API Documentation

class PyCD.Material(material_parameters)

Defines the properties and structure of working material

generate_sites(element_type_indices, cell_size)

Generates NumPy array of sites for the specified element types and cell size

Parameters:

• element_type_indices
• cell_size (np.ndarray) – size of the cell

Returns:

return_sites – Object of cell_coordinates, quantum_index_list, system_element_index_list

Return type:

object

class PyCD.Neighbors(material, system_size, pbc)

Returns the neighbor list file :param system_size: size of the super cell in terms of number of

unit cell in three dimensions

compute_distance(system_size, system_element_index_1, system_element_index_2)

Returns the distance in atomic units between the two system element indices for a given system size :param system_size: :param system_element_index_1: :param system_element_index_2: :return:

generate_neighbor_list(dst_path, local_system_size)

Adds the neighbor list to the system object and returns the neighbor list :param dst_path: :param local_system_size: :return:

get_coordinates(system_size, system_element_index)

Returns the coordinates in atomic units of the given system element index for a given system size :param system_size: :param system_element_index: :return:

get_pairwise_min_image_vector_data(dst_path)

Returns cumulative displacement list for the given system size printed out to disk :param dst_path: :return:

get_quantum_indices(system_size, system_element_index)

Returns the quantum indices of the element :param system_size: :param system_element_index: :return:

get_system_element_index(system_size, quantum_indices)

Returns the system_element_index of the element :param system_size: :param quantum_indices: :return:

hop_neighbor_sites(bulk_sites, center_site_indices, neighbor_site_indices, cutoff_dist_limits, cutoff_dist_key)

Returns system_element_index_map and distances between center sites and its neighbor sites within cutoff distance :param bulk_sites: :param center_site_indices: :param neighbor_site_indices: :param cutoff_dist_limits: :param cutoff_dist_key: :return:

class PyCD.System(material_info, material_neighbors, hop_neighbor_list, pairwise_min_image_vector_data, alpha, r_cut, k_cut, precision_parameters, step_system_size_array, step_hop_neighbor_master_list)

defines the system we are working on

Attributes: size: An array (3 x 1) defining the system size in multiple of unit cells

get_precomputed_array(dst_path, compute_energy_contributions, return_k_vector_data)

Parameters:dst_path – Returns:

pot_k_ewald(k_max, alpha, k_cut)

Updates precomputed array with potential energy contributions from reciprocal-space

pot_k_ewald_with_k_vector_data(charge_list_prod, k_max, alpha, k_cut)

Updates precomputed array with potential energy contributions from reciprocal-space

pot_r_ewald(alpha, r_cut)

Generates precomputed array with potential energy contributions from real-space confined to simulation cell i.e. n_max=[0, 0, 0]

class PyCD.Run(system, precomputed_array, temp, ion_charge_type, species_charge_type, n_traj, t_final, time_interval, species_count, initial_occupancy, relative_energies, external_field, doping)

defines the subroutines for running Kinetic Monte Carlo and computing electrostatic interaction energies

charge_config(occupancy, dopant_site_indices)

Returns charge distribution of the current configuration :param occupancy: :param ion_charge_type: :param species_charge_type: :return:

do_kmc_steps(dst_path, output_data, random_seed, compute_mode)

Subroutine to run the KMC simulation by specified number of steps :param dst_path: :return:

generate_initial_occupancy(dopant_site_indices)

generates initial occupancy list based on species count :param species_count: :return:

preproduction(dst_path, random_seed)

Subroutine to setup input files to run the production stage of the simulation :param dst_path: :param random_seed: :return:

class PyCD.Analysis(material_info, n_dim, species_count, n_traj, t_final, time_interval, msd_t_final, trim_length, temp, repr_time='ns', repr_dist='Angstrom')

Post-simulation analysis methods

compute_msd(dst_path, output_data)

Returns the squared displacement of the trajectories :param dst_path: :return:

generate_msd_plot(msd_data, sem_data, display_error_bars, species_types, file_name, dst_path)

Returns a line plot of the MSD data :param msd_data: :param std_data: :param display_error_bars: :param species_types: :param file_name: :param dst_path: :return:

class PyCD.material_setup.ReturnValues(input_dict)

dummy class to return objects from methods defined inside other classes

PyCD.material_setup.material_setup(input_directory_path, system_size, pbc, generate_hop_neighbor_list, generate_pairwise_min_image_vector_data, generate_precomputed_array, compute_energy_contributions, return_k_vector_data)

Prepare material class object file, neighbor list and saves to the provided destination path

class PyCD.material_preprod.ReturnValues(input_dict)

dummy class to return objects from methods defined inside other classes

class PyCD.material_run.ReturnValues(input_dict)

dummy class to return objects from methods defined inside other classes

class PyCD.material_msd.ReturnValues(input_dict)

dummy class to return objects from methods defined inside other classes

PyCD.io.generate_report(start_time, dst_path, file_name, print_time_elapsed, prefix=None)

Generates a report file to the output directory This function writes out a VASP-style POSCAR file and returns dict containing a variety of POSCAR-derived quantities.

Parameters:

• start_time (datetime.datetime) – Time value when this function is called
• dst_path (str) – The location of the destination file to write out
• file_name (str) – The file name of the output report
• print_time_elapsed (bool) – Flag value to whether to print elapsed time or not
• prefix (str or NoneType) – String value to prefix the newly generated report content

Returns:

None

Return type:

NoneType

PyCD.io.read_poscar(input_file_path)

This function reads in a VASP-style POSCAR file and returns dict containing a variety of POSCAR-derived quantities.

Parameters:file_location (str) – The location of the POSCAR file to read in Returns:poscar_info – Dict of POSCAR-derived quantities Return type:dict of {str : np.ndarray, str : list, str : list, str : int, str : str, str : np.ndarray, str : str}

PyCD.io.write_poscar(src_file_path, dst_file_path, file_format, element_types_cluster, num_elements_cluster, coordinate_type, coordinates_cluster)

This function writes out a VASP-style POSCAR file and returns dict containing a variety of POSCAR-derived quantities.

Parameters:

• src_file_path (str) – The location of the source file to read in
• dst_file_path (str) – The location of the destination file to write out
• file_format (str) – The file format of the source coordinate file: VASP, VESTA, unknown
• element_types_cluster (list) – List of element types in the cluster
• num_elements_cluster (list) – List containing number of elements for each element type listed in element_types_cluster
• coordinate_type (str) – Nature of coordinates : Cartesian or Fractional
• coordinates_cluster (np.ndarray) – NumPy array containing the coordinates within the selected cluster

Returns:

None

Return type:

NoneType

class PyCD.scripts.data_profile.DataProfile(dst_path, system_directory_path, variable_quantity_type_index, variable_quantity_index, variable_quantity_list, species_count, t_final, time_interval, n_traj, external_field, doping)