def test_from_stress_dict(self):
stress_dict = dict(list(zip([IndependentStrain(def_matrix) for def_matrix
in self.def_stress_dict['deformations']],
[Stress(stress_matrix) for stress_matrix
in self.def_stress_dict['stresses']])))
et_from_sd = ElasticTensor.from_stress_dict(stress_dict)
self.assertArrayAlmostEqual(et_from_sd.round(2),
self.elastic_tensor_1)
def test_from_stress_dict(self):
stress_dict = dict(list(zip([IndependentStrain(def_matrix) for def_matrix
in self.def_stress_dict['deformations']],
[Stress(stress_matrix) for stress_matrix
in self.def_stress_dict['stresses']])))
with warnings.catch_warnings(record = True):
et_from_sd = ElasticTensor.from_stress_dict(stress_dict)
self.assertArrayAlmostEqual(et_from_sd.voigt_symmetrized.round(2),
self.elastic_tensor_1)
def run_task(self, fw_spec):
# Get optimized structure
# TODO: will this find the correct path if the workflow is rerun from the start?
optimize_loc = fw_spec["calc_locs"][0]["path"]
logger.info("PARSING INITIAL OPTIMIZATION DIRECTORY: {}".format(optimize_loc))
drone = VaspDrone()
optimize_doc = drone.assimilate(optimize_loc)
opt_struct = Structure.from_dict(optimize_doc["calcs_reversed"][0]["output"]["structure"])
d = {"analysis": {}, "deformation_tasks": fw_spec["deformation_tasks"],
"initial_structure": self['structure'].as_dict(),
"optimized_structure": opt_struct.as_dict()}
if fw_spec.get("tags",None):
d["tags"] = fw_spec["tags"]
dtypes = fw_spec["deformation_tasks"].keys()
defos = [fw_spec["deformation_tasks"][dtype]["deformation_matrix"]
for dtype in dtypes]
stresses = [fw_spec["deformation_tasks"][dtype]["stress"] for dtype in dtypes]
stress_dict = {IndependentStrain(defo) : Stress(stress) for defo, stress
in zip(defos, stresses)}
logger.info("ANALYZING STRESS/STRAIN DATA")
# DETERMINE IF WE HAVE 6 "UNIQUE" deformations
if len(set([de[:3] for de in dtypes])) == 6:
# Perform Elastic tensor fitting and analysis
result = ElasticTensor.from_stress_dict(stress_dict)
d["elastic_tensor"] = result.voigt.tolist()
kg_average = result.kg_average
d.update({"K_Voigt": kg_average[0], "G_Voigt": kg_average[1],
"K_Reuss": kg_average[2], "G_Reuss": kg_average[3],
"K_Voigt_Reuss_Hill": kg_average[4],
"G_Voigt_Reuss_Hill": kg_average[5]})
d["universal_anisotropy"] = result.universal_anisotropy
d["homogeneous_poisson"] = result.homogeneous_poisson
else:
raise ValueError("Fewer than 6 unique deformations")
d["state"] = "successful"
# Save analysis results in json or db
db_file = env_chk(self.get('db_file'), fw_spec)
if not db_file:
with open("elasticity.json", "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
db = MMVaspDb.from_db_file(db_file, admin=True)
db.collection = db.db["elasticity"]
db.collection.insert_one(d)
logger.info("ELASTIC ANALYSIS COMPLETE")
return FWAction()
def test_from_stress_dict(self):
stress_dict = dict(
list(
zip([
IndependentStrain(def_matrix)
for def_matrix in self.def_stress_dict['deformations']
], [
Stress(stress_matrix)
for stress_matrix in self.def_stress_dict['stresses']
])))
minimal_sd = {
k: v
for k, v in stress_dict.items()
if (abs(k[k.independent_deformation] - 0.015) < 1e-10
or abs(k[k.independent_deformation] - 0.01005) < 1e-10)
}
with warnings.catch_warnings(record=True):
et_from_sd = ElasticTensor.from_stress_dict(stress_dict)
et_from_minimal_sd = ElasticTensor.from_stress_dict(minimal_sd)
self.assertArrayAlmostEqual(et_from_sd.voigt_symmetrized.round(2),
self.elastic_tensor_1)
self.assertAlmostEqual(50.63394169, et_from_minimal_sd[0, 0, 0, 0])
def run_task(self, fw_spec):
db_dir = os.environ['DB_LOC']
db_path = os.path.join(db_dir, 'tasks_db.json')
i = fw_spec['original_task_id']
with open(db_path) as f:
db_creds = json.load(f)
connection = MongoClient(db_creds['host'], db_creds['port'])
tdb = connection[db_creds['database']]
tdb.authenticate(db_creds['admin_user'], db_creds['admin_password'])
tasks = tdb[db_creds['collection']]
elasticity = tdb['elasticity']
ndocs = tasks.find({"original_task_id": i,
"state":"successful"}).count()
existing_doc = elasticity.find_one({"relaxation_task_id" : i})
if existing_doc:
print "Updating: " + i
else:
print "New material: " + i
d = {"analysis": {}, "error": [], "warning": []}
d["ndocs"] = ndocs
o = tasks.find_one({"task_id" : i},
{"pretty_formula" : 1, "spacegroup" : 1,
"snl" : 1, "snl_final" : 1, "run_tags" : 1})
if not o:
raise ValueError("Cannot find original task id")
# Get stress from deformed structure
d["deformation_tasks"] = {}
ss_dict = {}
for k in tasks.find({"original_task_id": i},
{"deformation_matrix":1,
"calculations.output":1,
"state":1, "task_id":1}):
defo = k['deformation_matrix']
d_ind = np.nonzero(defo - np.eye(3))
delta = Decimal((defo - np.eye(3))[d_ind][0])
# Normal deformation
if d_ind[0] == d_ind[1]:
dtype = "_".join(["d", str(d_ind[0][0]),
"{:.0e}".format(delta)])
# Shear deformation
else:
dtype = "_".join(["s", str(d_ind[0] + d_ind[1]),
"{:.0e}".format(delta)])
sm = IndependentStrain(defo)
if dtype in d["deformation_tasks"].keys():
print "old_task: {}".format(d["deformation_tasks"][dtype]["task_id"])
print "new_task: {}".format(k["task_id"])
raise ValueError("Duplicate deformation task in database.")
d["deformation_tasks"][dtype] = {"state" : k["state"],
"deformation_matrix" : defo,
"strain" : sm.tolist(),
"task_id": k["task_id"]}
if k["state"] == "successful":
st = Stress(k["calculations"][-1]["output"] \
["ionic_steps"][-1]["stress"])
ss_dict[sm] = st
d["snl"] = o["snl"]
if "run_tags" in o.keys():
d["run_tags"] = o["run_tags"]
for tag in o["run_tags"]:
if isinstance(tag, dict):
if "input_id" in tag.keys():
d["input_mp_id"] = tag["input_id"]
d["snl_final"] = o["snl_final"]
d["pretty_formula"] = o["pretty_formula"]
# Old input mp-id style
if o["snl"]["about"].get("_mp_id"):
d["material_id"] = o["snl"]["about"]["_mp_id"]
# New style
elif "input_mp_id" in d:
d["material_id"] = d["input_mp_id"]
else:
d["material_id"] = None
d["relaxation_task_id"] = i
calc_struct = Structure.from_dict(o["snl_final"])
# TODO:
# JHM: This test is unnecessary at the moment, but should be redone
"""
conventional = is_conventional(calc_struct)
if conventional:
d["analysis"]["is_conventional"] = True
else:
d["analysis"]["is_conventional"] = False
"""
d["spacegroup"]=o.get("spacegroup", "Unknown")
if ndocs >= 20:
# Perform Elastic tensor fitting and analysis
result = ElasticTensor.from_stress_dict(ss_dict)
d["elastic_tensor"] = result.voigt.tolist()
kg_average = result.kg_average
d.update({"K_Voigt":kg_average[0], "G_Voigt":kg_average[1],
"K_Reuss":kg_average[2], "G_Reuss":kg_average[3],
"K_Voigt_Reuss_Hill":kg_average[4],
"G_Voigt_Reuss_Hill":kg_average[5]})
d["universal_anisotropy"] = result.universal_anisotropy
d["homogeneous_poisson"] = result.homogeneous_poisson
if ndocs < 24:
d["warning"].append("less than 24 tasks completed")
# Perform filter checks
symm_t = result.voigt_symmetrized
d["symmetrized_tensor"] = symm_t.voigt.tolist()
d["analysis"]["not_rare_earth"] = True
for s in calc_struct.species:
if s.is_rare_earth_metal:
d["analysis"]["not_rare_earth"] = False
eigvals = np.linalg.eigvals(symm_t.voigt)
eig_positive = np.all((eigvals > 0) & np.isreal(eigvals))
d["analysis"]["eigval_positive"] = bool(eig_positive)
c11 = symm_t.voigt[0][0]
c12 = symm_t.voigt[0][1]
c13 = symm_t.voigt[0][2]
c23 = symm_t.voigt[1][2]
d["analysis"]["c11_c12"]= not (abs((c11-c12)/c11) < 0.05
or c11 < c12)
d["analysis"]["c11_c13"]= not (abs((c11-c13)/c11) < 0.05
or c11 < c13)
d["analysis"]["c11_c23"]= not (abs((c11-c23)/c11) < 0.1
or c11 < c23)
d["analysis"]["K_R"] = not (d["K_Reuss"] < 2)
d["analysis"]["G_R"] = not (d["G_Reuss"] < 2)
d["analysis"]["K_V"] = not (d["K_Voigt"] < 2)
d["analysis"]["G_V"] = not (d["G_Voigt"] < 2)
filter_state = np.all(d["analysis"].values())
d["analysis"]["filter_pass"] = bool(filter_state)
d["analysis"]["eigval"] = list(eigvals)
# TODO:
# JHM: eventually we can reintroduce the IEEE conversion
# but as of now it's not being used, and it should
# be in pymatgen
"""
# IEEE Conversion
try:
ieee_tensor = IEEE_conversion.get_ieee_tensor(struct_final, result)
d["elastic_tensor_IEEE"] = ieee_tensor[0].tolist()
d["analysis"]["IEEE"] = True
except Exception as e:
d["elastic_tensor_IEEE"] = None
d["analysis"]["IEEE"] = False
d["error"].append("Unable to get IEEE tensor: {}".format(e))
"""
# Add thermal properties
nsites = calc_struct.num_sites
volume = calc_struct.volume
natoms = calc_struct.composition.num_atoms
weight = calc_struct.composition.weight
num_density = 1e30 * nsites / volume
mass_density = 1.6605e3 * nsites * volume * weight / \
(natoms * volume)
tot_mass = sum([e.atomic_mass for e in calc_struct.species])
avg_mass = 1.6605e-27 * tot_mass / natoms
y_mod = 9e9 * result.k_vrh * result.g_vrh / \
(3. * result.k_vrh * result.g_vrh)
trans_v = 1e9 * result.k_vrh / mass_density**0.5
long_v = 1e9 * result.k_vrh + \
4./3. * result.g_vrh / mass_density**0.5
clarke = 0.87 * 1.3806e-23 * avg_mass**(-2./3.) * \
mass_density**(1./6.) * y_mod**0.5
cahill = 1.3806e-23 / 2.48 * num_density**(2./3.) * long_v + \
2 * trans_v
snyder_ac = 0.38483 * avg_mass * \
(long_v + 2./3.*trans_v)**3. / \
(300. * num_density**(-2./3.) * nsites**(1./3.))
snyder_opt = 1.66914e-23 * (long_v + 2./3.*trans_v) / \
num_density**(-2./3.) * \
(1 - nsites**(-1./3.))
snyder_total = snyder_ac + snyder_opt
debye = 2.489e-11 * avg_mass**(-1./3.) * \
mass_density**(-1./6.) * y_mod**0.5
d["thermal"]={"num_density" : num_density,
"mass_density" : mass_density,
"avg_mass" : avg_mass,
"num_atom_per_unit_formula" : natoms,
"youngs_modulus" : y_mod,
"trans_velocity" : trans_v,
"long_velocity" : long_v,
"clarke" : clarke,
"cahill" : cahill,
"snyder_acou_300K" : snyder_ac,
"snyder_opt" : snyder_opt,
"snyder_total" : snyder_total,
"debye": debye
}
else:
d['state'] = "Fewer than 20 successful tasks completed"
return FWAction()
if o["snl"]["about"].get("_kpoint_density"):
d["kpoint_density"]= o["snl"]["about"].get("_kpoint_density")
if d["error"]:
raise ValueError("Elastic analysis failed: {}".format(d["error"]))
elif d["analysis"]["filter_pass"]:
d["state"] = "successful"
else:
d["state"] = "filter_failed"
elasticity.update({"relaxation_task_id": d["relaxation_task_id"]},
d, upsert=True)
return FWAction()
import pymatgen as mg
from pymatgen.analysis.elasticity.strain import DeformedStructureSet
import os
from shutil import copyfile
from pymatgen.io.vasp.outputs import Vasprun
from pymatgen.analysis.elasticity.stress import Stress
from pymatgen.analysis.elasticity.elastic import ElasticTensor
structure = mg.Structure.from_file("POSCAR")
def_set = DeformedStructureSet(structure)
strains = def_set.as_strain_dict()
calculations = []
for x in range(1, 25):
calculations.append('poscar%s' % x)
match_dict = {}
for calc in calculations:
struct = mg.Structure.from_file(calc + "/POSCAR")
vrun = Vasprun(calc + '/vasprun.xml', parse_dos=False, parse_eigen=False)
stress = Stress(vrun.ionic_steps[-1]['stress'])
for strain in strains:
if strains[strain].lattice == struct.lattice:
match_dict[strain] = stress
elastics = ElasticTensor.from_stress_dict(match_dict)
with open("elasts.txt", 'w') as f:
f.write(str(elastics.voigt) + '\n')
f.write(str(elastics.k_voigt) + '\n')
def run_task(self, fw_spec):
# Get optimized structure
optimize_loc = fw_spec["calc_locs"][0]["path"]
logger.info("PARSING INITIAL OPTIMIZATION DIRECTORY: {}".format(optimize_loc))
drone = VaspDrone()
optimize_doc = drone.assimilate(optimize_loc)
opt_struct = Structure.from_dict(
optimize_doc["calcs_reversed"][0]["output"]["structure"])
deformations = fw_spec['deformations']
d = {"analysis": {}, "deformation_tasks": {},
"initial_structure": self['structure'].as_dict(),
"optimized_structure": opt_struct.as_dict()}
stress_dict = {}
dtypes = []
for deformation in deformations:
defo = deformation['deformation']
d_ind = np.nonzero(defo - np.eye(3))
delta = Decimal((defo - np.eye(3))[d_ind][0])
# Shorthand is d_X_V, X is voigt index, V is value
dtype = "_".join(["d", str(reverse_voigt_map[d_ind][0]),
"{:.0e}".format(delta)])
strain = IndependentStrain(defo)
stress = Stress(deformation['stress'])
d["deformation_tasks"][dtype] = {'deformation_matrix': defo,
'strain': strain.tolist(),
'stress': deformation['stress']}
dtypes.append(dtype)
stress_dict[strain] = stress
logger.info("ANALYZING STRESS/STRAIN DATA")
# DETERMINE IF WE HAVE 6 "UNIQUE" deformations
if len(set([de[:3] for de in dtypes])) == 6:
# Perform Elastic tensor fitting and analysis
result = ElasticTensor.from_stress_dict(stress_dict)
d["elastic_tensor"] = result.voigt.tolist()
kg_average = result.kg_average
d.update({"K_Voigt": kg_average[0], "G_Voigt": kg_average[1],
"K_Reuss": kg_average[2], "G_Reuss": kg_average[3],
"K_Voigt_Reuss_Hill": kg_average[4],
"G_Voigt_Reuss_Hill": kg_average[5]})
d["universal_anisotropy"] = result.universal_anisotropy
d["homogeneous_poisson"] = result.homogeneous_poisson
else:
raise ValueError("Fewer than 6 unique deformations")
d["state"] = "successful"
# Save analysis results in json or db
db_file = env_chk(self.get('db_file'), fw_spec)
if not db_file:
with open("elasticity.json", "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
db = MMDb.from_db_file(db_file, admin=True)
db.collection = db.db["elasticity"]
db.collection.insert_one(d)
logger.info("ELASTIC ANALYSIS COMPLETE")
return FWAction()
def run_task(self, fw_spec):
d = {
"analysis": {},
"deformation_tasks": fw_spec["deformation_tasks"],
"initial_structure": self['structure'].as_dict()
}
# Get optimized structure
calc_locs_opt = [
cl for cl in fw_spec['calc_locs'] if 'optimize' in cl['name']
]
if calc_locs_opt:
optimize_loc = calc_locs_opt[-1]['path']
logger.info("Parsing initial optimization directory: {}".format(
optimize_loc))
drone = VaspDrone()
optimize_doc = drone.assimilate(optimize_loc)
opt_struct = Structure.from_dict(
optimize_doc["calcs_reversed"][0]["output"]["structure"])
d.update({"optimized_structure": opt_struct.as_dict()})
# TODO: @montoyjh: does the below have anything to do with elastic tensor? If not, try
# the more general fw_spec_field approach in the VaspToDb rather than hard-coding the
# tags insertion here. -computron
if fw_spec.get("tags", None):
d["tags"] = fw_spec["tags"]
results = fw_spec["deformation_tasks"].values()
defos = [r["deformation_matrix"] for r in results]
stresses = [r["stress"] for r in results]
strains = np.array([Strain(r["strain"]).voigt for r in results])
stress_dict = {
IndependentStrain(defo): Stress(stress)
for defo, stress in zip(defos, stresses)
}
logger.info("Analyzing stress/strain data")
# Determine if we have 6 unique deformations
# TODO: @montoyjh: what if it's a cubic system? don't need 6. -computron
if np.linalg.matrix_rank(strains) == 6:
# Perform Elastic tensor fitting and analysis
result = ElasticTensor.from_stress_dict(stress_dict)
d["elastic_tensor"] = result.voigt.tolist()
d.update(result.property_dict)
else:
raise ValueError("Fewer than 6 unique deformations")
d["state"] = "successful"
# Save analysis results in json or db
db_file = env_chk(self.get('db_file'), fw_spec)
if not db_file:
with open("elasticity.json", "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
db = VaspCalcDb.from_db_file(db_file, admin=True)
db.collection = db.db["elasticity"]
db.collection.insert_one(d)
logger.info("Elastic analysis complete.")
return FWAction()
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
"""
root_mats = [
mat for mat in item["mats"]
if mat.get("inputs", {}).get("structure optimization", None)
]
deform_mats = [mat for mat in item["mats"] if mat not in root_mats]
docs = []
# TODO: What structure matcher parameters to use?
# TODO: Should SM parameters be configurable?
sm = StructureMatcher(primitive_cell=True,
scale=True,
attempt_supercell=False,
allow_subset=False,
comparator=ElementComparator())
for r_mat in root_mats:
# Enumerate over all deformations
r_struc = Structure.from_dict(r_mat['initial_structure'])
defos = []
stresses = []
strains = []
m_ids = []
for d_mat in deform_mats:
# Find deformation matrix
d_struc = Structure.from_dict(d_mat["initial_structure"])
transform_matrix = np.transpose(
np.linalg.solve(r_struc.lattice.matrix,
d_struc.lattice.matrix))
# apply deformation matrix to root_mat and check if the two structures match
dfm = Deformation(transform_matrix)
dfm_struc = dfm.apply_to_structure(r_struc)
# if match store stress and strain matrix
if sm.fit(dfm_struc, d_struc):
# This is a deformtion of the root struc
defos.append(dfm)
stresses.append(d_mat['stress'])
strains.append(dfm.green_lagrange_strain)
m_ids.append(d_mat['material_id'])
stress_dict = {
IndependentStrain(defo): Stress(stress)
for defo, stress in zip(defos, stresses)
}
self.__logger.info("Analyzing stress/strain data")
# Determine if we have 6 unique deformations
if np.linalg.matrix_rank(strains) == 6:
# Perform Elastic tensor fitting and analysis
result = ElasticTensor.from_stress_dict(stress_dict)
d = {
"material_id": r_mat["material_id"],
"elasticity": {
"elastic_tensor": result.voigt.tolist(),
"material_ids": m_ids
}
}
d["elasticity"].update(result.property_dict)
docs.append(d)
else:
self.__logger.warn(
"Fewer than 6 unique deformations for {}".format(
r_mat["material_id"]))
return docs
def run_task(self, fw_spec):
# Get optimized structure
# TODO: will this find the correct path if the workflow is rerun from the start?
optimize_loc = fw_spec["calc_locs"][0]["path"]
logger.info(
"PARSING INITIAL OPTIMIZATION DIRECTORY: {}".format(optimize_loc))
drone = VaspDrone()
optimize_doc = drone.assimilate(optimize_loc)
opt_struct = Structure.from_dict(
optimize_doc["calcs_reversed"][0]["output"]["structure"])
d = {
"analysis": {},
"deformation_tasks": fw_spec["deformation_tasks"],
"initial_structure": self['structure'].as_dict(),
"optimized_structure": opt_struct.as_dict()
}
if fw_spec.get("tags", None):
d["tags"] = fw_spec["tags"]
dtypes = fw_spec["deformation_tasks"].keys()
defos = [
fw_spec["deformation_tasks"][dtype]["deformation_matrix"]
for dtype in dtypes
]
stresses = [
fw_spec["deformation_tasks"][dtype]["stress"] for dtype in dtypes
]
stress_dict = {
IndependentStrain(defo): Stress(stress)
for defo, stress in zip(defos, stresses)
}
logger.info("ANALYZING STRESS/STRAIN DATA")
# DETERMINE IF WE HAVE 6 "UNIQUE" deformations
if len(set([de[:3] for de in dtypes])) == 6:
# Perform Elastic tensor fitting and analysis
result = ElasticTensor.from_stress_dict(stress_dict)
d["elastic_tensor"] = result.voigt.tolist()
kg_average = result.kg_average
d.update({
"K_Voigt": kg_average[0],
"G_Voigt": kg_average[1],
"K_Reuss": kg_average[2],
"G_Reuss": kg_average[3],
"K_Voigt_Reuss_Hill": kg_average[4],
"G_Voigt_Reuss_Hill": kg_average[5]
})
d["universal_anisotropy"] = result.universal_anisotropy
d["homogeneous_poisson"] = result.homogeneous_poisson
else:
raise ValueError("Fewer than 6 unique deformations")
d["state"] = "successful"
# Save analysis results in json or db
db_file = env_chk(self.get('db_file'), fw_spec)
if not db_file:
with open("elasticity.json", "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
db = MMVaspDb.from_db_file(db_file, admin=True)
db.collection = db.db["elasticity"]
db.collection.insert_one(d)
logger.info("ELASTIC ANALYSIS COMPLETE")
return FWAction()
