self_consistent_harmonic_model(atoms_ideal, calc, cs, T, alpha, n_iterations, n_structures, parameters_start=None)¶
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.
- 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
sequence of parameter vectors generated while iterating to self-consistency
This module contains various support/utility functions.
Shell(types, distance, count=0)¶
Neighbor Shell class
- 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
Computes the smallest possible displacements from positions and ideal positions given a cell metric.
- assumes pbc=[True, True, True].
get_neighbor_shells(atoms, cutoff, dist_tol=1e-05)¶
Gets list of neighbor shells.
Distances are grouped into shells via the following algorithm:
- Find smallest atomic distance d_min
- Find all pair distances in the range d_min + 1 * dist_tol
- Construct a shell from these and pop them from distance list
- Go to 1.
- 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