forces
Return type:: float, list(list(float))

implemented_properties: List[str] = ['energy', 'forces']: Properties calculator can handle (energy, forces, …)

ForceConstants

This module provides functionality for storing and handling of force constants.

class hiphive.force_constants.ForceConstants(supercell)[source]

Base class for force constants

assert_acoustic_sum_rules(order=None, tol=1e-06)[source]

Asserts that force constants obey acoustic sum rules.

Parameters:

order (int) – specifies which order to check, if None all are checked
tol (float) – numeric tolerance for checking sum rules

Raises:

AssertionError – if acoustic sum rules are violated

property clusters: list: sorted list of clusters

compute_gamma_frequencies()[source]

Returns the Gamma frequencies in THz using the second-order force constants.

Return type:: ndarray

classmethod from_arrays(supercell, fc2_array=None, fc3_array=None)[source]

Constructs FCs from numpy arrays.

One or both of fc2_array and fc3_array must not be None

Parameters:

supercell (Atoms) – supercell structure
fc2_array (ndarray) – second-order force constant in phonopy format, i.e. must have shape (N, N, 3, 3)
fc3_array (ndarray) – third-order force constant in phonopy format, i.e. must have shape (N, N, N, 3, 3, 3)

classmethod from_dense_dict(fc_dict, supercell)[source]

All permutations of clusters that are not zero must be listed, if label symmetries are fullfilled will return a SortedForceConstants

Parameters:

fc_dict (dict) – keys corresponding to clusters and values to the force constants
supercell (Atoms) – atomic configuration

classmethod from_sparse_dict(fc_dict, supercell)[source]

Assumes label symmetries, meaning only one cluster for each permuation should be included

Parameters:

fc_dict (dict) – keys corresponding to clusters and values to the force constants
supercell (Atoms) – atomic configuration

get_fc_array(order, format='phonopy')[source]

Returns force constants in array format for specified order.

Parameters:

order (int) – force constants for this order will be returned
format (str) – specify which format (shape) the NumPy array should have, possible values are phonopy and ase

Return type:

ndarray

Returns:

NumPy array with shape (N,)*order + (3,)*order where N is
the number of atoms

get_fc_dict(order=None)[source]

Returns force constant dictionary for one specific order.

The returned dict may be sparse or may be dense depending on the underlying force constants.

Parameters:

order (int) – force constants returned for this order

Return type:

dict

Returns:

dictionary with keys corresponding to clusters and values to
respective force constant

property n_atoms: int: number of atoms

print_force_constant(cluster)[source]

Prints force constants for a cluster in a nice format.

Parameters:: cluster (Tuple[int]) – sites belonging to the cluster
Return type:: None

classmethod read(fname)[source]

Reads ForceConstants from file.

Parameters:: fname (str) – name of file from which to read

classmethod read_phono3py(supercell, fname)[source]

Reads force constants from a phono3py calculation.

Parameters:

supercell (Atoms) – supercell structure (SPOSCAR)
fname (str) – name of third-order force constant file

classmethod read_phonopy(supercell, fname, format=None)[source]

Reads force constants from a phonopy calculation.

Parameters:

supercell (Atoms) – supercell structure (SPOSCAR)
fname (str) – name of second-order force constant file
format (str) – format for second-order force constants; possible values: “text”, “hdf5”

classmethod read_shengBTE(supercell, fname, prim, symprec=1e-05)[source]

Reads third order force constants from a shengBTE calculation.

shengBTE force constants will be mapped onto a supercell.

Parameters:

supercell (Atoms) – supercell structure
fname (str) – name of third-order force constant file
prim (Atoms) – primitive configuration (must be equivalent to structure used in the shengBTE calculation)
symprec – structural symmetry tolerance

property supercell: Atoms: supercell associated with force constants

write(fname)[source]

Writes entire ForceConstants object to file.

Parameters:: fname (str) – name of file to which to write
Return type:: None

write_to_phono3py(fname)[source]

Writes force constants in phono3py format.

Parameters:: fname (str) – name of file to which to write third-order force constant
Return type:: None

write_to_phonopy(fname, format=None)[source]

Writes force constants in phonopy format.

Parameters:

fname (str) – name of file to which to write second-order force constant
format (str) –

format for second-order force constants;
possible values: “text”, “hdf5”

Return type:

None

write_to_shengBTE(fname, prim, **kwargs)[source]

Writes third order force constants in shengBTE format.

Parameters:

fname (str) – name of file to which to write third-order force constant
prim (Atoms) – primitive configuration (must be equivalent to structure used in the shengBTE calculation)

Return type:

None

class hiphive.force_constants.RawForceConstants(fc_dict, supercell)[source]

Force constants without label symmetries.

Parameters:

fc_dict (dict) – keys corresponding to clusters and values to the force constants, should contain all clusters with nonzero force constants
supercell (ase.Atoms)

property orders: List[int]: orders for which force constants exist

class hiphive.force_constants.SortedForceConstants(fc_dict, supercell)[source]

Force constants with label symmetries.

Parameters:

fc_dict (dict) – keys corresponding to clusters and values to the force constants, should only contain sorted clusters
supercell (ase.Atoms)

property orders: List[int]: orders for which force constants exist

write_to_GPUMD(fname_fc, fname_clusters, order, tol=1e-10)[source]

Writes force constants of the specified order in GPUMD format.

Parameters:

fname_fc (str) – name of file which contains the lookup force constants
fname_clusters (str) – name of file which contains the clusters and the fc lookup index
order (int) – force constants for this order will be written to file
tol (float) – if the norm of a force constant is less than tol then it is not written. if two force-constants are within tol; they are considered equal.

hiphive.force_constants.array_to_dense_dict(fc_array, fc_tol=1e-10)[source]

Constructs a dense dict from an fc array in phonopy format.

Force constants with norm smaller than fc_tol will be considered zero and therefore not included in the fc_dict.

Parameters:

fc_array (ndarray) – force constant array in phonopy format
fc_tol (float) – tolerance for considering force constants zero or not

Return type:

dict

hiphive.force_constants.check_label_symmetries(fc_dict)[source]

Checks label symmetries for dense fc dict.

Todo

tol, which one to use etc

Parameters:: fc_dict (dict) – keys corresponding to clusters and values to the force constants
Return type:: bool

hiphive.force_constants.dense_dict_to_sparse_dict(fc_dict)[source]

Converts dense dict to sparse dict.

This does not check if label symmetry is True, but rather will just keep the sorted clusters and their force constants.

Parameters:: fc_dict (dict) – keys corresponding to clusters and values to the force constants
Return type:: dict

hiphive.force_constants.symbolize_force_constant(fc, tol=1e-10)[source]

Carries out a symbolic symmetrization of a force constant tensor.

Parameters:

fc (ndarray) – force constant tensor
tol (float) – tolerance used to decide whether two elements are identical

Return type:

symbolic representation of force constant matrix

Constraints

hiphive.enforce_rotational_sum_rules(cs, parameters, sum_rules=None, alpha=1e-06, **ridge_kwargs)[source]

Enforces rotational sum rules by projecting parameters.

Note

The interface to this function might change in future releases.

Parameters:

cs (ClusterSpace) – the underlying cluster space
parameters (ndarray) – parameters to be constrained
sum_rules (List[str]) – type of sum rules to enforce; possible values: ‘Huang’, ‘Born-Huang’
alpha (float) – hyperparameter to the ridge regression algorithm; keyword argument passed to the optimizer; larger values specify stronger regularization, i.e., less correction but higher stability [default: 1e-6]
ridge_kwargs (dict) – kwargs to be passed to sklearn Ridge

Returns:

constrained parameters

Return type:

numpy.ndarray

Examples

The rotational sum rules can be enforced to the parameters before constructing a force constant potential as illustrated by the following snippet:

cs = ClusterSpace(reference_structure, cutoffs)
sc = StructureContainer(cs)
# add structures to structure container
opt = Optimizer(sc.get_fit_data())
opt.train()
new_params = enforce_rotational_sum_rules(cs, opt.parameters,
    sum_rules=['Huang', 'Born-Huang'])
fcp = ForceConstantPotential(cs, new_params)