def test_approximate_rotational_invariance(self):
descriptor = DISH(configs=self.configs)
# build crystal structures - two different random rotations
fcc_al = crystal('Al', [(0, 0, 0)],
spacegroup=225,
cellpar=[4.05, 4.05, 4.05, 90, 90, 90])
fcc_all_supercell_rot1 = create_supercell(fcc_al, target_nb_atoms=128)
fcc_all_supercell_rot2 = create_supercell(fcc_al, target_nb_atoms=128)
# rotate 1st structure
alphas = (random.random() * 360.0, random.random() * 360.0,
random.random() * 360.0)
fcc_all_supercell_rot1.rotate(alphas[0],
'x',
rotate_cell=True,
center='COU')
fcc_all_supercell_rot1.rotate(alphas[1],
'y',
rotate_cell=True,
center='COU')
fcc_all_supercell_rot1.rotate(alphas[2],
'z',
rotate_cell=True,
center='COU')
# rotate 2nd structure
alphas = (random.random() * 360.0, random.random() * 360.0,
random.random() * 360.0)
fcc_all_supercell_rot2.rotate(alphas[0],
'x',
rotate_cell=True,
center='COU')
fcc_all_supercell_rot2.rotate(alphas[1],
'y',
rotate_cell=True,
center='COU')
fcc_all_supercell_rot2.rotate(alphas[2],
'z',
rotate_cell=True,
center='COU')
# calculate the descriptor
descriptor.calculate(fcc_all_supercell_rot1)
descriptor.calculate(fcc_all_supercell_rot2)
spectrum_rot1 = fcc_all_supercell_rot1.info['descriptor'][
'diffraction_3d_sh_spectrum']
spectrum_rot2 = fcc_all_supercell_rot2.info['descriptor'][
'diffraction_3d_sh_spectrum']
# test if the two diffraction fingerprints are different by less than 20% (20% is arbitrary)
self.assertLessEqual(np.abs(np.amax(spectrum_rot1 - spectrum_rot2)),
0.20)
def test_approximate_size_invariance(self):
descriptor = DISH(configs=self.configs)
# build crystal structures - two supercell sizes
fcc_al = crystal('Al', [(0, 0, 0)],
spacegroup=225,
cellpar=[4.05, 4.05, 4.05, 90, 90, 90])
structure_128 = create_supercell(fcc_al, target_nb_atoms=128)
structure_256 = create_supercell(fcc_al, target_nb_atoms=256)
# calculate the descriptor
descriptor.calculate(structure_128)
descriptor.calculate(structure_256)
intensity_128 = structure_128.info['descriptor'][
'diffraction_3d_sh_spectrum']
intensity_256 = structure_256.info['descriptor'][
'diffraction_3d_sh_spectrum']
# test if the two diffraction fingerprints are different by less than 20% (20% is arbitrary)
self.assertLessEqual(np.abs(np.amax(intensity_256 - intensity_128)),
0.20)
def test_approximate_translational_invariance(self):
# build crystal structures - one translated w.r.t. the other by an arbitrary vector
structure = crystal('Al', [(0, 0, 0)],
spacegroup=225,
cellpar=[4.05, 4.05, 4.05, 90, 90, 90])
trans_vector = [1.0, -2.0, 5.0]
structure_translated = structure.copy()
structure_translated.translate(trans_vector)
structure = create_supercell(structure, target_nb_atoms=128)
structure_translated = create_supercell(structure_translated,
target_nb_atoms=128)
# calculate the descriptor
descriptor = DISH(configs=self.configs)
descriptor.calculate(structure)
descriptor.calculate(structure_translated)
intensity = structure.info['descriptor']['diffraction_3d_sh_spectrum']
intensity_translated = structure_translated.info['descriptor'][
'diffraction_3d_sh_spectrum']
# test if the two diffraction fingerprints are different by less than 20% (20% is arbitrary)
np.testing.assert_allclose(intensity, intensity_translated, 0.2)
def test_normalization(self):
descriptor = DISH(configs=self.configs)
# build crystal structure
bcc_fe = crystal('Fe', [(0, 0, 0)],
spacegroup=229,
cellpar=[4.05, 4.05, 4.05, 90, 90, 90])
structure = create_supercell(bcc_fe, target_nb_atoms=256)
# calculate the descriptor
descriptor.calculate(structure)
intensity = structure.info['descriptor']['diffraction_3d_sh_spectrum']
# check if intensity_rgb is normalized
self.assertAlmostEqual(np.amax(intensity), 1.0)
self.assertAlmostEqual(np.amin(intensity), 0.0)
def test_size_invariance(self):
# replicating a unit cell must not change the atomic features
structure = crystal('C', [(0, 0, 0)], spacegroup=227, cellpar=[3.57, 3.57, 3.57, 90, 90, 90])
structure_supercell = create_supercell(structure, target_nb_atoms=128)
# define descriptor
selected_feature_list = ['atomic_ionization_potential', 'atomic_electron_affinity', 'atomic_rs_max']
kwargs = {'feature_order_by': 'atomic_mulliken_electronegativity', 'energy_unit': 'eV',
'length_unit': 'angstrom'}
descriptor = AtomicFeatures(configs=self.configs, **kwargs)
# calculate descriptor
descriptor.calculate(structure, selected_feature_list=selected_feature_list)
df_atomic_features = structure.info['descriptor']['atomic_features_table']
descriptor.calculate(structure_supercell, selected_feature_list=selected_feature_list)
df_atomic_features_supercell = structure_supercell.info['descriptor']['atomic_features_table']
self.assertIsInstance(df_atomic_features, pd.DataFrame)
self.assertIsInstance(df_atomic_features_supercell, pd.DataFrame)
random_rotation_before=True,
cell_type='standard_no_symmetries',
optimal_supercell=False)),
(random_displace_atoms,
dict(noise_distribution='gaussian',
displacement=0.10,
create_replicas_by='nb_atoms',
min_nb_atoms=32,
target_nb_atoms=256,
random_rotation=False,
random_rotation_before=True,
cell_type='standard_no_symmetries',
optimal_supercell=False))]
# =============================================================================
# Descriptor calculation
# =============================================================================
# create the fcc aluminium structure
nacl = crystal(['Na', 'Cl'], [(0, 0, 0), (0.5, 0.5, 0.5)],
spacegroup=225,
cellpar=[5.64, 5.64, 5.64, 90, 90, 90])
structure = create_supercell(nacl, target_nb_atoms=256)
# calculate the two-dimensional diffraction fingerprint
structure_result = descriptor.calculate(structure)
rdf = structure_result.info['descriptor']['rdf']
logger.info("Calculation completed.")
# setup folder and files
main_folder = '/home/ziletti/Documents/nomadml_docs'
desc_folder = os.path.abspath(os.path.normpath(os.path.join(main_folder, 'desc_folder')))
# checkpoint_folder = os.path.abspath(os.path.normpath(os.path.join(main_folder, 'saved_models')))
figure_folder = os.path.abspath(os.path.normpath(os.path.join(main_folder, 'attentive_resp_maps')))
checkpoint_folder = os.path.abspath(os.path.normpath('../assets/data_examples/'))
# build crystal structures
fcc_al = crystal('Al', [(0, 0, 0)], spacegroup=225, cellpar=[4.05, 4.05, 4.05, 90, 90, 90])
bcc_fe = crystal('Fe', [(0, 0, 0)], spacegroup=229, cellpar=[2.87, 2.87, 2.87, 90, 90, 90])
diamond_c = crystal('C', [(0, 0, 0)], spacegroup=227, cellpar=[3.57, 3.57, 3.57, 90, 90, 90])
hcp_mg = crystal('Mg', [(1. / 3., 2. / 3., 3. / 4.)], spacegroup=194, cellpar=[3.21, 3.21, 5.21, 90, 90, 120])
# create supercells - pristine
fcc_al_supercell = create_supercell(fcc_al, target_nb_atoms=128, cell_type='standard_no_symmetries')
bcc_fe_supercell = create_supercell(bcc_fe, target_nb_atoms=128, cell_type='standard_no_symmetries')
diamond_c_supercell = create_supercell(diamond_c, target_nb_atoms=128, cell_type='standard_no_symmetries')
hcp_mg_supercell = create_supercell(hcp_mg, target_nb_atoms=128, cell_type='standard_no_symmetries')
ase_atoms_list = [fcc_al_supercell, bcc_fe_supercell, diamond_c_supercell, hcp_mg_supercell]
# calculate the two-dimensional diffraction fingerprint for all four structures
descriptor = Diffraction2D(configs=configs)
diffraction_fingerprints_rgb = [descriptor.calculate(ase_atoms).info['descriptor']['diffraction_2d_intensity'] for ase_atoms in ase_atoms_list]
neural_network_name = 'ziletti_et_2018_rgb'
model_weights_file = os.path.abspath(os.path.normpath(os.path.join(checkpoint_folder, neural_network_name + '.h5')))
model_arch_file = os.path.abspath(os.path.normpath(os.path.join(checkpoint_folder, neural_network_name + '.json')))
# convert list of diffraction fingerprint images to to numpy array
