def test_convert_strain_to_deformation(self):
strain = Tensor(np.random.random((3, 3))).symmetrized
while not (np.linalg.eigvals(strain) > 0).all():
strain = Tensor(np.random.random((3, 3))).symmetrized
upper = convert_strain_to_deformation(strain, shape="upper")
symm = convert_strain_to_deformation(strain, shape="symmetric")
self.assertArrayAlmostEqual(np.triu(upper), upper)
self.assertTrue(Tensor(symm).is_symmetric())
for defo in upper, symm:
self.assertArrayAlmostEqual(defo.green_lagrange_strain, strain)
def diff_fit(strains, stresses, eq_stress=None, order=2, tol=1e-10):
"""
nth order elastic constant fitting function based on
central-difference derivatives with respect to distinct
strain states. The algorithm is summarized as follows:
1. Identify distinct strain states as sets of indices
for which nonzero strain values exist, typically
[(0), (1), (2), (3), (4), (5), (0, 1) etc.]
2. For each strain state, find and sort strains and
stresses by strain value.
3. Find first, second .. nth derivatives of each stress
with respect to scalar variable corresponding to
the smallest perturbation in the strain.
4. Use the pseudoinverse of a matrix-vector expression
corresponding to the parameterized stress-strain
relationship and multiply that matrix by the respective
calculated first or second derivatives from the
previous step.
5. Place the calculated nth-order elastic
constants appropriately.
Args:
order (int): order of the elastic tensor set to return
strains (nx3x3 array-like): Array of 3x3 strains
to use in fitting of ECs
stresses (nx3x3 array-like): Array of 3x3 stresses
to use in fitting ECs. These should be PK2 stresses.
eq_stress (3x3 array-like): stress corresponding to
equilibrium strain (i. e. "0" strain state).
If not specified, function will try to find
the state in the list of provided stresses
and strains. If not found, defaults to 0.
tol (float): value for which strains below
are ignored in identifying strain states.
Returns:
Set of tensors corresponding to nth order expansion of
the stress/strain relation
"""
strain_state_dict = get_strain_state_dict(
strains, stresses, eq_stress=eq_stress, tol=tol,
add_eq=True, sort=True)
# Collect derivative data
c_list = []
dei_dsi = np.zeros((order - 1, 6, len(strain_state_dict)))
for n, (strain_state, data) in enumerate(strain_state_dict.items()):
hvec = data["strains"][:, strain_state.index(1)]
for i in range(1, order):
coef = get_diff_coeff(hvec, i)
dei_dsi[i-1, :, n] = np.dot(coef, data["stresses"])
m, absent = generate_pseudo(list(strain_state_dict.keys()), order)
for i in range(1, order):
cvec, carr = get_symbol_list(i+1)
svec = np.ravel(dei_dsi[i-1].T)
cmap = dict(zip(cvec, np.dot(m[i-1], svec)))
c_list.append(v_subs(carr, cmap))
return [Tensor.from_voigt(c) for c in c_list]
def diff_fit(strains, stresses, eq_stress=None, order=2, tol=1e-10):
"""
nth order elastic constant fitting function based on
central-difference derivatives with respect to distinct
strain states. The algorithm is summarized as follows:
1. Identify distinct strain states as sets of indices
for which nonzero strain values exist, typically
[(0), (1), (2), (3), (4), (5), (0, 1) etc.]
2. For each strain state, find and sort strains and
stresses by strain value.
3. Find first, second .. nth derivatives of each stress
with respect to scalar variable corresponding to
the smallest perturbation in the strain.
4. Use the pseudoinverse of a matrix-vector expression
corresponding to the parameterized stress-strain
relationship and multiply that matrix by the respective
calculated first or second derivatives from the
previous step.
5. Place the calculated nth-order elastic
constants appropriately.
Args:
order (int): order of the elastic tensor set to return
strains (nx3x3 array-like): Array of 3x3 strains
to use in fitting of ECs
stresses (nx3x3 array-like): Array of 3x3 stresses
to use in fitting ECs. These should be PK2 stresses.
eq_stress (3x3 array-like): stress corresponding to
equilibrium strain (i. e. "0" strain state).
If not specified, function will try to find
the state in the list of provided stresses
and strains. If not found, defaults to 0.
tol (float): value for which strains below
are ignored in identifying strain states.
Returns:
Set of tensors corresponding to nth order expansion of
the stress/strain relation
"""
strain_state_dict = get_strain_state_dict(
strains, stresses, eq_stress=eq_stress, tol=tol,
add_eq=True, sort=True)
# Collect derivative data
c_list = []
dei_dsi = np.zeros((order - 1, 6, len(strain_state_dict)))
for n, (strain_state, data) in enumerate(strain_state_dict.items()):
hvec = data["strains"][:, strain_state.index(1)]
for i in range(1, order):
coef = get_diff_coeff(hvec, i)
dei_dsi[i-1, :, n] = np.dot(coef, data["stresses"])
m, absent = generate_pseudo(list(strain_state_dict.keys()), order)
for i in range(1, order):
cvec, carr = get_symbol_list(i+1)
svec = np.ravel(dei_dsi[i-1].T)
cmap = dict(zip(cvec, np.dot(m[i-1], svec)))
c_list.append(v_subs(carr, cmap))
return [Tensor.from_voigt(c) for c in c_list]
def get_symmetric_wallace_tensor(self, tau):
"""
Gets the symmetrized wallace tensor for determining
yield strength criteria.
Args:
tau (3x3 array-like): stress at which to evaluate
the wallace tensor.
"""
wallace = self.get_wallace_tensor(tau)
return Tensor(0.5 * (wallace + np.transpose(wallace, [2, 3, 0, 1])))
def generate_elastic_workflow(structure, tags=None):
"""
Generates a standard production workflow.
Notes:
Uses a primitive structure transformed into
the conventional basis (for equivalent deformations).
Adds the "minimal" category to the minimal portion
of the workflow necessary to generate the elastic tensor,
and the "minimal_full_stencil" category to the portion that
includes all of the strain stencil, but is symmetrically complete
"""
if tags == None:
tags = []
# transform the structure
ieee_rot = Tensor.get_ieee_rotation(structure)
if not SquareTensor(ieee_rot).is_rotation(tol=0.005):
raise ValueError("Rotation matrix does not satisfy rotation conditions")
symm_op = SymmOp.from_rotation_and_translation(ieee_rot)
ieee_structure = structure.copy()
ieee_structure.apply_operation(symm_op)
# construct workflow
wf = wf_elastic_constant(ieee_structure)
# Set categories, starting with optimization
opt_fws = get_fws_and_tasks(wf, fw_name_constraint="optimization")
wf.fws[opt_fws[0][0]].spec['elastic_category'] = "minimal"
# find minimal set of fireworks using symmetry reduction
fws_by_strain = {Strain(fw.tasks[-1]['pass_dict']['strain']): n
for n, fw in enumerate(wf.fws) if 'deformation' in fw.name}
unique_tensors = symmetry_reduce(list(fws_by_strain.keys()), ieee_structure)
for unique_tensor in unique_tensors:
fw_index = get_tkd_value(fws_by_strain, unique_tensor)
if np.isclose(unique_tensor, 0.005).any():
wf.fws[fw_index].spec['elastic_category'] = "minimal"
else:
wf.fws[fw_index].spec['elastic_category'] = "minimal_full_stencil"
# Add tags
if tags:
wf = add_tags(wf, tags)
wf = add_modify_incar(wf)
priority = 500 - structure.num_sites
wf = add_priority(wf, priority)
for fw in wf.fws:
if fw.spec.get('elastic_category') == 'minimal':
fw.spec['_priority'] += 2000
elif fw.spec.get('elastic_category') == 'minimal_full_stencil':
fw.spec['_priority'] += 1000
return wf
def process_item(self, item):
"""
Process the tasks and materials into a dielectrics collection
Args:
item dict: a dict of material_id, structure, and tasks
Returns:
dict: a dieletrics dictionary
"""
def poly(matrix):
diags = np.diagonal(matrix)
return np.prod(diags) / np.sum(
np.prod(comb) for comb in combinations(diags, 2))
d = {self.dielectric.key: item[self.materials.key]}
structure = Structure.from_dict(item["structure"])
if item.get("dielectric", False):
ionic = Tensor(
item["dielectric"]["ionic"]).symmetrized.fit_to_structure(
structure).convert_to_ieee(structure)
static = Tensor(
item["dielectric"]["static"]).symmetrized.fit_to_structure(
structure).convert_to_ieee(structure)
total = ionic + static
d["dielectric"] = {
"total": total,
"ionic": ionic,
"static": static,
"e_total": poly(total),
"e_ionic": poly(ionic),
"e_static": poly(static)
}
sga = SpacegroupAnalyzer(structure)
# Update piezo if non_centrosymmetric
if item.get("piezo", False) and not sga.is_laue():
static = PiezoTensor.from_voigt(np.array(
item['piezo']["static"])).symmetrized.fit_to_structure(
structure).convert_to_ieee(structure).voigt
ionic = PiezoTensor.from_voigt(np.array(
item['piezo']["ionic"])).symmetrized.fit_to_structure(
structure).convert_to_ieee(structure).voigt
total = ionic + static
directions, charges, strains = np.linalg.svd(total)
max_index = np.argmax(np.abs(charges))
d["piezo"] = {
"total": total,
"ionic": ionic,
"static": static,
"e_ij_max": charges[max_index],
"max_direction": directions[max_index],
"strain_for_max": strains[max_index]
}
if len(d) > 1:
return d
return None
def process_item(self, item):
"""
Process the tasks and materials into a dielectrics collection
Args:
item dict: a dict of material_id, structure, and tasks
Returns:
dict: a dieletrics dictionary
"""
def poly(matrix):
diags = np.diagonal(matrix)
return np.prod(diags) / np.sum(
np.prod(comb) for comb in combinations(diags, 2))
d = {"material_id": item["material_id"]}
structure = Structure.from_dict(item["structure"])
if item.get("dielectric") is not None:
ionic = Tensor(d["dielectric"]["ionic"])
static = Tensor(d["dielectric"]["static"])
total = ionic + static
d["dielectric"] = {
"total":
total.symmetrized.fit_to_structure(structure).convert_to_ieee(
structure),
"ionic":
ionic.symmetrized.fit_to_structure(structure).convert_to_ieee(
structure),
"static":
static.symmetrized.fit_to_structure(structure).convert_to_ieee(
structure),
"e_total":
poly(total),
"e_ionic":
poly(ionic),
"e_static":
poly(static)
}
# Update piezo if non_centrosymmetric
if item.get("piezo") is not None:
static = PiezoTensor.from_voigt(
np.array(item['piezo']["piezo_tensor"]))
ionic = PiezoTensor.from_voigt(
np.array(item['piezo']["piezo_ionic_tensor"]))
total = ionic + static
d["piezo"] = {
"total":
total.symmetrized.fit_to_structure(structure).convert_to_ieee(
structure).voigt,
"ionic":
ionic.symmetrized.fit_to_structure(structure).convert_to_ieee(
structure).voigt,
"static":
static.symmetrized.fit_to_structure(structure).convert_to_ieee(
structure).voigt,
"e_ij_max":
np.max(total.voigt)
}
# TODO Add in more analysis: v_max ?
# TODO: Add in unstable phonon mode analysis of piezoelectric for potentially ferroelectric
if len(d) > 1:
return d
return None
