Source code for asr.c2db.stiffness

"""Stiffness tensor."""
import typing

from ase import Atoms
import numpy as np

import asr
from asr.core import (
    command, option, ASRResult, prepare_result, AtomsFile,
from asr.database.browser import (matrixtable, describe_entry, dl,
from asr.c2db.relax import main as relax

panel_description = make_panel_description(
The stiffness tensor (C) is a rank-4 tensor that relates the stress of a
material to the applied strain. In Voigt notation, C is expressed as a NxN
matrix relating the N independent components of the stress and strain
tensors. C is calculated as a finite difference of the stress under an applied
strain with full relaxation of atomic coordinates. A negative eigenvalue of C
indicates a dynamical instability.

def webpanel(result, context):
    stiffnessdata = result  #['results-asr.c2db.stiffness.json']
    c_ij = stiffnessdata['stiffness_tensor'].copy()
    eigs = stiffnessdata['eigenvalues'].copy()
    nd = context.ndim

    if nd == 2:
        c_ij = np.zeros((4, 4))
        c_ij[1:, 1:] = stiffnessdata['stiffness_tensor']
        ctable = matrixtable(
            title='C<sub>ij</sub> (N/m)',
            columnlabels=['xx', 'yy', 'xy'],
            rowlabels=['xx', 'yy', 'xy'])

        eigrows = ([['<b>Stiffness tensor eigenvalues<b>', '']]
                   + [[f'Eigenvalue {ie}', f'{eig.real:.2f} N/m']
                      for ie, eig in enumerate(sorted(eigs,
                                                      key=lambda x: x.real))])
    elif nd == 3:
        eigs *= 1e-9
        c_ij *= 1e-9
        ctable = matrixtable(
            title='C<sub>ij</sub> (10⁹ N/m²)',
            columnlabels=['xx', 'yy', 'zz', 'yz', 'xz', 'xy'],
            rowlabels=['xx', 'yy', 'zz', 'yz', 'xz', 'xy'])

        eigrows = ([['<b>Stiffness tensor eigenvalues<b>', '']]
                   + [[f'Eigenvalue {ie}', f'{eig.real:.2f} · 10⁹ N/m²']
                      for ie, eig
                      in enumerate(sorted(eigs, key=lambda x: x.real))])
        ctable = dict(
        eig = complex(eigs[0])
        eigrows = ([['<b>Stiffness tensor eigenvalues<b>', '']]
                   + [[f'Eigenvalue', f'{eig.real:.2f} * 10⁻¹⁰ N']])

    eigtable = dict(

    panel = {
        'title': describe_entry(
            'Stiffness tensor', description=panel_description
        'columns': [[ctable], [eigtable]],
        'sort': 2}

    dynstab = result['dynamic_stability_stiffness']

    row = [
            'Dynamical (stiffness)',
            'Classifier for the dynamical stability of a material '
            'based on the minimum eigenvalue of the stiffness tensor.'
            + dl(
                    ["LOW", dynstab_text_low],
                    ["HIGH", dynstab_text_high],

    summary = {'title': 'Summary',
               'columns': [[{'type': 'table',
                             'header': ['Stability', 'Category'],
                             'rows': [row],
               'sort': 3}

    return [panel, summary]

[docs]@prepare_result class Result(ASRResult): c_11: float c_12: float c_13: float c_14: float c_15: float c_16: float c_21: float c_22: float c_23: float c_24: float c_25: float c_26: float c_31: float c_32: float c_33: float c_34: float c_35: float c_36: float c_41: float c_42: float c_43: float c_44: float c_45: float c_46: float c_51: float c_52: float c_53: float c_54: float c_55: float c_56: float c_61: float c_62: float c_63: float c_64: float c_65: float c_66: float stiffness_tensor: typing.List[typing.List[float]] eigenvalues: typing.List[complex] dynamic_stability_stiffness: str speed_of_sound_x: float speed_of_sound_y: float key_descriptions = { "c_11": "Stiffness tensor 11-component.", "c_12": "Stiffness tensor 12-component.", "c_13": "Stiffness tensor 13-component.", "c_14": "Stiffness tensor 14-component.", "c_15": "Stiffness tensor 15-component.", "c_16": "Stiffness tensor 16-component.", "c_21": "Stiffness tensor 21-component.", "c_22": "Stiffness tensor 22-component.", "c_23": "Stiffness tensor 23-component.", "c_24": "Stiffness tensor 24-component.", "c_25": "Stiffness tensor 25-component.", "c_26": "Stiffness tensor 26-component.", "c_31": "Stiffness tensor 31-component.", "c_32": "Stiffness tensor 32-component.", "c_33": "Stiffness tensor 33-component.", "c_34": "Stiffness tensor 34-component.", "c_35": "Stiffness tensor 35-component.", "c_36": "Stiffness tensor 36-component.", "c_41": "Stiffness tensor 41-component.", "c_42": "Stiffness tensor 42-component.", "c_43": "Stiffness tensor 43-component.", "c_44": "Stiffness tensor 44-component.", "c_45": "Stiffness tensor 45-component.", "c_46": "Stiffness tensor 46-component.", "c_51": "Stiffness tensor 51-component.", "c_52": "Stiffness tensor 52-component.", "c_53": "Stiffness tensor 53-component.", "c_54": "Stiffness tensor 54-component.", "c_55": "Stiffness tensor 55-component.", "c_56": "Stiffness tensor 56-component.", "c_61": "Stiffness tensor 61-component.", "c_62": "Stiffness tensor 62-component.", "c_63": "Stiffness tensor 63-component.", "c_64": "Stiffness tensor 64-component.", "c_65": "Stiffness tensor 65-component.", "c_66": "Stiffness tensor 66-component.", "eigenvalues": "Stiffness tensor eigenvalues.", "speed_of_sound_x": "Speed of sound (x) [m/s]", "speed_of_sound_y": "Speed of sound (y) [m/s]", "stiffness_tensor": "Stiffness tensor [`N/m^{dim-1}`]", "dynamic_stability_stiffness": "Stiffness dynamic stability (low/high)", } formats = {'webpanel2': webpanel}
sel = asr.Selector() sel.version = sel.EQ(-1) @asr.migration(selector=sel) def transform_stiffness_resultfile_record(record): """Remove fixcell and allow_symmetry_breaking from dependency_parameters.""" dep_params = record.parameters['dependency_parameters'] relax_dep_params = dep_params['asr.c2db.relax'] delparams = {'fixcell', 'allow_symmetry_breaking'} for param in delparams: del relax_dep_params[param] return record
[docs]@command( module='asr.c2db.stiffness', ) @option('--atoms', type=AtomsFile(), help='Atoms to be strained.', default='structure.json') @asr.calcopt @option('--strain-percent', help='Magnitude of applied strain.', type=float) @option('--d3/--nod3', help='Relax with vdW D3.', is_flag=True) def main(atoms: Atoms, calculator: dict = relax.defaults.calculator, strain_percent: float = 1.0, d3: bool = False) -> Result: """Calculate stiffness tensor.""" from asr.setup.strains import main as make_strained_atoms from asr.setup.strains import get_relevant_strains from ase.units import J ij = get_relevant_strains(atoms.pbc) ij_to_voigt = [[0, 5, 4], [5, 1, 3], [4, 3, 2]] stiffness = np.zeros((6, 6), float) for i, j in ij: dstress = np.zeros((6,), float) for sign in [-1, 1]: strained_atoms = make_strained_atoms( atoms, strain_percent=sign * strain_percent, i=i, j=j) relaxresult = relax( strained_atoms, calculator=calculator, fixcell=True, allow_symmetry_breaking=True, ) stress = relaxresult.stress dstress += stress * sign stiffness[:, ij_to_voigt[i][j]] = dstress / (strain_percent * 0.02) stiffness = np.array(stiffness, float) # We work with Mandel notation which is conventional and convenient stiffness[3:, :] *= 2**0.5 stiffness[:, 3:] *= 2**0.5 # Convert the stiffness tensor from [eV/Ang^3] -> [J/m^3]=[N/m^2] stiffness *= 10**30 / J # Now do some post processing data = {} nd = sum(atoms.pbc) speed_of_sound_x = None speed_of_sound_y = None if nd == 2: cell = atoms.get_cell() # We have to normalize with the supercell size z = cell[2, 2] stiffness = stiffness[[0, 1, 5], :][:, [0, 1, 5]] * z * 1e-10 from ase.units import kg from ase.units import m as meter area = atoms.get_volume() / cell[2, 2] mass = sum(atoms.get_masses()) area_density = (mass / kg) / (area / meter**2) # speed of sound in m/s speed_of_sound_x = np.sqrt(stiffness[0, 0] / area_density) speed_of_sound_y = np.sqrt(stiffness[1, 1] / area_density) elif nd == 1: cell = atoms.get_cell() area = atoms.get_volume() / cell[2, 2] stiffness = stiffness[[2], :][:, [2]] * area * 1e-20 # typical values for 1D are of the order of 10^(-10) N elif nd == 0: raise RuntimeError('Cannot compute stiffness tensor of 0D material.') data['speed_of_sound_x'] = speed_of_sound_x data['speed_of_sound_y'] = speed_of_sound_y for i in range(6): for j in range(6): data[f'c_{i + 1}{j + 1}'] = None stiffness_shape = stiffness.shape for i in range(stiffness_shape[0]): for j in range(stiffness_shape[1]): data[f'c_{i + 1}{j + 1}'] = stiffness[i, j] data['stiffness_tensor'] = stiffness if nd == 1: eigs = stiffness else: eigs = np.linalg.eigvals(stiffness) data['eigenvalues'] = eigs data['dynamic_stability_stiffness'] = dynamic_stability_stiffness( eigs.min()) return Result(data=data)
dynstab_text_high = 'Minimum stiffness tensor eigenvalue > 0' dynstab_text_low = 'Minimum stiffness tensor eigenvalue ≤ 0' def dynamic_stability_stiffness(mineig): if mineig > 0: return 'high' else: return 'low' if __name__ == '__main__': main.cli()