def from_dict(self, d={}):
"""Load from a dictionary."""
return LammpsJob(
atoms=Atoms.from_dict(d["atoms"]),
element_order=self.element_order,
parameters=d["parameters"],
lammps_cmd=d["lammps_cmd"],
output_file=d["output_file"],
stderr_file=d["stderr_file"],
jobname=d["jobname"],
attempts=d["attempts"],
copy_files=d["copy_files"],
)
def from_dict(self, d={}):
"""Convert class from a dictionary."""
return Wannier90win(
struct=Atoms.from_dict(d["struct"]),
efermi=d["efermi"],
soc=d["soc"],
dis_num_iter=d["dis_num_iter"],
dis_mix_ratio=d["dis_mix_ratio"],
num_iter=d["num_iter"],
num_print_cycles=d["num_print_cycles"],
frozen_tol=d["frozen_tol"],
semi_core_states=d["semi_core_states"],
kmesh_tol=d["kmesh_tol"],
)
def from_dict(self, d={}):
"""Construct class from a dictionary."""
return WTin(
atoms=Atoms.from_dict(d["atoms"]),
nelect=d["nelect"],
wannierout=d["wannierout"],
efermi=d["efermi"],
soc=d["soc"],
exclude=d["exclude"],
nwan=d["nwan"],
wtin=d["wtin"],
miller=d["miller"],
semi_core_states=d["semi_core_states"],
)
def test_magnetism_setup():
from jarvis.db.figshare import get_jid_data
atoms = Atoms.from_dict(
get_jid_data(jid="JVASP-78681", dataset="dft_3d")["atoms"]
)
mag = MagneticOrdering(atoms)
symm_list, ss = mag.get_minimum_configs(min_configs=3)
assert len(symm_list) == 3
assert ss.num_atoms == 8
mag_atoms = mag.get_mag_ions()
assert mag_atoms == ["Mn"]
tc = mag.tc_mean_field()
assert round(tc["Tc"], 2) == round(3868.17, 2)
def get_2d_hetero_jids(jid1="JVASP-664", jid2="JVASP-52"):
from jarvis.db.figshare import get_jid_data
m1 = get_jid_data(jid1)["atoms"]
m2 = get_jid_data(jid2)["atoms"]
mat1 = Atoms.from_dict(m1)
mat2 = Atoms.from_dict(m2)
vac = max(mat1.lattice_mat[2][2], mat2.lattice_mat[2][2])
print("jid1", jid1)
print(mat1)
print("jid2", jid2)
print(mat2)
combined = make_interface(
film=mat1.center_around_origin(),
# max_area=1000,
max_area=400,
max_area_ratio_tol=0.09,
# ltol=0.5,
ltol=0.05,
# atol=1.1,
atol=0.1,
subs=mat2.center_around_origin(),
)["interface"]
return combined
def test_vacancy():
box = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]
coords = [[0, 0, 0], [0.25, 0.25, 0.25]]
elements = ["Si", "Si"]
Si = Atoms(lattice_mat=box, coords=coords, elements=elements)
v = Vacancy(atoms=Si)
vacs = v.generate_defects(enforce_c_size=10.0)
td = vacs[0].to_dict()
fd = Vacancy.from_dict(td)
# print ( len(vacs),(vacs[0].to_dict()["defect_structure"].num_atoms))
# print (vacs[0].to_dict()["defect_structure"])
assert (
len(vacs),
Atoms.from_dict(vacs[0].to_dict()["defect_structure"]).num_atoms,
) == (1, 53)
def test_graph():
from jarvis.core.atoms import Atoms
from jarvis.db.figshare import get_jid_data
atoms = Atoms.from_dict(get_jid_data("JVASP-664")["atoms"])
feature_sets = ("atomic_number", "basic", "cfid", "cgcnn")
for i in feature_sets:
g = Graph.atom_dgl_multigraph(atoms=atoms, atom_features=i)
print(i, g)
g = Graph.from_atoms(atoms=atoms, features="atomic_number")
g = Graph.from_atoms(atoms=atoms, features="atomic_fraction")
g = Graph.from_atoms(
atoms=atoms,
features="basic",
get_prim=True,
zero_diag=True,
node_atomwise_angle_dist=True,
node_atomwise_rdf=True,
)
g = Graph.from_atoms(
atoms=atoms,
features="cfid",
get_prim=True,
zero_diag=True,
node_atomwise_angle_dist=True,
node_atomwise_rdf=True,
)
g = Graph.from_atoms(
atoms=atoms,
features="atomic_number",
get_prim=True,
zero_diag=True,
node_atomwise_angle_dist=True,
node_atomwise_rdf=True,
)
g = Graph.from_atoms(atoms=atoms, features="basic")
g = Graph.from_atoms(atoms=atoms,
features=["Z", "atom_mass", "max_oxid_s"])
g = Graph.from_atoms(atoms=atoms, features="cfid", max_cut=10000)
print(g)
d = g.to_dict()
g = Graph.from_dict(d)
num_nodes = g.num_nodes
num_edges = g.num_edges
print(num_nodes, num_edges)
assert num_nodes == 48
assert num_edges == 2256
assert (g.adjacency_matrix.shape) == (48, 48)
def from_dict(self, d={}):
"""Construct class from a dictionary."""
if d["atoms"] is not None:
atoms = Atoms.from_dict(d["atoms"])
else:
atoms = None
return Chgcar(
filename=d["filename"],
atoms=atoms,
chg=d["chg"],
dim=d["dim"],
chgdif=d["chgdif"],
aug=d["aug"],
augdiff=d["augdiff"],
nsets=d["nsets"],
)
def test_magnetism_setup():
from jarvis.db.figshare import get_jid_data
from jarvis.core.atoms import get_supercell_dims
atoms = Atoms.from_dict(
get_jid_data(jid="JVASP-78681", dataset="dft_3d")["atoms"]
)
dim = get_supercell_dims(atoms)
print("dim=", dim)
dim = [2, 2, 2]
symm_list, ss = get_unique_magnetic_structures(
atoms, supercell_dim=dim, magnetic_ions=["Mn"]
)
print("dim=", dim, len(symm_list))
assert len(symm_list)==5
assert ss.num_atoms == 16
def test_dataset():
d = data("dft_2d")
d = pd.DataFrame(d[:100])
graphs = []
for i, row in d.iterrows():
structure = Atoms.from_dict(row["atoms"])
g = Graph.atom_dgl_multigraph(
structure,
atom_features="atomic_number",
compute_line_graph=False,
)
graphs.append(g)
s = StructureDataset(d, graphs, "formation_energy_peratom")
col = s.collate
def __init__(
self,
structures,
targets,
ids=None,
cutoff=8.0,
maxrows=np.inf,
atom_features="atomic_number",
transform=None,
enforce_undirected=False,
max_neighbors=12,
classification=False,
):
"""Initialize the class."""
self.graphs = []
self.labels = []
self.ids = []
for idx, (structure, target,
id) in enumerate(tqdm(zip(structures, targets, ids))):
if idx >= maxrows:
break
a = Atoms.from_dict(structure)
g = Graph.atom_dgl_multigraph(
a,
atom_features=atom_features,
enforce_undirected=enforce_undirected,
cutoff=cutoff,
max_neighbors=max_neighbors,
id=id,
)
self.graphs.append(g)
self.labels.append(target)
self.ids.append(id)
self.labels = torch.tensor(self.labels).type(torch.get_default_dtype())
if classification:
self.labels = self.labels.view(-1).long()
print("Classification dataset.", self.labels)
self.transform = transform
def test_graph():
from jarvis.core.atoms import Atoms
from jarvis.db.figshare import get_jid_data
atoms = Atoms.from_dict(get_jid_data("JVASP-664")["atoms"])
g = Graph.from_atoms(atoms=atoms, features="atomic_number")
g = Graph.from_atoms(
atoms=atoms,
features="basic",
get_prim=True,
zero_diag=True,
node_atomwise_angle_dist=True,
node_atomwise_rdf=True,
)
g = Graph.from_atoms(
atoms=atoms,
features="cfid",
get_prim=True,
zero_diag=True,
node_atomwise_angle_dist=True,
node_atomwise_rdf=True,
)
g = Graph.from_atoms(
atoms=atoms,
features="atomic_number",
get_prim=True,
zero_diag=True,
node_atomwise_angle_dist=True,
node_atomwise_rdf=True,
)
g = Graph.from_atoms(atoms=atoms, features="basic")
g = Graph.from_atoms(atoms=atoms,
features=["Z", "atom_mass", "max_oxid_s"])
g = Graph.from_atoms(atoms=atoms, features="cfid")
print(g)
d = g.to_dict()
g = Graph.from_dict(d)
num_nodes = g.num_nodes
num_edges = g.num_edges
print(num_nodes, num_edges)
assert num_nodes == 48
assert num_edges == 2304
assert (g.adjacency_matrix.shape) == (48, 48)
def test_lat():
box = [[10, 0, 0], [0, 10, 0], [0, 0, 10]]
lat = Lattice(box)
td = lat.to_dict()
fd = Lattice.from_dict(td)
frac_coords = [[0, 0, 0], [0.5, 0.5, 0.5]]
cart_coords = [[0, 0, 0], [5, 5, 5]]
lll = lat._calculate_lll()
# print ('lll',lll[0][0][0])
lll_red = lat.get_lll_reduced_lattice()._lat
# print("lll_educed", lat.get_lll_reduced_lattice()._lat[0][0])
assert (
lat.lat_lengths(),
lat.lat_angles(),
round(lat.inv_lattice()[0][0], 2),
[round(i, 2) for i in lat.lat_angles(radians=True)],
lat.cart_coords(frac_coords)[1][1],
lat.frac_coords(cart_coords)[1][1],
lat.volume,
lat.parameters,
lll[0][0][0],
lll_red[0][0],
) == (
[10.0, 10.0, 10.0],
[90.0, 90.0, 90.0],
0.1,
[1.57, 1.57, 1.57],
5.0,
0.5,
1000.0,
[10.0, 10.0, 10.0, 90.0, 90.0, 90.0],
10.0,
10.0,
)
d = data('dft_3d')
for i in d:
if i['jid'] == 'JVASP-588':
atoms = Atoms.from_dict(i['atoms'])
lll = atoms.lattice._calculate_lll()
assert lll[1][0][0] == -1
def test_dataset():
d = data("dft_2d")
d = pd.DataFrame(d[:100])
graphs = []
for i, row in d.iterrows():
structure = Atoms.from_dict(row["atoms"])
g = Graph.atom_dgl_multigraph(
structure,
atom_features="atomic_number",
compute_line_graph=False,
)
graphs.append(g)
s = StructureDataset(
d, graphs, "formation_energy_peratom", line_graph=True
)
col = s.collate
col1 = s.collate_line_graph
ix = s[0]
sz = len(s)
s_std = s.setup_standardizer([1, 2])
def test_basic_atoms():
box = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]
coords = [[0, 0, 0], [0.25, 0.2, 0.25]]
elements = ["Si", "Si"]
Si = Atoms(lattice_mat=box, coords=coords, elements=elements)
dim = get_supercell_dims(Si)
assert dim == [3, 3, 3]
polar = Si.check_polar
Si.props = ["a", "a"]
vac_pad = VacuumPadding(Si)
den_2d = round(vac_pad.get_effective_2d_slab().density, 2)
den_0d = round(vac_pad.get_effective_molecule().density, 2)
den_lll_red = round(Si.get_lll_reduced_structure().density, 2)
strng = Si.get_string()
scell_nat = Si.make_supercell([2, 2, 2]).num_atoms
scell_nat2 = Si.make_supercell_matrix([[2, 0, 0], [0, 2, 0],
[0, 0, 2]]).num_atoms
# print("scell_nat,scell_nat2", scell_nat, scell_nat2)
# print(Si.make_supercell([2, 2, 2]))
# print()
# print(Si.make_supercell_matrix([[2, 0, 0], [0, 2, 0], [0, 0, 2]]))
com = round(Si.get_center_of_mass()[0], 3)
rem = (Si.make_supercell([2, 2, 2]).remove_site_by_index(site=0)).num_atoms
prim = Si.get_primitive_atoms
print(prim.cart_coords)
assert round(prim.cart_coords[0][0], 2) == round(4.37815150, 2)
# print ('raw_distance_matrix', prim.raw_distance_matrix)
# print ('raw_distance_matrix', Si.raw_distance_matrix)
# print ('distance_matrix', Si.pymatgen_converter().distance_matrix)
assert round(prim.raw_distance_matrix[0][1],
2) == round(4.42386329832851, 2)
print(prim.raw_angle_matrix)
d = Si.to_dict()
new_at = Atoms.from_dict(d)
angs_a = d["angles"][0]
Si_2_den = Atoms(
lattice_mat=d["lattice_mat"],
coords=d["coords"],
elements=d["elements"],
).density
Si_xyz = Si.get_xyz_string
Si.write_xyz(filename="atoms.xyz")
tmp = Atoms.from_xyz(filename="atoms.xyz")
cmd = 'rm atoms.xyz'
os.system(cmd)
Si.center_around_origin()
# print ('scell_nat', Si_2)
assert (
round(Si.volume, 2),
Si.atomic_numbers,
Si.num_atoms,
Si.frac_coords[0][0],
Si.cart_coords[0][0],
round(Si.density, 2),
Si.spacegroup(),
Si.pymatgen_converter() != {},
polar,
Si.props[0],
den_2d,
den_0d,
round(Si.packing_fraction, 2),
Si.composition.to_dict(),
strng != "",
den_lll_red,
scell_nat,
com,
rem,
angs_a,
round(Si_2_den, 2),
) == (
40.03,
[14, 14],
2,
0,
0.0,
2.33,
"C2/m (12)",
True,
False,
"a",
0.35,
0.01,
0.28,
{
"Si": 2
},
True,
2.33,
16,
0.679,
15,
60.0,
2.33,
)
cc = Si.center()
cc = Si.center(axis=[0, 0, 1])
m1 = Atoms.from_dict(get_jid_data("JVASP-6640")["atoms"])
assert m1.check_polar == True
print("Strain test")
print(m1)
m1.apply_strain(0.1)
print(m1)
assert m1.lattice_mat[2][2] == 32.8717576
m1.apply_strain([0, 0, 0.1])
assert m1.lattice_mat[2][2] == 36.158933360000006
filename = "atoms.cif"
m1.write_cif(filename)
a = Atoms.from_cif(filename)
filename = "POSCAR"
m1.write_poscar(filename)
m2 = Atoms.from_poscar(filename)
filename = "atoms.xyz"
m1.write_xyz(filename)
m3 = Atoms.from_xyz(filename)
cmd = "rm atoms.xyz POSCAR atoms.cif"
os.system(cmd)
def test_extra_spgs():
from jarvis.db.figshare import data
d = data("dft_3d")
few_spgs = {
"JVASP-4663": 119,
"JVASP-4666": 194,
"JVASP-588": 160,
"JVASP-4669": 191,
"JVASP-4672": 141,
"JVASP-581": 164,
"JVASP-4687": 139,
"JVASP-4693": 62,
"JVASP-4696": 187,
"JVASP-4711": 225,
"JVASP-4714": 2,
"JVASP-4723": 60,
"JVASP-32": 167,
"JVASP-107": 186,
"JVASP-4756": 63,
"JVASP-96": 216,
"JVASP-4334": 123,
"JVASP-4222": 11,
"JVASP-4804": 156,
"JVASP-329": 129,
"JVASP-4852": 163,
"JVASP-155": 61,
"JVASP-4340": 51,
"JVASP-4343": 15,
"JVASP-4361": 14,
"JVASP-137": 72,
"JVASP-4879": 166,
"JVASP-4885": 19,
"JVASP-4894": 31,
"JVASP-4216": 12,
"JVASP-4918": 121,
"JVASP-4948": 25,
"JVASP-4957": 221,
"JVASP-4960": 65,
"JVASP-5059": 189,
"JVASP-5071": 66,
"JVASP-5074": 150,
"JVASP-5086": 74,
"JVASP-4388": 64,
"JVASP-4391": 136,
"JVASP-5155": 127,
"JVASP-5185": 43,
"JVASP-5197": 59,
"JVASP-5212": 29,
"JVASP-164": 176,
"JVASP-5224": 137,
"JVASP-5227": 148,
"JVASP-4234": 193,
"JVASP-5257": 7,
"JVASP-5266": 140,
"JVASP-4397": 33,
"JVASP-5317": 8,
"JVASP-5332": 13,
"JVASP-5353": 16,
"JVASP-5371": 4,
"JVASP-5407": 229,
"JVASP-5416": 147,
"JVASP-5509": 52,
"JVASP-5536": 70,
"JVASP-5560": 71,
"JVASP-5680": 28,
"JVASP-5740": 6,
"JVASP-5839": 162,
"JVASP-5863": 79,
"JVASP-110": 99,
"JVASP-579": 38,
"JVASP-4501": 5,
"JVASP-91": 227,
"JVASP-41": 154,
"JVASP-4516": 96,
"JVASP-4564": 82,
"JVASP-4645": 130,
"JVASP-152": 55,
"JVASP-4792": 88,
"JVASP-5041": 107,
"JVASP-5425": 87,
"JVASP-5464": 115,
"JVASP-5650": 157,
"JVASP-4450": 36,
"JVASP-22520": 152,
"JVASP-22523": 205,
"JVASP-11998": 53,
"JVASP-22528": 58,
"JVASP-22533": 41,
"JVASP-12091": 32,
"JVASP-22541": 215,
"JVASP-22543": 97,
"JVASP-22549": 9,
"JVASP-12103": 138,
"JVASP-22575": 40,
"JVASP-22602": 180,
"JVASP-22611": 182,
"JVASP-12022": 85,
"JVASP-22637": 111,
"JVASP-12139": 10,
"JVASP-22709": 92,
"JVASP-12060": 20,
"JVASP-12064": 1,
"JVASP-12194": 185,
"JVASP-13885": 128,
"JVASP-14096": 56,
"JVASP-14020": 122,
"JVASP-13904": 54,
"JVASP-13783": 135,
"JVASP-14213": 26,
"JVASP-14034": 23,
"JVASP-14158": 113,
"JVASP-14256": 21,
"JVASP-32150": 217,
"JVASP-28392": 224,
"JVASP-32180": 146,
"JVASP-32197": 57,
"JVASP-31813": 67,
"JVASP-31819": 219,
"JVASP-29262": 39,
"JVASP-29281": 102,
"JVASP-31825": 226,
"JVASP-29425": 143,
"JVASP-29441": 42,
"JVASP-29520": 73,
"JVASP-29526": 18,
"JVASP-29555": 149,
"JVASP-29597": 151,
"JVASP-29705": 174,
"JVASP-31921": 199,
"JVASP-33147": 161,
"JVASP-33220": 46,
"JVASP-33344": 114,
"JVASP-30263": 44,
"JVASP-33832": 155,
"JVASP-30458": 69,
"JVASP-30461": 144,
"JVASP-30518": 132,
"JVASP-36540": 198,
"JVASP-36548": 220,
"JVASP-36568": 3,
"JVASP-36573": 145,
"JVASP-35061": 131,
"JVASP-35137": 125,
"JVASP-35222": 204,
"JVASP-35344": 109,
"JVASP-42053": 173,
"JVASP-40216": 91,
"JVASP-38594": 165,
"JVASP-37330": 86,
"JVASP-37573": 84,
"JVASP-36714": 47,
"JVASP-36754": 98,
"JVASP-59313": 230,
"JVASP-46893": 95,
"JVASP-46896": 76,
"JVASP-45779": 30,
"JVASP-45831": 158,
"JVASP-46446": 35,
"JVASP-44393": 159,
"JVASP-44773": 22,
"JVASP-47741": 78,
"JVASP-47811": 181,
"JVASP-48055": 94,
"JVASP-48916": 34,
"JVASP-49907": 190,
"JVASP-50342": 223,
"JVASP-50360": 68,
"JVASP-50431": 37,
"JVASP-52902": 142,
"JVASP-52377": 24,
"JVASP-50791": 214,
"JVASP-54512": 108,
"JVASP-56567": 213,
"JVASP-54867": 126,
"JVASP-55180": 81,
"JVASP-57780": 212,
"JVASP-57807": 118,
"JVASP-57816": 50,
"JVASP-57100": 197,
"JVASP-57138": 116,
"JVASP-58233": 124,
"JVASP-59682": 200,
"JVASP-20180": 206,
"JVASP-21448": 203,
"JVASP-40468": 90,
"JVASP-42914": 17,
"JVASP-21594": 100,
"JVASP-21706": 188,
"JVASP-22783": 218,
"JVASP-24006": 202,
"JVASP-30879": 83,
"JVASP-31186": 49,
"JVASP-21031": 110,
"JVASP-21116": 192,
"JVASP-25245": 134,
"JVASP-7066": 169,
"JVASP-13017": 112,
"JVASP-58953": 105,
"JVASP-8682": 183,
"JVASP-9902": 80,
"JVASP-34882": 208,
"JVASP-34330": 179,
"JVASP-34502": 48,
"JVASP-62982": 178,
}
"""
mem=[]
spgs=[]
pos=[]
info=defaultdict()
for i in d:
a=Atoms.from_dict(i['atoms'])
spg=Spacegroup3D(a).space_group_number
if spg not in spgs:
spgs.append(spg)
pos.append(i['jid'])
info[i['jid']]=spg
print (info)
if len(spgs)==230:
break
"""
for i, j in few_spgs.items():
for ii in d:
if ii["jid"] == i:
a = Atoms.from_dict(ii["atoms"])
spg = Spacegroup3D(a).space_group_number
assert j == spg
lattice_mat = a.lattice_mat
kp = Kpoints3D().automatic_length_mesh(lattice_mat=lattice_mat,
length=40)
sym = kp.high_symm_path(a)._path
# print (ii['jid'],a)
# TODO: following line failing for JVASP-4222
# x, y = kp.interpolated_points(a)
break
def from_dict(self, d={}):
"""Construct Poscar object from a dictionary."""
return Poscar(atoms=Atoms.from_dict(d["atoms"]), comment=d["comment"])
from jarvis.tasks.lammps.lammps import LammpsJob, JobFactory
from jarvis.core.atoms import Atoms
from jarvis.db.figshare import get_jid_data
from jarvis.analysis.structure.spacegroup import Spacegroup3D
# atoms = Atoms.from_poscar('POSCAR')
# Get Aluminum FCC
tmp_dict = get_jid_data(jid="JVASP-816", dataset="dft_3d")["atoms"]
atoms = Atoms.from_dict(tmp_dict)
# Get conventional cell
spg = Spacegroup3D(atoms)
cvn_atoms = spg.conventional_standard_structure
ff = "/users/knc6/Software/LAMMPS/lammps-master/potentials/Al_zhou.eam.alloy"
mod = "/users/knc6/Software/Devs/jarvis/jarvis/tasks/lammps/templates/inelast.mod"
cmd = "/users/knc6/Software/LAMMPS/lammps-master/src/lmp_serial<in.main>out"
parameters = {
"pair_style": "eam/alloy",
"pair_coeff": ff,
"atom_style": "charge",
"control_file": mod,
}
# Test LammpsJob
lmp = LammpsJob(atoms=cvn_atoms,
parameters=parameters,
lammps_cmd=cmd,
jobname="Test").runjob()
# Test high-throughput
job_fact = JobFactory(pair_style="eam/alloy", name="my_first_lammps_run")
job_fact.all_props_eam_alloy(atoms=cvn_atoms, ff_path=ff, lammps_cmd=cmd)
def test_spg229():
d = loadjson(os.path.join(os.path.dirname(__file__), "spg229.json"))
for i in d:
atoms = Atoms.from_dict(i["atoms"])
spg = Spacegroup3D(atoms).space_group_number
assert spg == i["spg_number"]
def upload_sample_data(curate_xml=False):
"""
Generate and upload XML files.
Set curate_xml==True to upload all the XML documents.
"""
d = data("dft_2d")
count = 0
for i in d[0:1]:
filname = str(i["jid"]) + str(".xml")
if not os.path.exists(filname):
count = count + 1
energy = (str(i["optb88vdw_total_energy"]) + str(",") +
str(i["formation_energy_peratom"]))
atoms = Atoms.from_dict(i["atoms"])
formula = str(atoms.composition.reduced_formula)
sgp = str(Spacegroup3D(atoms).space_group_symbol)
name = str(i["jid"])
print(name)
# ref = str("")
func = str("OptB88vdW")
elem = ""
species = atoms.elements
for j in species:
elem = str(elem) + str(j) + str("-")
encut = str(i["encut"])
kpoints = (str(i["kpoints_array"][0]) + str("x") +
str(i["kpoints_array"][1]) + str("x") +
str(i["kpoints_array"][2]))
el_tens = "na"
try:
el_tens = str(",".join(
map(str,
np.array(i["elastic_tensor"]).flatten())))
except Exception:
pass
KV = str(i["bulk_modulus_kv"])
GV = str(i["shear_modulus_gv"])
op_eg = str(i["optb88vdw_bandgap"])
mbj_eg = str(i["mbj_bandgap"])
realx_arr = str(i["epsx"])
mrealx_arr = str(i["mepsx"])
realy_arr = str(i["epsy"])
mrealy_arr = str(i["mepsy"])
realz_arr = str(i["epsz"])
mrealz_arr = str(i["mepsz"])
typ = str("2D") # 3D
other = str(
"Citation: 1) DOI:10.1038/s41598-017-05402-0" +
",2) DOI: 10.1038/sdata.2018.82, 3) arXiv:1804.01033v2 ")
struct = atoms.get_string()
data_json(
other=other,
energy=energy,
typ=typ,
formula=formula,
sgp=sgp,
name=name,
func=func,
elem=elem,
encut=encut,
kpoints=kpoints,
el_tens=el_tens,
KV=KV,
GV=GV,
op_eg=op_eg,
mbj_eg=mbj_eg,
realx_arr=realx_arr,
mrealx_arr=mrealx_arr,
realy_arr=realy_arr,
mrealy_arr=mrealy_arr,
realz_arr=realz_arr,
mrealz_arr=mrealz_arr,
struct=struct,
curate_xml=curate_xml,
)
from jarvis.db.figshare import data, get_jid_data
from jarvis.core.atoms import Atoms, crop_square
from jarvis.io.prismatic.inputs import write_prismatic_xyz
a = Atoms.from_dict(get_jid_data("JVASP-667")["atoms"])
def test_inputs():
c = crop_square(a)
lines = write_prismatic_xyz(c)
from jarvis.tasks.vasp.vasp import VaspJob, write_jobfact_optb88vdw
from jarvis.io.vasp.inputs import Potcar, Incar, Poscar
from jarvis.db.jsonutils import dumpjson
from jarvis.core.atoms import Atoms
from jarvis.core.kpoints import Kpoints3D
from jarvis.tasks.queue_jobs import Queue
import os
from jarvis.tasks.vasp.vasp import JobFactory, write_jobfact_optb88vdw
from jarvis.db.figshare import get_jid_data
tmp_dict = get_jid_data(jid="JVASP-816", dataset="dft_3d")["atoms"]
atoms = Atoms.from_dict(tmp_dict)
vasp_cmd = "mpirun /users/knc6/VASP/vasp54/src/vasp.5.4.1DobbySOC2/bin/vasp_std"
copy_files = ["/users/knc6/bin/vdw_kernel.bindat"]
submit_cmd = ["qsub", "submit_job"]
jids = ["JVASP-1002", "JVASP-1067"]
for jid in jids:
d = get_jid_data(jid=jid, dataset="dft_3d")
atoms = Atoms.from_dict(d["atoms"]).get_primitive_atoms
mat = Poscar(atoms)
mat.comment = "bulk@" + str(jid)
cwd_home = os.getcwd()
dir_name = d["jid"] + "_" + str("PBEBO")
if not os.path.exists(dir_name):
os.makedirs(dir_name)
os.chdir(dir_name)
job = JobFactory(
vasp_cmd=vasp_cmd,
poscar=mat,
def get_enp_jid(jid=""):
for i in dft2d:
if i["jid"] == jid:
return (i["optb88vdw_total_energy"] /
Atoms.from_dict(i["atoms"]).num_atoms)
def test_2d():
p = Poscar.from_file(C).atoms
vacs = Vacancy(atoms=p).generate_defects(enforce_c_size=10.0, extend=1)
# print (vacs[0]._defect_structure)
assert (Atoms.from_dict(
vacs[0].to_dict()["defect_structure"]).num_atoms == 49)
