# Other functions¶

## Self-consistent phonons¶

hiphive.self_consistent_phonons.self_consistent_harmonic_model(atoms_ideal, calc, cs, T, alpha, n_iterations, n_structures, parameters_start=None, fit_kwargs={})[source]

Constructs a set of self-consistent second-order force constants that provides the closest match to the potential energy surface at a the specified temperature.

Parameters: atoms_ideal (ase.Atoms) – ideal structure calc (ASE calculator object) – calculator to be used as reference potential cs (ClusterSpace) – clusterspace onto which to project the reference potential T (float) – temperature in K alpha (float) – stepsize in optimization algorithm n_iterations (int) – number of iterations in poor mans n_structures (int) – number of structures to use when fitting parameters_start (numpy.ndarray) – parameters from which to start the optimization fit_kwargs (dict) – kwargs to be used in the fitting process (via Optimizer) sequence of parameter vectors generated while iterating to self-consistency list(numpy.ndarray)

## Utilities¶

This module contains various support/utility functions.

class hiphive.utilities.Shell(types, distance, count=0)[source]

Neighbor Shell class

Parameters: types (list or tuple) – atomic types for neighbor shell distance (float) – interatomic distance for neighbor shell count (int) – number of pairs in the neighbor shell
hiphive.utilities.get_displacements(atoms, atoms_ideal)[source]

Computes the smallest possible displacements from displaced atoms relative to ideal atoms.

Notes

Parameters: atoms (ase.Atoms) – configuration with displaced atoms atoms_ideal (ase.Atoms) – ideal configuration relative to which displacements are computed displacements numpy.ndarray
hiphive.utilities.get_neighbor_shells(atoms, cutoff, dist_tol=1e-05)[source]

Gets list of neighbor shells.

Distances are grouped into shells via the following algorithm:

1. Find smallest atomic distance d_min
2. Find all pair distances in the range d_min + 1 * dist_tol
3. Construct a shell from these and pop them from distance list
4. Go to 1.
Parameters: atoms (ase.Atoms) – Atoms used for finding shells cutoff (float) – exclude neighbor shells which have a distance larger than cutoff dist_tol (float) – distance tolerance neighbor shells list(Shell)
hiphive.utilities.prepare_structures(structures, atoms_ideal, calc)[source]

Prepares a set of structures in the format suitable for a StructureContainer.

Note: Changes the structures in place.

## Enforcing rotational sum rules¶

hiphive.core.rotational_constraints.enforce_rotational_sum_rules(cs, parameters, sum_rules, **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 (numpy.ndarray) – parameters to be constrained sum_rules (list(str)) – type of sum rules to enforce; possible values: ‘Huang’, ‘Born-Huang’ ridge_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] iterations (int) – number of iterations to run the projection since each step projects the solution down to each nullspace in serial; keyword argument passed to the optimizer [default: 10] constrained parameters 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)