Model Predicting
Within the ACEhamiltonians
framework, predictions are made via the predict
methods. To construct the real space matrix for a given system one may call the predict
method and provide it with i) a model
with which to make predictions, ii) an JuLIP.Atoms
entity representing to system for which predictions are to be made, and iii) the cell translation vectors. The real-space matrix may then be used to construct the complex matrix at a specific k-point via the real_to_complex
method.
# K-point for which the complex matrix is to be constructed for
k_point = [ 0, 0, 0]
# Load the JuLIP.Atoms object of the system to make predictions for
atoms = h5open(database_path) do database
# The argument recentre` is only required when requiring comparability
# with the FHI-aims real-space matrix format.
load_atoms(database[target_systems[1]]; recentre=true)
end
# Specify the cell translation vectors; needed when wanting to compute real-space matrices
images = cell_translations(atoms, model)
# Predict the real-space matrix
predicted_real = predict(model, atoms, images)
# Construct the complex matrix
prdicted_k = real_to_complex(predicted_real, images, k_point)
The cell translation vectors control which cells are included when constructing the real space matrix. The cell_translations
method can be used to make a reliable estimate based on the distance cutoffs present within the model.