Cluster expansions¶

class
icet.
ClusterExpansion
(cluster_space, parameters)[source]¶ Cluster expansions are obtained by combining a cluster space with a set of effective cluster interactions (ECIs). Instances of this class allow one to predict the property of interest for a given structure.

cluster_space
¶ cluster space that was used for constructing the cluster expansion
Type: icet.ClusterSpace

parameters
¶ effective cluster interactions (ECIs)
Type: np.ndarray
Example
The following snippet illustrates the initialization and usage of a ClusterExpansion object. Here, the ECIs are taken to be a list of ones. Usually, they would be obtained by training with respect to a set of reference data:
from ase.build import bulk from icet import ClusterSpace, ClusterExpansion # create cluster expansion prim = bulk('Au') cs = ClusterSpace(prim, cutoffs=[7.0, 5.0], chemical_symbols=[['Au', 'Pd']]) ecis = 14 * [1.0] ce = ClusterExpansion(cs, ecis) # make prediction for supercell sc = prim.repeat(3) for k in [1, 4, 7]: sc[k].symbol = 'Pd' print(ce.predict(sc))

cluster_space
cluster space on which cluster expansion is based
Return type: ClusterSpace

orders
¶ orders included in cluster expansion
Return type: List
[int
]

parameters
effective cluster interactions (ECIs)
Return type: List
[float
]

parameters_as_dataframe
¶ dataframe containing orbit data and ECIs
Return type: DataFrame

predict
(structure)[source]¶ Predicts the property of interest (e.g., the energy) for the input structure using the cluster expansion.
Parameters: structure ( Union
[Atoms
,Structure
]) – atomic configurationReturns: property value of predicted by the cluster expansion Return type: float

prune
(indices=None, tol=0)[source]¶ Removes orbits from the cluster expansion (CE), for which the effective cluster interactions (ECIs; parameters) are zero or close to zero. This commonly reduces the computational cost for evaluating the CE and is therefore recommended prior to using it in production. If the method is called without arguments orbits will be pruned, for which the ECIs are strictly zero. Less restrictive pruning can be achived by setting the tol keyword.
Parameters:  indices (
Optional
[List
[int
]]) – indices to parameters to remove in the cluster expansion.  tol (
float
) – orbits for which the absolute ECIs is/are within this value will be pruned
 indices (
