entries

entries#

Store data for individual defect calculation

Classes

DefectEntry(defect[, energy_diff, ...])

Object to store the results of a defect calculation.

Functions

`fermi_dirac`	Returns the defect occupation as a function of the formation energy, using the Fermi-Dirac distribution with chemical potential equal to 0.
`maxwell_boltzmann`	Maxwell-Boltzmann distribution function.

class DefectEntry(defect, energy_diff=None, corrections={}, data=None, formation_energy_function=None, defect_concentration_function=None)[source]#

Bases: MSONable

Object to store the results of a defect calculation. This object is usually created automatically when importing results, either from directories or DataFrame.

Parameters:

defect (Defect) – Defect object (Vacancy, Interstitial, Substitution, Polaron or DefectComplex)
energy_diff (float) – Difference btw energy of defect structure and energy of pristine (bulk) structure in eV.
corrections (dict) – Dictionary of corrections in eV to apply to defect formation energy. All values will be added to the defect formation energy. Keys indicate the correction type.
data (dict) – Store additional data in dict format.
label (str) – Additional label to add to defect specie.
formation_energy_function (function) – Custom function for calculation of defect formation energy. Check documentation in formation_energy function for more details.
defect_concentration_function (function) – Custom function for calculation of defect concentration. Check documentation in defect_concentration function for more details.

as_dict()[source]#

Returns:: Json-serializable dict representation of DefectEntry.

property bulk_structure#: Structure of pristine material.

property charge#

property corrections#: Dictionary with corrections to the defect energy. Its values will be added to the formation energy.

property data#: Dictionary containing additional data.

property defect#: Defect object.

defect_concentration(vbm=0, chemical_potentials=None, temperature=300, fermi_level=0.0, per_unit_volume=True, eform_kwargs={}, **kwargs)[source]#

Compute the defect concentration. If defect_concentration_function is set, the custom function is called, otherwise, the concentration is computed in the dilute limit as:

n = N * (1 / e^(Ef/kT) + 1), where:

N is the site multiplicity (in cm^-3 or per unit cell)
Ef is the formation energy of the defect.
k is the Boltzmann constant.
T is the temperature.

A custom function can be set when initializing the DefectEntry or by using the ‘set_defect_concentration_function` method. Use reset_defect_concentration_function to restore the default behaviour. If a custom function is given, the input must have the same args as this function, with the possibility to add more kwargs.

Parameters:

vbm (float) – Valence band maximum of bulk calculation in eV
chemical_potentials (dict) – Chemical potentials of the elements involved in the defect.
temperature (float) – Temperature in Kelvin.
fermi_level (float) – Fermi level in eV (with respect to the VBM).
per_unit_volume (bool) – Compute concentrations per unit volume using self.defect.bulk_volume.
eform_kwargs (dict) – Kwargs to pass to self.formation_energy.
kwargs (dict) – Kwargs to pass to custom function.
Returns
--------
conc (float) – Defect concentration in cm^-3 or per unit cell.

property defect_concentration_function#

property defect_specie#: Species involved in defect.

property defect_type#: Defect type.

property delta_atoms#: Dictionary with Element as keys and particle difference between defect structure and bulk structure as values.

property energy_diff#

formation_energy(vbm=0, chemical_potentials=None, fermi_level=0, temperature=0, **kwargs)[source]#

Compute the formation energy of the defect. If formation_energy_function is set, the custom function is called, otherwise, the formation energy is computed as:

Ef = E_D - E_B + q(eVBM + fermi_level) + Ecorr - sum_i [(ni(D) - ni(B))* mu_i]

where:

E_D is the energy of the defective cell
E_B is the energy of the pristine (bulk) cell
Note: E_D - E_B is self.energy_diff
eVBM is the valence band maximum energy
fermi_level is the chemical potential of electrons
Ecorr are correction terms
ni are the number of particles in the defective and pristine cells
mu_i are the chemical potentials of the elements.

A custom function can be set when initializing the DefectEntry or by using the ‘set_formation_energy_function` method. Use reset_formation_energy_function to restore the default behaviour. If a custom function is given, the input must have the same args as this function, with the possibility to add more kwargs.

Parameters:

vbm (float) – Valence band maximum of bulk calculation in eV
chemical_potentials (dict) – Chemical potentials of the elements involved in the defect.
fermi_level (float) – Fermi level in eV (with respect to the VBM).
temperature (float) – Temperature in Kelvin. If a custom function is not passed, this parameter has no effect.
kwargs (dict) – Kwargs to pass to custom function.

Returns:

formation_energy – Formation energy in eV.

Return type:

float

property formation_energy_function#

static from_computed_entries(computed_entry_defect, computed_entry_bulk, corrections, multiplicity=1, data=None, label=None, initial_structure=False, **kwargs)[source]#

Generate DefectEntry object from Pymatgen’s ComputedStructureEntry objects.

Parameters:

computed_entry_defect (VaspJob) – ComputedStructureEntry of the defect calculation.
computed_entry_bulk (VaspJob) – ComputedStructureEntry of the bulk calculation.
corrections (dict) – Dict of corrections for defect formation energy. All values will be summed and added to the defect formation energy.
multiplicity (int) – Multiplicity of defect within the supercell. If set to None is attempted to be determined automatically.
data (dict) – Store additional data in dict format.
label (str) – Additional label to add to defect specie. Does not influence non equilibrium calculations.
initial_structure (bool) – Use initial structure for defect recognition. Useful when relaxations are large and defect_finder struggles to find the right defects.
kwargs (dict) – Kwargs to pass to defect_finder.

Returns:

entry – DefectEntry object

Return type:

DefectEntry

classmethod from_dict(d)[source]#

Reconstitute a DefectEntry object from a dict representation created using as_dict().

Parameters:: d (dict) – Dictionary representation of DefectEntry.
Returns:: entry – DefectEntry object
Return type:: DefectEntry

static from_structures(defect_structure, bulk_structure, energy_diff, corrections, charge=0, multiplicity=1, data=None, label=None, **kwargs)[source]#

Generate DefectEntry object from Structure objects.

Parameters:

defect_structure (Structure) – Defect structure.
bulk_structure (Structure) – Bulk structure.
energy_diff (float) – Difference btw energy of defect structure and energy of pristine structure
corrections (dict) – Dict of corrections in eV for defect formation energy. All values will be summed and added to the defect formation energy.
charge (int or float) – Charge of the defect system. The default is 0.
multiplicity (int) – multiplicity of defect within the supercell. If set to None is attempted to be determined automatically with Pymatgen. The default is 1.
data (dict) – Store additional data in dict format.
label (str) – Additional label to add to defect specie. Does not influence non equilibrium calculations.
kwargs (dict) – Kwargs to pass to defect_finder. ‘verbose’ is set to True by default.

Returns:

entry – DefectEntry object

Return type:

DefectEntry

static from_vasp_directories(path_defect, computed_entry_bulk=None, path_bulk=None, corrections={}, multiplicity=1, data=None, label=None, initial_structure=False, function=None, function_kwargs={}, computed_entry_kwargs={}, finder_kwargs={})[source]#

Generate DefectEntry object from VASP directories read with Pymatgen.

Parameters:

path_defect (str) – Path of VASP defect calculation.
computed_entry_bulk (VaspJob) – ComputedStructureEntry of the bulk calculation.
path_bulk (str) – If computed_entry_bulk is not provided, read directly from VASP directory.
corrections (dict) – Dict of corrections for defect formation energy. All values will be summed and added to the defect formation energy.
multiplicity (int) – Multiplicity of defect within the supercell. If set to None is attempted to be determined automatically with Pymatgen. The default is 1.
data (dict) – Store additional data in dict format.
label (str) – Additional label to add to defect specie. Does not influence non equilibrium calculations.
initial_structure (bool) – Use initial structure for defect recognition. Useful when relaxations are large and defect_finder struggles to find the right defects.
function (function) – Function to apply to DefectEntry. Useful to automate custom entry modification. The function can modify entry attributes and returns None.
function_kwargs (dict) – Kwargs to pass to custom function.
computed_entr_kwargs (dict) – Kwargs to pass to Vasprun.get_computed_entry.
finder_kwargs (dict) – Kwargs to pass to defect_finder.

Returns:

entry – DefectEntry object

Return type:

DefectEntry

property label#

property multiplicity#

property name#

relaxation_volume(stress_bulk, bulk_modulus, stress_defect=None, corrections={})[source]#

Calculate relaxation volume from stresses. Stress of defect calculation needs to be provided either directly or in data dict with “stress” key.

Parameters:

stress_bulk (np.array) – Stresses from bulk calculation in kbar (units of VASP output)
bulk_volume (float) – Cell volume of bulk calculation in A°^3.
bulk_modulus (float) – Bulk modulus in GPa.
stress_defect (np.array) – Stresses from defect calculation in kbar (units of VASP output)
corrections (bool) – Add correction terms to the residual stress tensor.

Returns:

rel_volume – Relaxation volume in A°^3.

Return type:

float

reset_defect_concentration_function()[source]#: Reset formation energy function to default.

reset_formation_energy_function()[source]#: Reset formation energy function to default.

set_charge(new_charge=0)[source]#: Set defect charge

set_corrections(**kwargs)[source]#: Set defect corrections. Pass either dict or kwargs.

set_data(new_data)[source]#: Set data dictionary

set_defect_concentration_function(new_function)[source]#: Set custom defect concentration function.

set_formation_energy_function(new_function)[source]#: Set custom defect concentrations function.

set_label(new_label)[source]#: Set defect label.

set_multiplicity(new_multiplicity=None)[source]#: Sets the Multiplicity of the defect. If new_multiplicity is not provided it is determined with self.defect.get_multiplicity

property structure#

property symbol#

property symbol_charge#: Defect symbol with charge formatted with numbers.

property symbol_kroger#: Defect symbol with charge formatted with Kröger-Vink notation.

fermi_dirac(E, T)[source]#

Returns the defect occupation as a function of the formation energy, using the Fermi-Dirac distribution with chemical potential equal to 0.

Parameters:

E (float) – Energy in eV
T (float) – Temperature in Kelvin.

maxwell_boltzmann(E, T, clip=True)[source]#

Maxwell-Boltzmann distribution function. Can be clipped to prevent divergence.

Parameters:

E (float) – Energy in eV.
fermi (float) – Fermi level in eV.
T (float) – Temperature in kelvin.
clip (bool) – Clip exponential factor to 700 to avoid divergence, returns 1e304.

Returns:

occupation – Maxwell-Boltzmann occupation probability at energy E.

Return type:

float

entries

Contents

entries#