def traj_to_json(atoms : ase.Atoms) -> str:
"""
Serialize an Atoms object in a human readable way
"""
def rounder(y : float)->float:
return round(y,3) + 0 #+0 turns -0 into 0
atomdata = []
const = atoms.constraints
if const: const_list = const[0].get_indices().tolist()
else: const_list = []
for a in atoms: atomdata.append({'number' : int(a.number)
,'x' : rounder(a.x)
,'y' : rounder(a.y)
,'z' : rounder(a.z)
,'magmom' : rounder(a.magmom)
,'tag' : int(a.tag)
,'constrained' : int(a.index in const_list)
,'index' : int(a.index)})
out = {'cell': [[rounder(x) for x in xx] for xx in atoms.get_cell().tolist()]
,'atomdata':atomdata}
return json.dumps(out)
def get_system_type(atoms: ase.Atoms) -> str:
"""
Helpful docstring
"""
############################################################################
# Parameters
#------------
cutoff = 6 # A
# Preprocessing
atoms.center() # In case dealing with Nerds Rope & Co.
minx, miny, minz, maxx, maxy, maxz = 1000, 1000, 1000, -1000, -1000, -1000
for a in atoms:
if a.position[0] < minx: minx = a.position[0]
if a.position[1] < miny: miny = a.position[1]
if a.position[2] < minz: minz = a.position[2]
if a.position[0] > maxx: maxx = a.position[0]
if a.position[1] > maxy: maxy = a.position[1]
if a.position[2] > maxz: maxz = a.position[2]
cell_abc = list(map(np.linalg.norm, atoms.get_cell()[:3]))
thickness = [maxx - minx, maxy - miny, maxz - minz]
pbc = [cell_abc[i] - thickness[i] < cutoff for i in range(3)]
if all(pbc): return 'bulk'
elif any(pbc): return 'surface'
else: return 'molecule'
def test_struc_from_ase():
from ase import Atoms
from ase.calculators.singlepoint import SinglePointCalculator
results = {
"forces": np.random.randn(20, 3),
"energy": np.random.rand(),
"stress": np.random.randn(6),
}
uc = Atoms(
["Pd" for i in range(10)] + ["Ag" for i in range(10)],
positions=np.random.rand(20, 3),
cell=np.random.rand(3, 3),
)
calculator = SinglePointCalculator(uc, **results)
uc.set_calculator(calculator)
new_struc = Structure.from_ase_atoms(uc)
assert np.all(new_struc.species_labels == uc.get_chemical_symbols())
assert np.all(new_struc.positions == uc.get_positions())
assert np.all(new_struc.cell == uc.get_cell())
assert np.all(new_struc.forces == results["forces"])
assert np.all(new_struc.energy == results["energy"])
assert np.all(new_struc.stress == results["stress"])
def test_electrostatics(self):
"""Tests that the results are consistent with the electrostatic
interpretation. Each matrix [i, j] element should correspond to the
Coulomb energy of a system consisting of the pair of atoms i, j.
"""
system = H2O
n_atoms = len(system)
a = 0.5
desc = EwaldSumMatrix(n_atoms_max=3, permutation="none", flatten=False)
# The Ewald matrix contains the electrostatic interaction between atoms
# i and j. Here we construct the total electrostatic energy for a
# system consisting of atoms i and j.
matrix = desc.create(system, a=a, rcut=rcut, gcut=gcut)
energy_matrix = np.zeros(matrix.shape)
for i in range(n_atoms):
for j in range(n_atoms):
if i == j:
energy_matrix[i, j] = matrix[i, j]
else:
energy_matrix[i, j] = matrix[i, j] + matrix[i, i] + matrix[j, j]
# Converts unit of q*q/r into eV
conversion = 1e10 * scipy.constants.e / (4 * math.pi * scipy.constants.epsilon_0)
energy_matrix *= conversion
# The value in each matrix element should correspond to the Coulomb
# energy of a system with with only those atoms. Here the energies from
# the Ewald matrix are compared against the Ewald energy calculated
# with pymatgen.
positions = system.get_positions()
atomic_num = system.get_atomic_numbers()
for i in range(n_atoms):
for j in range(n_atoms):
if i == j:
pos = [positions[i]]
sym = [atomic_num[i]]
else:
pos = [positions[i], positions[j]]
sym = [atomic_num[i], atomic_num[j]]
i_sys = Atoms(
cell=system.get_cell(),
positions=pos,
symbols=sym,
pbc=True,
)
structure = Structure(
lattice=i_sys.get_cell(),
species=i_sys.get_atomic_numbers(),
coords=i_sys.get_scaled_positions(),
)
structure.add_oxidation_state_by_site(i_sys.get_atomic_numbers())
ewald = EwaldSummation(structure, eta=a, real_space_cut=rcut, recip_space_cut=gcut)
energy = ewald.total_energy
# Check that the energy given by the pymatgen implementation is
# the same as given by the descriptor
self.assertTrue(np.allclose(energy_matrix[i, j], energy, atol=0.00001, rtol=0))
def sphere(atomlist, fill_factor=0.74, radius=None, cell=None):
"""Generates a random sphere of particles given an
atomlist and radius. If radius is None, one is
automatically estimated. min_dist and tries_b4_expand
are parameters that govern how stricly the proximity
of atoms are enforced.
"""
if radius is None:
radius = get_particle_radius(atomlist, fill_factor)
# Create a list of random order of the atoms
chemical_symbols = []
for atom in atomlist:
chemical_symbols += [atom[0]] * atom[1]
random.shuffle(chemical_symbols)
unit_vec = np.array([random_three_vector() for i in range(len(chemical_symbols))])
D = radius * np.random.sample(size=len(chemical_symbols)) ** (1.0/3.0)
positions = np.array([D]).T * unit_vec
indiv = Atoms(symbols=chemical_symbols, positions=positions)
if cell is not None:
indiv.set_cell(cell)
cell_center = np.sum(indiv.get_cell(), axis=1) / 2.0
indiv.translate(indiv.get_center_of_mass() + cell_center)
indiv.set_pbc(True)
return indiv
def get_structure_info(self):
path = os.path.join(self.maindir, 'sgroup.out')
if not os.path.isfile(path):
return
self.structure.mainfile = path
cellpar = self.structure.get('cellpar', None)
sym_pos = self.structure.get('sym_pos', {})
sym = sym_pos.get('symbols', None)
pos = sym_pos.get('positions', None)
if cellpar is None or sym is None or pos is None:
return
structure = Atoms(cell=cellpar,
scaled_positions=pos,
symbols=sym,
pbc=True)
positions = structure.get_positions()
positions = positions * ureg.angstrom
cell = structure.get_cell()
cell = cell * ureg.angstrom
return sym, positions, cell
def _compute_rdf_1frame(box,
posis,
atype,
sel_type=[None, None],
max_r=5,
nbins=100):
all_types = list(set(list(np.sort(atype))))
if sel_type[0] is None:
sel_type[0] = all_types
if sel_type[1] is None:
sel_type[1] = all_types
if type(sel_type[0]) is not list:
sel_type[0] = [sel_type[0]]
if type(sel_type[1]) is not list:
sel_type[1] = [sel_type[1]]
natoms = len(posis)
from ase import Atoms
import ase.neighborlist
atoms = Atoms(positions=posis, cell=box, pbc=[1, 1, 1])
nlist = ase.neighborlist.NeighborList(
max_r,
self_interaction=False,
bothways=True,
primitive=ase.neighborlist.NewPrimitiveNeighborList)
nlist.update(atoms)
stat = np.zeros(nbins)
hh = max_r / float(nbins)
for ii in range(natoms):
# atom "0"
if atype[ii] in sel_type[0]:
indices, offsets = nlist.get_neighbors(ii)
for jj, os in zip(indices, offsets):
# atom "1"
if atype[jj] in sel_type[1]:
posi_jj = atoms.positions[jj] + np.dot(
os, atoms.get_cell())
diff = posi_jj - atoms.positions[ii]
dr = np.linalg.norm(diff)
# if (np.linalg.norm(diff- diff_1)) > 1e-12 :
# raise RuntimeError
si = int(dr / hh)
if si < nbins:
stat[si] += 1
# count the number of atom1
c0 = 0
for ii in sel_type[0]:
c0 += np.sum(atype == ii)
# count the number of atom1
c1 = 0
for ii in sel_type[1]:
c1 += np.sum(atype == ii)
rho1 = c1 / np.linalg.det(box)
# compute rdf
for ii in range(nbins):
vol = 4. / 3. * np.pi * (((ii + 1) * hh)**3 - ((ii) * hh)**3)
rho = stat[ii] / vol
stat[ii] = rho / rho1 / c0
xx = np.arange(0, max_r - 1e-12, hh)
return xx, stat
def test_struc_from_ase():
from ase import Atoms
uc = Atoms(['Pd' for i in range(10)] + ['Ag' for i in range(10)],
positions=np.random.rand(20, 3),
cell=np.random.rand(3, 3))
new_struc = Structure.from_ase_atoms(uc)
assert np.all(new_struc.species_labels == uc.get_chemical_symbols())
assert np.all(new_struc.positions == uc.get_positions())
assert np.all(new_struc.cell == uc.get_cell())
def test_k2_weights_and_geoms_periodic(self):
"""Tests that the values of the weight and geometry functions are
correct for the k=2 term in periodic systems.
"""
atoms = Atoms(
cell=[
[10, 0, 0],
[10, 10, 0],
[10, 0, 10],
],
symbols=["H", "C"],
scaled_positions=[
[0.1, 0.5, 0.5],
[0.9, 0.5, 0.5],
]
)
mbtr = MBTR(
[1, 6],
k=[2],
grid=default_grid,
periodic=True,
weighting={
"k2": {
"function": "exponential",
"scale": 0.8,
"cutoff": 1e-3
},
},
)
mbtr.create(atoms)
weights = mbtr._k2_weights
geoms = mbtr._k2_geoms
# Test against the assumed geometry values
pos = atoms.get_positions()
distances = np.array([
np.linalg.norm(pos[0] - pos[1]),
np.linalg.norm(pos[0] - pos[1] + atoms.get_cell()[0, :]),
np.linalg.norm(pos[1] - pos[0] - atoms.get_cell()[0, :])
])
assumed_geoms = {
(0, 1): 1/distances
}
self.dict_comparison(geoms, assumed_geoms)
# Test against the assumed weights
weight_list = np.exp(-0.8*distances)
# The periodic distances are halved
weight_list[1:3] /= 2
assumed_weights = {
(0, 1): weight_list
}
self.dict_comparison(weights, assumed_weights)
def test_change_cell_dimensions_and_pbc(factory, dimer_params, lj_epsilons):
"""Ensure that post_change_box commands are actually executed after
changing the dimensions of the cell or its periodicity. This is done by
setting up an isolated dimer with a Lennard-Jones potential and a set of
post_changebox_cmds that specify the same potential but with a rescaled
energy (epsilon) parameter. The energy is then computed twice, once before
changing the cell dimensions and once after, and the values are compared to
the expected values based on the two different epsilons to ensure that the
modified LJ potential is used for the second calculation. The procedure is
repeated but where the periodicity of the cell boundaries is changed rather
than the cell dimensions.
"""
# Make a dimer in a large nonperiodic box
dimer = Atoms(**dimer_params)
spec, a = extract_dimer_species_and_separation(dimer)
# Ensure LJ cutoff is large enough to encompass the dimer
lj_cutoff = 3 * a
calc_params = calc_params_lj_changebox(spec, lj_cutoff, **lj_epsilons)
dimer.calc = factory.calc(**calc_params)
energy_orig = dimer.get_potential_energy()
# Shrink the box slightly to invalidate cached energy and force change_box
# to be issued. This shouldn't actually affect the energy in and of itself
# since our dimer has non-periodic boundaries.
cell_orig = dimer.get_cell()
dimer.set_cell(cell_orig * 1.01, scale_atoms=False)
energy_modified = dimer.get_potential_energy()
eps_scaling_factor = lj_epsilons["eps_modified"] / lj_epsilons["eps_orig"]
assert energy_modified == pytest.approx(eps_scaling_factor * energy_orig,
rel=1e-4)
# Reset dimer cell. Also, create and attach new calculator so that
# previous post_changebox_cmds won't be in effect.
dimer.set_cell(cell_orig, scale_atoms=False)
dimer.calc = factory.calc(**calc_params)
# Compute energy of original configuration again so that a change_box will
# be triggered on the next calculation after we change the pbcs
energy_orig = dimer.get_potential_energy()
# Change the periodicity of the cell along one direction. This shouldn't
# actually affect the energy in and of itself since the cell is large
# relative to the dimer
dimer.set_pbc([False, True, False])
energy_modified = dimer.get_potential_energy()
assert energy_modified == pytest.approx(eps_scaling_factor * energy_orig,
rel=1e-4)
def view_atoms_classifier(image, mg_label, mn_label, o_label, view=True):
x = image.reshape(-1, 3)
mg = x[2:10, :]
mn = x[10:18, :]
o = x[18:, :]
l = x[0, :] * 30
a = x[1, :] * 180
c = np.hstack((l, a))
atoms = Atoms('H')
atoms.set_cell(c)
cell = atoms.get_cell()
t = np.isnan(cell)
tt = np.sum(t)
isnan = False
if not tt == 0:
isnan = True
print(cell)
print(l)
print(a)
_, mg_index = torch.max(mg_label, dim=1)
_, mn_index = torch.max(mn_label, dim=1)
_, o_index = torch.max(o_label, dim=1)
mg_index = mg_index.reshape(8, ).detach().cpu().numpy()
mn_index = mn_index.reshape(8, ).detach().cpu().numpy()
o_index = o_index.reshape(12, ).detach().cpu().numpy()
mg_pos = mg[np.where(mg_index)]
mn_pos = mn[np.where(mn_index)]
o_pos = o[np.where(o_index)]
n_mg = len(mg_pos)
n_mn = len(mn_pos)
n_o = len(o_pos)
if n_mg == 0:
mg_pos = np.array([0.1667, 0.1667, 0.1667]).reshape(1, 3)
n_mg = 1
if n_mn == 0:
mn_pos = np.array([0.1667, 0.1667, 0.1667]).reshape(1, 3)
n_mn = 1
if n_o == 0:
o_pos = np.array([0.1667, 0.1667, 0.1667]).reshape(1, 3)
n_o = 1
pos = np.vstack((mg_pos, mn_pos, o_pos))
scaled_pos = back_to_10_cell(pos, n_mg, n_mn, n_o)
atoms = back_to_real_cell(scaled_pos, cell, n_mg, n_mn, n_o)
atoms.set_pbc([1, 1, 1])
if view:
atoms.edit()
return atoms, x, isnan
def test_emt():
import numpy as np
from ase.calculators.emt import EMT
from ase import Atoms
a = 3.60
b = a / 2
cu = Atoms('Cu',
positions=[(0, 0, 0)],
cell=[(0, b, b), (b, 0, b), (b, b, 0)],
pbc=1,
calculator=EMT())
e0 = cu.get_potential_energy()
print(e0)
cu.set_cell(cu.get_cell() * 1.001, scale_atoms=True)
e1 = cu.get_potential_energy()
V = a**3 / 4
B = 2 * (e1 - e0) / 0.003**2 / V * 160.2
print(B)
for i in range(4):
x = 0.001 * i
A = np.array([(x, b, b + x), (b, 0, b), (b, b, 0)])
cu.set_cell(A, scale_atoms=True)
e = cu.get_potential_energy() - e0
if i == 0:
print(i, e)
else:
print(i, e, e / x**2)
A = np.array([(0, b, b), (b, 0, b), (6 * b, 6 * b, 0)])
R = np.zeros((2, 3))
for i in range(1, 2):
R[i] = i * A[2] / 6
print((Atoms('Cu2', positions=R, pbc=1, cell=A,
calculator=EMT()).get_potential_energy() - 2 * e0) / 2)
A = np.array([(0, b, b), (b, 0, b), (10 * b, 10 * b, 0)])
R = np.zeros((3, 3))
for i in range(1, 3):
R[i] = i * A[2] / 10
print((Atoms('Cu3', positions=R, pbc=1, cell=A,
calculator=EMT()).get_potential_energy() - 3 * e0) / 2)
A = np.array([(0, b, b), (b, 0, b), (b, b, 0)])
R = np.zeros((3, 3))
for i in range(1, 3):
R[i] = i * A[2]
print((Atoms('Cu3', positions=R, pbc=(1, 1, 0), cell=A,
calculator=EMT()).get_potential_energy() - 3 * e0) / 2)
def _get_reduced_indices(atoms: Atoms, tol: float = 1e-5) -> List[int]:
"""Get a list of the reduced atomic indices using spglib.
Note: Does no checks to see if spglib is installed.
:param atoms: ase Atoms object to reduce
:param tol: ``float``, numeric tolerance for positional comparisons
"""
import spglib
# Create input for spglib
spglib_cell = (atoms.get_cell(), atoms.get_scaled_positions(),
atoms.numbers)
symmetry_data = spglib.get_symmetry_dataset(spglib_cell, symprec=tol)
return list(set(symmetry_data['equivalent_atoms']))
def _preprocess_atoms(self, atoms: Atoms) -> Dict[str, Optional[Tensor]]:
pos = torch.tensor(
atoms.get_positions(), device=self.device, dtype=self.dtype, requires_grad=True
)
Z = torch.tensor(atoms.get_atomic_numbers(), device=self.device)
if any(atoms.pbc):
cell = torch.tensor(
atoms.get_cell(), device=self.device, dtype=self.dtype, requires_grad=True
)
else:
cell = None
pbc = torch.tensor(atoms.pbc, device=self.device)
edge_index, S = self._calc_edge_index(pos, cell, pbc)
input_dicts = dict(pos=pos, Z=Z, cell=cell, pbc=pbc, edge_index=edge_index, shift=S)
return input_dicts
def prepare_ga(dbfile='godb.db', N=20):
''' This method creates a database with the desired number
of randomly generated structures.
'''
blocks = [('Si', 7)] # the building blocks
l = [list(Atoms(block).numbers) * count for block, count in blocks]
stoichiometry = [int(item) for sublist in l for item in sublist]
atom_numbers = list(set(stoichiometry))
# This dictionary will be used to check that the shortest
# Si-Si distances are above a certain threshold
# (here 1.5 Angstrom):
blmin = {(14, 14): 1.5}
# This defines the cubic simulation cell:
slab = Atoms('', positions=np.zeros((0, 3)), cell=[16] * 3)
# This defines the smaller box in which the
# initial coordinates are allowed to vary
density = 0.12 # in atoms per cubic Angstrom
aspect_ratios = np.array([1.0, 1.0, 1.0])
v = len(stoichiometry) / density
l = np.cbrt(v / np.product(aspect_ratios))
cell = np.identity(3) * aspect_ratios * l
p0 = 0.5 * (np.diag(slab.get_cell() - cell))
box = [p0, cell]
# Seed the random number generators using the system time,
# to ensure that no two runs produce the same results:
np.random.seed()
seed()
# Generate the random structures and add them to the database:
sg = StartGenerator(slab=slab, atom_numbers=stoichiometry,
closest_allowed_distances=blmin,
box_to_place_in=box)
da = PrepareDB(db_file_name=dbfile,
simulation_cell=slab,
stoichiometry=stoichiometry)
for i in range(N):
a = sg.get_new_candidate()
da.add_unrelaxed_candidate(a)
return
def create_water_region(cell):
"""Creates a region of water for the cell specified. The density is assumed to be that
of normal water at 1atm and 300K.
Internally, this first creates a grid of Ne atoms, and then rattle them very slightly
(std = 1/10 of a H2O molecule size). The Ne atoms are then replaced with H2O molecules
centered on Ne. Be _very_ careful that the atoms are not out of bounds. It can happen
after some rotations. Recommend to put in a slightly smaller cell to give some margins.
Parameters:
cell: list | numpy array
Returns:
H2O_bulk: Atoms | list of Atom
"""
aq_cell = cell
H2O_volume = (1e+27)/(1000/18 * units.mol)
Atoms_grid = Atoms('Ne', pbc=True, cell=[H2O_volume**(1/3)]*3)
Atoms_grid = Atoms_grid.repeat(
tuple([int(x/(H2O_volume**(1/3))) for x in aq_cell]))
Atoms_grid.set_cell(aq_cell)
Atoms_grid.center()
Atoms_grid.rattle(stdev=H2O_volume**(1/3)/10)
H2O_bulk = molecule('H2O')
H2O_bulk.euler_rotate(rand()*360, rand()*360, rand()*360, center="COM")
H2O_bulk.translate(Atoms_grid.get_positions()[0])
H2O_bulk.set_cell(Atoms_grid.get_cell())
H2O_bulk.set_pbc(True)
for new_pos in Atoms_grid.get_positions()[1:]:
new_molecule = molecule('H2O')
new_molecule.euler_rotate(
rand()*360, rand()*360, rand()*360, center="COM")
new_molecule.translate(new_pos)
H2O_bulk += new_molecule.copy()
H2O_bulk.center()
return H2O_bulk
def is_cell_hexagonal(atoms: Atoms):
"""
Function to check whether the cell of an ASE atoms object is hexagonal.
Parameters
----------
atoms : ASE atoms object
The atoms that should be checked.
"""
cell = atoms.get_cell()
a = np.linalg.norm(cell[0], axis=0)
b = np.linalg.norm(cell[1], axis=0)
c = np.linalg.norm(cell[2], axis=0)
angle = np.arccos(np.dot(cell[0], cell[1]) / (a * b))
return np.isclose(a, b) & (np.isclose(angle, np.pi / 3) | np.isclose(
angle, 2 * np.pi / 3)) & (c == cell[2, 2])
def ase_to_quip(ase_atoms: ase.Atoms, quip_atoms=None):
"""
Converter to put the info from an ase atoms object into a quip atoms object.
Copies everything to make sure there is not linking back.
Checks if the
:param ase_atoms:
:param quip_atoms:
:return:
"""
lattice = ase_atoms.get_cell().T.copy()
if quip_atoms is not None:
if isinstance(quip_atoms, quippy.atoms_types_module.Atoms):
# check if the length matches, otherwise make a new one in place of that
if len(ase_atoms) != quip_atoms.n:
# need to regenerate the quip atoms object
quip_atoms = quippy.atoms_types_module.Atoms(len(ase_atoms), lattice)
else:
# but the cell needs to be set anyways
quip_atoms.set_lattice(lattice, scale_positions=False)
else:
# raise an error for the wrong object given
raise TypeError('quip_atoms argument is not of valid type, cannot work with it')
else:
# need to regenerate the quip atoms object
quip_atoms = quippy.atoms_types_module.Atoms(len(ase_atoms), lattice)
quip_atoms.pos[:] = ase_atoms.get_positions().T.copy()
quip_atoms.is_periodic[:] = ase_atoms.get_pbc()
quip_atoms.z[:] = ase_atoms.numbers
quip_atoms.set_atoms(quip_atoms.z) # set species and mass
if ase_atoms.has('momenta'):
# if ase atoms has momenta then add velocities to the quip object
# workaround for the interfaces not behaving properly in the wrapped code, see f90wrap issue #86
_quippy.f90wrap_atoms_add_property_real_2da(this=quip_atoms._handle, name='velo',
value=velocities_ase_to_quip(ase_atoms.get_velocities()))
# go through all properties
return quip_atoms
def get_charge(self):
q, ecoul, eself, evdw = [], [], [], []
print('- get charges by QEq ... \n')
A = Atoms(symbols=self.atom_name,
positions=self.x[0],
cell=self.cell,
pbc=(1, 1, 1))
Qe = qeq(p=self.p, atoms=A)
for nf in range(self.batch):
# print('* get charges of batch {0}/{1} ...\r'.format(nf,self.batch),end='\r')
A = Atoms(symbols=self.atom_name,
positions=self.x[nf],
cell=self.cell,
pbc=(1, 1, 1))
positions = A.get_positions()
cell = A.get_cell()
Qe.calc(cell, positions)
q.append(Qe.q[:-1])
self.q = np.array(q)
def sphere(atomlist, cell, fill_factor=0.74, radius=None):
"""Generates a random sphere of particles given an
atomlist and radius.
Parameters
----------
atomlist : list
A list of [sym, n] pairs where sym is the chemical symbol
and n is the number of of sym's to include in the individual
cell : list
The x, y, and z dimensions of the cell which holds the particle.
fill_factor : float
How densely packed the sphere should be. Ranges from 0 to 1.
radius : float
The radius of the sphere. If None, estimated from the
atomic radii
"""
if radius is None:
radius = get_particle_radius(atomlist, fill_factor)
# Create a list of random order of the atoms
chemical_symbols = []
for atom in atomlist:
chemical_symbols += [atom[0]] * atom[1]
random.shuffle(chemical_symbols)
unit_vec = np.array([random_three_vector() for i in range(len(chemical_symbols))])
D = radius * np.random.sample(size=len(chemical_symbols)) ** (1.0/3.0)
positions = np.array([D]).T * unit_vec
indiv = Atoms(symbols=chemical_symbols, positions=positions)
if cell is not None:
indiv.set_cell(cell)
cell_center = np.sum(indiv.get_cell(), axis=1) / 2.0
indiv.translate(indiv.get_center_of_mass() + cell_center)
indiv.set_pbc(True)
return indiv
def standardize_cell(atoms: Atoms, tol: float = 1e-12):
"""
Standardize the cell of an ASE atoms object. The atoms are rotated so one of the lattice vectors in the xy-plane
aligns with the x-axis, then all of the lattice vectors are made positive.
Parameters
----------
atoms : ASE atoms object
The atoms that should be standardized
tol : float
Components of the lattice vectors below this value are considered to be zero.
Returns
-------
atoms : ASE atoms object
The standardized atoms.
"""
cell = np.array(atoms.cell)
vertical_vector = np.where(np.all(np.abs(cell[:, :2]) < tol, axis=1))[0]
if len(vertical_vector) != 1:
raise RuntimeError('Invalid cell: no vertical lattice vector')
cell[[vertical_vector[0], 2]] = cell[[2, vertical_vector[0]]]
r = np.arctan2(atoms.cell[0, 1], atoms.cell[0, 0]) / np.pi * 180
atoms.set_cell(cell)
if r != 0.:
atoms.rotate(-r, 'z', rotate_cell=True)
if not is_cell_valid(atoms, tol):
raise RuntimeError(
'This cell cannot be made orthogonal using currently implemented methods.'
)
atoms.set_cell(np.abs(atoms.get_cell()))
atoms.wrap()
return atoms
def test():
"""Simple test function to test atoms2json and json2atoms function"""
from ase import Atoms
teststructure = Atoms('N2', [(0, 0, 0), (0, 0, 1.1)])
teststring = atoms2json(teststructure,
additional_information={"abc": "def"
}) #convert to the string
newstructure = json2atoms(teststring) #and convert back
assert (newstructure.get_atomic_numbers() ==
teststructure.get_atomic_numbers()).all()
assert (
newstructure.get_positions() == teststructure.get_positions()).all()
assert (newstructure.get_cell() == teststructure.get_cell()).all()
assert (newstructure.get_pbc() == teststructure.get_pbc()).all()
assert newstructure.info["key_value_pairs"]["abc"] == "def"
print("JSON Conversion in both directions: apparently no issue")
from ase import Atoms
from ase.lattice import bulk
from ase.optimize.bfgs import BFGS
from ase.constraints import UnitCellFilter
from gpaw import GPAW
from gpaw import PW
import numpy as np
cell = bulk('Si', 'fcc', a=6.0).get_cell()
# Experimental Lattice constant is a=5.421 A
a = Atoms('Si2', cell=cell, pbc=True,
scaled_positions=((0,0,0), (0.25,0.25,0.25)))
calc = GPAW(xc='PBE',
mode=PW(400),
#mode=PW(400, cell=a.get_cell()), # fix no of planewaves!
kpts=(4,4,4),
#convergence={'eigenstates': 1.e-10}, # converge tightly!
txt='stress.txt')
a.set_calculator(calc)
uf = UnitCellFilter(a)
relax = BFGS(uf)
relax.run(fmax=0.05) # Consider much tighter fmax!
a = np.dot(a.get_cell()[0], a.get_cell()[0])**0.5 * 2**0.5
print 'Relaxed lattice parameter: a = %s A' % a
def rotct_rand(ind1, ind2, Optimizer):
"""Rotate atoms cut and splice
Rotates atoms randomly around center of mass and cuts with xy plane
Maintains number of atoms
Maintains concentration of atoms
Returns individuals to standard positions at end (un-rotates)
"""
if 'CX' in Optimizer.debug:
debug = True
else:
debug = False
Optimizer.output.write('Random Rotate Cut/Splice Cx between individual '+repr(ind1.index)+' and individual '+repr(ind2.index)+'\n')
#Perserve starting conditions of individual
indi1 = ind1[0].copy()
indi2 =ind2[0].copy()
#Translate individuals so COM is at (0,0,0)
com1 = indi1.get_center_of_mass()
indi1.translate(-1*com1)
com2 = indi2.get_center_of_mass()
indi2.translate(-1*com2)
#Select random axis, random angle, and random position and rotate individuals
cmax = [min(numpy.maximum.reduce(indi1.get_positions())[i],numpy.maximum.reduce(indi2.get_positions())[i]) for i in range(3)]
cmin = [min(numpy.minimum.reduce(indi1.get_positions())[i],numpy.minimum.reduce(indi2.get_positions())[i]) for i in range(3)]
n=0
while n<10:
rax = random.choice(['x', '-x','y','-y','z','-z'])
rang = random.random()*90
rpos = [random.uniform(cmin[i]*0.8,cmax[i]*0.8) for i in range(3)]
indi1.rotate(rax,a=rang,center=rpos,rotate_cell=False)
#Search for atoms in individual 1 that are above the xy plane
group1 = Atoms(cell=ind1[0].get_cell(),pbc=ind1[0].get_pbc())
indices1=[]
for one in indi1:
if one.position[2] >= 0:
group1.append(one)
indices1.append(one.index)
if len(group1) > 2 and len(group1) < len(indi1):
break
else:
n+=1
indi1.rotate(rax,a=-1*rang,center=rpos, rotate_cell=False)
indi2.rotate(rax,a=rang,center=rpos,rotate_cell=False)
if debug:
print 'Group1 size = ', len(group1)
print 'Position = ', rpos
print 'Angle = ', rang
print 'Axis = ', rax
print 'Number of tries = ', n
if len(group1) != 0:
#Apply concentration forcing if needed
group2 = Atoms(cell=ind2[0].get_cell(),pbc=ind2[0].get_pbc())
indices2 = []
dellist = []
for one in indi2:
if one.position[2] >= 0:
group2.append(one)
indices2.append(one.index)
if Optimizer.forcing=='Concentration':
symlist = list(set(indi1.get_chemical_symbols()))
seplist = [[atm for atm in group2 if atm.symbol == sym] for sym in symlist]
group2n=Atoms(cell=group2.get_cell(),pbc=group2.get_pbc())
indices2n = []
dellist = []
for one in group1:
sym1=one.symbol
listpos=[i for i,s in enumerate(symlist) if s==sym1][0]
if len(seplist[listpos]) > 0:
pos = random.choice(range(len(seplist[listpos])))
group2n.append(seplist[listpos][pos])
indices2n.append(indices2[seplist[listpos][pos].index])
del seplist[listpos][pos]
else:
dellist.append(one.index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
indices2=indices2n
group2=group2n.copy()
else:
dellist = []
while len(group2) < len(group1)-len(dellist):
#Too many atoms in group 1
dellist.append(random.choice(group1).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
dellist = []
while len(group1) < len(group2)-len(dellist):
#Too many atoms in group 2
dellist.append(random.choice(group2).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group2[one]
del indices2[one]
other2 = Atoms(cell=ind2[0].get_cell(),pbc=ind2[0].get_pbc())
for one in indi2:
if one.index not in indices2:
other2.append(one)
other1 = Atoms(cell=ind1[0].get_cell(),pbc=ind1[0].get_pbc())
for one in indi1:
if one.index not in indices1:
other1.append(one)
indi1 = group2.copy()
indi1.extend(other1)
indi2 = group1.copy()
indi2.extend(other2)
#DEBUG: Write crossover to file
if debug:
write_xyz(Optimizer.debugfile, group1,'group1')
write_xyz(Optimizer.debugfile, other1,'other1')
write_xyz(Optimizer.debugfile, group2,'group2')
write_xyz(Optimizer.debugfile, other2,'other2')
print 'Length of group1 = ',len(group1),'Length of group2',len(group2)
#DEBUG: Check structure of atoms exchanged
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in indi1 if atm.symbol==sym])
Optimizer.output.write('CX RANDROTCT: Individual 1 contains '+repr(nc)+' '+repr(sym)+' atoms\n')
nc=len([atm for atm in indi2 if atm.symbol==sym])
Optimizer.output.write('CX RANDROTCT: Individual 2 contains '+repr(nc)+' '+repr(sym)+' atoms\n')
if Optimizer.forcing !='Concentration':
for i in range(len(Optimizer.atomlist)):
atms1=[inds for inds in indi1 if inds.symbol==Optimizer.atomlist[i][0]]
atms2=[inds for inds in indi2 if inds.symbol==Optimizer.atomlist[i][0]]
if len(atms1)==0:
if len(atms2)==0:
indi1[random.randint(0,len(indi1)-1)].symbol==Optimizer.atomlist[i][0]
indi2[random.randint(0,len(indi2)-1)].symbol==Optimizer.atomlist[i][0]
else:
indi1.append(atms2[random.randint(0,len(atms2)-1)])
indi1.pop(random.randint(0,len(indi1)-2))
else:
if len(atms2)==0:
indi2.append(atms1[random.randint(0,len(atms1)-1)])
indi2.pop(random.randint(0,len(indi2)-2))
indi1.rotate(rax,a=-1*rang,center=rpos, rotate_cell=False)
indi2.rotate(rax,a=-1*rang,center=rpos, rotate_cell=False)
indi1.translate(com1)
indi2.translate(com2)
#DEBUG: Check structure and number of atoms in crystal
if Optimizer.structure=='Defect':
solid1=Atoms()
solid1.extend(indi1)
solid1.extend(ind1.bulki)
solid2=Atoms()
solid2.extend(indi2)
solid2.extend(ind2.bulki)
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in solid1 if atm.symbol==sym])
Optimizer.output.write('CX RANDROTCT: Defect 1 configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n')
nc=len([atm for atm in solid2 if atm.symbol==sym])
Optimizer.output.write('CX RANDROTCT: Defect 2 configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n')
if debug: Optimizer.output.flush()
#pdb.set_trace()
ind1[0]=indi1
ind2[0]=indi2
return ind1, ind2
def eval_energy(input):
"""Function to evaluate energy of an individual
Inputs:
input = [Optimizer class object with parameters, Individual class structure to be evaluated]
Outputs:
energy, bul, individ, signal
energy = energy of Individual evaluated
bul = bulk structure of Individual if simulation structure is Defect
individ = Individual class structure evaluated
signal = string of information about evaluation
"""
if input[0]==None:
energy=0
bul=0
individ=0
rank = MPI.COMM_WORLD.Get_rank()
signal='Evaluated none individual on '+repr(rank)+'\n'
else:
[Optimizer, individ]=input
if Optimizer.calc_method=='MAST':
energy = individ.energy
bul = individ.energy
signal = 'Recieved MAST structure\n'
else:
if Optimizer.parallel: rank = MPI.COMM_WORLD.Get_rank()
if not Optimizer.genealogy:
STR='----Individual ' + str(individ.index)+ ' Optimization----\n'
else:
STR='----Individual ' + str(individ.history_index)+ ' Optimization----\n'
indiv=individ[0]
if 'EE' in Optimizer.debug:
debug = True
else:
debug = False
if debug:
write_xyz(Optimizer.debugfile,indiv,'Recieved by eval_energy')
Optimizer.debugfile.flush()
if Optimizer.structure=='Defect':
indi=indiv.copy()
if Optimizer.alloy==True:
bulk=individ.bulki
else:
bulk=individ.bulko
nat=indi.get_number_of_atoms()
csize=bulk.get_cell()
totalsol=Atoms(cell=csize, pbc=True)
totalsol.extend(indi)
totalsol.extend(bulk)
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in totalsol if atm.symbol==sym])
STR+='Defect configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n'
elif Optimizer.structure=='Surface':
totalsol=Atoms()
totalsol.extend(indiv)
nat=indiv.get_number_of_atoms()
totalsol.extend(individ.bulki)
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in totalsol if atm.symbol==sym])
STR+='Surface-Bulk configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n'
cell=numpy.maximum.reduce(indiv.get_cell())
totalsol.set_cell([cell[0],cell[1],500])
totalsol.set_pbc([True,True,False])
if Optimizer.constrain_position:
ts = totalsol.copy()
indc,indb,vacant,swap,stro = find_defects(ts,Optimizer.solidbulk,0)
sbulk = Optimizer.solidbulk.copy()
bcom = sbulk.get_center_of_mass()
#totalsol.translate(-bulkcom)
#indc.translate(-bulkcom)
#totalsol.append(Atom(position=[0,0,0]))
# for one in indc:
# index = [atm.index for atm in totalsol if atm.position[0]==one.position[0] and atm.position[1]==one.position[1] and atm.position[2]==one.position[2]][0]
# if totalsol.get_distance(-1,index) > Optimizer.sf:
# r = random.random()
# totalsol.set_distance(-1,index,Optimizer.sf*r,fix=0)
# totalsol.pop()
# totalsol.translate(bulkcom)
com = indc.get_center_of_mass()
dist = (sum((bcom[i] - com[i])**2 for i in range(3)))**0.5
if dist > Optimizer.sf:
STR+='Shifting structure to within region\n'
r = random.random()*Optimizer.sf
comv = numpy.linalg.norm(com)
ncom = [one*r/comv for one in com]
trans = [ncom[i]-com[i] for i in range(3)]
indices = []
for one in indc:
id = [atm.index for atm in totalsol if atm.position[0]==one.position[0] and atm.position[1]==one.position[1] and atm.position[2]==one.position[2]][0]
totalsol[id].position += trans
# Check for atoms that are too close
min_len=0.7
#pdb.set_trace()
if not Optimizer.fixed_region:
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
cutoffs=[2.0 for one in totalsol]
nl=NeighborList(cutoffs,bothways=True,self_interaction=False)
nl.update(totalsol)
for one in totalsol[0:nat]:
nbatoms=Atoms()
nbatoms.append(one)
indices, offsets=nl.get_neighbors(one.index)
for index, d in zip(indices,offsets):
index = int(index)
sym=totalsol[index].symbol
pos=totalsol[index].position + numpy.dot(d,totalsol.get_cell())
at=Atom(symbol=sym,position=pos)
nbatoms.append(at)
while True:
dflag=False
for i in range(1,len(nbatoms)):
d=nbatoms.get_distance(0,i)
if d < min_len:
nbatoms.set_distance(0,i,min_len+.01,fix=0.5)
STR+='--- WARNING: Atoms too close (<0.7A) - Implement Move ---\n'
dflag=True
if dflag==False:
break
for i in range(len(indices)):
totalsol[indices[i]].position=nbatoms[i+1].position
totalsol[one.index].position=nbatoms[0].position
nl.update(totalsol)
if debug:
write_xyz(Optimizer.debugfile,totalsol,'After minlength check')
Optimizer.debugfile.flush()
else:
for i in range(len(indiv)):
for j in range(len(indiv)):
if i != j:
d=indiv.get_distance(i,j)
if d < min_len:
indiv.set_distance(i,j,min_len,fix=0.5)
STR+='--- WARNING: Atoms too close (<0.7A) - Implement Move ---\n'
if debug:
write_xyz(Optimizer.debugfile,indiv,'After minlength check')
Optimizer.debugfile.flush()
# Set calculator to use to get forces/energies
if Optimizer.parallel:
calc = setup_calculator(Optimizer)
if Optimizer.fixed_region:
pms=copy.deepcopy(calc.parameters)
try:
pms['mass'][len(pms['mass'])-1] += '\ngroup RO id >= '+repr(nat)+'\nfix freeze RO setforce 0.0 0.0 0.0\n'
except KeyError:
pms['pair_coeff'][0] += '\ngroup RO id >= '+repr(nat)+'\nfix freeze RO setforce 0.0 0.0 0.0\n'
calc = LAMMPS(parameters=pms, files=calc.files, keep_tmp_files=calc.keep_tmp_files, tmp_dir=calc.tmp_dir)
lmin = copy.copy(Optimizer.lammps_min)
Optimizer.lammps_min = None
Optimizer.static_calc = setup_calculator(Optimizer)
Optimizer.lammps_min = lmin
else:
calc=Optimizer.calc
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
totalsol.set_calculator(calc)
totalsol.set_pbc(True)
else:
indiv.set_calculator(calc)
indiv.set_pbc(True) #Current bug in ASE optimizer-Lammps prevents pbc=false
if Optimizer.structure=='Cluster':
indiv.set_cell([500,500,500])
indiv.translate([250,250,250])
cwd=os.getcwd()
# Perform Energy Minimization
if not Optimizer.parallel:
Optimizer.output.flush()
if Optimizer.ase_min == True:
try:
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
dyn=BFGS(totalsol)
else:
dyn=BFGS(indiv)
dyn.run(fmax=Optimizer.ase_min_fmax, steps=Optimizer.ase_min_maxsteps)
except OverflowError:
STR+='--- Error: Infinite Energy Calculated - Implement Random ---\n'
box=Atoms()
indiv=gen_pop_box(Optimizer.natoms, Optimizer.atomlist, Optimizer.size)
indiv.set_calculator(calc)
dyn=BFGS(indiv)
dyn.run(fmax=fmax, steps=steps)
except numpy.linalg.linalg.LinAlgError:
STR+='--- Error: Singular Matrix - Implement Random ---\n'
indiv=gen_pop_box(Optimizer.natoms, Optimizer.atomlist, Optimizer.size)
indiv.set_calculator(calc)
dyn=BFGS(indiv)
dyn.run(fmax=fmax, steps=steps)
# Get Energy of Minimized Structure
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
en=totalsol.get_potential_energy()
#force=numpy.maximum.reduce(abs(totalsol.get_forces()))
if Optimizer.fitness_scheme == 'enthalpyfit':
pressure=totalsol.get_isotropic_pressure(totalsol.get_stress())
cell_max=numpy.maximum.reduce(totalsol.get_positions())
cell_min=numpy.minimum.reduce(totalsol.get_positions())
cell=cell_max-cell_min
volume=cell[0]*cell[1]*cell[2]
else:
pressure=0
volume=0
na=totalsol.get_number_of_atoms()
ena=en/na
energy=en
individ[0]=totalsol[0:nat]
bul=totalsol[(nat):len(totalsol)]
STR+='Number of positions = '+repr(len(bul)+len(individ[0]))+'\n'
individ[0].set_cell(csize)
indiv=individ[0]
else:
en=indiv.get_potential_energy()
if Optimizer.fitness_scheme == 'enthalpyfit':
pressure=indiv.get_isotropic_pressure(indiv.get_stress())
cell_max=numpy.maximum.reduce(indiv.get_positions())
cell_min=numpy.minimum.reduce(indiv.get_positions())
cell=cell_max-cell_min
volume=cell[0]*cell[1]*cell[2]
else:
pressure=0
volume=0
na=indiv.get_number_of_atoms()
ena=en/na
energy=ena
individ[0]=indiv
bul=0
else:
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
if Optimizer.calc_method=='VASP':
en=totalsol.get_potential_energy()
calcb=Vasp(restart=True)
totalsol=calcb.get_atoms()
stress=calcb.read_stress()
else:
try:
totcop=totalsol.copy()
if debug: write_xyz(Optimizer.debugfile,totcop,'Individual sent to lammps')
OUT=totalsol.calc.calculate(totalsol)
totalsol=OUT['atoms']
totalsol.set_pbc(True)
if Optimizer.fixed_region:
if debug:
print 'Energy of fixed region calc = ', OUT['thermo'][-1]['pe']
totalsol.set_calculator(Optimizer.static_calc)
OUT=totalsol.calc.calculate(totalsol)
totalsol=OUT['atoms']
totalsol.set_pbc(True)
if debug:
print 'Energy of static calc = ', OUT['thermo'][-1]['pe']
en=OUT['thermo'][-1]['pe']
stress=numpy.array([OUT['thermo'][-1][i] for i in ('pxx','pyy','pzz','pyz','pxz','pxy')])*(-1e-4*GPa)
#force=numpy.maximum.reduce(abs(totalsol.get_forces()))
if debug:
write_xyz(Optimizer.debugfile,totalsol,'After Lammps Minimization')
Optimizer.debugfile.flush()
except Exception, e:
os.chdir(cwd)
STR+='WARNING: Exception during energy eval:\n'+repr(e)+'\n'
f=open('problem-structures.xyz','a')
write_xyz(f,totcop,data='Starting structure hindex='+individ.history_index)
write_xyz(f,totalsol,data='Lammps Min structure')
en=10
stress=0
f.close()
if Optimizer.fitness_scheme == 'enthalpyfit':
pressure=totalsol.get_isotropic_pressure(stress)
cell_max=numpy.maximum.reduce(totalsol.get_positions())
cell_min=numpy.minimum.reduce(totalsol.get_positions())
cell=cell_max-cell_min
volume=cell[0]*cell[1]*cell[2]
else:
pressure=totalsol.get_isotropic_pressure(stress)
volume=0
na=totalsol.get_number_of_atoms()
ena=en/na
energy=en
if Optimizer.structure=='Defect':
if Optimizer.fixed_region==True or Optimizer.finddefects==False:
individ[0]=totalsol[0:nat]
bul=totalsol[(nat):len(totalsol)]
individ[0].set_cell(csize)
else:
if 'FI' in Optimizer.debug:
outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=Optimizer.debugfile)
else:
outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=False)
individ[0]=outt[0]
bul=outt[1]
individ.vacancies = outt[2]
individ.swaps = outt[3]
STR += outt[4]
indiv=individ[0]
else:
top,bul=find_top_layer(totalsol,Optimizer.surftopthick)
indiv=top.copy()
individ[0]=top.copy()
else:
# Takes 30 mins with 8 cores in domain-parallel (memory ~500mb, max 1.5gb)
import numpy as np
from ase import Atom, Atoms
from gpaw import GPAW, MixerDif, mpi
from gpaw.lrtddft import LrTDDFT
from gpaw.pes.dos import DOSPES
from gpaw.pes.tddft import TDDFTPES
atoms = Atoms([Atom('C', (7.666263598984184, 7.637780850431168, 8.289450797111844)),
Atom('O', (8.333644370007132, 8.362384430165646, 7.710230973847514))])
atoms.center(vacuum=8)
h = 0.15
N_c = np.round(atoms.get_cell().diagonal() / h / 16) * 16
m_c = GPAW(gpts=N_c, nbands=6, mixer=MixerDif(0.1, 5, weight=100.0),
#convergence={'bands':10},
parallel={'domain': mpi.size},
xc='PBE', txt='CO-m.txt', spinpol=True)
m = atoms.copy()
m.set_initial_magnetic_moments([-1,1])
m.set_calculator(m_c)
m.get_potential_energy()
d_c = GPAW(gpts=N_c, nbands=16, mixer=MixerDif(0.1, 5, weight=100.0),
convergence={'bands':10},
parallel={'domain': mpi.size},
xc='PBE', txt='CO-d.txt', spinpol=True)
# Calculate potential energy per atom for orthogonal unitcell
atoms = hcp0001('C', a=a / sqrt(3), vacuum=d, size=(3, 2, 1), orthogonal=True)
del atoms[[1, -1]]
atoms.center(axis=0)
atoms.set_calculator(calc)
kpts_c = np.ceil(50 / np.sum(atoms.get_cell()**2, axis=1)**0.5).astype(int)
kpts_c[~atoms.get_pbc()] = 1
calc.set(kpts=kpts_c)
eppa1 = atoms.get_potential_energy() / len(atoms)
F1_av = atoms.get_forces()
equal(np.abs(F1_av).max(), 0, 5e-3)
# Redo calculation with non-orthogonal unitcell
atoms = Atoms(symbols='C2',
pbc=(True, True, False),
positions=[(a / 2, -sqrt(3) / 6 * a, d),
(a / 2, sqrt(3) / 6 * a, d)],
cell=[(a / 2, -sqrt(3) / 2 * a, 0), (a / 2, sqrt(3) / 2 * a, 0),
(0, 0, 2 * d)])
atoms.set_calculator(calc)
kpts_c = np.ceil(50 / np.sum(atoms.get_cell()**2, axis=1)**0.5).astype(int)
kpts_c[~atoms.get_pbc()] = 1
calc.set(kpts=kpts_c)
eppa2 = atoms.get_potential_energy() / len(atoms)
F2_av = atoms.get_forces()
equal(np.abs(F2_av).max(), 0, 5e-3)
equal(eppa1, eppa2, 1e-3)
def rotct_rand_defect(ind1, ind2, Optimizer):
"""Rotate atoms cut and splice
Translates atoms to center of positions first
Rotates atoms randomly around center of mass and cuts with xy plane
Maintains number of atoms
Maintains concentration of atoms
Returns individuals to standard positions at end (un-rotates)
"""
if "CX" in Optimizer.debug:
debug = True
else:
debug = False
Optimizer.output.write(
"Random Rotate Cut/Splice Cx for defects between individual "
+ repr(ind1.index)
+ " and individual "
+ repr(ind2.index)
+ "\n"
)
# Perserve starting conditions of individual
indi1 = ind1[0].copy()
indi2 = ind2[0].copy()
# Translate individuals so COP is at (0,0,0)
if Optimizer.structure == "Defect":
# Identify center of positions for defect structure
indi1c, indi1b, vacant1, swap1, stro1 = find_defects(indi1, Optimizer.solidbulk, 0)
com1 = position_average(indi1c)
indi1 = shift_atoms(indi1, com1)
# Do the same for second individual
indi2c, indi2b, vacant2, swap2, stro2 = find_defects(indi2, Optimizer.solidbulk, 0)
com2 = position_average(indi2c)
indi2 = shift_atoms(indi2, com2)
else:
com1 = indi1.get_center_of_mass()
indi1.translate(-1 * com1)
com2 = indi2.get_center_of_mass()
indi2.translate(-1 * com2)
# Select random axis, random angle, and random position and rotate individuals
cmax = [
min(numpy.maximum.reduce(indi1.get_positions())[i], numpy.maximum.reduce(indi2.get_positions())[i])
for i in range(3)
]
cmin = [
min(numpy.minimum.reduce(indi1.get_positions())[i], numpy.minimum.reduce(indi2.get_positions())[i])
for i in range(3)
]
n = 0
while n < 10:
rax = random.choice(["x", "-x", "y", "-y", "z", "-z"])
rang = random.random() * 90
rpos = [random.uniform(cmin[i] * 0.8, cmax[i] * 0.8) for i in range(3)]
indi1.rotate(rax, a=rang, center=rpos, rotate_cell=False)
# Search for atoms in individual 1 that are above the xy plane
group1 = Atoms(cell=ind1[0].get_cell(), pbc=ind1[0].get_pbc())
indices1 = []
for one in indi1:
if one.position[2] >= 0:
group1.append(one)
indices1.append(one.index)
if len(group1) > 2 and len(group1) < len(indi1):
break
else:
n += 1
indi1.rotate(rax, a=-1 * rang, center=rpos, rotate_cell=False)
indi2.rotate(rax, a=rang, center=rpos, rotate_cell=False)
if debug:
print "Group1 size = ", len(group1)
print "Position = ", rpos
print "Angle = ", rang
print "Axis = ", rax
print "Number of tries = ", n
if len(group1) != 0:
# Apply concentration forcing if needed
group2 = Atoms(cell=ind2[0].get_cell(), pbc=ind2[0].get_pbc())
indices2 = []
dellist = []
for one in indi2:
if one.position[2] >= 0:
group2.append(one)
indices2.append(one.index)
if Optimizer.forcing == "Concentration":
symlist = list(set(indi1.get_chemical_symbols()))
seplist = [[atm for atm in group2 if atm.symbol == sym] for sym in symlist]
group2n = Atoms(cell=group2.get_cell(), pbc=group2.get_pbc())
indices2n = []
dellist = []
for one in group1:
sym1 = one.symbol
listpos = [i for i, s in enumerate(symlist) if s == sym1][0]
if len(seplist[listpos]) > 0:
pos = random.choice(range(len(seplist[listpos])))
group2n.append(seplist[listpos][pos])
indices2n.append(indices2[seplist[listpos][pos].index])
del seplist[listpos][pos]
else:
dellist.append(one.index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
indices2 = indices2n
group2 = group2n.copy()
else:
dellist = []
while len(group2) < len(group1) - len(dellist):
# Too many atoms in group 1
dellist.append(random.choice(group1).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
dellist = []
while len(group1) < len(group2) - len(dellist):
# Too many atoms in group 2
dellist.append(random.choice(group2).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group2[one]
del indices2[one]
other2 = Atoms(cell=ind2[0].get_cell(), pbc=ind2[0].get_pbc())
for one in indi2:
if one.index not in indices2:
other2.append(one)
other1 = Atoms(cell=ind1[0].get_cell(), pbc=ind1[0].get_pbc())
for one in indi1:
if one.index not in indices1:
other1.append(one)
indi1 = group2.copy()
indi1.extend(other1)
indi2 = group1.copy()
indi2.extend(other2)
# DEBUG: Write crossover to file
if debug:
write_xyz(Optimizer.debugfile, group1, "group1")
write_xyz(Optimizer.debugfile, other1, "other1")
write_xyz(Optimizer.debugfile, group2, "group2")
write_xyz(Optimizer.debugfile, other2, "other2")
print "Length of group1 = ", len(group1), "Length of group2", len(group2)
# DEBUG: Check structure of atoms exchanged
for sym, c, m, u in Optimizer.atomlist:
nc = len([atm for atm in indi1 if atm.symbol == sym])
Optimizer.output.write(
"CX RANDROTCT_Defect: Individual 1 contains " + repr(nc) + " " + repr(sym) + " atoms\n"
)
nc = len([atm for atm in indi2 if atm.symbol == sym])
Optimizer.output.write(
"CX RANDROTCT_Defect: Individual 2 contains " + repr(nc) + " " + repr(sym) + " atoms\n"
)
if Optimizer.forcing != "Concentration":
for i in range(len(Optimizer.atomlist)):
atms1 = [inds for inds in indi1 if inds.symbol == Optimizer.atomlist[i][0]]
atms2 = [inds for inds in indi2 if inds.symbol == Optimizer.atomlist[i][0]]
if len(atms1) == 0:
if len(atms2) == 0:
indi1[random.randint(0, len(indi1) - 1)].symbol == Optimizer.atomlist[i][0]
indi2[random.randint(0, len(indi2) - 1)].symbol == Optimizer.atomlist[i][0]
else:
indi1.append(atms2[random.randint(0, len(atms2) - 1)])
indi1.pop(random.randint(0, len(indi1) - 2))
else:
if len(atms2) == 0:
indi2.append(atms1[random.randint(0, len(atms1) - 1)])
indi2.pop(random.randint(0, len(indi2) - 2))
indi1.rotate(rax, a=-1 * rang, center=rpos, rotate_cell=False)
indi2.rotate(rax, a=-1 * rang, center=rpos, rotate_cell=False)
if Optimizer.structure == "Defect":
indi1 = shift_atoms(indi1, [-p for p in com1])
indi2 = shift_atoms(indi2, [-p for p in com2])
else:
indi1.translate(com1)
indi2.translate(com2)
# DEBUG: Check structure and number of atoms in crystal
if Optimizer.structure == "Defect":
solid1 = Atoms()
solid1.extend(indi1)
solid1.extend(ind1.bulki)
solid2 = Atoms()
solid2.extend(indi2)
solid2.extend(ind2.bulki)
for sym, c, m, u in Optimizer.atomlist:
nc = len([atm for atm in solid1 if atm.symbol == sym])
Optimizer.output.write(
"CX RANDROTCT_Defect: Defect 1 configuration contains " + repr(nc) + " " + repr(sym) + " atoms\n"
)
nc = len([atm for atm in solid2 if atm.symbol == sym])
Optimizer.output.write(
"CX RANDROTCT_Defect: Defect 2 configuration contains " + repr(nc) + " " + repr(sym) + " atoms\n"
)
if debug:
Optimizer.output.flush()
# pdb.set_trace()
ind1[0] = indi1
ind2[0] = indi2
return ind1, ind2
def write_pw_in(d,a,p):
if 'iofile' in p.keys() :
# write special varians of the pw input
fh=open(os.path.join(d,p['iofile']+'.in'),'w')
else :
# write standard pw input
fh=open(os.path.join(d,'pw.in'),'w')
# ----------------------------------------------------------
# CONTROL section
# ----------------------------------------------------------
fh.write(' &CONTROL\n')
fh.write(" calculation = '%s',\n" % p['calc'])
pwin_k=['tstress', 'tprnfor','nstep','pseudo_dir','outdir',
'wfcdir', 'prefix','forc_conv_thr', 'etot_conv_thr']
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# SYSTEM section
# ----------------------------------------------------------
fh.write(' &SYSTEM\n')
if p['use_symmetry'] :
# Need to use symmetry properly
# create a dummy atoms object for primitive cell
primcell=write_cell_params(fh,a,p)
if primcell :
# primitive cell has been found - let us use it
cr=Atoms(cell=primcell[0],scaled_positions=primcell[1],numbers=primcell[2],pbc=True)
else :
# no primitive cell found - drop the symmetry
cr=a
else :
cr=a
p['ibrav']=0
fh.write(" nat = %d,\n" % (cr.get_number_of_atoms()))
fh.write(" ntyp = %d,\n" % (len(set(cr.get_atomic_numbers()))))
pwin_k=['ecutwfc','ibrav','nbnd','occupations','degauss','smearing','ecutrho','nbnd']
# must also take into account degauss and smearing
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# ELECTRONS section
# ----------------------------------------------------------
fh.write(' &ELECTRONS\n')
# must also take into account mixing_beta mixing_mode and diagonalization
pwin_k=['conv_thr','mixing_beta','mixing_mode','diagonalization',
'mixing_ndim','electron_maxstep']
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# | IONS section
# ----------------------------------------------------------
if p['calc'] in ['vc-relax', 'vc-md', 'md', 'relax']:
fh.write(' &IONS\n')
pwin_k=['ion_dynamics','ion_positions', 'phase_space', 'pot_extrapolation']
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# | CELL section
# ----------------------------------------------------------
if p['calc'] in ['vc-relax', 'vc-md']:
fh.write('&CELL\n')
pwin_k=['cell_dynamics','press', 'cell_dofree']
write_section_params(fh, pwin_k, p)
fh.write('/\n')
# ----------------------------------------------------------
# Card: ATOMIC_SPECIES
# ----------------------------------------------------------
fh.write('ATOMIC_SPECIES\n')
xc=p['xc']
pp_type=p['pp_type']
pp_format=p['pp_format']
# we check if the desired potential exists. Get the PP locations
if 'ESPRESSO_PSEUDO' in os.environ:
pppaths = os.environ['ESPRESSO_PSEUDO']
else:
pppaths = p['pseudo_dir'] # default
pppaths = pppaths.split(':')
# search for element PP in each location (in principle with QE only one location)
for nm, mass in set(zip(cr.get_chemical_symbols(),cr.get_masses())):
name = "%s_%s_%s.%s" % (nm, xc, pp_type, pp_format)
found = False
for path in pppaths:
filename = os.path.join(path, name)
match = glob.glob(filename)
if match: # the exact name as expected
found = True
name = match[0]
if not found: # a more permissive name with element.*.format
name = "%s.*.%s" % (nm, pp_format)
filename = os.path.join(path, name)
match = glob.glob(filename)
if match:
found = True
name = match[0] # the first match
if not found: # a more permissive name with element_*.format
name = "%s_*.%s" % (nm, pp_format)
filename = os.path.join(path, name)
match = glob.glob(filename)
if match:
found = True
name = match[0] # the first match
if not found: # a more permissive name with just the element_*
name = "%s_*" % (nm)
filename = os.path.join(path, name)
match = glob.glob(filename)
if match:
found = True
name = match[0] # the first match
if not found: # a more permissive name with just the element.*
name = "%s.*" % (nm)
filename = os.path.join(path, name)
match = glob.glob(filename)
if match:
found = True
name = match[0] # the first match
if not found:
raise RuntimeError('Espresso: No pseudopotential for %s. Aborting.' % nm)
fh.write(" %s %g %s \n" % (nm, mass, os.path.basename(name)))
# ----------------------------------------------------------
# Card: ATOMIC_POSITIONS
# ----------------------------------------------------------
fh.write('ATOMIC_POSITIONS crystal\n')
# Now we can write it out
for n,v in zip(cr.get_chemical_symbols(),cr.get_scaled_positions()):
fh.write(" %s %g %g %g\n" % (n, v[0], v[1], v[2]))
# ----------------------------------------------------------
# Card: CELL_PARAMETERS
# ----------------------------------------------------------
# Write out only if ibrav==0 - no symmetry used
if not p['use_symmetry'] or p['ibrav']==0:
fh.write('CELL_PARAMETERS angstrom\n')
for v in cr.get_cell():
fh.write(' %f %f %f\n' % tuple(v))
# ----------------------------------------------------------
# Card: K_POINTS
# ----------------------------------------------------------
if p['kpt_type'] in ['automatic','edos'] :
fh.write('K_POINTS %s\n' % 'automatic')
else :
fh.write('K_POINTS %s\n' % p['kpt_type'])
if p['kpt_type'] is 'automatic':
fh.write(' %d %d %d %d %d %d\n' % (tuple(p['kpts'])+tuple(p['kpt_shift'])))
elif p['kpt_type'] is 'edos':
fh.write(' %d %d %d %d %d %d\n' % (tuple(p['nkdos'])+tuple([0,0,0])))
elif not p['kpt_type'] is 'gamma':
write_q_path(fh,p['qpath'],p['points'])
fh.close()
def write_cell_params(fh, a, p):
'''
Write the specification of the cell using a traditional A,B,C,
alpha, beta, gamma scheme. The symmetry and parameters are determined
from the atoms (a) object. The atoms object is translated into a
primitive unit cell but *not* converted. This is just an internal procedure.
If you wish to work with primitive unit cells in ASE, you need to make
a conversion yourself. The cell params are in angstrom.
Input
-----
fh file handle of the opened pw.in file
a atoms object
p parameters dictionary
Output
------
Primitive cell tuple of arrays: (lattice, atoms, atomic numbers)
'''
assert(p['use_symmetry'])
# Get a spacegroup name and number for the a
sg,sgn=spglib.get_spacegroup(a).split()
# Extract the number
sgn=int(sgn[1:-1])
# Extract the lattice type
ltyp=sg[0]
# Find a primitive unit cell for the system
# puc is a tuple of (lattice, atoms, atomic numbers)
puc=spglib.find_primitive(a)
cell=puc[0]
apos=puc[1]
anum=puc[2]
icell=a.get_cell()
A=norm(icell[0])
B=norm(icell[1])
C=norm(icell[2])
# Select appropriate ibrav
if sgn >= 195 :
# Cubic lattice
if ltyp=='P':
p['ibrav']=1 # Primitive
qepc=array([[1,0,0],[0,1,0],[0,0,1]])
elif ltyp=='F':
p['ibrav']=2 # FCC
qepc=array([[-1,0,1],[0,1,1],[-1,1,0]])/2.0
elif ltyp=='I':
p['ibrav']=3 # BCC
qepc=array([[1,1,1],[-1,1,1],[-1,-1,1]])/2.0
else :
print 'Impossible lattice symmetry! Contact the author!'
raise NotImplementedError
#a=sqrt(2)*norm(cell[0])
qepc=A*qepc
fh.write(' A = %f,\n' % (A,))
elif sgn >= 143 :
# Hexagonal and trigonal
if ltyp=='P' :
p['ibrav']=4 # Primitive
qepc=array([[1,0,0],[-1/2,sqrt(3)/2,0],[0,0,C/A]])
elif ltyp=='R' :
p['ibrav']=5 # Trigonal rhombohedral
raise NotImplementedError
else :
print 'Impossible lattice symmetry! Contact the author!'
raise NotImplementedError
qepc=A*qepc
fh.write(' A = %f,\n' % (A,))
fh.write(' C = %f,\n' % (C,))
elif sgn >= 75 :
raise NotImplementedError
elif sgn ==1 :
# P1 symmetry - no special primitive cell signal to the caller
p['ibrav']=0
return None
else :
raise NotImplementedError
cp=Atoms(cell=puc[0], scaled_positions=puc[1], numbers=puc[2], pbc=True)
qepc=Atoms(cell=qepc,
positions=cp.get_positions(),
numbers=cp.get_atomic_numbers(), pbc=True)
return qepc.get_cell(), qepc.get_scaled_positions(), qepc.get_atomic_numbers()
return ed
if __name__ == '__main__':
import matplotlib.pyplot as plt
from ase import Atoms
from ase.visualize import view
from ase.cluster import FaceCenteredCubic
import ase.io as aseio
# atoms = Atoms('Pt', [(0, 0, 0)])
atoms = Atoms(
FaceCenteredCubic('Pt', [[1, 0, 0], [1, 1, 0], [1, 1, 1]], (2, 3, 2)))
# atoms = atoms[[atom.index for atom in atoms if atom.position[2]< 1.5]]
view(atoms)
atoms.set_cell(atoms.get_cell() * 1.2)
atoms.center()
cell = atoms.get_cell()
print(cell, len(atoms))
resolution = .3 * np.ones(3)
c = np.diagonal(cell)
v = tuple(np.int32(np.ceil(c / resolution)))
voxels = np.zeros(v)
ed = np.zeros(v)
i = 0
for atom in atoms:
print(i)
ed += get_atomic_electron_density(atom, voxels, resolution)
i += 1
print(ed[:, :, 0])
plt.imshow(np.sum(ed, axis=2), cmap='viridis')
import numpy as np
from ase import Atom, Atoms
from ase.optimize import BFGS
from ase.parallel import parprint
from ase.units import Hartree
import gpaw.mpi as mpi
from gpaw import GPAW
from gpaw.test import equal
from gpaw.lrtddft.kssingle import KSSingles
Be = Atoms('Be')
Be.center(vacuum=6)
if 1:
# introduce a sligth non-orthgonality
cell = Be.get_cell()
cell[1] += 0.001 * cell[0]
Be.set_cell(cell)
txt = None
#txt='-'
eigensolver = None
#eigensolver = 'rmm-diis'
#modes = ['lcao', 'fd']
modes = ['fd']
for mode in modes:
energy = {}
osz = {}
for pbc in [False, True]:
def write_pw_in(d,a,p):
if 'iofile' in p.keys() :
# write special varians of the pw input
fh=open(os.path.join(d,p['iofile']+'.in'),'w')
else :
# write standard pw input
fh=open(os.path.join(d,'pw.in'),'w')
# ----------------------------------------------------------
# CONTROL section
# ----------------------------------------------------------
fh.write(' &CONTROL\n')
fh.write(" calculation = '%s',\n" % p['calc'])
pwin_k=['tstress', 'tprnfor','nstep','pseudo_dir','outdir',
'wfcdir', 'prefix','forc_conv_thr', 'etot_conv_thr']
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# SYSTEM section
# ----------------------------------------------------------
fh.write(' &SYSTEM\n')
if p['use_symmetry'] :
# Need to use symmetry properly
# create a dummy atoms object for primitive cell
primcell=write_cell_params(fh,a,p)
if primcell :
# primitive cell has been found - let us use it
cr=Atoms(cell=primcell[0],scaled_positions=primcell[1],numbers=primcell[2],pbc=True)
else :
# no primitive cell found - drop the symmetry
cr=a
else :
cr=a
p['ibrav']=0
fh.write(" nat = %d,\n" % (cr.get_number_of_atoms()))
fh.write(" ntyp = %d,\n" % (len(set(cr.get_atomic_numbers()))))
pwin_k=['ecutwfc','ibrav','nbnd','occupations']
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# ELECTRONS section
# ----------------------------------------------------------
fh.write(' &ELECTRONS\n')
fh.write(' /\n')
# ----------------------------------------------------------
# | IONS section
# ----------------------------------------------------------
if p['calc'] in ['vc-relax', 'vc-md', 'md', 'relax']:
fh.write(' &IONS\n')
pwin_k=['ion_dynamics','ion_positions', 'phase_space', 'pot_extrapolation']
write_section_params(fh, pwin_k, p)
fh.write(' /\n')
# ----------------------------------------------------------
# | CELL section
# ----------------------------------------------------------
if p['calc'] in ['vc-relax', 'vc-md']:
fh.write('&CELL\n')
pwin_k=['cell_dynamics','press', 'cell_dofree']
write_section_params(fh, pwin_k, p)
fh.write('/\n')
# ----------------------------------------------------------
# Card: ATOMIC_SPECIES
# ----------------------------------------------------------
fh.write('ATOMIC_SPECIES\n')
xc=p['xc']
pp_type=p['pp_type']
pp_format=p['pp_format']
for nm, mass in set(zip(cr.get_chemical_symbols(),cr.get_masses())):
fh.write(" %s %g %s_%s_%s.%s \n" % (nm, mass, nm, xc, pp_type, pp_format))
# ----------------------------------------------------------
# Card: ATOMIC_POSITIONS
# ----------------------------------------------------------
fh.write('ATOMIC_POSITIONS crystal\n')
# Now we can write it out
for n,v in zip(cr.get_chemical_symbols(),cr.get_scaled_positions()):
fh.write(" %s %g %g %g\n" % (n, v[0], v[1], v[2]))
# ----------------------------------------------------------
# Card: CELL_PARAMETERS
# ----------------------------------------------------------
# Write out only if ibrav==0 - no symmetry used
if not p['use_symmetry'] or p['ibrav']==0:
fh.write('CELL_PARAMETERS angstrom\n')
for v in cr.get_cell():
fh.write(' %f %f %f\n' % tuple(v))
# ----------------------------------------------------------
# Card: K_POINTS
# ----------------------------------------------------------
if p['kpt_type'] in ['automatic','edos'] :
fh.write('K_POINTS %s\n' % 'automatic')
else :
fh.write('K_POINTS %s\n' % p['kpt_type'])
if p['kpt_type'] is 'automatic':
fh.write(' %d %d %d %d %d %d\n' % (tuple(p['kpts'])+tuple(p['kpt_shift'])))
elif p['kpt_type'] is 'edos':
fh.write(' %d %d %d %d %d %d\n' % (tuple(p['nkdos'])+tuple([0,0,0])))
elif not p['kpt_type'] is 'gamma':
write_q_path(fh,p['qpath'],p['points'])
fh.close()
from ase import Atoms, Atom
import numpy as np
b = 7.1
atoms = Atoms([Atom('C', [0., 0., 0.]),
Atom('O', [1.1, 0., 0.])],
cell=[[b, b, 0.],
[b, 0., b],
[0., b, b]])
# get unit cell vectors and their lengths
(a1, a2, a3) = atoms.get_cell()
print '|a1| = {0:1.2f} Ang'.format(np.sum(a1**2)**0.5)
print '|a2| = {0:1.2f} Ang'.format(np.linalg.norm(a2))
print '|a3| = {0:1.2f} Ang'.format(np.sum(a3**2)**0.5)
import numpy as np
from ase import Atom,Atoms
from ase.io import write
atoms = Atoms('Ni4', positions= [(0, 0, 0),
(0.45, 0, 0),
(0, 0.5, 0),
(0.5, 0.5, 0),])
atoms[1].x = 0.5
a = 3.55
cell = [(2 / np.sqrt(2.0) * a, 0, 0),
(1 / np.sqrt(2.0) * a, np.sqrt(3.0 / 2.0) * a, 0),
(0, 0, 10 * np.sqrt(3.0) / 3.0 * a)]
atoms.set_cell(cell, scale_atoms=True)
write('a1.png', atoms, rotation='-73x', show_unit_cell=2)
a = atoms.repeat((3, 3, 2))
a.set_cell(atoms.get_cell())
write('a2.png', a, rotation='-73x', show_unit_cell=True)
xyzcell = np.identity(3) # The 3x3 unit matrix
atoms.set_cell(xyzcell, scale_atoms=True) # Set the unit cell and rescale
atoms.append(Atom('Ni', (1/6., 1/6., .1)))
atoms.set_cell(cell, scale_atoms=True) # Set the unit cell and scale back
a = atoms.repeat((3, 3, 1))
a.set_cell(atoms.get_cell())
write('a3.png', a, show_unit_cell=True)
[-3.68163760377696e+00, -5.94997793843316e+00, +1.14910098375475e+01]],
[[+1.13578166916005e+01, +0.00000000000000e+00, +0.00000000000000e+00],
[-4.62236725820948e+00, +1.28309672640153e+01, +0.00000000000000e+00],
[-3.36772471669551e+00, -6.41548363200768e+00, +1.15542200082913e+01]],
[[+1.18321595661992e+01, +0.00000000000000e+00, +0.00000000000000e+00],
[-4.71877792223422e+00, +1.29511827614560e+01, +0.00000000000000e+00],
[-3.55669082198251e+00, -6.47559138072800e+00, +1.16368667031408e+01]],
[[+4.81947971142972e-03, +0.00000000000000e+00, +0.00000000000000e+00],
[+4.12397039845618e-03, +4.86743859122682e+01, +0.00000000000000e+00],
[+4.62732595971025e-03, +1.13797841621313e+01, +6.81149615940608e+01]],
[[+1.43683914413843e-01, +0.00000000000000e+00, +0.00000000000000e+00],
[+4.73841211849216e-02, +8.02075186538656e+00, +0.00000000000000e+00],
[+9.29303317118020e-03, +8.28854375915883e-01, +1.93660401476964e+01]],
[[+5.02420000000000e+00, +0.00000000000000e+00, +0.00000000000000e+00],
[-2.40035596861745e+00, +1.25083680303996e+01, +0.00000000000000e+00],
[-2.37319883118274e+00, -5.49894680458153e+00, +2.86098306766757e+01]],
[[+5.02419976114664e+00, +0.00000000000000e+00, +0.00000000000000e+00],
[-2.40036499209593e+00, +1.25083662987906e+01, +0.00000000000000e+00],
[-2.37320481266200e+00, -5.49892622854049e+00, +2.86097847514890e+01]],
])
conf = Atoms(pbc=True)
for i, cell in enumerate(cells_in):
conf.set_cell(cell)
niggli_reduce(conf)
cell = conf.get_cell()
diff = np.linalg.norm(cell - cells_out[i])
assert diff < 1e-5, \
'Difference between unit cells is too large! ({0})'.format(diff)
from ase.optimize import QuasiNewton, FIRE
from ase.constraints import FixAtoms
from ase.neb import NEB
from ase.io import write, PickleTrajectory
from ase.calculators.emt import ASAP
# Distance between Cu atoms on a (100) surface:
d = 2.74
h1 = d * sqrt(3) / 2
h2 = d * sqrt(2.0 / 3)
initial = Atoms(symbols='Pt',
positions=[(0, 0, 0)],#(1.37,0.79,2.24),(2.74,1.58,4.48),(0,0,6.72),(1.37,0.79,8.96),(2.74,1.58,11.2)],
cell=([(d,0,0),(d/2,h1,0),(d/2,h1/3,-h2)]),
pbc=(True, True, True))
initial *= (7, 8, 6) # 5x5 (100) surface-cell
cell = initial.get_cell()
cell[2] = (0, 0, 22)
initial.set_cell(cell)
#initial.set_pbc((True,True,False))
# Approximate height of Ag atom on Cu(100) surfece:
h0 = 2.2373
initial += Atom('Pt', (10.96, 11.074, h0))
initial += Atom('Pt', (13.7, 11.074, h0))
initial += Atom('Pt', (9.59, 8.701, h0))
initial += Atom('Pt', (12.33, 8.701, h0))
initial += Atom('Pt', (15.07, 8.701, h0))
initial += Atom('Pt', (10.96, 6.328, h0))
initial += Atom('Pt', (13.7, 6.328, h0))
if 0:
view(initial)
calc.set(kpts=(20, 20, 7), fixdensity=True)
atoms.get_potential_energy()
# The result should also be converged with respect to bands:
calc.diagonalize_full_hamiltonian(nbands=60)
calc.write('graphite.gpw', 'all')
# Part 2: Spectra calculations
f = paropen('graphite_q_list', 'w') # write q
for i in range(1, 6): # loop over different q
df = DielectricFunction(calc='graphite.gpw',
domega0=0.01,
eta=0.2, # Broadening parameter.
ecut=100,
# write different output for different q:
txt='out_df_%d.txt' % i)
q_c = [i / 20.0, 0.0, 0.0] # Gamma - M excitation
df.get_eels_spectrum(q_c=q_c, filename='graphite_EELS_%d' % i)
# Calculate cartesian momentum vector:
cell_cv = atoms.get_cell()
bcell_cv = 2 * np.pi * np.linalg.inv(cell_cv).T
q_v = np.dot(q_c, bcell_cv)
print(sqrt(np.inner(q_v, q_v)), file=f)
f.close()
def read_pmd(fname='pmd0000',specorder=[],fmvs=[(True,True,True),]):
"""
Import pmd format file.
fname
Name of the file to be read.
specorder
Order of species appeared in pmd file.
fmvs
Constraints of motion in bool of x,y,z direction.
e.g., fmv = ((True,True,True),(True,True,False),)
True means fix and False means free.
"""
f = open(fname,'r')
# 1st line: lattice_constant
lattice_constant = float(f.readline().split()[0])
# 2-4 lines: lattice vectors
a = []
for ii in range(3):
s = f.readline().split()
floatvect = float(s[0]), float(s[1]), float(s[2])
a.append(floatvect)
basis_vectors = np.array(a) * lattice_constant
# 5-7 lines: velocities of lattice vectors
tmp = f.readline()
tmp = f.readline()
tmp = f.readline()
natm = int(f.readline().split()[0])
positions = np.zeros((natm,3),dtype=float)
symbols = []
ifmvs = np.zeros((natm),dtype=int)
maxifmv = 0
for ia in range(natm):
data = f.readline().split()
tag = float(data[0])
for ii in range(3):
positions[ia,ii] = float(data[ii+1])
sid,symbol,ifmv,aid = decode_tag(tag,specorder)
symbols.append(symbol)
ifmvs[ia] = ifmv
maxifmv = max(maxifmv,ifmv)
f.close()
if maxifmv > len(fmvs)+1:
raise ValueError('Length of fmvs are too short.')
atoms = Atoms(symbols=symbols,cell=basis_vectors,pbc=True)
atoms.set_scaled_positions(positions)
#set constraints
constraints = []
indices = []
for ia in range(natm):
if ifmvs[ia] == 0:
indices.append(ia)
else:
ifmv = ifmvs[ia]
fmv = fmvs[ifmv-1]
if all(fmv):
indices.append(ia)
elif any(fmv):
constraints.append(FixScaled(atoms.get_cell(),ia,fmv))
if indices:
constraints.append(FixAtoms(indices))
if constraints:
atoms.set_constraint(constraints)
return atoms
def newclus(ind1, ind2, Optimizer):
"""Select a box in the cluster configuration"""
if 'CX' in Optimizer.debug:
debug = True
else:
debug = False
Optimizer.output.write('Box Cluster Cx between individual {} and individual {}'.format(repr(ind1.index), repr(ind2.index)))
# Perserve starting conditions of individual
solid1 = ind1[0].copy()
solid2 = ind2[0].copy()
cello1 = ind1[0].get_cell()
cello2 = ind2[0].get_cell()
cell1 = numpy.maximum.reduce(solid1.get_positions())
cell1m = numpy.minimum.reduce(solid1.get_positions())
cell2 = numpy.maximum.reduce(solid2.get_positions())
cell2m = numpy.minimum.reduce(solid2.get_positions())
cell = numpy.minimum(cell1, cell2)
pbc1 = solid1.get_pbc()
pbc2 = solid2.get_pbc()
# Get starting concentrations and number of atoms
nat1 = len(solid1)
nat2 = len(solid2)
# Pick a origin point for box in the cell
pt1 = random.choice(solid1)
pt1f = [(pt1.position[i]-cell1m[i])/cell1[i] for i in range(3)]
pt2 = [pt1f[i]*cell2[i]+cell2m[i] for i in range(3)]
solid2.append(Atom(position=pt2))
pt2 = solid2[len(solid2)-1]
# Find max neighborsize of circle cut
r = random.uniform(0, min(nat1, nat2)/5.0)
if debug:
print('DEBUG CX: Point one = {}'.format(pt1.position))
print('DEBUG CX: Point two = {}'.format(pt2.position))
# Find atoms within sphere of neighborsize r for both individuals
# Make sure that crossover is only selection of atoms not all
while True:
ctoff = [r for on in solid1]
nl = NeighborList(ctoff, bothways=True, self_interaction=False)
nl.update(solid1)
indices1, offsets = nl.get_neighbors(pt1.index)
if len(indices1) == 0:
r = r*1.2
elif len(indices1) < nat1*.75:
break
else:
r = r*0.8
if debug:
print('Neighborsize of box = {}'.format(repr(r)))
print('Position in solid1 = {}'.format(repr(pt1.position)))
print('Position in solid2 = {}'.format(repr(pt2.position)))
group1 = Atoms(cell=solid1.get_cell(), pbc=solid1.get_pbc())
group1.append(pt1)
indices1a = [pt1.index]
for index, d in zip(indices1, offsets):
if index not in indices1a:
index = int(index)
pos = solid1[index].position + numpy.dot(d, solid1.get_cell())
group1.append(Atom(symbol=solid1[index].symbol, position=pos))
indices1a.append(index)
indices1 = indices1a
ctoff = [r for on in solid2]
nl = NeighborList(ctoff, bothways=True, self_interaction=False)
nl.update(solid2)
indices2, offsets = nl.get_neighbors(pt2.index)
group2 = Atoms(cell=solid2.get_cell(), pbc=solid2.get_pbc())
indices2a = []
for index, d in zip(indices2, offsets):
if index not in indices2a:
index = int(index)
pos = solid2[index].position + numpy.dot(d, solid2.get_cell())
group2.append(Atom(symbol=solid2[index].symbol, position=pos))
indices2a.append(index)
indices2 = indices2a
if len(indices2) == 0:
for one in group1:
while True:
sel = random.choice(solid2)
if sel.symbol == one.symbol:
if sel.index not in indices2:
group2.append(sel)
indices2.append(sel.index)
break
if Optimizer.forcing == 'Concentration':
symlist = list(set(group1.get_chemical_symbols()))
seplist = [[atm for atm in group2 if atm.symbol == sym] for sym in symlist]
group2n = Atoms(cell=group2.get_cell(), pbc=group2.get_pbc())
indices2n = []
dellist = []
for one in group1:
sym1 = one.symbol
listpos = [i for i, s in enumerate(symlist) if s == sym1][0]
if len(seplist[listpos]) > 0:
pos = random.choice(range(len(seplist[listpos])))
group2n.append(seplist[listpos][pos])
indices2n.append(indices2[seplist[listpos][pos].index])
del seplist[listpos][pos]
else:
dellist.append(one.index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
indices2n.append(pt2.index)
indices2 = indices2n
group2 = group2n.copy()
else:
dellist = []
while len(group2) < len(group1)-len(dellist):
# Too many atoms in group 1
dellist.append(random.choice(group1).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
dellist = []
while len(group1) < len(group2)-len(dellist):
# Too many atoms in group 2
dellist.append(random.choice(group2).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group2[one]
del indices2[one]
other2 = Atoms(cell=solid2.get_cell(), pbc=solid2.get_pbc())
for one in solid2:
if one.index not in indices2:
other2.append(one)
other1 = Atoms(cell=solid1.get_cell(), pbc=solid1.get_pbc())
for one in solid1:
if one.index not in indices1:
other1.append(one)
# Exchange atoms in sphere and build new solids
nsolid1 = other1.copy()
nsolid1.extend(group2.copy())
nsolid2 = other2.copy()
nsolid2.extend(group1.copy())
# DEBUG: Write crossover to file
if debug:
write_xyz(Optimizer.debugfile, nsolid1, 'CX(randalloybx):nsolid1')
write_xyz(Optimizer.debugfile, nsolid2, 'CX(randalloybx):nsolid2')
# DEBUG: Check structure of atoms exchanged
for sym, c, m, u in Optimizer.atomlist:
if Optimizer.structure == 'Defect':
nc = len([atm for atm in nsolid1 if atm.symbol == sym])
nc += len([atm for atm in ind1.bulki if atm.symbol == sym])
oc = len([atm for atm in solid1 if atm.symbol == sym])
oc += len([atm for atm in ind1.bulki if atm.symbol == sym])
else:
nc = len([atm for atm in nsolid1 if atm.symbol == sym])
oc = len([atm for atm in solid1 if atm.symbol == sym])
Optimizer.output.write('CX(clustbx):New solid1 contains '+repr(nc)+' '+repr(sym)+' atoms\n')
if debug:
print('DEBUG CX: New solid1 contains {} {} atoms'.format(repr(nc), repr(sym)))
if oc != nc:
# pdb.set_trace()
print('CX: Issue in maintaining atom concentration\n Dropping new individual')
Optimizer.output.write('CX: Issue in maintaining atom concentration\n Dropping new individual 1\n')
nsolid1 = solid1
for sym, c, m, u in Optimizer.atomlist:
if Optimizer.structure == 'Defect':
nc = len([atm for atm in nsolid2 if atm.symbol == sym])
nc += len([atm for atm in ind2.bulki if atm.symbol == sym])
oc = len([atm for atm in solid2 if atm.symbol == sym])
oc += len([atm for atm in ind2.bulki if atm.symbol == sym])
else:
nc = len([atm for atm in nsolid2 if atm.symbol == sym])
oc = len([atm for atm in solid2 if atm.symbol == sym])
Optimizer.output.write('CX(clustbx):New solid2 contains '+repr(nc)+' '+repr(sym)+' atoms\n')
if debug:
print('DEBUG CX: New solid2 contains {} and {} atoms'.format(repr(nc), repr(sym)))
if oc != nc:
#pdb.set_trace()
print('CX: Issue in maintaining atom concentration\n Dropping new individual')
Optimizer.output.write('CX: Issue in maintaining atom concentration\n Dropping new individual 2\n')
solid2.pop()
nsolid2 = solid2
if Optimizer.forcing != 'Concentration':
for i in range(len(Optimizer.atomlist)):
atms1 = [inds for inds in nsolid1 if inds.symbol == Optimizer.atomlist[i][0]]
atms2 = [inds for inds in nsolid2 if inds.symbol == Optimizer.atomlist[i][0]]
if len(atms1) == 0:
if len(atms2) == 0:
nsolid1[random.randint(0, len(indi1)-1)].symbol == Optimizer.atomlist[i][0]
nsolid2[random.randint(0, len(indi2)-1)].symbol == Optimizer.atomlist[i][0]
else:
nsolid1.append(atms2[random.randint(0, len(atms2)-1)])
nsolid1.pop(random.randint(0, len(nsolid1)-2))
else:
if len(atms2) == 0:
nsolid2.append(atms1[random.randint(0, len(atms1)-1)])
nsolid2.pop(random.randint(0, len(nsolid2)-2))
nsolid1.set_cell(cello1)
nsolid2.set_cell(cello2)
nsolid1.set_pbc(pbc1)
nsolid2.set_pbc(pbc2)
ind1[0] = nsolid1.copy()
ind2[0] = nsolid2.copy()
return ind1, ind2
from ase.calculators.emt import EMT
from ase import Atoms
a = 3.60
b = a / 2
cu = Atoms('Cu',
positions=[(0, 0, 0)],
cell=[(0, b, b),
(b, 0, b),
(b, b, 0)],
pbc=1,
calculator=EMT())
e0 = cu.get_potential_energy()
print e0
cu.set_cell(cu.get_cell() * 1.001, scale_atoms=True)
e1 = cu.get_potential_energy()
V = a**3 / 4
B = 2 * (e1 - e0) / 0.003**2 / V * 160.2
print B
for i in range(4):
x = 0.001 * i
A = np.array([(x, b, b+x),
(b, 0, b),
(b, b, 0)])
cu.set_cell(A, scale_atoms=True)
e = cu.get_potential_energy() - e0
if i == 0:
print i, e
else:
d_bond = 1.1
d_disp = 0.04
# Timestep and expected oscillatory period in attoseconds
timestep = 5.0
period = 1.414e4 # ~292.4 meV cf. CRC Handbook of Phys. & Chem. #09_08_91
ndiv = int(np.ceil(0.1e3 / timestep)) # update stats every 0.1 fs
niter = ndiv * int(np.ceil(2 * period / (ndiv * timestep)))
if __name__ == '__main__':
if not os.path.isfile(name + '_gs.gpw'):
atoms = Atoms('N2', positions=[(0, 0, 0), (0, 0, d_bond + d_disp)])
atoms.set_pbc(False)
atoms.center(vacuum=6.0)
cell_c = np.sum(atoms.get_cell()**2, axis=1)**0.5
N_c = 8 * np.round(cell_c / (0.2 * 8))
calc = GPAW(gpts=N_c, nbands=5, basis='dzp', txt=name + '_gs.txt',
eigensolver='rmm-diis')
atoms.set_calculator(calc)
atoms.get_potential_energy()
calc.write(name + '_gs.gpw', mode='all')
del atoms, calc
time.sleep(10)
while not os.path.isfile(name + '_gs.gpw'):
print('Node %d waiting for file...' % world.rank)
time.sleep(10)
world.barrier()
tdcalc = TDDFT(name + '_gs.gpw', txt=name + '_td.txt', propagator='EFSICN')
import numpy as np
from ase import Atoms
from ase.io.trajectory import PickleTrajectory
from ase.calculators.emt import EMT
a = 4.0 # approximate lattice constant
b = a / 2
ag = Atoms('Ag',
cell=[(0,b,b), (b,0,b), (b,b,0)],
pbc=1,
calculator=EMT()) # use EMT potential
cell = ag.get_cell()
traj = PickleTrajectory('Ag.traj', 'w')
for x in np.linspace(0.95, 1.05, 5):
ag.set_cell(cell * x, scale_atoms=True)
traj.write(ag)
def read_vasp(filename='CONTCAR'):
"""Import POSCAR/CONTCAR type file.
Reads unitcell, atom positions and constraints from the POSCAR/CONTCAR
file and tries to read atom types from POSCAR/CONTCAR header, if this fails
the atom types are read from OUTCAR or POTCAR file.
"""
from ase import Atoms, Atom
from ase.constraints import FixAtoms, FixScaled
from ase.data import chemical_symbols
import numpy as np
if isinstance(filename, str):
f = open(filename)
else: # Assume it's a file-like object
f = filename
# The first line is in principle a comment line, however in VASP
# 4.x a common convention is to have it contain the atom symbols,
# eg. "Ag Ge" in the same order as later in the file (and POTCAR
# for the full vasp run). In the VASP 5.x format this information
# is found on the fifth line. Thus we save the first line and use
# it in case we later detect that we're reading a VASP 4.x format
# file.
line1 = f.readline()
lattice_constant = float(f.readline().split()[0])
# Now the lattice vectors
a = []
for ii in range(3):
s = f.readline().split()
floatvect = float(s[0]), float(s[1]), float(s[2])
a.append(floatvect)
basis_vectors = np.array(a) * lattice_constant
# Number of atoms. Again this must be in the same order as
# in the first line
# or in the POTCAR or OUTCAR file
atom_symbols = []
numofatoms = f.readline().split()
# Check whether we have a VASP 4.x or 5.x format file. If the
# format is 5.x, use the fifth line to provide information about
# the atomic symbols.
vasp5 = False
try:
int(numofatoms[0])
except ValueError:
vasp5 = True
atomtypes = numofatoms
numofatoms = f.readline().split()
# check for comments in numofatoms line and get rid of them if necessary
commentcheck = np.array(['!' in s for s in numofatoms])
if commentcheck.any():
# only keep the elements up to the first including a '!':
numofatoms = numofatoms[:np.arange(len(numofatoms))[commentcheck][0]]
if not vasp5:
atomtypes = line1.split()
numsyms = len(numofatoms)
if len(atomtypes) < numsyms:
# First line in POSCAR/CONTCAR didn't contain enough symbols.
# Sometimes the first line in POSCAR/CONTCAR is of the form
# "CoP3_In-3.pos". Check for this case and extract atom types
if len(atomtypes) == 1 and '_' in atomtypes[0]:
atomtypes = get_atomtypes_from_formula(atomtypes[0])
else:
atomtypes = atomtypes_outpot(f.name, numsyms)
else:
try:
for atype in atomtypes[:numsyms]:
if atype not in chemical_symbols:
raise KeyError
except KeyError:
atomtypes = atomtypes_outpot(f.name, numsyms)
for i, num in enumerate(numofatoms):
numofatoms[i] = int(num)
[atom_symbols.append(atomtypes[i]) for na in range(numofatoms[i])]
# Check if Selective dynamics is switched on
sdyn = f.readline()
selective_dynamics = sdyn[0].lower() == 's'
# Check if atom coordinates are cartesian or direct
if selective_dynamics:
ac_type = f.readline()
else:
ac_type = sdyn
cartesian = ac_type[0].lower() == 'c' or ac_type[0].lower() == 'k'
tot_natoms = sum(numofatoms)
atoms_pos = np.empty((tot_natoms, 3))
if selective_dynamics:
selective_flags = np.empty((tot_natoms, 3), dtype=bool)
for atom in range(tot_natoms):
ac = f.readline().split()
atoms_pos[atom] = (float(ac[0]), float(ac[1]), float(ac[2]))
if selective_dynamics:
curflag = []
for flag in ac[3:6]:
curflag.append(flag == 'F')
selective_flags[atom] = curflag
# Done with all reading
if isinstance(filename, str):
f.close()
if cartesian:
atoms_pos *= lattice_constant
atoms = Atoms(symbols=atom_symbols, cell=basis_vectors, pbc=True)
if cartesian:
atoms.set_positions(atoms_pos)
else:
atoms.set_scaled_positions(atoms_pos)
if selective_dynamics:
constraints = []
indices = []
for ind, sflags in enumerate(selective_flags):
if sflags.any() and not sflags.all():
constraints.append(FixScaled(atoms.get_cell(), ind, sflags))
elif sflags.all():
indices.append(ind)
if indices:
constraints.append(FixAtoms(indices))
if constraints:
atoms.set_constraint(constraints)
return atoms
def gen_pop_box(atomlist,size,crystal=False):
"""Function to generate a random structure of atoms.
Inputs:
atomlist = List of tuples with structure of atoms and quantity
[('Sym1',int(concentration1), float(mass1),float(chempotential1)),
('Sym2',int(concentration2), float(mass2),float(chempotential2)),...]
size = Float of length of side of cube within which to generate atoms
crystal = False/List of crystal cell shape options
list('cubic','orthorhombic','tetragonal','hexagonal','monoclinic','triclinic')
cell shape will be adjusted accordingly
Outputs:
Returns individual of class Atoms (see ase manual for info on Atoms class)
and if crystal list provided also outputs combined string with output information
"""
indiv=Atoms()
indiv.set_cell([size,size,size])
# Get list of atom types for all atoms in cluster
for s,c,m,u in atomlist:
if c > 0:
for i in range(c):
pos = [random.uniform(0,size) for j in range(3)]
at=Atom(symbol=s,position=pos)
indiv.append(at)
if crystal:
stro=''
natoms=sum([c for s,c,m,u in atomlist])
pos=indiv.get_scaled_positions()
structure=random.choice(crystal)
cello=indiv.get_cell()
if structure=='cubic':
#Set to cubic shape
an,bn,cn = [numpy.linalg.norm(v) for v in cello]
a=(an+bn+cn)/3.0
celln=numpy.array([[a,0,0],[0,a,0],[0,0,a]])
stro+='Setting cell to cubic\n'
elif structure=='orthorhombic':
#Set to orthorhombic
a=random.uniform(2,natoms**0.3333*size)
b=random.uniform(2,natoms**0.3333*size)
c=random.uniform(2,natoms**0.3333*size)
celln=numpy.array([[a,0,0],[0,b,0],[0,0,c]])
stro+='Setting cell to orthorhombic\n'
elif structure=='tetragonal':
#Set to tetragonal shape
an,bn,cn = [numpy.linalg.norm(v) for v in cello]
a=(an+bn)/2.0
c=cn
if c==a:
c=random.uniform(1,natoms**0.3333*size)
celln=numpy.array([[a,0,0],[0,a,0],[0,0,c]])
stro+='Setting cell to tetragonal\n'
elif structure=='hexagonal':
#Set to hexagonal shape
an,bn,cn = [numpy.linalg.norm(v) for v in cello]
a=(an+bn)/2.0
c=cn
if c<=a:
c=random.uniform(a+1,natoms**0.3333*size)
trans=numpy.array([[1,0,0],[-0.5,(3.0**0.5)/2.0,0],[0,0,1]])
trans[0]=[a*i for i in trans[0]]
trans[1]=[a*i for i in trans[1]]
trans[2]=[c*i for i in trans[2]]
celln=trans
stro+='Setting cell to Hexagonal\n'
elif structure=='monoclinic':
#Set to monoclinic
a,b,c = [numpy.linalg.norm(v) for v in cello]
if a==b:
b=random.uniform(1,natoms**0.3333*size)
trans=numpy.array([(1+random.random())*c, 0, (1+random.random())*c])
celln=numpy.array([[a,0,0],[0,b,0],[0,0,0]])
celln[2]=trans
stro+='Setting cell to monoclinic\n'
elif structure=='triclinic':
#Set to triclinic
a,b,c = [numpy.linalg.norm(v) for v in cello]
celln=numpy.array([[a,0,0],[(1+random.random())*b,(1+random.random())*b,0],[(1+random.random())*c,0,(1+random.random())*c]])
stro+='Setting cell to triclinic\n'
indiv.set_cell(celln)
indiv.set_scaled_positions(pos)
stro+=repr(indiv.get_cell())+'\n'
return indiv, stro
return indiv
def newclus(ind1, ind2, Optimizer):
"""Select a box in the cluster configuration"""
if 'CX' in Optimizer.debug:
debug = True
else:
debug = False
Optimizer.output.write('Box Cluster Cx between individual ' +
repr(ind1.index) + ' and individual ' +
repr(ind2.index) + '\n')
#Perserve starting conditions of individual
solid1 = ind1[0].copy()
solid2 = ind2[0].copy()
cello1 = ind1[0].get_cell()
cello2 = ind2[0].get_cell()
cell1 = numpy.maximum.reduce(solid1.get_positions())
cell1m = numpy.minimum.reduce(solid1.get_positions())
cell2 = numpy.maximum.reduce(solid2.get_positions())
cell2m = numpy.minimum.reduce(solid2.get_positions())
cell = numpy.minimum(cell1, cell2)
pbc1 = solid1.get_pbc()
pbc2 = solid2.get_pbc()
#Get starting concentrations and number of atoms
nat1 = len(solid1)
nat2 = len(solid2)
# Pick a origin point for box in the cell
pt1 = random.choice(solid1)
pt1f = [(pt1.position[i] - cell1m[i]) / cell1[i] for i in range(3)]
pt2 = [pt1f[i] * cell2[i] + cell2m[i] for i in range(3)]
solid2.append(Atom(position=pt2))
pt2 = solid2[len(solid2) - 1]
#Find max neighborsize of circle cut
r = random.uniform(0, min(nat1, nat2) / 5.0)
if debug:
print 'DEBUG CX: Point one =', pt1.position
print 'DEBUG CX: Point two =', pt2.position
#Find atoms within sphere of neighborsize r for both individuals
#Make sure that crossover is only selection of atoms not all
while True:
ctoff = [r for on in solid1]
nl = NeighborList(ctoff, bothways=True, self_interaction=False)
nl.update(solid1)
indices1, offsets = nl.get_neighbors(pt1.index)
if len(indices1) == 0:
r = r * 1.2
elif len(indices1) < nat1 * .75:
break
else:
r = r * 0.8
if debug:
print 'Neighborsize of box = ' + repr(
r) + '\nPosition in solid1 = ' + repr(
pt1.position) + '\nPosition in solid2 = ' + repr(pt2.position)
group1 = Atoms(cell=solid1.get_cell(), pbc=solid1.get_pbc())
group1.append(pt1)
indices1a = [pt1.index]
for index, d in zip(indices1, offsets):
if index not in indices1a:
index = int(index)
pos = solid1[index].position + numpy.dot(d, solid1.get_cell())
group1.append(Atom(symbol=solid1[index].symbol, position=pos))
indices1a.append(index)
indices1 = indices1a
ctoff = [r for on in solid2]
nl = NeighborList(ctoff, bothways=True, self_interaction=False)
nl.update(solid2)
indices2, offsets = nl.get_neighbors(pt2.index)
group2 = Atoms(cell=solid2.get_cell(), pbc=solid2.get_pbc())
indices2a = []
for index, d in zip(indices2, offsets):
if index not in indices2a:
index = int(index)
pos = solid2[index].position + numpy.dot(d, solid2.get_cell())
group2.append(Atom(symbol=solid2[index].symbol, position=pos))
indices2a.append(index)
indices2 = indices2a
if len(indices2) == 0:
for one in group1:
while True:
sel = random.choice(solid2)
if sel.symbol == one.symbol:
if sel.index not in indices2:
group2.append(sel)
indices2.append(sel.index)
break
if Optimizer.forcing == 'Concentration':
symlist = list(set(group1.get_chemical_symbols()))
seplist = [[atm for atm in group2 if atm.symbol == sym]
for sym in symlist]
group2n = Atoms(cell=group2.get_cell(), pbc=group2.get_pbc())
indices2n = []
dellist = []
for one in group1:
sym1 = one.symbol
listpos = [i for i, s in enumerate(symlist) if s == sym1][0]
if len(seplist[listpos]) > 0:
pos = random.choice(range(len(seplist[listpos])))
group2n.append(seplist[listpos][pos])
indices2n.append(indices2[seplist[listpos][pos].index])
del seplist[listpos][pos]
else:
dellist.append(one.index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
indices2n.append(pt2.index)
indices2 = indices2n
group2 = group2n.copy()
else:
dellist = []
while len(group2) < len(group1) - len(dellist):
#Too many atoms in group 1
dellist.append(random.choice(group1).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group1[one]
del indices1[one]
dellist = []
while len(group1) < len(group2) - len(dellist):
#Too many atoms in group 2
dellist.append(random.choice(group2).index)
if len(dellist) != 0:
dellist.sort(reverse=True)
for one in dellist:
del group2[one]
del indices2[one]
other2 = Atoms(cell=solid2.get_cell(), pbc=solid2.get_pbc())
for one in solid2:
if one.index not in indices2:
other2.append(one)
other1 = Atoms(cell=solid1.get_cell(), pbc=solid1.get_pbc())
for one in solid1:
if one.index not in indices1:
other1.append(one)
#Exchange atoms in sphere and build new solids
nsolid1 = other1.copy()
nsolid1.extend(group2.copy())
nsolid2 = other2.copy()
nsolid2.extend(group1.copy())
#DEBUG: Write crossover to file
if debug:
write_xyz(Optimizer.debugfile, nsolid1, 'CX(randalloybx):nsolid1')
write_xyz(Optimizer.debugfile, nsolid2, 'CX(randalloybx):nsolid2')
#DEBUG: Check structure of atoms exchanged
for sym, c, m, u in Optimizer.atomlist:
if Optimizer.structure == 'Defect':
nc = len([atm for atm in nsolid1 if atm.symbol == sym])
nc += len([atm for atm in ind1.bulki if atm.symbol == sym])
oc = len([atm for atm in solid1 if atm.symbol == sym])
oc += len([atm for atm in ind1.bulki if atm.symbol == sym])
else:
nc = len([atm for atm in nsolid1 if atm.symbol == sym])
oc = len([atm for atm in solid1 if atm.symbol == sym])
Optimizer.output.write('CX(clustbx):New solid1 contains ' + repr(nc) +
' ' + repr(sym) + ' atoms\n')
if debug:
print 'DEBUG CX: New solid1 contains ' + repr(nc) + ' ' + repr(
sym) + ' atoms'
if oc != nc:
#pdb.set_trace()
print 'CX: Issue in maintaining atom concentration\n Dropping new individual'
Optimizer.output.write(
'CX: Issue in maintaining atom concentration\n Dropping new individual 1\n'
)
nsolid1 = solid1
for sym, c, m, u in Optimizer.atomlist:
if Optimizer.structure == 'Defect':
nc = len([atm for atm in nsolid2 if atm.symbol == sym])
nc += len([atm for atm in ind2.bulki if atm.symbol == sym])
oc = len([atm for atm in solid2 if atm.symbol == sym])
oc += len([atm for atm in ind2.bulki if atm.symbol == sym])
else:
nc = len([atm for atm in nsolid2 if atm.symbol == sym])
oc = len([atm for atm in solid2 if atm.symbol == sym])
Optimizer.output.write('CX(clustbx):New solid2 contains ' + repr(nc) +
' ' + repr(sym) + ' atoms\n')
if debug:
print 'DEBUG CX: New solid2 contains ' + repr(nc) + ' ' + repr(
sym) + ' atoms'
if oc != nc:
#pdb.set_trace()
print 'CX: Issue in maintaining atom concentration\n Dropping new individual'
Optimizer.output.write(
'CX: Issue in maintaining atom concentration\n Dropping new individual 2\n'
)
solid2.pop()
nsolid2 = solid2
if Optimizer.forcing != 'Concentration':
for i in range(len(Optimizer.atomlist)):
atms1 = [
inds for inds in nsolid1
if inds.symbol == Optimizer.atomlist[i][0]
]
atms2 = [
inds for inds in nsolid2
if inds.symbol == Optimizer.atomlist[i][0]
]
if len(atms1) == 0:
if len(atms2) == 0:
nsolid1[random.randint(
0,
len(indi1) - 1)].symbol == Optimizer.atomlist[i][0]
nsolid2[random.randint(
0,
len(indi2) - 1)].symbol == Optimizer.atomlist[i][0]
else:
nsolid1.append(atms2[random.randint(0, len(atms2) - 1)])
nsolid1.pop(random.randint(0, len(nsolid1) - 2))
else:
if len(atms2) == 0:
nsolid2.append(atms1[random.randint(0, len(atms1) - 1)])
nsolid2.pop(random.randint(0, len(nsolid2) - 2))
nsolid1.set_cell(cello1)
nsolid2.set_cell(cello2)
nsolid1.set_pbc(pbc1)
nsolid2.set_pbc(pbc2)
ind1[0] = nsolid1.copy()
ind2[0] = nsolid2.copy()
return ind1, ind2
#PBS -q medium -l nodes=1:ppn=8:xeon5570 -m abe -V
# Takes 30 mins with 8 cores in domain-parallel (memory ~500mb, max 1.5gb)
import numpy as np
from ase import Atom, Atoms
from gpaw import GPAW, MixerDif, mpi
from gpaw.lrtddft import LrTDDFT
from gpaw.pes.dos import DOSPES
from gpaw.pes.tddft import TDDFTPES
atoms = Atoms([Atom('C', (7.666263598984184, 7.637780850431168, 8.289450797111844)),
Atom('O', (8.333644370007132, 8.362384430165646, 7.710230973847514))])
atoms.center(vacuum=8)
h = 0.15
N_c = np.round(atoms.get_cell().diagonal() / h / 16) * 16
m_c = GPAW(gpts=N_c, nbands=6, mixer=MixerDif(0.1, 5, weight=100.0),
#convergence={'bands':10},
parallel={'domain': mpi.size},
xc='PBE', txt='CO-m.txt', spinpol=True)
m = atoms.copy()
m.set_initial_magnetic_moments([-1,1])
m.set_calculator(m_c)
m.get_potential_energy()
d_c = GPAW(gpts=N_c, nbands=16, mixer=MixerDif(0.1, 5, weight=100.0),
convergence={'bands':10},
parallel={'domain': mpi.size},
xc='PBE', txt='CO-d.txt', spinpol=True)
calc = GPAW(h=0.15, width=0.1, xc='LDA', nbands=-4, txt='-',
basis='dzp', convergence={'energy': 1e-5, 'density': 1e-5})
# Calculate potential energy per atom for orthogonal unitcell
atoms = hcp0001('C', a=a/sqrt(3), vacuum=d, size=(3,2,1), orthogonal=True)
del atoms[[1,-1]]
atoms.center(axis=0)
atoms.set_calculator(calc)
kpts_c = np.ceil(50 / np.sum(atoms.get_cell()**2, axis=1)**0.5).astype(int)
kpts_c[~atoms.get_pbc()] = 1
calc.set(kpts=kpts_c)
eppa1 = atoms.get_potential_energy() / len(atoms)
F1_av = atoms.get_forces()
equal(np.abs(F1_av).max(), 0, 5e-3)
# Redo calculation with non-orthogonal unitcell
atoms = Atoms(symbols='C2', pbc=(True,True,False),
positions=[(a/2,-sqrt(3)/6*a,d), (a/2,sqrt(3)/6*a,d)],
cell=[(a/2,-sqrt(3)/2*a,0), (a/2,sqrt(3)/2*a,0), (0,0,2*d)])
atoms.set_calculator(calc)
kpts_c = np.ceil(50 / np.sum(atoms.get_cell()**2, axis=1)**0.5).astype(int)
kpts_c[~atoms.get_pbc()] = 1
calc.set(kpts=kpts_c)
eppa2 = atoms.get_potential_energy() / len(atoms)
F2_av = atoms.get_forces()
equal(np.abs(F2_av).max(), 0, 5e-3)
equal(eppa1, eppa2, 1e-3)
u = 0.3 # internal degree of freedom!
#primitive vectors
a1 = a * np.array([1.0, 0.0, 0.0])
a2 = a * np.array([0.0, 1.0, 0.0])
a3 = c * np.array([0.0, 0.0, 1.0])
atoms = Atoms([
Atom('Ti', [0., 0., 0.]),
Atom('Ti', 0.5 * a1 + 0.5 * a2 + 0.5 * a3),
Atom('O', u * a1 + u * a2),
Atom('O', -u * a1 - u * a2),
Atom('O', (0.5 + u) * a1 + (0.5 - u) * a2 + 0.5 * a3),
Atom('O', (0.5 - u) * a1 + (0.5 + u) * a2 + 0.5 * a3)
],
cell=[a1, a2, a3])
v0 = atoms.get_volume()
cell0 = atoms.get_cell()
factors = [0.9, 0.95, 1.0, 1.05, 1.1] #to change volume by
energies, volumes = [], []
ready = True
for f in factors:
v1 = f * v0
cell_factor = (v1 / v0)**(1. / 3.)
atoms.set_cell(cell0 * cell_factor, scale_atoms=True)
calc = Vasp(
'bulk/tio2/step1-{0:1.2f}'.format(f),
encut=520,
kpts=[5, 5, 5],
isif=2, # relax internal degrees of freedom
ibrion=1,
nsw=50,
xc='PBE',
from ase import Atom, Atoms
# http://cst-www.nrl.navy.mil/lattice/struk/c5.html
B = 'Ti'; X = 'O'; a = 3.7842; c = 2*4.7573; z = 0.0831;
a1 = a * np.array([1.0, 0.0, 0.0])
a2 = a * np.array([0.0, 1.0, 0.0])
a3 = np.array([0.5 * a, 0.5 * a, 0.5 * c])
atoms = Atoms([Atom(B, -0.125 * a1 + 0.625 * a2 + 0.25 * a3),
Atom(B, 0.125 * a1 + 0.375 * a2 + 0.75 * a3),
Atom(X, -z*a1 + (0.25-z)*a2 + 2.*z*a3),
Atom(X, -(0.25+z)*a1 + (0.5-z)*a2 + (0.5+2*z)*a3),
Atom(X, z*a1 - (0.25 - z)*a2 + (1-2*z)*a3),
Atom(X, (0.25 + z)*a1 + (0.5 + z)*a2 + (0.5-2*z)*a3)],
cell=[a1,a2,a3])
nTiO2 = len(atoms) / 3.
v0 = atoms.get_volume()
cell0 = atoms.get_cell()
volumes = [30., 33., 35., 37., 39.] #vol of one TiO2
for v in volumes:
atoms.set_cell(cell0 * ((nTiO2*v/v0)**(1./3.)),
scale_atoms=True)
with jasp('bulk/TiO2/anatase/anatase-{0}'.format(v),
encut=350,
kpts=(6, 6, 6),
xc='PBE',
ismear=0,
sigma=0.001,
isif=2,
ibrion=2,
nsw=20,
atoms=atoms) as calc:
try:
def read_vasp(filename='CONTCAR'):
"""Import POSCAR/CONTCAR type file.
Reads unitcell, atom positions and constraints from the POSCAR/CONTCAR
file and tries to read atom types from POSCAR/CONTCAR header, if this fails
the atom types are read from OUTCAR or POTCAR file.
"""
from ase import Atoms
from ase.constraints import FixAtoms, FixScaled
from ase.data import chemical_symbols
import numpy as np
if isinstance(filename, basestring):
f = open(filename)
else: # Assume it's a file-like object
f = filename
# The first line is in principle a comment line, however in VASP
# 4.x a common convention is to have it contain the atom symbols,
# eg. "Ag Ge" in the same order as later in the file (and POTCAR
# for the full vasp run). In the VASP 5.x format this information
# is found on the fifth line. Thus we save the first line and use
# it in case we later detect that we're reading a VASP 4.x format
# file.
line1 = f.readline()
lattice_constant = float(f.readline().split()[0])
# Now the lattice vectors
a = []
for ii in range(3):
s = f.readline().split()
floatvect = float(s[0]), float(s[1]), float(s[2])
a.append(floatvect)
basis_vectors = np.array(a) * lattice_constant
# Number of atoms. Again this must be in the same order as
# in the first line
# or in the POTCAR or OUTCAR file
atom_symbols = []
numofatoms = f.readline().split()
# Check whether we have a VASP 4.x or 5.x format file. If the
# format is 5.x, use the fifth line to provide information about
# the atomic symbols.
vasp5 = False
try:
int(numofatoms[0])
except ValueError:
vasp5 = True
atomtypes = numofatoms
numofatoms = f.readline().split()
# check for comments in numofatoms line and get rid of them if necessary
commentcheck = np.array(['!' in s for s in numofatoms])
if commentcheck.any():
# only keep the elements up to the first including a '!':
numofatoms = numofatoms[:np.arange(len(numofatoms))[commentcheck][0]]
if not vasp5:
atomtypes = line1.split()
numsyms = len(numofatoms)
if len(atomtypes) < numsyms:
# First line in POSCAR/CONTCAR didn't contain enough symbols.
# Sometimes the first line in POSCAR/CONTCAR is of the form
# "CoP3_In-3.pos". Check for this case and extract atom types
if len(atomtypes) == 1 and '_' in atomtypes[0]:
atomtypes = get_atomtypes_from_formula(atomtypes[0])
else:
atomtypes = atomtypes_outpot(f.name, numsyms)
else:
try:
for atype in atomtypes[:numsyms]:
if atype not in chemical_symbols:
raise KeyError
except KeyError:
atomtypes = atomtypes_outpot(f.name, numsyms)
for i, num in enumerate(numofatoms):
numofatoms[i] = int(num)
[atom_symbols.append(atomtypes[i]) for na in range(numofatoms[i])]
# Check if Selective dynamics is switched on
sdyn = f.readline()
selective_dynamics = sdyn[0].lower() == 's'
# Check if atom coordinates are cartesian or direct
if selective_dynamics:
ac_type = f.readline()
else:
ac_type = sdyn
cartesian = ac_type[0].lower() == 'c' or ac_type[0].lower() == 'k'
tot_natoms = sum(numofatoms)
atoms_pos = np.empty((tot_natoms, 3))
if selective_dynamics:
selective_flags = np.empty((tot_natoms, 3), dtype=bool)
for atom in range(tot_natoms):
ac = f.readline().split()
atoms_pos[atom] = (float(ac[0]), float(ac[1]), float(ac[2]))
if selective_dynamics:
curflag = []
for flag in ac[3:6]:
curflag.append(flag == 'F')
selective_flags[atom] = curflag
# Done with all reading
if isinstance(filename, basestring):
f.close()
if cartesian:
atoms_pos *= lattice_constant
atoms = Atoms(symbols=atom_symbols, cell=basis_vectors, pbc=True)
if cartesian:
atoms.set_positions(atoms_pos)
else:
atoms.set_scaled_positions(atoms_pos)
if selective_dynamics:
constraints = []
indices = []
for ind, sflags in enumerate(selective_flags):
if sflags.any() and not sflags.all():
constraints.append(FixScaled(atoms.get_cell(), ind, sflags))
elif sflags.all():
indices.append(ind)
if indices:
constraints.append(FixAtoms(indices))
if constraints:
atoms.set_constraint(constraints)
return atoms
atoms=atoms) as calc:
atoms = calc.get_atoms()
x, y, z, cd = calc.get_charge_density()
mlab.figure(bgcolor=(1, 1, 1))
# plot the atoms as spheres
for atom in atoms:
mlab.points3d(atom.x,
atom.y,
atom.z,
scale_factor=vdw_radii[atom.number]/5.,
resolution=20,
# a tuple is required for the color
color=tuple(cpk_colors[atom.number]),
scale_mode='none')
# draw the unit cell - there are 8 corners, and 12 connections
a1, a2, a3 = atoms.get_cell()
origin = [0, 0, 0]
cell_matrix = [[origin, a1],
[origin, a2],
[origin, a3],
[a1, a1 + a2],
[a1, a1 + a3],
[a2, a2 + a1],
[a2, a2 + a3],
[a3, a1 + a3],
[a3, a2 + a3],
[a1 + a2, a1 + a2 + a3],
[a2 + a3, a1 + a2 + a3],
[a1 + a3, a1 + a3 + a2]]
for p1, p2 in cell_matrix:
mlab.plot3d([p1[0], p2[0]], # x-positions
pass
else:
w += ['png', 'eps']
for format in w:
print format, 'O',
fname1 = 'io-test.1.' + format
fname2 = 'io-test.2.' + format
write(fname1, atoms, format=format)
if format not in ['cube', 'png', 'eps', 'cfg', 'struct', 'etsf', 'gen']:
write(fname2, images, format=format)
if format in r:
print 'I'
a1 = read(fname1)
assert np.all(np.abs(a1.get_positions() -
atoms.get_positions()) < 1e-6)
if format in ['traj', 'cube', 'cfg', 'struct', 'gen']:
assert np.all(np.abs(a1.get_cell() - atoms.get_cell()) < 1e-6)
if format in ['cfg']:
assert np.all(np.abs(a1.get_array('extra') -
atoms.get_array('extra')) < 1e-6)
if format not in ['cube', 'png', 'eps', 'cfg', 'struct', 'etsf',
'gen']:
a2 = read(fname2)
a3 = read(fname2, index=0)
a4 = read(fname2, index=slice(None))
assert len(a4) == 2
else:
print
def gen_pop_plate(atomlist, sizeinput, dir_par=[1,-1,0], dir_per=[1,1,1], crystal=False):
'''
Last edited by Hyunseok Ko on 10-15-2014
Function to generate defects in a plate. The center of plate is equal to the center of mass of the bulk structure.
-- Plate orientation can be specified by following parameters::
dir_par and dir_per is a directional vector (do not necessarily be a unit vector) which sets the axis of the plate
-- Plate dimension can be chosen (Needs development. Manual setup for now) by setting 'size' in this module.
'''
# Setup axis vectors for plate
size=[sizeinput, sizeinput, 3]
#print 'HKK :: plate vector', size
# Set unit vectors for plate
dir_crs=numpy.cross(dir_par, dir_per)
uvec_par=[x/((dir_par[0]**2+dir_par[1]**2+dir_par[2]**2)**0.5) for x in dir_par]
uvec_crs=[x/((dir_crs[0]**2+dir_crs[1]**2+dir_crs[2]**2)**0.5) for x in dir_crs]
uvec_per=[x/((dir_per[0]**2+dir_per[1]**2+dir_per[2]**2)**0.5) for x in dir_per]
#print 'Unit vectors of new xyz axis'
#print uvec_par, uvec_crs, uvec_per
# Set vectors for plate
vec_par=[x*size[0] for x in uvec_par]
vec_crs=[x*size[1] for x in uvec_crs]
vec_per=[x*size[2] for x in uvec_per]
# Compute rotation angles from primary axis to axis for plate (If loop to avoid floating error)
temp=[-uvec_per[1]/((1-uvec_per[2]**2)**0.5), uvec_per[2],uvec_crs[2]/((1-uvec_per[2]**2)**0.5)]
if [temp[x] for x in range(3)] >=1:
temp[x]=1
alpha=numpy.arccos(temp[0])
beta=numpy.arccos(temp[1])
gamma=numpy.arccos(temp[2])
# Get list of atom types for all atoms in cluster
indiv=Atoms()
indiv.set_cell(size)
for s,c,m,u in atomlist:
if c > 0:
for i in range(c):
temppos = [random.uniform(0,size[j]) for j in range(3)]
# Rotating the defect position to the plate
pos = rot_vec(temppos,alpha,beta,gamma)
at=Atom(symbol=s,position=pos)
indiv.append(at)
if crystal:
stro=''
natoms=sum([c for s,c,m,u in atomlist])
pos=indiv.get_scaled_positions()
structure=random.choice(crystal)
cello=indiv.get_cell()
if structure=='cubic':
#Set to cubic shape
an,bn,cn = [numpy.linalg.norm(v) for v in cello]
a=(an+bn+cn)/3.0
celln=numpy.array([[a,0,0],[0,a,0],[0,0,a]])
stro+='Setting cell to cubic\n'
elif structure=='orthorhombic':
#Set to orthorhombic
a=random.uniform(2,natoms**0.3333*size)
b=random.uniform(2,natoms**0.3333*size)
c=random.uniform(2,natoms**0.3333*size)
celln=numpy.array([[a,0,0],[0,b,0],[0,0,c]])
stro+='Setting cell to orthorhombic\n'
elif structure=='tetragonal':
#Set to tetragonal shape
an,bn,cn = [numpy.linalg.norm(v) for v in cello]
a=(an+bn)/2.0
c=cn
if c==a:
c=random.uniform(1,natoms**0.3333*size)
celln=numpy.array([[a,0,0],[0,a,0],[0,0,c]])
stro+='Setting cell to tetragonal\n'
elif structure=='hexagonal':
#Set to hexagonal shape
an,bn,cn = [numpy.linalg.norm(v) for v in cello]
a=(an+bn)/2.0
c=cn
if c<=a:
c=random.uniform(a+1,natoms**0.3333*size)
trans=numpy.array([[1,0,0],[-0.5,(3.0**0.5)/2.0,0],[0,0,1]])
trans[0]=[a*i for i in trans[0]]
trans[1]=[a*i for i in trans[1]]
trans[2]=[c*i for i in trans[2]]
celln=trans
stro+='Setting cell to Hexagonal\n'
elif structure=='monoclinic':
#Set to monoclinic
a,b,c = [numpy.linalg.norm(v) for v in cello]
if a==b:
b=random.uniform(1,natoms**0.3333*size)
trans=numpy.array([(1+random.random())*c, 0, (1+random.random())*c])
celln=numpy.array([[a,0,0],[0,b,0],[0,0,0]])
celln[2]=trans
stro+='Setting cell to monoclinic\n'
elif structure=='triclinic':
#Set to triclinic
a,b,c = [numpy.linalg.norm(v) for v in cello]
celln=numpy.array([[a,0,0],[(1+random.random())*b,(1+random.random())*b,0],[(1+random.random())*c,0,(1+random.random())*c]])
stro+='Setting cell to triclinic\n'
indiv.set_cell(celln)
indiv.set_scaled_positions(pos)
stro+=repr(indiv.get_cell())+'\n'
return indiv, stro
return indiv
for opt in OPTIONS:
a0 = 3.554 * opt / np.sqrt(2)
# c0 = np.sqrt(8 / 3.0) * a0
c0= 1.585 * a0
atoms = Atoms('Fe2',
scaled_positions=[(0, 0, 0),
(1./3., 1./3., 1./2.)],
cell = [[1./2.,sqrt(3)/2.,0],[-1./2., sqrt(3)/2., 0] ,[0,0,1]],
pbc=(1,1,1))
# atoms.set_cell([[1/2, sqrt(3)/2, 0],[-1/2, sqrt(3)/2, 0] ,[0,0,1]])
scale = [[a0,0,0],[0,a0,0],[0,0,c0]]
atoms.set_cell(np.dot( atoms.get_cell(), scale), scale_atoms=True)
atoms.set_tags([1, 1])
atoms = atoms*(1,1,2)
print atoms.get_cell()
print atoms.positions
alloys = []
alloys.append(Alloy(1, 'Fe', fe, 0.0))
alloys.append(Alloy(1, 'Cr', cr, 0.0))
calc = EMTO()
calc.set(dir='{0}/calc/{1}/opt-{2:0.3f}'.format(temp_dir, name, opt),
lat=9,
for i in range(2):
equal(p[i, 1], 0, 1e-10)
equal(p[i, 2], 0, 1e-10)
# rotate the nuclear axis to the direction (1,1,1)
CO.rotate(p[1] - p[0], (1, 1, 1))
q = CO.positions.copy()
for c in range(3):
equal(q[0, c], p[0, 0] / sqrt(3), 1e-10)
equal(q[1, c], p[1, 0] / sqrt(3), 1e-10)
# minimal box
b=4.0
CO = Cluster([Atom('C', (1, 0, 0)), Atom('O', (1, 0, R))])
CO.minimal_box(b)
cc = CO.get_cell()
for c in range(3):
width = 2*b
if c==2:
width += R
equal(cc[c, c], width, 1e-10)
# minimal box, ensure multiple of 4
h = .13
b = [2, 3, 4]
CO.minimal_box(b, h=h)
cc = CO.get_cell()
for c in range(3):
# print "cc[c,c], cc[c,c] / h % 4 =", cc[c, c], cc[c, c] / h % 4
for a in CO:
print(a.symbol, b[c], a.position[c], cc[c, c] - a.position[c])
def test_chain_operators(seed):
from ase.ga.startgenerator import StartGenerator
from ase.ga.cutandsplicepairing import CutAndSplicePairing
from ase.ga.standardmutations import StrainMutation
from ase.ga.utilities import (closest_distances_generator, atoms_too_close,
CellBounds)
import numpy as np
from ase import Atoms
# set up the random number generator
rng = np.random.RandomState(seed)
slab = Atoms('', cell=(0, 16, 16), pbc=[True, False, False])
blocks = ['C'] * 8
n_top = 8
use_tags = False
num_vcv = 1
box_volume = 8. * n_top
blmin = closest_distances_generator(atom_numbers=[6],
ratio_of_covalent_radii=0.6)
cellbounds = CellBounds(
bounds={
'phi': [0.1 * 180., 0.9 * 180.],
'chi': [0.1 * 180., 0.9 * 180.],
'psi': [0.1 * 180., 0.9 * 180.],
'a': [1, 6]
})
box_to_place_in = [[None, 6., 6.], [None, [0., 4., 0.], [0., 0., 4.]]]
sg = StartGenerator(slab,
blocks,
blmin,
box_volume=box_volume,
splits=None,
box_to_place_in=box_to_place_in,
number_of_variable_cell_vectors=num_vcv,
cellbounds=cellbounds,
test_too_far=True,
test_dist_to_slab=False,
rng=rng)
parents = []
for i in range(2):
a = None
while a is None:
a = sg.get_new_candidate()
a.info['confid'] = i
parents.append(a)
assert len(a) == n_top
assert len(np.unique(a.get_tags())) == 8
assert np.allclose(a.get_pbc(), slab.get_pbc())
p = a.get_positions()
assert np.min(p[:, 1:]) > 6.
assert np.max(p[:, 1:]) < 6. + 4.
assert not atoms_too_close(a, blmin, use_tags=use_tags)
c = a.get_cell()
assert np.allclose(c[1:], slab.get_cell()[1:])
assert cellbounds.is_within_bounds(c)
v = a.get_volume() * (4. / 16.)**2
assert abs(v - box_volume) < 1e-5
# Test cut-and-splice pairing and strain mutation
pairing = CutAndSplicePairing(slab,
n_top,
blmin,
number_of_variable_cell_vectors=num_vcv,
p1=1.,
p2=0.,
minfrac=0.15,
cellbounds=cellbounds,
use_tags=use_tags,
rng=rng)
strainmut = StrainMutation(blmin,
cellbounds=cellbounds,
number_of_variable_cell_vectors=num_vcv,
use_tags=use_tags,
rng=rng)
strainmut.update_scaling_volume(parents)
for operator in [pairing, strainmut]:
child = None
while child is None:
child, desc = operator.get_new_individual(parents)
assert not atoms_too_close(child, blmin, use_tags=use_tags)
cell = child.get_cell()
assert cellbounds.is_within_bounds(cell)
assert np.allclose(cell[1:], slab.get_cell()[1:])
def read_vasp(filename='CONTCAR'):
"""Import POSCAR/CONTCAR type file.
Reads unitcell, atom positions and constraints from the POSCAR/CONTCAR
file and tries to read atom types from POSCAR/CONTCAR header, if this fails
the atom types are read from OUTCAR or POTCAR file.
"""
from ase import Atoms, Atom
from ase.constraints import FixAtoms, FixScaled
from ase.data import chemical_symbols
import numpy as np
if isinstance(filename, str):
f = open(filename)
else: # Assume it's a file-like object
f = filename
# First line should contain the atom symbols , eg. "Ag Ge" in
# the same order
# as later in the file (and POTCAR for the full vasp run)
atomtypes = f.readline().split()
# Sometimes the first line in POSCAR/CONTCAR is of the form
# "CoP3_In-3.pos". Check for this case and extract atom types
if len(atomtypes) == 1 and '_' in atomtypes[0]:
atomtypes = get_atomtypes_from_formula(atomtypes[0])
lattice_constant = float(f.readline().split()[0])
# Now the lattice vectors
a = []
for ii in range(3):
s = f.readline().split()
floatvect = float(s[0]), float(s[1]), float(s[2])
a.append(floatvect)
basis_vectors = np.array(a) * lattice_constant
# Number of atoms. Again this must be in the same order as
# in the first line
# or in the POTCAR or OUTCAR file
atom_symbols = []
numofatoms = f.readline().split()
#vasp5.1 has an additional line which gives the atom types
#the following try statement skips this line
try:
int(numofatoms[0])
except ValueError:
numofatoms = f.readline().split()
# check for comments in numofatoms line and get rid of them if necessary
commentcheck = np.array(['!' in s for s in numofatoms])
if commentcheck.any():
# only keep the elements up to the first including a '!':
numofatoms = numofatoms[:np.arange(len(numofatoms))[commentcheck][0]]
numsyms = len(numofatoms)
if len(atomtypes) < numsyms:
# First line in POSCAR/CONTCAR didn't contain enough symbols.
atomtypes = atomtypes_outpot(f.name, numsyms)
else:
try:
for atype in atomtypes[:numsyms]:
if not atype in chemical_symbols:
raise KeyError
except KeyError:
atomtypes = atomtypes_outpot(f.name, numsyms)
for i, num in enumerate(numofatoms):
numofatoms[i] = int(num)
[atom_symbols.append(atomtypes[i]) for na in xrange(numofatoms[i])]
# Check if Selective dynamics is switched on
sdyn = f.readline()
selective_dynamics = sdyn[0].lower() == "s"
# Check if atom coordinates are cartesian or direct
if selective_dynamics:
ac_type = f.readline()
else:
ac_type = sdyn
cartesian = ac_type[0].lower() == "c" or ac_type[0].lower() == "k"
tot_natoms = sum(numofatoms)
atoms_pos = np.empty((tot_natoms, 3))
if selective_dynamics:
selective_flags = np.empty((tot_natoms, 3), dtype=bool)
for atom in xrange(tot_natoms):
ac = f.readline().split()
atoms_pos[atom] = (float(ac[0]), float(ac[1]), float(ac[2]))
if selective_dynamics:
curflag = []
for flag in ac[3:6]:
curflag.append(flag == 'F')
selective_flags[atom] = curflag
# Done with all reading
if type(filename) == str:
f.close()
if cartesian:
atoms_pos *= lattice_constant
atoms = Atoms(symbols = atom_symbols, cell = basis_vectors, pbc = True)
if cartesian:
atoms.set_positions(atoms_pos)
else:
atoms.set_scaled_positions(atoms_pos)
if selective_dynamics:
constraints = []
indices = []
for ind, sflags in enumerate(selective_flags):
if sflags.any() and not sflags.all():
constraints.append(FixScaled(atoms.get_cell(), ind, sflags))
elif sflags.all():
indices.append(ind)
if indices:
constraints.append(FixAtoms(indices))
if constraints:
atoms.set_constraint(constraints)
return atoms
