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 _match_material(self, doc):
"""
Returns the material_id that has the same structure as this doc as
determined by the structure matcher. Returns None if no match.
Args:
doc: a JSON-like document
Returns:
(int) matching material_id or None
"""
formula = doc["formula_reduced_abc"]
sgnum = doc["spacegroup"]["number"]
for m in self._materials.find({"formula_reduced_abc": formula, "sg_number": sgnum},
{"structure": 1, "material_id": 1}):
m_struct = Structure.from_dict(m["structure"])
t_struct = Structure.from_dict(doc["structure"])
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=True, scale=True,
attempt_supercell=False, allow_subset=False,
comparator=ElementComparator())
if sm.fit(m_struct, t_struct):
return m["material_id"]
return None
def test_ordering_enumeration(self):
# simple afm
structure = Structure.from_file(os.path.join(ref_dir, "ordering/LaMnO3.json"))
wf = MagneticOrderingsWF(structure)
self.assertEqual(wf.input_origin, "afm")
# ferrimagnetic (Cr produces net spin)
structure = Structure.from_file(os.path.join(ref_dir, "ordering/Cr2NiO4.json"))
wf = MagneticOrderingsWF(structure)
self.assertEqual(wf.input_origin, "ferri_by_Cr")
# antiferromagnetic on single magnetic site
structure = Structure.from_file(os.path.join(ref_dir, "ordering/Cr2WO6.json"))
wf = MagneticOrderingsWF(structure)
self.assertEqual(wf.input_origin, "afm_by_Cr")
# afm requiring large cell size
# (enable for further development of workflow, too slow for CI)
# structure = Structure.from_file(os.path.join(ref_dir, "CuO.json"))
# wf = MagneticOrderingsWF(structure, default_magmoms={'Cu': 1.73},
# transformation_kwargs={'max_cell_size': 4})
# self.assertEqual(wf.input_origin, "afm")
# antiferromagnetic by structural motif
structure = Structure.from_file(os.path.join(ref_dir, "ordering/Ca3Co2O6.json"))
wf = MagneticOrderingsWF(
structure,
strategies=("antiferromagnetic_by_motif",),
# this example just misses default cut-off, so do not truncate
truncate_by_symmetry=False,
transformation_kwargs={"max_cell_size": 2},
)
self.assertEqual(wf.input_origin, "afm_by_motif_2a")
def test_unique_structure_substitutions( self ):
# integration test
# Create a pymatgen structure with 16 sites in a 4x4 square grid
coords = np.array( [ [ 0.0, 0.0, 0.0 ],
[ 0.25, 0.0, 0.0 ],
[ 0.5, 0., 0.0 ],
[ 0.75, 0.0, 0.0 ],
[ 0.0, 0.25, 0.0 ],
[ 0.25, 0.25, 0.0 ],
[ 0.5, 0.25, 0.0 ],
[ 0.75, 0.25, 0.0 ],
[ 0.0, 0.5, 0.0 ],
[ 0.25, 0.5, 0.0 ],
[ 0.5, 0.5, 0.0 ],
[ 0.75, 0.5, 0.0 ],
[ 0.0, 0.75, 0.0 ],
[ 0.25, 0.75, 0.0 ],
[ 0.5, 0.75, 0.0 ],
[ 0.75, 0.75, 0.0 ] ] )
atom_list = [ 'Li' ] * len( coords )
lattice = Lattice.from_parameters( a = 3.0, b=3.0, c=3.0, alpha=90, beta=90, gamma=90 )
parent_structure = Structure( lattice, atom_list, coords )
parent_structure.replace( 0, 'O' ) # substitute one site with 'O'
ns = unique_structure_substitutions( parent_structure, 'Li', { 'Na':1, 'Li':14 } )
self.assertEqual( len( ns ), 5 )
distances = np.array( sorted( [ s.get_distance( s.indices_from_symbol('O')[0], s.indices_from_symbol('Na')[0] ) for s in ns ] ) )
np.testing.assert_array_almost_equal( distances, np.array( [ 0.75 , 1.06066 , 1.5 , 1.677051, 2.12132 ] ) )
np.testing.assert_array_equal( np.array( sorted( [ s.number_of_equivalent_configurations for s in ns ] ) ), np.array( [ 1, 2, 4, 4, 4 ] ) )
np.testing.assert_array_equal( np.array( sorted( [ s.full_configuration_degeneracy for s in ns ] ) ), np.array( [ 1, 2, 4, 4, 4 ] ) )
def setUp(self):
with open(os.path.join(test_dir, 'LobsterCompleteDos_spin.json'), 'r') as f:
data_spin = json.load(f)
self.LobsterCompleteDOS_spin = LobsterCompleteDos.from_dict(data_spin)
with open(os.path.join(test_dir, 'LobsterCompleteDos_nonspin.json'), 'r') as f:
data_nonspin = json.load(f)
self.LobsterCompleteDOS_nonspin = LobsterCompleteDos.from_dict(data_nonspin)
with open(os.path.join(test_dir, 'structure_KF.json'), 'r') as f:
data_structure = json.load(f)
self.structure = Structure.from_dict(data_structure)
with open(os.path.join(test_dir, 'LobsterCompleteDos_MnO.json'), 'r') as f:
data_MnO = json.load(f)
self.LobsterCompleteDOS_MnO = LobsterCompleteDos.from_dict(data_MnO)
with open(os.path.join(test_dir, 'LobsterCompleteDos_MnO_nonspin.json'), 'r') as f:
data_MnO_nonspin = json.load(f)
self.LobsterCompleteDOS_MnO_nonspin = LobsterCompleteDos.from_dict(data_MnO_nonspin)
with open(os.path.join(test_dir, 'structure_MnO.json'), 'r') as f:
data_MnO = json.load(f)
self.structure_MnO = Structure.from_dict(data_MnO)
def _match_material(self, taskdoc):
"""
Returns the material_id that has the same structure as this task as
determined by the structure matcher. Returns None if no match.
Args:
taskdoc (dict): a JSON-like task document
Returns:
(int) matching material_id or None
"""
formula = taskdoc["formula_reduced_abc"]
if "parent_structure" in taskdoc: # this is used to intentionally combine multiple data w/same formula but slightly different structure, e.g. from an ordering scheme
t_struct = Structure.from_dict(taskdoc["parent_structure"]["structure"])
q = {"formula_reduced_abc": formula, "parent_structure.spacegroup.number": taskdoc["parent_structure"]["spacegroup"]["number"]}
else:
sgnum = taskdoc["output"]["spacegroup"]["number"]
t_struct = Structure.from_dict(taskdoc["output"]["structure"])
q = {"formula_reduced_abc": formula, "sg_number": sgnum}
for m in self._materials.find(q, {"parent_structure": 1, "structure": 1, "material_id": 1}):
s_dict = m["parent_structure"]["structure"] if "parent_structure" in m else m["structure"]
m_struct = Structure.from_dict(s_dict)
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=True, scale=True,
attempt_supercell=False, allow_subset=False,
comparator=ElementComparator())
if sm.fit(m_struct, t_struct):
return m["material_id"]
return None
def test_disordered_primitive_to_ordered_supercell(self):
sm_atoms = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size='num_atoms',
comparator=OrderDisorderElementComparator())
sm_sites = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=False, scale=True,
attempt_supercell=True,
allow_subset=True,
supercell_size='num_sites',
comparator=OrderDisorderElementComparator())
lp = Lattice.orthorhombic(10, 20, 30)
pcoords = [[0, 0, 0],
[0.5, 0.5, 0.5]]
ls = Lattice.orthorhombic(20, 20, 30)
scoords = [[0, 0, 0],
[0.75, 0.5, 0.5]]
prim = Structure(lp, [{'Na': 0.5}, {'Cl': 0.5}], pcoords)
supercell = Structure(ls, ['Na', 'Cl'], scoords)
supercell.make_supercell([[-1, 1, 0], [0, 1, 1], [1, 0, 0]])
self.assertFalse(sm_sites.fit(prim, supercell))
self.assertTrue(sm_atoms.fit(prim, supercell))
self.assertRaises(ValueError, sm_atoms.get_s2_like_s1, prim, supercell)
self.assertEqual(len(sm_atoms.get_s2_like_s1(supercell, prim)), 4)
def poscar_from_sitelist( configs, labels, sitelists, structure, subset = None ):
"""
Uses pymatgen Structure.to() method to generates POSCAR files for a set of
configurations within a parent structure.
Args:
configs (list): site configurations
labels (list): atom labels to output
sitelist (list): list of sites in fractional coords
structure (pymatgen Structure): Parent structure
subset (Optional [list]): list of atom indices to output
"""
if subset:
species_clean = [ spec for i,spec in enumerate( structure.species ) if i not in subset ]
species_config = [ spec for i,spec in enumerate( structure.species ) if i in subset ]
frac_coords_clean = [ coord for i, coord in enumerate( structure.frac_coords ) if i not in subset ]
clean_structure = Structure( structure.lattice, species_clean, frac_coords_clean )
else:
clean_structure = Structure( structure.lattice, [], [] )
species_config = structure.species
for idx, config in enumerate( configs, start=1 ):
structure_config = clean_structure.copy()
for label in labels:
for pos in config.position( label ):
for sitelist in sitelists:
structure_config.append( species_config[ pos ], sitelist[ pos ] )
structure_config.to( filename="POSCAR_{}.vasp".format( idx ) )
def setUp(self):
self.structure = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3.5),
["Ni"], [[0, 0, 0]])
lattice = Lattice.cubic(3.010)
frac_coords = [[0.00000, 0.00000, 0.00000],
[0.00000, 0.50000, 0.50000],
[0.50000, 0.00000, 0.50000],
[0.50000, 0.50000, 0.00000],
[0.50000, 0.00000, 0.00000],
[0.50000, 0.50000, 0.50000],
[0.00000, 0.00000, 0.50000],
[0.00000, 0.50000, 0.00000]]
species = ['Mg', 'Mg', 'Mg', 'Mg', 'O', 'O', 'O', 'O']
self.MgO = Structure(lattice, species, frac_coords)
slabs = generate_all_slabs(self.structure, max_index=2,
min_slab_size=6.0, min_vacuum_size=15.0,
max_normal_search=1, center_slab=True)
self.slab_dict = {''.join([str(i) for i in slab.miller_index]):
slab for slab in slabs}
self.asf_211 = AdsorbateSiteFinder(self.slab_dict["211"])
self.asf_100 = AdsorbateSiteFinder(self.slab_dict["100"])
self.asf_111 = AdsorbateSiteFinder(self.slab_dict["111"])
self.asf_110 = AdsorbateSiteFinder(self.slab_dict["110"])
self.asf_struct = AdsorbateSiteFinder(
Structure.from_sites(self.slab_dict["111"].sites))
def run_task(self, fw_spec):
prev_dir = fw_spec.get('PREV_DIR', None)
self.custom_params = self.get('custom_params', None)
if isinstance(self["structure"], Structure):
s = self["structure"]
elif isinstance(self["structure"], dict):
s = Structure.from_dict(self["structure"])
else:
s = Structure.from_file(os.path.join(prev_dir, self["structure"]))
vis = load_class("pymatgen.io.vasp.sets", self["vasp_input_set"])(
**self.get("input_set_params", {}))
vis.write_input(s, ".")
# Write Custom KPOINTS settings if necessary
ksettings = self.custom_params.get('user_kpts_settings', None) if isinstance(
self.custom_params, dict) else None
if ksettings:
style = ksettings.get('kpts_style', 'Gamma')
kpoints = ksettings.get('kpts', [16,16,16])
shift = ksettings.get('kpts_shift', [0,0,0])
k = Kpoints(kpts=[kpoints], kpts_shift=shift)
k.style = style
k.write_file("KPOINTS")
def test_structure(self):
quartz = self.quartz.structure
np.testing.assert_array_equal(quartz.lattice.matrix,
[[4.913400, 0, 0],
[-2.456700, 4.255129, 0],
[0, 0, 5.405200]])
self.assertEqual(quartz.formula, "Si3 O6")
self.assertNotIn("molecule-ID", self.quartz.atoms.columns)
ethane = self.ethane.structure
np.testing.assert_array_equal(ethane.lattice.matrix,
np.diag([10.0] * 3))
lbounds = np.array(self.ethane.box.bounds)[:, 0]
coords = self.ethane.atoms[["x", "y", "z"]].values - lbounds
np.testing.assert_array_equal(ethane.cart_coords, coords)
np.testing.assert_array_equal(ethane.site_properties["charge"],
self.ethane.atoms["q"])
tatb = self.tatb.structure
frac_coords = tatb.frac_coords[381]
real_frac_coords = frac_coords - np.floor(frac_coords)
np.testing.assert_array_almost_equal(real_frac_coords,
[0.01553397,
0.71487872,
0.14134139])
co = Structure.from_spacegroup(194,
Lattice.hexagonal(2.50078, 4.03333),
["Co"], [[1/3, 2/3, 1/4]])
ld_co = LammpsData.from_structure(co)
self.assertEqual(ld_co.structure.composition.reduced_formula, "Co")
ni = Structure.from_spacegroup(225, Lattice.cubic(3.50804),
["Ni"], [[0, 0, 0]])
ld_ni = LammpsData.from_structure(ni)
self.assertEqual(ld_ni.structure.composition.reduced_formula, "Ni")
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):
from pymatgen.analysis.eos import EOS
eos = self.get("eos", "vinet")
tag = self["tag"]
db_file = env_chk(self.get("db_file"), fw_spec)
summary_dict = {"eos": eos}
to_db = self.get("to_db", True)
# collect and store task_id of all related tasks to make unique links with "tasks" collection
all_task_ids = []
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one({"task_label": "{} structure optimization".format(tag)})
all_task_ids.append(d["task_id"])
structure = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
summary_dict["structure"] = structure.as_dict()
summary_dict["formula_pretty"] = structure.composition.reduced_formula
# 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)
all_task_ids.append(d["task_id"])
summary_dict["energies"] = energies
summary_dict["volumes"] = volumes
summary_dict["all_task_ids"] = all_task_ids
# fit the equation of state
eos = EOS(eos)
eos_fit = eos.fit(volumes, energies)
summary_dict["bulk_modulus"] = eos_fit.b0_GPa
# TODO: find a better way for passing tags of the entire workflow to db - albalu
if fw_spec.get("tags", None):
summary_dict["tags"] = fw_spec["tags"]
summary_dict["results"] = dict(eos_fit.results)
summary_dict["created_at"] = datetime.utcnow()
# db_file itself is required but the user can choose to pass the results to db or not
if to_db:
mmdb.collection = mmdb.db["eos"]
mmdb.collection.insert_one(summary_dict)
else:
with open("bulk_modulus.json", "w") as f:
f.write(json.dumps(summary_dict, default=DATETIME_HANDLER))
# TODO: @matk86 - there needs to be a builder to put it into materials collection... -computron
logger.info("Bulk modulus calculation complete.")
def test_apply_transformation_mult(self):
# Test returning multiple structures from each transformation.
disord = Structure(np.eye(3) * 4.209, [{"Cs+": 0.5, "K+": 0.5}, "Cl-"], [[0, 0, 0], [0.5, 0.5, 0.5]])
disord.make_supercell([2, 2, 1])
tl = [EnumerateStructureTransformation(), OrderDisorderedStructureTransformation()]
t = SuperTransformation(tl, nstructures_per_trans=10)
self.assertEqual(len(t.apply_transformation(disord, return_ranked_list=20)), 8)
t = SuperTransformation(tl)
self.assertEqual(len(t.apply_transformation(disord, return_ranked_list=20)), 2)
def setUp(self):
c1 = [[0.5] * 3, [0.9] * 3]
c2 = [[0.5] * 3, [0.9, 0.1, 0.1]]
s1 = Structure(Lattice.cubic(5), ['Si', 'Si'], c1)
s2 = Structure(Lattice.cubic(5), ['Si', 'Si'], c2)
structs = []
for s in s1.interpolate(s2, 3, pbc=True):
structs.append(Structure.from_sites(s.sites, to_unit_cell=True))
self.structures = structs
self.vis = MITNEBSet(self.structures)
def test_supercell_fit(self):
sm = StructureMatcher(attempt_supercell=False)
s1 = Structure.from_file(os.path.join(test_dir, "Al3F9.json"))
s2 = Structure.from_file(os.path.join(test_dir, "Al3F9_distorted.json"))
self.assertFalse(sm.fit(s1, s2))
sm = StructureMatcher(attempt_supercell=True)
self.assertTrue(sm.fit(s1, s2))
self.assertTrue(sm.fit(s2, s1))
def test_write_inputs(self):
c1 = [[0.5] * 3, [0.9] * 3]
c2 = [[0.5] * 3, [0.9, 0.1, 0.1]]
s1 = Structure(Lattice.cubic(5), ['Si', 'Si'], c1)
s2 = Structure(Lattice.cubic(5), ['Si', 'Si'], c2)
structs = []
for s in s1.interpolate(s2, 3, pbc=True):
structs.append(Structure.from_sites(s.sites, to_unit_cell=True))
fc = self.vis._process_structures(structs)[2].frac_coords
self.assertTrue(np.allclose(fc, [[0.5]*3,[0.9, 1.033333, 1.0333333]]))
def run(self):
if os.path.exists("CONTCAR"):
structure = Structure.from_file("CONTCAR")
elif (not self.contcar_only) and os.path.exists("POSCAR"):
structure = Structure.from_file("POSCAR")
else:
raise RuntimeError("No CONTCAR/POSCAR detected to generate input!")
modname, classname = self.input_set.rsplit(".", 1)
mod = __import__(modname, globals(), locals(), [classname], 0)
vis = getattr(mod, classname)(structure, **self.kwargs)
vis.write_input(".")
def test_dict_to_object(self):
coords = [[0, 0, 0], [0.75, 0.5, 0.75]]
lattice = Lattice([[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603]])
struct = Structure(lattice, ["Si"] * 2, coords)
d = {'structure_dict': [struct.as_dict(), struct.as_dict()]}
df = DataFrame(data=d)
df["structure"] = dict_to_object(df["structure_dict"])
self.assertEqual(df["structure"].tolist()[0], struct)
self.assertEqual(df["structure"].tolist()[1], struct)
def setUp(self):
# trivial example, simple square lattice for testing
structure = Structure(Lattice.tetragonal(5.0, 50.0), ['H'], [[0, 0, 0]])
self.square_sg = StructureGraph.with_empty_graph(structure, edge_weight_name="", edge_weight_units="")
self.square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(1, 0, 0))
self.square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(-1, 0, 0))
self.square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(0, 1, 0))
self.square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(0, -1, 0))
# body-centered square lattice for testing
structure = Structure(Lattice.tetragonal(5.0, 50.0), ['H', 'He'], [[0, 0, 0], [0.5, 0.5, 0.5]])
self.bc_square_sg = StructureGraph.with_empty_graph(structure, edge_weight_name="", edge_weight_units="")
self.bc_square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(1, 0, 0))
self.bc_square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(-1, 0, 0))
self.bc_square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(0, 1, 0))
self.bc_square_sg.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(0, -1, 0))
self.bc_square_sg.add_edge(0, 1, from_jimage=(0, 0, 0), to_jimage=(0, 0, 0))
self.bc_square_sg.add_edge(0, 1, from_jimage=(0, 0, 0), to_jimage=(-1, 0, 0))
self.bc_square_sg.add_edge(0, 1, from_jimage=(0, 0, 0), to_jimage=(-1, -1, 0))
self.bc_square_sg.add_edge(0, 1, from_jimage=(0, 0, 0), to_jimage=(0, -1, 0))
# body-centered square lattice for testing
# directions reversed, should be equivalent to as bc_square
structure = Structure(Lattice.tetragonal(5.0, 50.0), ['H', 'He'], [[0, 0, 0], [0.5, 0.5, 0.5]])
self.bc_square_sg_r = StructureGraph.with_empty_graph(structure, edge_weight_name="", edge_weight_units="")
self.bc_square_sg_r.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(1, 0, 0))
self.bc_square_sg_r.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(-1, 0, 0))
self.bc_square_sg_r.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(0, 1, 0))
self.bc_square_sg_r.add_edge(0, 0, from_jimage=(0, 0, 0), to_jimage=(0, -1, 0))
self.bc_square_sg_r.add_edge(0, 1, from_jimage=(0, 0, 0), to_jimage=(0, 0, 0))
self.bc_square_sg_r.add_edge(1, 0, from_jimage=(-1, 0, 0), to_jimage=(0, 0, 0))
self.bc_square_sg_r.add_edge(1, 0, from_jimage=(-1, -1, 0), to_jimage=(0, 0, 0))
self.bc_square_sg_r.add_edge(1, 0, from_jimage=(0, -1, 0), to_jimage=(0, 0, 0))
# MoS2 example, structure graph obtained from critic2
# (not ground state, from mp-1023924, single layer)
stdout_file = os.path.join(os.path.dirname(__file__), "..", "..", "..",
'test_files/critic2/MoS2_critic2_stdout.txt')
with open(stdout_file, 'r') as f:
reference_stdout = f.read()
self.structure = Structure.from_file(os.path.join(os.path.dirname(__file__), "..", "..", "..",
'test_files/critic2/MoS2.cif'))
c2o = Critic2Output(self.structure, reference_stdout)
self.mos2_sg = c2o.structure_graph(edge_weight="bond_length", edge_weight_units="Å")
latt = Lattice.cubic(4.17)
species = ["Ni", "O"]
coords = [[0, 0, 0],
[0.5, 0.5, 0.5]]
self.NiO = Structure.from_spacegroup(225, latt, species, coords).get_primitive_structure()
def run_task(self, fw_spec):
from atomate.vasp.analysis.phonopy 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 = VaspCalcDb.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()
summary_dict["formula_pretty"] = structure.composition.reduced_formula
# 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))
# TODO: @matk86 - there needs to be a way to insert this into a database! And also
# a builder to put it into materials collection... -computron
logger.info("Thermal expansion coefficient calculation complete.")
def test_partial_disorder(self):
s = Structure.from_file(filename=os.path.join(test_dir, "garnet.cif"))
a = SpacegroupAnalyzer(s, 0.1)
prim = a.find_primitive()
s = prim.copy()
s["Al3+"] = {"Al3+": 0.5, "Ga3+": 0.5}
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 7)
for s in structures:
self.assertEqual(s.formula, 'Ca12 Al4 Ga4 Si12 O48')
s = prim.copy()
s["Ca2+"] = {"Ca2+": 1/3, "Mg2+": 2/3}
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 20)
for s in structures:
self.assertEqual(s.formula, 'Ca4 Mg8 Al8 Si12 O48')
s = prim.copy()
s["Si4+"] = {"Si4+": 1/3, "Ge4+": 2/3}
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 18)
for s in structures:
self.assertEqual(s.formula, 'Ca12 Al8 Si4 Ge8 O48')
def run_task(self, fw_spec):
s = Structure.from_dict(self["structure"])
mod = __import__("pymatgen.io.vaspio_set", globals(), locals(),
[self["vasp_input_set"]], -1)
vis = load_class("pymatgen.io.vaspio_set", self["vasp_input_set"])(
**self.get("input_set_params", {}))
vis.write_input(s, ".")
def test_timeout(self):
s = Structure.from_file(filename=os.path.join(test_dir, "garnet.cif"))
a = SpacegroupAnalyzer(s, 0.1)
s["Al3+"] = {"Al3+": 0.5, "Ga3+": 0.5}
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01,
timeout=0.0000000000001)
self.assertRaises(TimeoutError, adaptor._run_multienum)
def generate_diffraction_plot(args):
s = Structure.from_file(args.filenames[0])
c = XRDCalculator()
if args.outfile:
c.get_xrd_plot(s).savefig(args.outfile[0])
else:
c.show_xrd_plot(s)
def _create_new_material(self, taskdoc):
"""
Create a new material document.
Args:
taskdoc (dict): a JSON-like task document
Returns:
(int) - material_id of the new document
"""
doc = {"created_at": datetime.utcnow()}
doc["_tasksbuilder"] = {"all_task_ids": [], "prop_metadata":
{"labels": {}, "task_ids": {}}, "updated_at": datetime.utcnow()}
doc["spacegroup"] = taskdoc["output"]["spacegroup"]
doc["sg_symbol"] = doc["spacegroup"]["symbol"]
doc["sg_number"] = doc["spacegroup"]["number"]
doc["structure"] = taskdoc["output"]["structure"]
doc["material_id"] = self.mid_str(self._counter.find_one_and_update(
{"_id": "materialid"}, {"$inc": {"c": 1}}, return_document=ReturnDocument.AFTER)["c"])
for x in ["formula_anonymous", "formula_pretty", "formula_reduced_abc",
"elements", "nelements", "chemsys"]:
doc[x] = taskdoc[x]
if "parent_structure" in taskdoc:
doc["parent_structure"] = taskdoc["parent_structure"]
t_struct = Structure.from_dict(taskdoc["parent_structure"]["structure"])
doc["parent_structure"]["formula_reduced_abc"] = t_struct.composition.reduced_formula
self._materials.insert_one(doc)
return doc["material_id"]
def run(self):
print("MaterialsEhullBuilder starting...")
self._build_indexes()
q = {"thermo.energy": {"$exists": True}}
if not self.update_all:
q["stability"] = {"$exists": False}
mats = [m for m in self._materials.find(q, {"calc_settings": 1, "structure": 1,
"thermo.energy": 1, "material_id": 1})]
pbar = tqdm(mats)
for m in pbar:
pbar.set_description("Processing materials_id: {}".format(m['material_id']))
try:
params = {}
for x in ["is_hubbard", "hubbards", "potcar_spec"]:
params[x] = m["calc_settings"][x]
structure = Structure.from_dict(m["structure"])
energy = m["thermo"]["energy"]
my_entry = ComputedEntry(structure.composition, energy, parameters=params)
self._materials.update_one({"material_id": m["material_id"]},
{"$set": {"stability": self.mpr.get_stability([my_entry])[0]}})
mpids = self.mpr.find_structure(structure)
self._materials.update_one({"material_id": m["material_id"]}, {"$set": {"mpids": mpids}})
except:
import traceback
print("<---")
print("There was an error processing material_id: {}".format(m))
traceback.print_exc()
print("--->")
print("MaterialsEhullBuilder finished processing.")
def create_df():
newcoll = db['abc3']
newcoll.drop()
x = 0
for doc in db['pauling_file_min_tags'].find().batch_size(75):
x += 1
if x % 1000 == 0:
print x
if doc['metadata']['_structure']['anonymized_formula'] == 'ABC3' and doc['is_ht'] in [True,False] \
and 'TiO3' in doc['metadata']['_structure']['reduced_cell_formula']:
newcoll.insert(doc)
cursor = newcoll.find()
df = pd.DataFrame(list(cursor))
for i, row in df.iterrows():
df.set_value(i, 'reduced_cell_formula', row['metadata']['_structure']['reduced_cell_formula'])
try:
df.set_value(i, 'space_group', int(row['metadata']['_Springer']['geninfo']['Space Group']))
except:
df.set_value(i, 'space_group', None)
try:
df.set_value(i, 'density', float(row['metadata']['_Springer']['geninfo']['Density'].split()[2]))
except IndexError as e:
df.set_value(i, 'density', None)
structure = Structure.from_dict(row['structure'])
num_density = (structure.num_sites/structure.volume)
no_of_atoms = Composition(structure.composition).num_atoms
num_vol = (structure.volume/no_of_atoms)
df.set_value(i, 'number_density', num_density)
df.set_value(i, 'number_volume', num_vol)
if row['metadata']['_structure']['is_ordered']:
df.set_value(i, 'is_ordered', 1)
else:
df.set_value(i, 'is_ordered', 0)
df.to_pickle('abc3.pkl')
def setUp(self):
with open(os.path.join(test_dir, "TiO2_entries.json"), 'r') as fp:
entries = json.load(fp, cls=MontyDecoder)
self.struct_list = [e.structure for e in entries]
self.oxi_structs = [self.get_structure("Li2O"),
Structure.from_file(os.path.join(
test_dir, "POSCAR.Li2O"))]
def get(self, cid, name):
"""Retrieve structure for contribution in CIF format.
---
operationId: get_cif
parameters:
- name: cid
in: path
type: string
pattern: '^[a-f0-9]{24}$'
required: true
description: contribution ID (ObjectId)
- name: name
in: path
type: string
required: true
description: name of structure
responses:
200:
description: structure in CIF format
schema:
type: string
"""
mask = [f'content.structures.{name}']
entry = Contributions.objects.only(*mask).get(id=cid)
structure = Structure.from_dict(entry.content.structures.get(name))
if structure:
return CifWriter(structure, symprec=1e-10).__str__()
return f"Structure with name {name} not found for {cid}!" # TODO raise 404?
def substrate(self):
"""
Return the substrate (Structure) of the interface.
"""
return Structure.from_sites(self.substrate_sites)
def setUp(self):
self.single_bond = Structure(
Lattice.cubic(10),
["H", "H", "H"], [[1, 0, 0], [0, 0, 0], [6, 0, 0]],
validate_proximity=False,
to_unit_cell=False, coords_are_cartesian=True,
site_properties=None)
self.linear = Structure(
Lattice.cubic(10),
["H", "H", "H"], [[1, 0, 0], [0, 0, 0], [2, 0, 0]],
validate_proximity=False,
to_unit_cell=False, coords_are_cartesian=True,
site_properties=None)
self.bent45 = Structure(
Lattice.cubic(10), ["H", "H", "H"],
[[0, 0, 0], [0.707, 0.707, 0], [0.707, 0, 0]],
validate_proximity=False,
to_unit_cell=False, coords_are_cartesian=True,
site_properties=None)
self.cubic = Structure(
Lattice.cubic(1),
["H"], [[0, 0, 0]], validate_proximity=False,
to_unit_cell=False, coords_are_cartesian=False,
site_properties=None)
self.bcc = Structure(
Lattice.cubic(1),
["H", "H"], [[0, 0, 0], [0.5, 0.5, 0.5]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=False, site_properties=None)
self.fcc = Structure(
Lattice.cubic(1), ["H", "H", "H", "H"],
[[0, 0, 0], [0, 0.5, 0.5], [0.5, 0, 0.5], [0.5, 0.5, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=False, site_properties=None)
self.hcp = Structure(
Lattice.hexagonal(1, 1.633),
["H", "H"],
[[0.3333, 0.6667, 0.25], [0.6667, 0.3333, 0.75]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=False, site_properties=None)
self.diamond = Structure(
Lattice.cubic(1), ["H", "H", "H", "H", "H", "H", "H", "H"],
[[0, 0, 0.5], [0.75, 0.75, 0.75], [0, 0.5, 0], [0.75, 0.25, 0.25],
[0.5, 0, 0], [0.25, 0.75, 0.25], [0.5, 0.5, 0.5],
[0.25, 0.25, 0.75]], validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=False, site_properties=None)
self.trigonal_off_plane = Structure(
Lattice.cubic(100),
["H", "H", "H", "H"],
[[0.50, 0.50, 0.50], [0.25, 0.75, 0.25],
[0.25, 0.25, 0.75], [0.75, 0.25, 0.25]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.regular_triangle = Structure(
Lattice.cubic(30), ["H", "H", "H", "H"],
[[15, 15.28867, 15.65], [14.5, 15, 15], [15.5, 15, 15],
[15, 15.866, 15]], validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.trigonal_planar = Structure(
Lattice.cubic(30), ["H", "H", "H", "H"],
[[15, 15.28867, 15], [14.5, 15, 15], [15.5, 15, 15],
[15, 15.866, 15]], validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.square_planar = Structure(
Lattice.cubic(30), ["H", "H", "H", "H", "H"],
[[15, 15, 15], [14.75, 14.75, 15], [14.75, 15.25, 15],
[15.25, 14.75, 15], [15.25, 15.25, 15]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.square = Structure(
Lattice.cubic(30), ["H", "H", "H", "H", "H"],
[[15, 15, 15.707], [14.75, 14.75, 15], [14.75, 15.25, 15],
[15.25, 14.75, 15], [15.25, 15.25, 15]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.T_shape = Structure(
Lattice.cubic(30), ["H", "H", "H", "H"],
[[15, 15, 15], [15, 15, 15.5], [15, 15.5, 15],
[15, 14.5, 15]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.square_pyramid = Structure(
Lattice.cubic(30), ["H", "H", "H", "H", "H", "H"],
[[15, 15, 15], [15, 15, 15.3535], [14.75, 14.75, 15],
[14.75, 15.25, 15], [15.25, 14.75, 15], [15.25, 15.25, 15]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.pentagonal_planar = Structure(
Lattice.cubic(30), ["Xe", "F", "F", "F", "F", "F"],
[[0, -1.6237, 0], [1.17969, 0, 0], [-1.17969, 0, 0],
[1.90877, -2.24389, 0], [-1.90877, -2.24389, 0], [0, -3.6307, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.pentagonal_pyramid = Structure(
Lattice.cubic(30), ["Xe", "F", "F", "F", "F", "F", "F"],
[[0, -1.6237, 0], [0, -1.6237, 1.17969], [1.17969, 0, 0],
[-1.17969, 0, 0], [1.90877, -2.24389, 0],
[-1.90877, -2.24389, 0], [0, -3.6307, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.pentagonal_bipyramid = Structure(
Lattice.cubic(30),
["Xe", "F", "F", "F", "F", "F", "F", "F"],
[[0, -1.6237, 0], [0, -1.6237, -1.17969],
[0, -1.6237, 1.17969], [1.17969, 0, 0],
[-1.17969, 0, 0], [1.90877, -2.24389, 0],
[-1.90877, -2.24389, 0], [0, -3.6307, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.hexagonal_planar = Structure(
Lattice.cubic(30),
["H", "C", "C", "C", "C", "C", "C"],
[[0, 0, 0], [0.71, 1.2298, 0],
[-0.71, 1.2298, 0], [0.71, -1.2298, 0], [-0.71, -1.2298, 0],
[1.4199, 0, 0], [-1.4199, 0, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.hexagonal_pyramid = Structure(
Lattice.cubic(30),
["H", "Li", "C", "C", "C", "C", "C", "C"],
[[0, 0, 0], [0, 0, 1.675], [0.71, 1.2298, 0],
[-0.71, 1.2298, 0], [0.71, -1.2298, 0], [-0.71, -1.2298, 0],
[1.4199, 0, 0], [-1.4199, 0, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.hexagonal_bipyramid = Structure(
Lattice.cubic(30),
["H", "Li", "Li", "C", "C", "C", "C", "C", "C"],
[[0, 0, 0], [0, 0, 1.675], [0, 0, -1.675],
[0.71, 1.2298, 0], [-0.71, 1.2298, 0],
[0.71, -1.2298, 0], [-0.71, -1.2298, 0],
[1.4199, 0, 0], [-1.4199, 0, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.trigonal_pyramid = Structure(
Lattice.cubic(30),
["P", "Cl", "Cl", "Cl", "Cl"],
[[0, 0, 0], [0, 0, 2.14], [0, 2.02, 0],
[1.74937, -1.01, 0], [-1.74937, -1.01, 0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.trigonal_bipyramidal = Structure(
Lattice.cubic(30), ["P", "Cl", "Cl", "Cl", "Cl", "Cl"],
[[0, 0, 0], [0, 0, 2.14], [0, 2.02, 0],
[1.74937, -1.01, 0], [-1.74937, -1.01, 0], [0, 0, -2.14]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.cuboctahedron = Structure(
Lattice.cubic(30),
["H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H", "H"],
[[15, 15, 15], [15, 14.5, 14.5], [15, 14.5, 15.5],
[15, 15.5, 14.5], [15, 15.5, 15.5],
[14.5, 15, 14.5], [14.5, 15, 15.5], [15.5, 15, 14.5], [15.5, 15, 15.5],
[14.5, 14.5, 15], [14.5, 15.5, 15], [15.5, 14.5, 15], [15.5, 15.5, 15]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.see_saw_rect = Structure(
Lattice.cubic(30),
["H", "H", "H", "H", "H"],
[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, -1.0, 0.0],
[0.0, 0.0, -1.0], [-1.0, 0.0, 0.0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
self.sq_face_capped_trig_pris = Structure(
Lattice.cubic(30),
["H", "H", "H", "H", "H", "H", "H", "H"],
[[0, 0, 0], [-0.6546536707079771, -0.37796447300922725, 0.6546536707079771],
[0.6546536707079771, -0.37796447300922725, 0.6546536707079771],
[0.0, 0.7559289460184545, 0.6546536707079771],
[-0.6546536707079771, -0.37796447300922725, -0.6546536707079771],
[0.6546536707079771, -0.37796447300922725, -0.6546536707079771],
[0.0, 0.7559289460184545, -0.6546536707079771], [0.0, -1.0, 0.0]],
validate_proximity=False, to_unit_cell=False,
coords_are_cartesian=True, site_properties=None)
def setUp(self):
self.diamond = Structure(
Lattice([[2.189, 0, 1.264], [0.73, 2.064, 1.264], [0, 0, 2.528]]),
["C", "C"], [[2.554, 1.806, 4.423], [0.365, 0.258, 0.632]],
coords_are_cartesian=True
)
def test_oxide_type(self):
el_li = Element("Li")
el_o = Element("O")
latt = Lattice([[3.985034, 0.0, 0.0], [0.0, 4.881506, 0.0],
[0.0, 0.0, 2.959824]])
elts = [el_li, el_li, el_o, el_o, el_o, el_o]
coords = list()
coords.append([0.500000, 0.500000, 0.500000])
coords.append([0.0, 0.0, 0.0])
coords.append([0.632568, 0.085090, 0.500000])
coords.append([0.367432, 0.914910, 0.500000])
coords.append([0.132568, 0.414910, 0.000000])
coords.append([0.867432, 0.585090, 0.000000])
struct = Structure(latt, elts, coords)
self.assertEqual(oxide_type(struct, 1.1), "superoxide")
el_li = Element("Li")
el_o = Element("O")
elts = [el_li, el_o, el_o, el_o]
latt = Lattice.from_parameters(3.999911, 3.999911, 3.999911,
133.847504, 102.228244, 95.477342)
coords = [[0.513004, 0.513004,
1.000000], [0.017616, 0.017616, 0.000000],
[0.649993, 0.874790, 0.775203],
[0.099587, 0.874790, 0.224797]]
struct = Structure(latt, elts, coords)
self.assertEqual(oxide_type(struct, 1.1), "ozonide")
latt = Lattice.from_parameters(3.159597, 3.159572, 7.685205, 89.999884,
89.999674, 60.000510)
el_li = Element("Li")
el_o = Element("O")
elts = [el_li, el_li, el_li, el_li, el_o, el_o, el_o, el_o]
coords = [[0.666656, 0.666705,
0.750001], [0.333342, 0.333378, 0.250001],
[0.000001, 0.000041,
0.500001], [0.000001, 0.000021, 0.000001],
[0.333347, 0.333332,
0.649191], [0.333322, 0.333353, 0.850803],
[0.666666, 0.666686, 0.350813],
[0.666665, 0.666684, 0.149189]]
struct = Structure(latt, elts, coords)
self.assertEqual(oxide_type(struct, 1.1), "peroxide")
el_li = Element("Li")
el_o = Element("O")
el_h = Element("H")
latt = Lattice.from_parameters(3.565276, 3.565276, 4.384277, 90.000000,
90.000000, 90.000000)
elts = [el_h, el_h, el_li, el_li, el_o, el_o]
coords = [[0.000000, 0.500000,
0.413969], [0.500000, 0.000000, 0.586031],
[0.000000, 0.000000,
0.000000], [0.500000, 0.500000, 0.000000],
[0.000000, 0.500000, 0.192672],
[0.500000, 0.000000, 0.807328]]
struct = Structure(latt, elts, coords)
self.assertEqual(oxide_type(struct, 1.1), "hydroxide")
el_li = Element("Li")
el_n = Element("N")
el_h = Element("H")
latt = Lattice.from_parameters(3.565276, 3.565276, 4.384277, 90.000000,
90.000000, 90.000000)
elts = [el_h, el_h, el_li, el_li, el_n, el_n]
coords = [[0.000000, 0.500000,
0.413969], [0.500000, 0.000000, 0.586031],
[0.000000, 0.000000,
0.000000], [0.500000, 0.500000, 0.000000],
[0.000000, 0.500000, 0.192672],
[0.500000, 0.000000, 0.807328]]
struct = Structure(latt, elts, coords)
self.assertEqual(oxide_type(struct, 1.1), "None")
el_o = Element("O")
latt = Lattice.from_parameters(4.389828, 5.369789, 5.369789, 70.786622,
69.244828, 69.244828)
elts = [el_o, el_o, el_o, el_o, el_o, el_o, el_o, el_o]
coords = [[0.844609, 0.273459,
0.786089], [0.155391, 0.213911, 0.726541],
[0.155391, 0.726541,
0.213911], [0.844609, 0.786089, 0.273459],
[0.821680, 0.207748,
0.207748], [0.178320, 0.792252, 0.792252],
[0.132641, 0.148222, 0.148222],
[0.867359, 0.851778, 0.851778]]
struct = Structure(latt, elts, coords)
self.assertEqual(oxide_type(struct, 1.1), "None")
def run_task(self, fw_spec):
gibbs_dict = {}
tag = self["tag"]
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)
poisson = self.get("poisson", 0.25)
anharmonic_contribution = self.get("anharmonic_contribution", False)
gibbs_dict["metadata"] = self.get("metadata", {})
db_file = env_chk(self.get("db_file"), fw_spec)
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one(
{"task_label": "{} structure optimization".format(tag)},
{"calcs_reversed": 1})
structure = Structure.from_dict(
d["calcs_reversed"][-1]["output"]['structure'])
gibbs_dict["structure"] = structure.as_dict()
gibbs_dict["formula_pretty"] = structure.composition.reduced_formula
# 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
}, {"calcs_reversed": 1})
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_dict["energies"] = energies
gibbs_dict["volumes"] = volumes
if qha_type not in ["debye_model"]:
gibbs_dict["force_constants"] = force_constants
try:
# use quasi-harmonic debye approximation
if qha_type in ["debye_model"]:
from pymatgen.analysis.quasiharmonic import QuasiharmonicDebyeApprox
qhda = QuasiharmonicDebyeApprox(
energies,
volumes,
structure,
t_min,
t_step,
t_max,
eos,
pressure=pressure,
poisson=poisson,
anharmonic_contribution=anharmonic_contribution)
gibbs_dict.update(qhda.get_summary_dict())
gibbs_dict["anharmonic_contribution"] = anharmonic_contribution
gibbs_dict["success"] = True
# use the phonopy interface
else:
from atomate.vasp.analysis.phonopy import get_phonopy_gibbs
G, T = get_phonopy_gibbs(energies, volumes, force_constants,
structure, t_min, t_step, t_max, mesh,
eos, pressure)
gibbs_dict["gibbs_free_energy"] = G
gibbs_dict["temperatures"] = T
gibbs_dict["success"] = True
# quasi-harmonic analysis failed, set the flag to false
except:
import traceback
logger.warn("Quasi-harmonic analysis failed!")
gibbs_dict["success"] = False
gibbs_dict["traceback"] = traceback.format_exc()
gibbs_dict['metadata'].update({"task_label_tag": tag})
gibbs_dict["created_at"] = datetime.utcnow()
gibbs_dict = jsanitize(gibbs_dict)
# TODO: @matk86: add a list of task_ids that were used to construct the analysis to DB?
# -computron
if not db_file:
dump_file = "gibbs.json"
logger.info("Dumping the analysis summary to {}".format(dump_file))
with open(dump_file, "w") as f:
f.write(json.dumps(gibbs_dict, default=DATETIME_HANDLER))
else:
coll = mmdb.db["gibbs_tasks"]
coll.insert_one(gibbs_dict)
logger.info("Gibbs free energy calculation complete.")
if not gibbs_dict["success"]:
return FWAction(defuse_children=True)
def test_ensure_vacuum_for_SiP(self):
# Compound test for add_vacuum and get_spacing.
os.chdir(ROOT)
structure = Structure.from_file('POSCAR_SiP')
structure = ensure_vacuum(structure, vacuum=15)
self.assertAlmostEqual(get_spacing(structure), 15.0)
def get_structure(self, sid):
"""Convenience function to get pymatgen structure."""
return Structure.from_dict(
self.structures.get_entry(pk=sid,
_fields=["lattice", "sites",
"charge"]).result())
[['Fe', 'Y'], [u'sandbox-mvc-819'], [u'sandbox-mvc-1830']],
[['Co', 'Y'], [u'sandbox-mvc-1596'], []],
[['Ni', 'Y'], [u'mp-25595'], [u'sandbox-mvc-1778']]
]
batt_list = []
for result in results:
if [] not in result:
id_charge = result[1][0]
id_discharge = result[2][0]
dict_charge = materials.find({"task_id": id_charge})[0]
dict_discharge = materials.find({"task_id": id_discharge})[0]
charge_struc = Structure.from_dict(dict_charge["structure"])
charge_energy = dict_charge["final_energy"]
entry_charge = ComputedStructureEntry(charge_struc, charge_energy)
charge_ehull = dict_charge["e_above_hull"]
discharge_struc = Structure.from_dict(dict_discharge["structure"])
discharge_energy = dict_discharge["final_energy"]
entry_discharge = ComputedStructureEntry(discharge_struc,
discharge_energy)
discharge_ehull = dict_discharge["e_above_hull"]
mpr = MPRester(api_key=API_KEY, host="www.materialsproject.org")
entries = mpr.get_entries(result[0][1], inc_structure="final")
counter = 0
energies = []
def main():
"""
logicMain function. Does the cli
"""
options = docopt(__doc__)
poscar_path = options['<poscar>']
if not exists(poscar_path):
raise IOError('POSCAR file "{0}" does not exist.'.format(poscar_path))
else:
try:
structure = Poscar.from_file(poscar_path).structure
species = set(
map_structure(structure, apply=lambda s: s.specie.symbol))
except:
raise IOError(
'Could not parse POSCAR file "{0}"'.format(poscar_path))
if options['--incar']:
incar_path = options['--incar']
if not exists(incar_path):
raise IOError(
'INCAR file "{0}" does not exist.'.format(incar_path))
else:
try:
incar = Incar.from_file(incar_path)
except:
raise IOError(
'Could not parse INCAR file "{0}"'.format(incar_path))
if options['supercell']:
try:
sx, sy, sz = [int(options['<s{0}>'.format(k)]) for k in 'xyz']
except:
raise ValueError('Could not parse supercell parameters')
else:
structure.make_supercell([sx, sy, sz])
if options['direction']:
try:
dx, dy, dz = [int(options['<d{0}>'.format(k)]) for k in 'xyz']
except:
raise ValueError('Could not parse direction')
else:
cl_direction = unit_vector(np.array([dx, dy, dz]))
else:
cl_direction = unit_vector(np.array([1.0, 0.0, 0.0]))
lengths = parse_lengths(options['<lengths>'])
if not all([k in species for k in lengths.keys()]):
missing_elements = set(lengths.keys()) - species
raise ValueError(
'The following element{0} {1} not specified in "{2}": {3}'.format(
's' if len(missing_elements) > 1 else '',
'are' if len(missing_elements) > 1 else 'is', poscar_path,
', '.join(list(missing_elements))))
for missing_element in species - set(lengths.keys()):
lengths[missing_element] = 0.0
if options['ncl']:
magmoms = generate_spins_vecs_ncl(structure, lengths)
# Check if net magnetic moment is really zero
sum_vec = np.array([array for _, v in magmoms.items() for array in v])
assert np.isclose(np.sum(sum_vec, axis=0), np.zeros((3, ))).all()
if options['cl']:
if options['--imbalanced']:
balanced = False
else:
balanced = True
magmoms = generate_spins_vecs_cl(structure,
lengths,
direction=cl_direction,
balanced=balanced)
# Check if net magnetic moment is really zero
sum_vec = np.array([array for _, v in magmoms.items() for array in v])
assert np.isclose(np.sum(sum_vec, axis=0), np.zeros((3, ))).all()
if options['fm']:
magmoms = generate_spins_vecs_fm(structure,
lengths,
direction=cl_direction)
frac_coords_list = []
species_list = []
magmom_str = []
magmoms_site_property = {'magmom': []}
for spec, magm in magmoms.items():
# Assemble the species list and the fractional coords in the same order as the generated magnetic moments
current_frac_coords = map_structure(
structure,
predicate=lambda site: site.specie.symbol == spec,
apply=lambda site: site.frac_coords)
species_list.extend([spec] * len(current_frac_coords))
frac_coords_list.extend(current_frac_coords)
# If direction is given calculate the projection of the moment onto the direction which gives length and
# orientation
if options['direction'] or options['ncl']:
magmom_str.extend([
' '.join([make_format_crumb(i)
for i in range(3)]).format(mag_x, mag_y, mag_z)
for mag_x, mag_y, mag_z in magm
])
magmoms_site_property['magmom'].extend(
[mag_x, mag_y, mag_z] for mag_x, mag_y, mag_z in magm)
else:
magmom_str.extend([
make_format_crumb(0).format(
np.dot(cl_direction, np.array([mag_x, mag_y, mag_z])))
for mag_x, mag_y, mag_z in magm
])
magmoms_site_property['magmom'].extend([
np.dot(cl_direction, np.array([mag_x, mag_y, mag_z]))
for mag_x, mag_y, mag_z in magm
])
# Create the new structure
new_structure = Structure(structure.lattice,
species_list,
frac_coords_list,
site_properties=magmoms_site_property)
magmom_str = ' '.join(magmom_str)
# Write the new Posar file
Poscar(new_structure).write_file(
file_exists('{0}{1}'.format(poscar_path, OUTPUT_SUFFIX)))
if options['--incar']:
incar['MAGMOM'] = magmom_str
incar.write_file(file_exists('INCAR{0}'.format(OUTPUT_SUFFIX)))
else:
# Print the magnetic moment vector to the terminal
print('MAGMOM = {0}'.format(magmom_str))
if options['--plot']:
plot_magmoms(magmoms)
if options['--cif']:
cif_writer = CifWriter(new_structure, write_magmoms=True)
cif_writer.write_file(
file_exists('{0}{1}.cif'.format(poscar_path, OUTPUT_SUFFIX)))
def test_calculate(self):
s1 = Structure.from_spacegroup(227, Lattice.cubic(5.46873),
['Si'],
[[0, 0, 0]])
profile1 = dict(Si=dict(r=0.5, w=1.9), C=dict(r=0.5, w=2.55))
diag1 = np.random.randint(4)
tjm1 = np.random.randint(1, 11)
calculator1 = SpectralNeighborAnalysis(rcutfac=5, twojmax=tjm1,
element_profile=profile1,
quadratic=False,
diagonalstyle=diag1)
sna1, snad1, snav1, elem1 = calculator1.calculate([s1])[0]
n1 = calculator1.n_bs
self.assertAlmostEqual(sna1[0][0], 585.920)
self.assertEqual(sna1.shape, (len(s1), n1))
self.assertEqual(snad1.shape, (len(s1), n1 * 3 * len(profile1)))
self.assertEqual(snav1.shape, (len(s1), n1 * 6 * len(profile1)))
self.assertEqual(len(np.unique(elem1)), 1)
calculator4 = SpectralNeighborAnalysis(rcutfac=5, twojmax=tjm1,
element_profile=profile1,
quadratic=True,
diagonalstyle=diag1)
sna4, snad4, snav4, elem4 = calculator4.calculate([s1])[0]
n4 = calculator4.n_bs
n4 += int((1 + n4) * n4 / 2)
self.assertAlmostEqual(sna4[0][0], 585.920)
self.assertEqual(sna4.shape, (len(s1), n4))
self.assertEqual(snad4.shape, (len(s1), n4 * 3 * len(profile1)))
self.assertEqual(snav4.shape, (len(s1), n4 * 6 * len(profile1)))
self.assertEqual(len(np.unique(elem4)), 1)
s2 = Structure.from_spacegroup(225, Lattice.cubic(5.69169),
['Na', 'Cl'],
[[0, 0, 0], [0, 0, 0.5]])
profile2 = dict(Na=dict(r=0.3, w=0.9),
Cl=dict(r=0.7, w=3.0))
diag2 = np.random.randint(4)
tjm2 = np.random.randint(1, 11)
calculator2 = SpectralNeighborAnalysis(rcutfac=5, twojmax=tjm2,
element_profile=profile2,
quadratic=False,
diagonalstyle=diag2)
sna2, snad2, snav2, elem2 = calculator2.calculate([s2])[0]
n2 = calculator2.n_bs
self.assertAlmostEqual(sna2[0][0], 525.858)
self.assertEqual(sna2.shape, (len(s2), n2))
self.assertEqual(snad2.shape, (len(s2), n2 * 3 * len(profile2)))
self.assertEqual(snav2.shape, (len(s2), n2 * 6 * len(profile2)))
self.assertEqual(len(np.unique(elem2)), len(profile2))
calculator5 = SpectralNeighborAnalysis(rcutfac=5, twojmax=tjm2,
element_profile=profile2,
quadratic=True,
diagonalstyle=diag2)
sna5, snad5, snav5, elem5 = calculator5.calculate([s2])[0]
n5 = calculator5.n_bs
n5 += int((1 + n5) * n5 / 2)
self.assertAlmostEqual(sna5[0][0], 525.858)
self.assertEqual(sna5.shape, (len(s2), n5))
self.assertEqual(snad5.shape, (len(s2), n5 * 3 * len(profile2)))
self.assertEqual(snav5.shape, (len(s2), n5 * 6 * len(profile2)))
self.assertEqual(len(np.unique(elem5)), len(profile2))
s3 = Structure.from_spacegroup(221, Lattice.cubic(3.88947),
['Ca', 'Ti', 'O'],
[[0.5, 0.5, 0.5],
[0, 0, 0],
[0, 0, 0.5]])
profile3 = dict(Ca=dict(r=0.4, w=1.0),
Ti=dict(r=0.3, w=1.5),
O=dict(r=0.75, w=3.5))
diag3 = np.random.randint(4)
tjm3 = np.random.randint(1, 11)
calculator3 = SpectralNeighborAnalysis(rcutfac=5, twojmax=tjm3,
element_profile=profile3,
quadratic=False,
diagonalstyle=diag3)
sna3, snad3, snav3, elem3 = calculator3.calculate([s3])[0]
n3 = calculator3.n_bs
self.assertAlmostEqual(sna3[0][0], 25506.3)
self.assertEqual(sna3.shape, (len(s3), n3))
self.assertEqual(snad3.shape, (len(s3), n3 * 3 * len(profile3)))
self.assertEqual(snav3.shape, (len(s3), n3 * 6 * len(profile3)))
self.assertEqual(len(np.unique(elem3)), len(profile3))
calculator6 = SpectralNeighborAnalysis(rcutfac=5, twojmax=tjm3,
element_profile=profile3,
quadratic=True,
diagonalstyle=diag3)
sna6, snad6, snav6, elem6 = calculator6.calculate([s3])[0]
n6 = calculator6.n_bs
n6 += int((1 + n6) * n6 / 2)
self.assertAlmostEqual(sna6[0][0], 25506.3)
self.assertEqual(sna6.shape, (len(s3), n6))
self.assertEqual(snad6.shape, (len(s3), n6 * 3 * len(profile3)))
self.assertEqual(snav6.shape, (len(s3), n6 * 6 * len(profile3)))
self.assertEqual(len(np.unique(elem6)), len(profile3))
def test_symmetrized(self):
filepath = os.path.join(test_dir, 'POSCAR')
poscar = Poscar.from_file(filepath)
writer = CifWriter(poscar.structure, symprec=0.1)
ans = """# generated using pymatgen
data_FePO4
_symmetry_space_group_name_H-M Pnma
_cell_length_a 10.41176687
_cell_length_b 6.06717188
_cell_length_c 4.75948954
_cell_angle_alpha 90.00000000
_cell_angle_beta 90.00000000
_cell_angle_gamma 90.00000000
_symmetry_Int_Tables_number 62
_chemical_formula_structural FePO4
_chemical_formula_sum 'Fe4 P4 O16'
_cell_volume 300.65685512
_cell_formula_units_Z 4
loop_
_symmetry_equiv_pos_site_id
_symmetry_equiv_pos_as_xyz
1 'x, y, z'
2 '-x, -y, -z'
3 '-x+1/2, -y, z+1/2'
4 'x+1/2, y, -z+1/2'
5 'x+1/2, -y+1/2, -z+1/2'
6 '-x+1/2, y+1/2, z+1/2'
7 '-x, y+1/2, -z'
8 'x, -y+1/2, z'
loop_
_atom_site_type_symbol
_atom_site_label
_atom_site_symmetry_multiplicity
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
_atom_site_occupancy
Fe Fe1 4 0.218728 0.250000 0.525133 1
P P2 4 0.094613 0.750000 0.581757 1
O O3 8 0.165710 0.546072 0.714616 1
O O4 4 0.043372 0.250000 0.292862 1
O O5 4 0.096642 0.750000 0.258680 1"""
for l1, l2 in zip(str(writer).split("\n"), ans.split("\n")):
self.assertEqual(l1.strip(), l2.strip())
ans = """# generated using pymatgen
data_LiFePO4
_symmetry_space_group_name_H-M Pnma
_cell_length_a 10.41037000
_cell_length_b 6.06577000
_cell_length_c 4.74480000
_cell_angle_alpha 90.00000000
_cell_angle_beta 90.00000000
_cell_angle_gamma 90.00000000
_symmetry_Int_Tables_number 62
_chemical_formula_structural LiFePO4
_chemical_formula_sum 'Li4 Fe4 P4 O16'
_cell_volume 299.619458734
_cell_formula_units_Z 4
loop_
_symmetry_equiv_pos_site_id
_symmetry_equiv_pos_as_xyz
1 'x, y, z'
2 '-x, -y, -z'
3 '-x+1/2, -y, z+1/2'
4 'x+1/2, y, -z+1/2'
5 'x+1/2, -y+1/2, -z+1/2'
6 '-x+1/2, y+1/2, z+1/2'
7 '-x, y+1/2, -z'
8 'x, -y+1/2, z'
loop_
_atom_site_type_symbol
_atom_site_label
_atom_site_symmetry_multiplicity
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
_atom_site_occupancy
Li Li1 4 0.000000 0.000000 0.000000 1.0
Fe Fe2 4 0.218845 0.750000 0.474910 1.0
P P3 4 0.094445 0.250000 0.417920 1.0
O O4 8 0.165815 0.044060 0.286540 1.0
O O5 4 0.043155 0.750000 0.708460 1.0
O O6 4 0.096215 0.250000 0.741480 1.0
"""
s = Structure.from_file(os.path.join(test_dir, 'LiFePO4.cif'))
writer = CifWriter(s, symprec=0.1)
s2 = CifParser.from_string(str(writer)).get_structures()[0]
m = StructureMatcher()
self.assertTrue(m.fit(s, s2))
s = self.get_structure("Li2O")
writer = CifWriter(s, symprec=0.1)
ans = """# generated using pymatgen
data_Li2O
_symmetry_space_group_name_H-M Fm-3m
_cell_length_a 4.61000000
_cell_length_b 4.61000000
_cell_length_c 4.61000000
_cell_angle_alpha 90.00000000
_cell_angle_beta 90.00000000
_cell_angle_gamma 90.00000000
_symmetry_Int_Tables_number 225
_chemical_formula_structural Li2O
_chemical_formula_sum 'Li8 O4'
_cell_volume 97.972181
_cell_formula_units_Z 4
loop_
_symmetry_equiv_pos_site_id
_symmetry_equiv_pos_as_xyz
1 'x, y, z'
2 '-x, -y, -z'
3 'z, y, -x'
4 '-z, -y, x'
5 '-x, y, -z'
6 'x, -y, z'
7 '-z, y, x'
8 'z, -y, -x'
9 'x, -y, -z'
10 '-x, y, z'
11 'z, -y, x'
12 '-z, y, -x'
13 '-x, -y, z'
14 'x, y, -z'
15 '-z, -y, -x'
16 'z, y, x'
17 'y, -z, -x'
18 '-y, z, x'
19 'y, x, -z'
20 '-y, -x, z'
21 'y, z, x'
22 '-y, -z, -x'
23 'y, -x, z'
24 '-y, x, -z'
25 '-y, z, -x'
26 'y, -z, x'
27 '-y, -x, -z'
28 'y, x, z'
29 '-y, -z, x'
30 'y, z, -x'
31 '-y, x, z'
32 'y, -x, -z'
33 '-z, x, -y'
34 'z, -x, y'
35 'x, z, -y'
36 '-x, -z, y'
37 'z, -x, -y'
38 '-z, x, y'
39 '-x, -z, -y'
40 'x, z, y'
41 'z, x, y'
42 '-z, -x, -y'
43 '-x, z, y'
44 'x, -z, -y'
45 '-z, -x, y'
46 'z, x, -y'
47 'x, -z, y'
48 '-x, z, -y'
49 'x+1/2, y+1/2, z'
50 '-x+1/2, -y+1/2, -z'
51 'z+1/2, y+1/2, -x'
52 '-z+1/2, -y+1/2, x'
53 '-x+1/2, y+1/2, -z'
54 'x+1/2, -y+1/2, z'
55 '-z+1/2, y+1/2, x'
56 'z+1/2, -y+1/2, -x'
57 'x+1/2, -y+1/2, -z'
58 '-x+1/2, y+1/2, z'
59 'z+1/2, -y+1/2, x'
60 '-z+1/2, y+1/2, -x'
61 '-x+1/2, -y+1/2, z'
62 'x+1/2, y+1/2, -z'
63 '-z+1/2, -y+1/2, -x'
64 'z+1/2, y+1/2, x'
65 'y+1/2, -z+1/2, -x'
66 '-y+1/2, z+1/2, x'
67 'y+1/2, x+1/2, -z'
68 '-y+1/2, -x+1/2, z'
69 'y+1/2, z+1/2, x'
70 '-y+1/2, -z+1/2, -x'
71 'y+1/2, -x+1/2, z'
72 '-y+1/2, x+1/2, -z'
73 '-y+1/2, z+1/2, -x'
74 'y+1/2, -z+1/2, x'
75 '-y+1/2, -x+1/2, -z'
76 'y+1/2, x+1/2, z'
77 '-y+1/2, -z+1/2, x'
78 'y+1/2, z+1/2, -x'
79 '-y+1/2, x+1/2, z'
80 'y+1/2, -x+1/2, -z'
81 '-z+1/2, x+1/2, -y'
82 'z+1/2, -x+1/2, y'
83 'x+1/2, z+1/2, -y'
84 '-x+1/2, -z+1/2, y'
85 'z+1/2, -x+1/2, -y'
86 '-z+1/2, x+1/2, y'
87 '-x+1/2, -z+1/2, -y'
88 'x+1/2, z+1/2, y'
89 'z+1/2, x+1/2, y'
90 '-z+1/2, -x+1/2, -y'
91 '-x+1/2, z+1/2, y'
92 'x+1/2, -z+1/2, -y'
93 '-z+1/2, -x+1/2, y'
94 'z+1/2, x+1/2, -y'
95 'x+1/2, -z+1/2, y'
96 '-x+1/2, z+1/2, -y'
97 'x+1/2, y, z+1/2'
98 '-x+1/2, -y, -z+1/2'
99 'z+1/2, y, -x+1/2'
100 '-z+1/2, -y, x+1/2'
101 '-x+1/2, y, -z+1/2'
102 'x+1/2, -y, z+1/2'
103 '-z+1/2, y, x+1/2'
104 'z+1/2, -y, -x+1/2'
105 'x+1/2, -y, -z+1/2'
106 '-x+1/2, y, z+1/2'
107 'z+1/2, -y, x+1/2'
108 '-z+1/2, y, -x+1/2'
109 '-x+1/2, -y, z+1/2'
110 'x+1/2, y, -z+1/2'
111 '-z+1/2, -y, -x+1/2'
112 'z+1/2, y, x+1/2'
113 'y+1/2, -z, -x+1/2'
114 '-y+1/2, z, x+1/2'
115 'y+1/2, x, -z+1/2'
116 '-y+1/2, -x, z+1/2'
117 'y+1/2, z, x+1/2'
118 '-y+1/2, -z, -x+1/2'
119 'y+1/2, -x, z+1/2'
120 '-y+1/2, x, -z+1/2'
121 '-y+1/2, z, -x+1/2'
122 'y+1/2, -z, x+1/2'
123 '-y+1/2, -x, -z+1/2'
124 'y+1/2, x, z+1/2'
125 '-y+1/2, -z, x+1/2'
126 'y+1/2, z, -x+1/2'
127 '-y+1/2, x, z+1/2'
128 'y+1/2, -x, -z+1/2'
129 '-z+1/2, x, -y+1/2'
130 'z+1/2, -x, y+1/2'
131 'x+1/2, z, -y+1/2'
132 '-x+1/2, -z, y+1/2'
133 'z+1/2, -x, -y+1/2'
134 '-z+1/2, x, y+1/2'
135 '-x+1/2, -z, -y+1/2'
136 'x+1/2, z, y+1/2'
137 'z+1/2, x, y+1/2'
138 '-z+1/2, -x, -y+1/2'
139 '-x+1/2, z, y+1/2'
140 'x+1/2, -z, -y+1/2'
141 '-z+1/2, -x, y+1/2'
142 'z+1/2, x, -y+1/2'
143 'x+1/2, -z, y+1/2'
144 '-x+1/2, z, -y+1/2'
145 'x, y+1/2, z+1/2'
146 '-x, -y+1/2, -z+1/2'
147 'z, y+1/2, -x+1/2'
148 '-z, -y+1/2, x+1/2'
149 '-x, y+1/2, -z+1/2'
150 'x, -y+1/2, z+1/2'
151 '-z, y+1/2, x+1/2'
152 'z, -y+1/2, -x+1/2'
153 'x, -y+1/2, -z+1/2'
154 '-x, y+1/2, z+1/2'
155 'z, -y+1/2, x+1/2'
156 '-z, y+1/2, -x+1/2'
157 '-x, -y+1/2, z+1/2'
158 'x, y+1/2, -z+1/2'
159 '-z, -y+1/2, -x+1/2'
160 'z, y+1/2, x+1/2'
161 'y, -z+1/2, -x+1/2'
162 '-y, z+1/2, x+1/2'
163 'y, x+1/2, -z+1/2'
164 '-y, -x+1/2, z+1/2'
165 'y, z+1/2, x+1/2'
166 '-y, -z+1/2, -x+1/2'
167 'y, -x+1/2, z+1/2'
168 '-y, x+1/2, -z+1/2'
169 '-y, z+1/2, -x+1/2'
170 'y, -z+1/2, x+1/2'
171 '-y, -x+1/2, -z+1/2'
172 'y, x+1/2, z+1/2'
173 '-y, -z+1/2, x+1/2'
174 'y, z+1/2, -x+1/2'
175 '-y, x+1/2, z+1/2'
176 'y, -x+1/2, -z+1/2'
177 '-z, x+1/2, -y+1/2'
178 'z, -x+1/2, y+1/2'
179 'x, z+1/2, -y+1/2'
180 '-x, -z+1/2, y+1/2'
181 'z, -x+1/2, -y+1/2'
182 '-z, x+1/2, y+1/2'
183 '-x, -z+1/2, -y+1/2'
184 'x, z+1/2, y+1/2'
185 'z, x+1/2, y+1/2'
186 '-z, -x+1/2, -y+1/2'
187 '-x, z+1/2, y+1/2'
188 'x, -z+1/2, -y+1/2'
189 '-z, -x+1/2, y+1/2'
190 'z, x+1/2, -y+1/2'
191 'x, -z+1/2, y+1/2'
192 '-x, z+1/2, -y+1/2'
loop_
_atom_type_symbol
_atom_type_oxidation_number
Li+ 1.0
O2- -2.0
loop_
_atom_site_type_symbol
_atom_site_label
_atom_site_symmetry_multiplicity
_atom_site_fract_x
_atom_site_fract_y
_atom_site_fract_z
_atom_site_occupancy
Li+ Li1 8 0.250000 0.250000 0.250000 1.0
O2- O2 4 0.000000 0.000000 0.000000 1.0"""
for l1, l2 in zip(str(writer).split("\n"), ans.split("\n")):
self.assertEqual(l1.strip(), l2.strip())
from pymatgen import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation
from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet
structure = Structure.from_file(
"D:/Program Files (x86)/python2/file/EntryWithCollCode418490.cif")
# （获取文件 当在自己电脑中调试的时候需要将相关的路径统一一下）
print(structure)
# loop over all sites in the structure
for i, site in enumerate(structure):
# （enumerate 为枚举类型）
# change the occupancy of Li+ disordered sites to 0.5
if not site.is_ordered:
structure[i] = {"Li+": 0.5}
print("The composition after adjustments is %s." %
structure.composition.reduced_formula)
analyzer = SpacegroupAnalyzer(structure)
# SpacegroupAnalyzer为空间群分析 晶体内部结构中全部对称要素的集合称为 “空间群” 。
# 一切晶体结构中总共只能有230种不同的对称要素组合方式，即230个空间群所谓点空间群，
# 是由一个平移群和一个点群对称操作组合而成的，它的一般对称操作可以写成（R | t (αβγ)），其中R表示点群对称操作，t(αβγ)表示平移操作
# 为了保持可排序的顺序，我们只在原始单元上执行枚举。原始单元可以使用*空间群分析器*获得
prim_cell = analyzer.find_primitive()
print(prim_cell)
enum = EnumerateStructureTransformation() # 变换枚举结构
enumerated = enum.apply_transformation(prim_cell, 90) # 在这需要限制返回结构的数量
structures = [d["structure"] for d in enumerated]
print("%d structures returned." % len(structures))
def test_get_structure_type_for_conventional_material(self):
os.chdir(ROOT)
structure = Structure.from_file('POSCAR_Fe')
test_type = get_structure_type(structure)
self.assertEqual(test_type, 'conventional')
def film(self):
"""
Return the film (Structure) of the interface.
"""
return Structure.from_sites(self.film_sites)
def get_wf_surface(slabs,
molecules=[],
bulk_structure=None,
slab_gen_params=None,
vasp_cmd="vasp",
db_file=None,
ads_structures_params={},
add_molecules_in_box=False):
"""
Args:
slabs (list of Slabs or Structures): slabs to calculate
molecules (list of Molecules): molecules to place as adsorbates
bulk_structure (Structure): bulk structure from which generate slabs
after reoptimization. If supplied, workflow will begin with
bulk structure optimization.
slab_gen_params (dict): dictionary of slab generation parameters
used to generate the slab, necessary to get the slab
that corresponds to the bulk structure if in that mode
ads_structures_params (dict): parameters to be supplied as
kwargs to AdsorbateSiteFinder.generate_adsorption_structures
add_molecules_in_box (boolean): flag to add calculation of molecule
energies to the workflow
db_file (string): path to database file
vasp_cmd (string): vasp command
Returns:
Workflow
"""
fws, parents = [], []
if bulk_structure:
vis = MVLSlabSet(bulk_structure, bulk=True)
fws.append(
OptimizeFW(structure=bulk_structure,
vasp_input_set=vis,
vasp_cmd="vasp",
db_file=db_file))
parents = fws[0]
for slab in slabs:
name = slab.composition.reduced_formula
if getattr(slab, "miller_index", None):
name += "_{}".format(slab.miller_index)
fws.append(
get_slab_fw(slab,
bulk_structure,
slab_gen_params,
db_file=db_file,
vasp_cmd=vasp_cmd,
parents=parents,
name=name + " slab optimization"))
for molecule in molecules:
ads_slabs = AdsorbateSiteFinder(
slab).generate_adsorption_structures(molecule,
**ads_structures_params)
for n, ads_slab in enumerate(ads_slabs):
ads_name = "{}-{} adsorbate optimization {}".format(
molecule.composition.formula, name, n)
fws.append(
get_slab_fw(ads_slab,
bulk_structure,
slab_gen_params,
db_file=db_file,
vasp_cmd=vasp_cmd,
parents=parents,
name=ads_name))
if add_molecules_in_box:
for molecule in molecules:
# molecule in box
m_struct = Structure(Lattice.cubic(10),
molecule.species_and_occu,
molecule.cart_coords,
coords_are_cartesian=True)
m_struct.translate_sites(
list(range(len(m_struct))),
np.array([0.5] * 3) - np.average(m_struct.frac_coords, axis=0))
vis = MVLSlabSet(m_struct)
fws.append(
OptimizeFW(structure=molecule,
job_type="normal",
vasp_input_set=vis,
db_file=db_file,
vasp_cmd=vasp_cmd))
# TODO: add analysis framework
return Workflow(fws,
name="{} surfaces wf, e.g., {}".format(
len(fws), fws[0].name))
import itertools
import json
import os
import collections
from collections import Counter
import numpy as np
from pymatgen import Structure
from statistics import mode
import shutil
# Step 1: #
## Open a folder and determine the 'cheapest' method of calculating simularities
p = Structure.from_file(filename='/Users/budmacaulay/Desktop/newruns/0' + '/POSCAR')
# Pretty sure it's more light weight to use dicts and such but im a terrible coder and honestly im painfully lazy
c = collections.Counter(p.species)
leastcom = []
for site in p:
if site.specie.name == c.most_common()[-1][0].name:
leastcom.append(site)
checkele = c.most_common()[-2][0].name
# # # - - - Checking first 100 desired sites are in correct position - - - # # #
thecount = 0
for folder in range(1, 100):
# 1 Open poscar
comped = Structure.from_file(filename='/Users/budmacaulay/Desktop/newtestssss/' + str(folder) + '/POSCAR')
# 2 Check if it compares
i = 0
import math
from pymatgen import Structure, Element
import os
import numpy as np
s = Structure.from_file('CONTCAR')
sr = []
ti = []
z = []
for i in s.sites:
if i.specie == Element.O:
z.append(i.z)
elif i.specie == Element.Ti or i.specie == Element.Ti:
ti.append(i.z)
elif i.specie is Element.Ba or i.specie == Element.Ba:
sr.append(i.z)
arr_Sr = [(x, x - y) for x,y in zip(sorted(sr), sorted(z)[2::3])]
arr_Ti = [(x, x - y) for x,y in zip(sorted(ti), sorted(z)[1::3])]
print(arr_Sr)
print(arr_Ti)
np.savetxt('sr.dat', arr_Sr, delimiter='\t')
np.savetxt('ti.dat', arr_Ti, delimiter='\t')
def add_magnetism(mat):
mag_types = {"NM": "Non-magnetic", "FiM": "Ferri", "AFM": "AFM", "FM": "FM"}
struc = Structure.from_dict(mat["structure"])
msa = CollinearMagneticStructureAnalyzer(struc)
mat["magnetic_type"] = mag_types[msa.ordering.value]
def test_get_magmom_string_for_FeCl2(self):
os.chdir(ROOT)
structure = Structure.from_file('POSCAR_FeCl2')
test_string = get_magmom_string(structure)
self.assertEqual(test_string, u'1.0*6.0 2.0*0.5')
def run_task(self, fw_spec):
ref_struct = self['structure']
d = {"analysis": {}, "initial_structure": self['structure'].as_dict()}
# Get optimized structure
calc_locs_opt = [
cl for cl in fw_spec.get('calc_locs', [])
if 'optimiz' 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()})
ref_struct = opt_struct
eq_stress = -0.1 * Stress(optimize_doc["calcs_reversed"][0]
["output"]["ionic_steps"][-1]["stress"])
else:
eq_stress = None
if self.get("fw_spec_field"):
d.update({
self.get("fw_spec_field"):
fw_spec.get(self.get("fw_spec_field"))
})
# Get the stresses, strains, deformations from deformation tasks
defo_dicts = fw_spec["deformation_tasks"].values()
stresses, strains, deformations = [], [], []
for defo_dict in defo_dicts:
stresses.append(Stress(defo_dict["stress"]))
strains.append(Strain(defo_dict["strain"]))
deformations.append(Deformation(defo_dict["deformation_matrix"]))
# Add derived stresses and strains if symmops is present
for symmop in defo_dict.get("symmops", []):
stresses.append(Stress(defo_dict["stress"]).transform(symmop))
strains.append(Strain(defo_dict["strain"]).transform(symmop))
deformations.append(
Deformation(
defo_dict["deformation_matrix"]).transform(symmop))
stresses = [-0.1 * s for s in stresses]
pk_stresses = [
stress.piola_kirchoff_2(deformation)
for stress, deformation in zip(stresses, deformations)
]
d['fitting_data'] = {
'cauchy_stresses': stresses,
'eq_stress': eq_stress,
'strains': strains,
'pk_stresses': pk_stresses,
'deformations': deformations
}
logger.info("Analyzing stress/strain data")
# TODO: @montoyjh: what if it's a cubic system? don't need 6. -computron
# TODO: Can add population method but want to think about how it should
# be done. -montoyjh
order = self.get('order', 2)
if order > 2:
method = 'finite_difference'
else:
method = self.get('fitting_method', 'finite_difference')
if method == 'finite_difference':
result = ElasticTensorExpansion.from_diff_fit(strains,
pk_stresses,
eq_stress=eq_stress,
order=order)
if order == 2:
result = ElasticTensor(result[0])
elif method == 'pseudoinverse':
result = ElasticTensor.from_pseudoinverse(strains, pk_stresses)
elif method == 'independent':
result = ElasticTensor.from_independent_strains(
strains, pk_stresses, eq_stress=eq_stress)
else:
raise ValueError(
"Unsupported method, method must be finite_difference, "
"pseudoinverse, or independent")
ieee = result.convert_to_ieee(ref_struct)
d.update({
"elastic_tensor": {
"raw": result.voigt,
"ieee_format": ieee.voigt
}
})
if order == 2:
d.update({
"derived_properties":
ieee.get_structure_property_dict(ref_struct)
})
else:
soec = ElasticTensor(ieee[0])
d.update({
"derived_properties":
soec.get_structure_property_dict(ref_struct)
})
d["formula_pretty"] = ref_struct.composition.reduced_formula
d["fitting_method"] = method
d["order"] = order
d = jsanitize(d)
# 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 run_task(self, fw_spec):
wfid = list(filter(lambda x: 'wfid' in x, fw_spec['tags'])).pop()
db_file = env_chk(self.get("db_file"), fw_spec)
vaspdb = VaspCalcDb.from_db_file(db_file, admin=True)
# ferroelectric workflow groups calculations by generated wfid tag
polarization_tasks = vaspdb.collection.find({
"tags": wfid,
"task_label": {
"$regex": ".*polarization"
}
})
tasks = []
outcars = []
structure_dicts = []
sort_weight = []
energies_per_atom = []
energies = []
zval_dicts = []
for p in polarization_tasks:
# Grab data from each polarization task
energies_per_atom.append(
p['calcs_reversed'][0]['output']['energy_per_atom'])
energies.append(p['calcs_reversed'][0]['output']['energy'])
tasks.append(p['task_label'])
outcars.append(p['calcs_reversed'][0]['output']['outcar'])
structure_dicts.append(
p['calcs_reversed'][0]['input']['structure'])
zval_dicts.append(
p['calcs_reversed'][0]['output']['outcar']['zval_dict'])
# Add weight for sorting
# Want polarization calculations in order of nonpolar to polar for Polarization object
# This number needs to be bigger than the number of calculations
max_sort_weight = 1000000
if 'nonpolar_polarization' in p['task_label']:
sort_weight.append(0)
elif "polar_polarization" in p['task_label']:
sort_weight.append(max_sort_weight)
elif "interpolation_" in p['task_label']:
num = 0
part = re.findall(r'interpolation_[0-9]+_polarization',
p['task_label'])
if part != []:
part2 = re.findall(r'[0-9]+', part.pop())
if part2 != []:
num = part2.pop()
sort_weight.append(max_sort_weight - int(num))
# Sort polarization tasks
# nonpolar -> interpolation_n -> interpolation_n-1 -> ... -> interpolation_1 -> polar
data = zip(tasks, structure_dicts, outcars, energies_per_atom,
energies, sort_weight)
data = sorted(data, key=lambda x: x[-1])
# Get the tasks, structures, etc in sorted order from the zipped data.
tasks, structure_dicts, outcars, energies_per_atom, energies, sort_weight = zip(
*data)
structures = [
Structure.from_dict(structure) for structure in structure_dicts
]
# If LCALCPOL = True then Outcar will parse and store the pseudopotential zvals.
zval_dict = zval_dicts.pop()
# Assumes that we want to calculate the ionic contribution to the dipole moment.
# VASP's ionic contribution is sometimes strange.
# See pymatgen.analysis.ferroelectricity.polarization.Polarization for details.
p_elecs = [outcar['p_elec'] for outcar in outcars]
p_ions = [
get_total_ionic_dipole(structure, zval_dict)
for structure in structures
]
polarization = Polarization(p_elecs, p_ions, structures)
p_change = polarization.get_polarization_change().A1.tolist()
p_norm = polarization.get_polarization_change_norm()
polarization_max_spline_jumps = polarization.max_spline_jumps()
same_branch = polarization.get_same_branch_polarization_data(
convert_to_muC_per_cm2=True)
raw_elecs, raw_ions = polarization.get_pelecs_and_pions()
quanta = polarization.get_lattice_quanta(convert_to_muC_per_cm2=True)
energy_trend = EnergyTrend(energies_per_atom)
energy_max_spline_jumps = energy_trend.max_spline_jump()
polarization_dict = {}
def split_abc(var, var_name):
d = {}
for i, j in enumerate('abc'):
d.update({var_name + "_{}".format(j): var[:, i].A1.tolist()})
return d
# Add some sort of id for the structures? Like cid but more general?
# polarization_dict.update({'cid': cid})
# General information
polarization_dict.update(
{'pretty_formula': structures[0].composition.reduced_formula})
polarization_dict.update({'wfid': wfid})
polarization_dict.update({'task_label_order': tasks})
# Polarization information
polarization_dict.update({'polarization_change': p_change})
polarization_dict.update({'polarization_change_norm': p_norm})
polarization_dict.update(
{'polarization_max_spline_jumps': polarization_max_spline_jumps})
polarization_dict.update(
split_abc(same_branch, "same_branch_polarization"))
polarization_dict.update(
split_abc(raw_elecs, "raw_electron_polarization"))
polarization_dict.update(
split_abc(raw_ions, "raw_electron_polarization"))
polarization_dict.update(split_abc(quanta, "polarization_quanta"))
polarization_dict.update({"zval_dict": zval_dict})
# Energy information
polarization_dict.update(
{'energy_per_atom_max_spline_jumps': energy_max_spline_jumps})
polarization_dict.update({"energies": energies})
polarization_dict.update({"energies_per_atom": energies_per_atom})
polarization_dict.update({'outcars': outcars})
polarization_dict.update({"structures": structure_dicts})
# Write all the info to db.
coll = vaspdb.db["polarization_tasks"]
coll.insert_one(polarization_dict)
def test_get_structure_type_for_0D_material(self):
os.chdir(ROOT)
structure = Structure.from_file('POSCAR_O2')
test_type = get_structure_type(structure)
self.assertEqual(test_type, 'molecular')
def run_task(self, fw_spec):
from pymatgen.analysis.eos import EOS
eos = self.get("eos", "vinet")
tag = self["tag"]
db_file = env_chk(self.get("db_file"), fw_spec)
summary_dict = {"eos": eos}
to_db = self.get("to_db", True)
# collect and store task_id of all related tasks to make unique links with "tasks" collection
all_task_ids = []
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one(
{"task_label": "{} structure optimization".format(tag)})
all_task_ids.append(d["task_id"])
structure = Structure.from_dict(
d["calcs_reversed"][-1]["output"]['structure'])
summary_dict["structure"] = structure.as_dict()
summary_dict["formula_pretty"] = structure.composition.reduced_formula
# 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)
all_task_ids.append(d["task_id"])
summary_dict["energies"] = energies
summary_dict["volumes"] = volumes
summary_dict["all_task_ids"] = all_task_ids
# fit the equation of state
eos = EOS(eos)
eos_fit = eos.fit(volumes, energies)
summary_dict["bulk_modulus"] = eos_fit.b0_GPa
# TODO: find a better way for passing tags of the entire workflow to db - albalu
if fw_spec.get("tags", None):
summary_dict["tags"] = fw_spec["tags"]
summary_dict["results"] = dict(eos_fit.results)
summary_dict["created_at"] = datetime.utcnow()
# db_file itself is required but the user can choose to pass the results to db or not
if to_db:
mmdb.collection = mmdb.db["eos"]
mmdb.collection.insert_one(summary_dict)
else:
with open("bulk_modulus.json", "w") as f:
f.write(json.dumps(summary_dict, default=DATETIME_HANDLER))
# TODO: @matk86 - there needs to be a builder to put it into materials collection... -computron
logger.info("Bulk modulus calculation complete.")
def test_get_structure_type_for_layered_material(self):
os.chdir(ROOT)
structure = Structure.from_file('POSCAR_FeCl2')
test_type = get_structure_type(structure)
self.assertEqual(test_type, 'layered')
from pymatgen import Structure
from pymatgen.core.surface import SlabGenerator
structure = Structure.from_file('POSCAR_conv')
structure.add_oxidation_state_by_element({"Ca": 2, "Bi": -3, "O": -2})
#structure.add_oxidation_state_by_guess()
# These are distances in Angstroms
dist = [10, 15, 20, 25, 30]
# We iterate through the distances twice, once for vac, once for slab
for vac in dist:
for thickness in dist:
slabgen = SlabGenerator(structure,
miller_index=(0, 0, 1),
min_slab_size=thickness,
min_vacuum_size=vac,
lll_reduce=True)
slabs = slabgen.get_slabs()
dipole_free_slabs = []
for slab in slabs:
if not slab.is_polar():
dipole_free_slabs.append(slab)
slab = dipole_free_slabs[0] # <-- put a number in here!
slab.to(fmt='poscar',
filename='slab_{}_{}.vasp'.format(thickness, vac))
def get_elastic_analysis(opt_task, defo_tasks):
"""
Performs the analysis of opt_tasks and defo_tasks necessary for
an elastic analysis
Args:
opt_task: task doc corresponding to optimization
defo_tasks: task_doc corresponding to deformations
Returns:
elastic document with fitted elastic tensor and analysis
"""
elastic_doc = {"warnings": []}
opt_struct = Structure.from_dict(opt_task['output']['structure'])
d_structs = [
Structure.from_dict(d['output']['structure']) for d in defo_tasks
]
defos = [
calculate_deformation(opt_struct, def_structure)
for def_structure in d_structs
]
# Warning if deformation is not equivalent to stored deformation
stored_defos = [d['transmuter']['transformation_params'][0]\
['deformation'] for d in defo_tasks]
if not np.allclose(defos, stored_defos, atol=1e-5):
wmsg = "Inequivalent stored and calc. deformations."
logger.warning(wmsg)
elastic_doc["warnings"].append(wmsg)
# Collect all fitting data and task ids
defos = [Deformation(d) for d in defos]
strains = [d.green_lagrange_strain for d in defos]
vasp_stresses = [d['output']['stress'] for d in defo_tasks]
cauchy_stresses = [-0.1 * Stress(s) for s in vasp_stresses]
pk_stresses = [
Stress(s.piola_kirchoff_2(d)) for s, d in zip(cauchy_stresses, defos)
]
defo_task_ids = [d['task_id'] for d in defo_tasks]
# Determine whether data is sufficient to fit tensor
# If raw data is insufficient but can be symmetrically transformed
# to provide a sufficient set, use the expanded set with appropriate
# symmetry transformations, fstresses/strains are "fitting
# strains" below.
vstrains = [s.voigt for s in strains]
if np.linalg.matrix_rank(vstrains) < 6:
symmops = SpacegroupAnalyzer(opt_struct).get_symmetry_operations()
fstrains = [[s.transform(symmop) for symmop in symmops]
for s in strains]
fstrains = list(chain.from_iterable(fstrains))
vfstrains = [s.voigt for s in fstrains]
if not np.linalg.matrix_rank(vfstrains) == 6:
logger.warning("Insufficient data to form SOEC")
elastic_doc['warnings'].append("insufficient strains")
return None
else:
fstresses = [[s.transform(symmop) for symmop in symmops]
for s in pk_stresses]
fstresses = list(chain.from_iterable(fstresses))
else:
fstrains = strains
fstresses = pk_stresses
with warnings.catch_warnings():
warnings.simplefilter('ignore')
if len(cauchy_stresses) == 24:
elastic_doc['legacy_fit'] = legacy_fit(strains, cauchy_stresses)
et_raw = ElasticTensor.from_pseudoinverse(fstrains, fstresses)
et = et_raw.voigt_symmetrized.convert_to_ieee(opt_struct)
defo_tasks = sorted(defo_tasks, key=lambda x: x['completed_at'])
vasp_input = opt_task['input']
vasp_input.pop('structure')
elastic_doc.update({
"deformation_task_ids": defo_task_ids,
"optimization_task_id": opt_task['task_id'],
"pk_stresses": pk_stresses,
"cauchy_stresses": cauchy_stresses,
"strains": strains,
"deformations": defos,
"elastic_tensor": et.voigt,
"elastic_tensor_raw": et_raw.voigt,
"optimized_structure": opt_struct,
"completed_at": defo_tasks[-1]['completed_at'],
"optimization_input": vasp_input
})
# Process input
elastic_doc['warnings'] = get_warnings(et, opt_struct) or None
#TODO: process MPWorks metadata?
#TODO: higher order
#TODO: add some of the relevant DFT params, kpoints
elastic_doc['state'] = "filter_failed" if elastic_doc['warnings']\
else "successful"
return elastic_doc
def run_task(self, fw_spec):
# handle arguments and database setup
db_file = env_chk(self.get("db_file"), fw_spec)
tag = self["tag"]
vasp_db = VaspCalcDb.from_db_file(db_file, admin=True)
# get the energies, volumes and DOS objects by searching for the tag
static_calculations = vasp_db.collection.find(
{'$and': [{
'metadata.tag': tag
}, {
'adopted': True
}]})
energies = []
volumes = []
dos_objs = [] # pymatgen.electronic_structure.dos.Dos objects
structure = None # single Structure for QHA calculation
for calc in static_calculations:
energies.append(calc['output']['energy'])
volumes.append(calc['output']['structure']['lattice']['volume'])
dos_objs.append(vasp_db.get_dos(calc['task_id']))
# get a Structure. We only need one for the masses and number of atoms in the unit cell.
if structure is None:
structure = Structure.from_dict(calc['output']['structure'])
# sort everything in volume order
# note that we are doing volume last because it is the thing we are sorting by!
energies = sort_x_by_y(energies, volumes)
dos_objs = sort_x_by_y(dos_objs, volumes)
volumes = sorted(volumes)
qha_result = {}
qha_result['structure'] = structure.as_dict()
qha_result['formula_pretty'] = structure.composition.reduced_formula
qha_result['elements'] = sorted(
[el.name for el in structure.composition.elements])
qha_result['metadata'] = self.get('metadata', {})
qha_result['has_phonon'] = self['phonon']
poisson = self.get('poisson', 0.363615)
bp2gru = self.get('bp2gru', 1)
# phonon properties
if self['phonon']:
# get the vibrational properties from the FW spec
phonon_calculations = list(vasp_db.db['phonon'].find(
{'$and': [{
'metadata.tag': tag
}, {
'adopted': True
}]}))
vol_vol = [
calc['volume'] for calc in phonon_calculations
] # these are just used for sorting and will be thrown away
vol_f_vib = [calc['F_vib'] for calc in phonon_calculations]
# sort them order of the unit cell volumes
vol_f_vib = sort_x_by_y(vol_f_vib, vol_vol)
f_vib = np.vstack(vol_f_vib)
qha = Quasiharmonic(energies,
volumes,
structure,
dos_objects=dos_objs,
F_vib=f_vib,
t_min=self['t_min'],
t_max=self['t_max'],
t_step=self['t_step'],
poisson=poisson,
bp2gru=bp2gru)
qha_result['phonon'] = qha.get_summary_dict()
qha_result['phonon']['temperatures'] = qha_result['phonon'][
'temperatures'].tolist()
# calculate the Debye model results no matter what
qha_debye = Quasiharmonic(energies,
volumes,
structure,
dos_objects=dos_objs,
F_vib=None,
t_min=self['t_min'],
t_max=self['t_max'],
t_step=self['t_step'],
poisson=poisson,
bp2gru=bp2gru)
# fit 0 K EOS for good measure
eos = Vinet(volumes, energies)
eos.fit()
errors = eos.func(volumes) - energies
sum_square_error = float(np.sum(np.square(errors)))
eos_res = {}
eos_res['b0_GPa'] = float(eos.b0_GPa)
eos_res['b0'] = float(eos.b0)
eos_res['b1'] = float(eos.b1)
eos_res['eq_volume'] = float(eos.v0)
eos_res['eq_energy'] = float(eos.e0)
eos_res['energies'] = energies
eos_res['volumes'] = volumes
eos_res['name'] = 'Vinet'
eos_res['error'] = {}
eos_res['error']['difference'] = errors.tolist(
) # volume by volume differences
eos_res['error']['sum_square_error'] = sum_square_error
qha_result['eos'] = eos_res
qha_result['debye'] = qha_debye.get_summary_dict()
qha_result['debye']['poisson'] = poisson
qha_result['debye']['bp2gru'] = bp2gru
qha_result['debye']['temperatures'] = qha_result['debye'][
'temperatures'].tolist()
qha_result['version_atomate'] = atomate_ver
qha_result['version_dfttk'] = dfttk_ver
volumes_false = []
energies_false = []
static_falses = vasp_db.collection.find(
{'$and': [{
'metadata.tag': tag
}, {
'adopted': False
}]})
for static_false in static_falses:
volumes_false.append(
static_false['output']['structure']['lattice']['volume'])
energies_false.append(static_false['output']['energy'])
qha_result['Volumes_fitting_false'] = volumes_false
qha_result['Energies_fitting_false'] = energies_false
print('Volumes_fitting_false : %s' % volumes_false)
print('Energies_fitting_false: %s' % energies_false)
# write to JSON for debugging purposes
import json
with open('qha_summary.json', 'w') as fp:
json.dump(qha_result, fp)
if self['phonon']:
vasp_db.db['qha_phonon'].insert_one(qha_result)
else:
vasp_db.db['qha'].insert_one(qha_result)
def run_task(self, fw_spec):
additional_fields = self.get("additional_fields", {})
# pass the additional_fields first to avoid overriding BoltztrapAnalyzer items
d = additional_fields.copy()
btrap_dir = os.path.join(os.getcwd(), "boltztrap")
d["boltztrap_dir"] = btrap_dir
bta = BoltztrapAnalyzer.from_files(btrap_dir)
d.update(bta.as_dict())
d["scissor"] = bta.intrans["scissor"]
# 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"]
bandstructure_dir = os.getcwd()
d["bandstructure_dir"] = bandstructure_dir
# add the structure
v, o = get_vasprun_outcar(bandstructure_dir,
parse_eigen=False,
parse_dos=False)
structure = v.final_structure
d["structure"] = structure.as_dict()
d["formula_pretty"] = structure.composition.reduced_formula
d.update(get_meta_from_structure(structure))
# 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:
del d["dos"]
with open(os.path.join(btrap_dir, "boltztrap.json"), "w") as f:
f.write(json.dumps(d, default=DATETIME_HANDLER))
else:
mmdb = VaspCalcDb.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)
for struct, fname in zip(structs, fnames):
filename = NAME + '_' + fname.lower() + '.' + fmt
if "POSCAR" in fname and flag:
# it is a bug in pymatgen
print("The string POSCAR in filename will be replaced by poscar")
flag = False
if fmt == 'lammps':
system = structure2system(struct)
system.to_lammps_lmp(filename)
continue
try:
print("Write {0:20s} {1:s}".format(fname, 'Pymatgen:Struture'))
struct.to(fmt, filename)
except:
print("Write {0:20s} {1:s} ".format(fname, 'Atoms:Atoms'))
atoms = pmg2ase(struct)
write(filename)
return True
if __name__ == "__main__":
from pymatgen import Structure
structure = Structure.from_file('POSCAR')
system = structure2system(structure)
print(system)
print('--')
covert_operation([structure], ["POSCAR"], 'lammps')
modify_incar_params = { 'Full relax': {'incar_update': {"LAECHG":False,"LCHARG":False,"LWAVE":False}},
'PreStatic': {'incar_update': {"LAECHG":False,"LCHARG":False,"LWAVE":False}},
'PS2': {'incar_update': {"LAECHG":False,"LCHARG":False,"LWAVE":False}},
'static': {'incar_update': {"LAECHG":False,"LCHARG":False,"LWAVE":False}},
"""
modify_incar_params = {}
#dict, dict of class ModifyKpoints with keywords in Workflow name, similar with modify_incar_params
modify_kpoints_params = {}
#bool, print(True) or not(False) some informations, used for debug
verbose = False
###################### DO NOT CHANGE THE FOLLOWING LINES ##############################
from pymatgen import MPRester, Structure
from dfttk.wflows import get_wf_gibbs
structure = Structure.from_file(TEMPLATE_STRUCTURE_FILENAME)
if magmom:
structure.add_site_property('magmom', magmom)
if not db_file:
from fireworks.fw_config import config_to_dict
from monty.serialization import loadfn
db_file = loadfn(config_to_dict()["FWORKER_LOC"])["env"]["db_file"]
wf = get_wf_gibbs(structure,
num_deformations=num_deformations,
deformation_fraction=deformation_fraction,
phonon=phonon,
phonon_supercell_matrix=phonon_supercell_matrix,
t_min=t_min,
