def run_task(self, fw_spec):
from pymatgen.analysis.eos import EOS
tag = self["tag"]
db_file = env_chk(self.get("db_file"), fw_spec)
summary_dict = {"eos": self["eos"]}
mmdb = MMVaspDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one({"task_label": "{} structure optimization".format(tag)})
structure = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
summary_dict["structure"] = structure.as_dict()
# get the data(energy, volume, force constant) from the deformation runs
docs = mmdb.collection.find({"task_label": {"$regex": "{} bulk_modulus*".format(tag)},
"formula_pretty": structure.composition.reduced_formula})
energies = []
volumes = []
for d in docs:
s = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
energies.append(d["calcs_reversed"][-1]["output"]['energy'])
volumes.append(s.volume)
summary_dict["energies"] = energies
summary_dict["volumes"] = volumes
# fit the equation of state
eos = EOS(self["eos"])
eos_fit = eos.fit(volumes, energies)
summary_dict["results"] = dict(eos_fit.results)
with open("bulk_modulus.json", "w") as f:
f.write(json.dumps(summary_dict, default=DATETIME_HANDLER))
logger.info("BULK MODULUS CALCULATION COMPLETE")
def run_task(self, fw_spec):
tag = self["tag"]
db_file = env_chk(self.get("db_file"), fw_spec)
t_step = self.get("t_step", 10)
t_min = self.get("t_min", 0)
t_max = self.get("t_max", 1000)
mesh = self.get("mesh", [20, 20, 20])
eos = self.get("eos", "vinet")
qha_type = self.get("qha_type", "debye_model")
pressure = self.get("pressure", 0.0)
gibbs_summary_dict = {}
mmdb = MMVaspDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one({"task_label": "{} structure optimization".format(tag)})
structure = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
gibbs_summary_dict["structure"] = structure.as_dict()
# get the data(energy, volume, force constant) from the deformation runs
docs = mmdb.collection.find({"task_label": {"$regex": "{} gibbs*".format(tag)},
"formula_pretty": structure.composition.reduced_formula})
energies = []
volumes = []
force_constants = []
for d in docs:
s = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
energies.append(d["calcs_reversed"][-1]["output"]['energy'])
if qha_type not in ["debye_model"]:
force_constants.append(d["calcs_reversed"][-1]["output"]['force_constants'])
volumes.append(s.volume)
gibbs_summary_dict["energies"] = energies
gibbs_summary_dict["volumes"] = volumes
if qha_type not in ["debye_model"]:
gibbs_summary_dict["force_constants"] = force_constants
G, T = None, None
# use debye model
if qha_type in ["debye_model"]:
from atomate.tools.analysis import get_debye_model_gibbs
G, T = get_debye_model_gibbs(energies, volumes, structure, t_min, t_step, t_max, eos,
pressure)
# use the phonopy interface
else:
from atomate.tools.analysis import get_phonopy_gibbs
G, T = get_phonopy_gibbs(energies, volumes, force_constants, structure, t_min, t_step,
t_max, mesh, eos, pressure)
gibbs_summary_dict["G"] = G
gibbs_summary_dict["T"] = T
with open("gibbs.json", "w") as f:
f.write(json.dumps(gibbs_summary_dict, default=DATETIME_HANDLER))
logger.info("GIBBS FREE ENERGY CALCULATION COMPLETE")
def run_task(self, fw_spec):
nm_norms = np.array(fw_spec["normalmodes"]["norms"])
nm_eigenvals = np.array(fw_spec["normalmodes"]["eigenvals"])
structure = fw_spec["normalmodes"]["structure"]
masses = np.array([site.specie.data['Atomic mass'] for site in structure])
# the eigenvectors read from vasprun.xml are not divided by sqrt(M_i)
nm_norms = nm_norms / np.sqrt(masses)
# To get the actual eigenvals, the values read from vasprun.xml must be multiplied by -1.
# frequency_i = sqrt(-e_i)
# To convert the frequency to THZ: multiply sqrt(-e_i) by 15.633
# To convert the frequency to cm^-1: multiply sqrt(-e_i) by 82.995
nm_frequencies = np.sqrt(np.abs(nm_eigenvals)) * 82.995 # cm^-1
d = {"structure": structure.as_dict(),
"normalmodes": {"eigenvals": fw_spec["normalmodes"]["eigenvals"],
"eigenvecs": fw_spec["normalmodes"]["eigenvecs"]
},
"frequencies": nm_frequencies.tolist()
}
mode_disps = fw_spec["raman_epsilon"].keys()
# store the displacement & epsilon for each mode in a dictionary
modes_eps_dict = defaultdict(list)
for md in mode_disps:
modes_eps_dict[fw_spec["raman_epsilon"][md]["mode"]].append(
[fw_spec["raman_epsilon"][md]["displacement"],
fw_spec["raman_epsilon"][md]["epsilon"]])
# raman tensor = finite difference derivative of epsilon wrt displacement.
raman_tensor_dict = {}
scale = np.sqrt(structure.volume/2.0) / 4.0 / np.pi
for k, v in modes_eps_dict.items():
raman_tensor = (np.array(v[0][1]) - np.array(v[1][1])) / (v[0][0] - v[1][0])
# frequency in cm^-1
omega = nm_frequencies[k]
if nm_eigenvals[k] > 0:
logger.warn("Mode: {} is UNSTABLE. Freq(cm^-1) = {}".format(k, -omega))
raman_tensor = scale * raman_tensor * np.sum(nm_norms[k]) / np.sqrt(omega)
raman_tensor_dict[str(k)] = raman_tensor.tolist()
d["raman_tensor"] = raman_tensor_dict
d["state"] = "successful"
# store the results
db_file = env_chk(self.get("db_file"), fw_spec)
if not db_file:
with open("raman.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["raman"]
db.collection.insert_one(d)
logger.info("RAMAN TENSOR CALCULATION COMPLETE")
logger.info("The frequencies are in the units of cm^-1")
logger.info("To convert the frequency to THz: multiply by 0.1884")
return FWAction()
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 run_task(self, fw_spec):
btrap_dir = os.path.join(os.getcwd(), "boltztrap")
bta = BoltztrapAnalyzer.from_files(btrap_dir)
d = bta.as_dict()
d["boltztrap_dir"] = btrap_dir
# trim the output
for x in ['cond', 'seebeck', 'kappa', 'hall', 'mu_steps',
'mu_doping', 'carrier_conc']:
del d[x]
if not self.get("hall_doping"):
del d["hall_doping"]
d["scissor"] = bta.intrans["scissor"]
# add the structure
bandstructure_dir = os.getcwd()
v, o = get_vasprun_outcar(bandstructure_dir, parse_eigen=False,
parse_dos=False)
structure = v.final_structure
d["structure"] = structure.as_dict()
d.update(get_meta_from_structure(structure))
d["bandstructure_dir"] = bandstructure_dir
# add the spacegroup
sg = SpacegroupAnalyzer(Structure.from_dict(d["structure"]), 0.1)
d["spacegroup"] = {"symbol": sg.get_space_group_symbol(),
"number": sg.get_space_group_number(),
"point_group": sg.get_point_group_symbol(),
"source": "spglib",
"crystal_system": sg.get_crystal_system(),
"hall": sg.get_hall()}
d["created_at"] = datetime.utcnow()
db_file = env_chk(self.get('db_file'), fw_spec)
if not db_file:
with open(os.path.join(btrap_dir, "boltztrap.json"), "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
mmdb = MMVaspDb.from_db_file(db_file, admin=True)
# dos gets inserted into GridFS
dos = json.dumps(d["dos"], cls=MontyEncoder)
fsid, compression = mmdb.insert_gridfs(
dos, collection="dos_boltztrap_fs", compress=True)
d["dos_boltztrap_fs_id"] = fsid
del d["dos"]
mmdb.db.boltztrap.insert(d)
def run_task(self, fw_spec):
from atomate.tools.analysis import get_phonopy_thermal_expansion
tag = self["tag"]
db_file = env_chk(self.get("db_file"), fw_spec)
t_step = self.get("t_step", 10)
t_min = self.get("t_min", 0)
t_max = self.get("t_max", 1000)
mesh = self.get("mesh", [20, 20, 20])
eos = self.get("eos", "vinet")
pressure = self.get("pressure", 0.0)
summary_dict = {}
mmdb = MMVaspDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one({"task_label": "{} structure optimization".format(tag)})
structure = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
summary_dict["structure"] = structure.as_dict()
# get the data(energy, volume, force constant) from the deformation runs
docs = mmdb.collection.find({"task_label": {"$regex": "{} thermal_expansion*".format(tag)},
"formula_pretty": structure.composition.reduced_formula})
energies = []
volumes = []
force_constants = []
for d in docs:
s = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
energies.append(d["calcs_reversed"][-1]["output"]['energy'])
volumes.append(s.volume)
force_constants.append(d["calcs_reversed"][-1]["output"]['force_constants'])
summary_dict["energies"] = energies
summary_dict["volumes"] = volumes
summary_dict["force_constants"] = force_constants
alpha, T = get_phonopy_thermal_expansion(energies, volumes, force_constants, structure,
t_min, t_step, t_max, mesh, eos, pressure)
summary_dict["alpha"] = alpha
summary_dict["T"] = T
with open("thermal_expansion.json", "w") as f:
f.write(json.dumps(summary_dict, default=DATETIME_HANDLER))
logger.info("THERMAL EXPANSION COEFF CALCULATION COMPLETE")
def run_task(self, fw_spec):
# get the directory that contains the VASP dir to parse
calc_dir = os.getcwd()
if "calc_dir" in self:
calc_dir = self["calc_dir"]
elif self.get("calc_loc"):
calc_dir = get_calc_loc(self["calc_loc"], fw_spec["calc_locs"])["path"]
# parse the VASP directory
logger.info("PARSING DIRECTORY: {}".format(calc_dir))
# get the database connection
db_file = env_chk(self.get('db_file'), fw_spec)
drone = VaspDrone(additional_fields=self.get("additional_fields"),
parse_dos=self.get("parse_dos", False), compress_dos=1,
bandstructure_mode=self.get("bandstructure_mode", False), compress_bs=1)
# assimilate (i.e., parse)
task_doc = drone.assimilate(calc_dir)
# Check for additional fields to add in the fw_spec
if self.get("fw_spec_field"):
task_doc.update(fw_spec[self.get("fw_spec_field")])
# db insertion
if not db_file:
with open("task.json", "w") as f:
f.write(json.dumps(task_doc, default=DATETIME_HANDLER))
else:
mmdb = MMVaspDb.from_db_file(db_file, admin=True)
# insert dos into GridFS
if self.get("parse_dos") and "calcs_reversed" in task_doc:
for idx, x in enumerate(task_doc["calcs_reversed"]):
if "dos" in task_doc["calcs_reversed"][idx]:
if idx == 0: # only store most recent DOS
dos = json.dumps(task_doc["calcs_reversed"][idx]["dos"], cls=MontyEncoder)
gfs_id, compression_type = mmdb.insert_gridfs(dos, "dos_fs")
task_doc["calcs_reversed"][idx]["dos_compression"] = compression_type
task_doc["calcs_reversed"][idx]["dos_fs_id"] = gfs_id
del task_doc["calcs_reversed"][idx]["dos"]
# insert band structure into GridFS
if self.get("bandstructure_mode") and "calcs_reversed" in task_doc:
for idx, x in enumerate(task_doc["calcs_reversed"]):
if "bandstructure" in task_doc["calcs_reversed"][idx]:
if idx == 0: # only store most recent band structure
bs = json.dumps(task_doc["calcs_reversed"][idx]["bandstructure"], cls=MontyEncoder)
gfs_id, compression_type = mmdb.insert_gridfs(bs, "bandstructure_fs")
task_doc["calcs_reversed"][idx]["bandstructure_compression"] = compression_type
task_doc["calcs_reversed"][idx]["bandstructure_fs_id"] = gfs_id
del task_doc["calcs_reversed"][idx]["bandstructure"]
# insert the task document
t_id = mmdb.insert(task_doc)
logger.info("Finished parsing with task_id: {}".format(t_id))
if self.get("defuse_unsuccessful", True):
defuse_children = (task_doc["state"] != "successful")
else:
defuse_children = False
return FWAction(stored_data={"task_id": task_doc.get("task_id", None)},
defuse_children=defuse_children)
