pyiron.atomistics.master.elastic module

class pyiron.atomistics.master.elastic.ElasticTensor(project, job_name)[source]

Bases: pyiron.atomistics.master.parallel.AtomisticParallelMaster

Class to calculate the elastic tensor and isotropic elastic constants.

Example:

>>> job = pr.create_job('SomeAtomisticJob', 'atomistic')
>>> job.structure = pr.create_structure('Fe', 'bcc', 2.83)
>>> elastic = job.create_job('ElasticTensor', 'elastic')
>>> elastic.run()

Input parameters:

min_num_measurements (int): Minimum number of measurements/simulations

to be launched

min_num_points (int): Minimum number of data points to fit data

(number of measurements times number of symmetry operations)

polynomial_order (int): Polynomial order to use (default: 2) additional_points (int): Additional points for linearly dependent

strains. Twice the number of strains will be created for +-strain. If additional_points > 0, polynomial order should be at least 3.

strain_matrices (numpy.ndarray): Strain tensors to be applied on

simulation boxes (optional)

rotations (numpy.ndarray): Rotation matrices for box symmetry normalize_magnitude (bool): Whether to normalize strains following

Frobenius norm or not. When polynomial_order = 2, strains should not be normalized in order for the fit to get both large and small strains. When polynomial order > 2, then they should be normalized to not get too small strains which could destabilize higher order fitting.

use_symmetry (bool): Whether or not exploit box symmetry (ignored if

rotations already specified)

use_elements (bool): Whether or not respect chemical elements for box

symmetry (ignored if rotations already specified)

fit_first_order (bool): Whether or not fit first order strains. In

principle it should not be necessary, but might stabilize the calculation if the reference structure is not exactly in the zero pressure state. Setting this to True does not correct the second order strains

The default input parameters might not be chosen adequately. If you have a large computation power, increase min_num_measurements. At the same time, make sure to choose an orientation which maximizes the number symmetry operations. Also if the child job does not support pressure, better increase the number of measurements - without pressure the constants must be fit to total cell energies, which contain only a fraction of the data that the pressure matrix does. This lack can be compensated by sampling more rotations.

collect_output()[source]

Collect the output files of the external executable and store the information in the HDF5 file. This method has to be implemented in the individual meta jobs derived from the ParallelMaster.

validate_ready_to_run()[source]

Returns:

pyiron.atomistics.master.elastic.calc_elastic_tensor(strain, stress=None, energy=None, volume=None, rotations=None, return_score=False, max_polynomial_order=None, fit_first_order=False)[source]

Calculate 6x6-elastic tensor from the strain and stress or strain and energy+volume.

Rotations matrices can be added to take box symmetries into account (unit matrix can be added but does not have to be included in the list)

Parameters

  • strain (numpy.ndarray) – nx3x3 strain tensors

  • stress (numpy.ndarray) – nx3x3 stress tensors

  • energy (numpy.ndarray) – n energy values

  • volume (numpy.ndarray) – n volume values

  • rotations (numpy.ndarray) – mx3x3 rotation matrices

  • return_score (numpy.ndarray) – return regression score (cf. sklearn.linear_mode.LinearRegression)

pyiron.atomistics.master.elastic.get_strain(max_strain=0.05, n_set=10, polynomial_order=2, additional_points=0, normalize=False)[source]
Parameters

  • max_strain (float) – Maximum strain (for each component)

  • n_set (int) – Number of strain values to return

  • polynomial_order (int) – This value determines the number of linear-dependent strain values. For a polynomial order of two, there will be +-strain and for 3, there will be +-strain and +-0.5*strain etc.

  • additional_points (int) – Additional linear-dependent points

  • normalize (bool) – Whether to normalize the strain values. If True, the norm (Frobenius norm) of all outer most strain (i.e. the greatest strain within the linear-dependent strains) will be equal to max_strain

Returns: numpy.ndarray of strains (n, 3, 3)