def __init__(self, basis='qz', xc='BH'):
label = 'H2O' #TODO: for now, change later
if xc == 'REVPBE': xc = 'revPBE'
fdf_arguments = {
'DM.MixingWeight': 0.3,
'DM.NumberPulay': 3,
'ElectronicTemperature': 5e-3,
'WriteMullikenPop': 0,
'MaxSCFIterations': 40
}
if basis == 'uf':
super().__init__(label=label,
xc='PBE',
mesh_cutoff=100 * Ry,
energy_shift=0.02 * Ry,
basis_set='SZ')
dmtol = 5e-4
elif not 'custom' in basis.lower():
super().__init__(label=label,
xc=xc,
mesh_cutoff=200 * Ry,
energy_shift=0.02 * Ry,
basis_set=basis.upper())
dmtol = 5e-4
else:
species_o = Species(symbol='O',
basis_set=PAOBasisBlock(
basis_sets['o_basis_{}'.format(basis)]))
species_h = Species(symbol='H',
basis_set=PAOBasisBlock(
basis_sets['h_basis_{}'.format(basis)]))
super().__init__(label='H2O',
xc=xc,
mesh_cutoff=200 * Ry,
basis_set='DZP',
species=[species_o, species_h],
energy_shift=0.02 * Ry)
dmtol = 5e-4
fdf_arguments['DM.UseSaveDM'] = 'True'
fdf_arguments['SCF.MustConverge'] = 'False'
fdf_arguments['DM.Tolerance'] = dmtol
fdf_arguments['MLCF.Use'] = False
fdf_arguments['SaveDeltaRho'] = True
allowed_keys = self.allowed_fdf_keywords
allowed_keys['SaveDeltaRho'] = False
allowed_keys['SaveRhoXC'] = False
allowed_keys['MLCF.Use'] = False
allowed_keys['SCF.MustConverge'] = False
self.allowed_keywords = allowed_keys
self.set_fdf_arguments(fdf_arguments)
def __init__(self,
atoms=None,
command=None,
xc='LDA',
spin='non-polarized',
ghosts=[],
**kwargs):
if atoms is not None:
finder = DojoFinder()
elems = list(dict.fromkeys(atoms.get_chemical_symbols()).keys())
elem_dict = dict(zip(elems, range(1, len(elems) + 1)))
symbols = atoms.get_chemical_symbols()
# ghosts
ghost_symbols = [symbols[i] for i in ghosts]
ghost_elems = list(dict.fromkeys(ghost_symbols).keys())
tags = [1 if i in ghosts else 0 for i in range(len(atoms))]
atoms.set_tags(tags)
pseudo_path = finder.get_pp_path(xc=xc)
if spin == 'spin-orbit':
rel = 'fr'
else:
rel = 'sr'
species = [
Species(symbol=elem,
pseudopotential=finder.get_pp_fname(elem,
xc=xc,
rel=rel),
ghost=False) for elem in elem_dict.keys()
]
for elem in ghost_elems:
species.append(
Species(symbol=elem,
pseudopotential=finder.get_pp_fname(elem,
xc=xc,
rel=rel),
tag=1,
ghost=True))
Siesta.__init__(self,
xc=xc,
spin=spin,
atoms=atoms,
pseudo_path=pseudo_path,
species=species,
**kwargs)
def get_species(atoms, xc, rel='sr'):
finder = DojoFinder()
elems = list(dict.fromkeys(atoms.get_chemical_symbols()).keys())
elem_dict = dict(zip(elems, range(1, len(elems) + 1)))
pseudo_path = finder.get_pp_path(xc=xc)
species = [
Species(symbol=elem,
pseudopotential=finder.get_pp_fname(elem, xc=xc, rel=rel),
ghost=False) for elem in elem_dict.keys()
]
return pseudo_path, species
def species(self, atoms):
"""Find all relevant species depending on the atoms object and
species input.
Parameters :
- atoms : An Atoms object.
"""
# For each element use default species from the species input, or set
# up a default species from the general default parameters.
symbols = np.array(atoms.get_chemical_symbols())
tags = atoms.get_tags()
species = list(self['species'])
default_species = [
s for s in species
if (s['tag'] is None) and s['symbol'] in symbols]
default_symbols = [s['symbol'] for s in default_species]
for symbol in symbols:
if symbol not in default_symbols:
spec = Species(symbol=symbol,
basis_set=self['basis_set'],
tag=None)
default_species.append(spec)
default_symbols.append(symbol)
assert len(default_species) == len(np.unique(symbols))
# Set default species as the first species.
species_numbers = np.zeros(len(atoms), int)
i = 1
for spec in default_species:
mask = symbols == spec['symbol']
species_numbers[mask] = i
i += 1
# Set up the non-default species.
non_default_species = [s for s in species if not s['tag'] is None]
for spec in non_default_species:
mask1 = (tags == spec['tag'])
mask2 = (symbols == spec['symbol'])
mask = np.logical_and(mask1, mask2)
if sum(mask) > 0:
species_numbers[mask] = i
i += 1
all_species = default_species + non_default_species
return all_species, species_numbers
np.array([[0.000000, 0.000000, 0.100000],
[0.682793, 0.682793, 0.682793],
[-0.682793, -0.682793, 0.68279],
[-0.682793, 0.682793, -0.682793],
[0.682793, -0.682793, -0.682793]]),
cell=[10, 10, 10])
c_basis = """2 nodes 1.00
0 1 S 0.20 P 1 0.20 6.00
5.00
1.00
1 2 S 0.20 P 1 E 0.20 6.00
6.00 5.00
1.00 0.95"""
species = Species(symbol='C', basis_set=PAOBasisBlock(c_basis))
calc = Siesta(
label='ch4',
basis_set='SZ',
xc='LYP',
mesh_cutoff=300 * Ry,
species=[species],
restart='ch4.XV',
fdf_arguments={
'DM.Tolerance': 1E-5,
'DM.MixingWeight': 0.15,
'DM.NumberPulay': 3,
'MaxSCFIterations': 200,
'ElectronicTemperature': (0.02585, 'eV'), # 300 K
'SaveElectrostaticPotential': True
})
def __init__(self,
atoms=None,
command=None,
xc='LDA',
spin='non-polarized',
basis_set='DZP',
ghosts=[],
input_basis_set={},
pseudo_path=None,
input_pp={},
pp_accuracy='standard',
**kwargs):
# non-perturnbative polarized orbital.
self.npt_elems = set()
if atoms is not None:
finder = DojoFinder()
elems = list(dict.fromkeys(atoms.get_chemical_symbols()).keys())
self.elem_dict = dict(zip(elems, range(1, len(elems) + 1)))
symbols = atoms.get_chemical_symbols()
# ghosts
ghost_symbols = [symbols[i] for i in ghosts]
ghost_elems = list(dict.fromkeys(ghost_symbols).keys())
tags = [1 if i in ghosts else 0 for i in range(len(atoms))]
atoms.set_tags(tags)
if pseudo_path is None:
pseudo_path = finder.get_pp_path(xc=xc, accuracy=pp_accuracy)
if spin == 'spin-orbit':
rel = 'fr'
else:
rel = 'sr'
species = []
for elem, index in self.elem_dict.items():
if elem not in input_basis_set:
bselem = basis_set
if elem in ['Li', 'Be', 'Na', 'Mg']:
self.npt_elems.add(f"{elem}.{index}")
else:
bselem = PAOBasisBlock(input_basis_set[elem])
if elem not in input_pp:
pseudopotential = finder.get_pp_fname(
elem, xc=xc, rel=rel, accuracy=pp_accuracy)
else:
pseudopotential = os.path.join(
pseudo_path, input_pp[elem])
species.append(Species(symbol=elem,
pseudopotential=pseudopotential,
basis_set=bselem,
ghost=False))
for elem in ghost_elems:
species.append(
Species(symbol=elem,
pseudopotential=finder.get_pp_fname(
elem, xc=xc, rel=rel, accuracy=pp_accuracy),
tag=1,
ghost=True))
Siesta.__init__(self,
xc=xc,
spin=spin,
atoms=atoms,
pseudo_path=pseudo_path,
species=species,
**kwargs)
self.set_npt_elements()
# Test setting fdf-arguments after initiation.
siesta.set_fdf_arguments(
{'DM.Tolerance': 1e-2,
'ON.eta': (2, 'Ry')})
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as f:
lines = f.readlines()
assert 'MeshCutoff\t3000\teV\n' in lines
assert 'DM.Tolerance\t0.01\n' in lines
assert 'ON.eta\t2\tRy\n' in lines
# Test initiation using Species.
atoms = ch4.copy()
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 2, 2]))
siesta = Siesta(species=[Species(symbol='C', tag=1)])
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 2, 2]))
atoms.set_tags([0, 0, 0, 1, 0])
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 2, 2]))
siesta = Siesta(species=[Species(symbol='H', tag=1, basis_set='SZ')])
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 3, 2]))
siesta = Siesta(label='test_label', species=species)
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as f:
lines = f.readlines()
lines = [line.split() for line in lines]
assert ['1', '6', 'C.lda.1'] in lines
assert ['2', '1', 'H.lda.2'] in lines
# In this script the Virtual Crystal approximation is used to model
# a stronger affinity for positive charge on the H atoms.
# This could model interaction with other molecules not explicitly
# handled.
import numpy as np
from ase.calculators.siesta import Siesta
from ase.calculators.siesta.parameters import Species
from ase.optimize import QuasiNewton
from ase import Atoms
atoms = Atoms('CH4', np.array([
[0.000000, 0.000000, 0.000000],
[0.682793, 0.682793, 0.682793],
[-0.682793, -0.682793, 0.682790],
[-0.682793, 0.682793, -0.682793],
[0.682793, -0.682793, -0.682793]]))
siesta = Siesta(
species=[
Species(symbol='H', excess_charge=0.1)])
atoms.calc = siesta
dyn = QuasiNewton(atoms, trajectory='h.traj')
dyn.run(fmax=0.02)
def test_siesta(siesta_factory):
# Setup test structures.
h = Atoms('H', [(0.0, 0.0, 0.0)])
ch4 = Atoms(
'CH4',
np.array([[0.000000, 0.000000, 0.000000],
[0.682793, 0.682793, 0.682793],
[-0.682793, -0.682793, 0.682790],
[-0.682793, 0.682793, -0.682793],
[0.682793, -0.682793, -0.682793]]))
# Test the initialization.
siesta = siesta_factory.calc()
assert isinstance(siesta, FileIOCalculator)
assert isinstance(siesta.implemented_properties, tuple)
assert isinstance(siesta.default_parameters, dict)
assert isinstance(siesta.name, str)
assert isinstance(siesta.default_parameters, dict)
# Test simple fdf-argument case.
atoms = h.copy()
siesta = siesta_factory.calc(label='test_label',
fdf_arguments={'DM.Tolerance': 1e-3})
atoms.calc = siesta
siesta.write_input(atoms, properties=['energy'])
atoms = h.copy()
atoms.calc = siesta
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as fd:
lines = fd.readlines()
assert any([line.split() == ['DM.Tolerance', '0.001'] for line in lines])
# Test (slightly) more complex case of setting fdf-arguments.
siesta = siesta_factory.calc(label='test_label',
mesh_cutoff=3000,
fdf_arguments={
'DM.Tolerance': 1e-3,
'ON.eta': (5, 'Ry')
})
atoms.calc = siesta
siesta.write_input(atoms, properties=['energy'])
atoms = h.copy()
atoms.calc = siesta
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as f:
lines = f.readlines()
assert 'MeshCutoff\t3000\teV\n' in lines
assert 'DM.Tolerance\t0.001\n' in lines
assert 'ON.eta\t5\tRy\n' in lines
# Test setting fdf-arguments after initiation.
siesta.set_fdf_arguments({'DM.Tolerance': 1e-2, 'ON.eta': (2, 'Ry')})
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as f:
lines = f.readlines()
assert 'MeshCutoff\t3000\teV\n' in lines
assert 'DM.Tolerance\t0.01\n' in lines
assert 'ON.eta\t2\tRy\n' in lines
# Test initiation using Species.
atoms = ch4.copy()
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 2, 2]))
siesta = siesta_factory.calc(species=[Species(symbol='C', tag=1)])
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 2, 2]))
atoms.set_tags([0, 0, 0, 1, 0])
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 2, 2]))
siesta = siesta_factory.calc(
species=[Species(symbol='H', tag=1, basis_set='SZ')])
species, numbers = siesta.species(atoms)
assert all(numbers == np.array([1, 2, 2, 3, 2]))
siesta = siesta_factory.calc(label='test_label', species=species)
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as f:
lines = f.readlines()
lines = [line.split() for line in lines]
assert ['1', '6', 'C.lda.1'] in lines
assert ['2', '1', 'H.lda.2'] in lines
assert ['3', '1', 'H.lda.3'] in lines
assert ['C.lda.1', 'DZP'] in lines
assert ['H.lda.2', 'DZP'] in lines
assert ['H.lda.3', 'SZ'] in lines
# Test if PAO block can be given as species.
c_basis = """2 nodes 1.00
0 1 S 0.20 P 1 0.20 6.00
5.00
1.00
1 2 S 0.20 P 1 E 0.20 6.00
6.00 5.00
1.00 0.95"""
basis_set = PAOBasisBlock(c_basis)
species = Species(symbol='C', basis_set=basis_set)
siesta = siesta_factory.calc(label='test_label', species=[species])
siesta.write_input(atoms, properties=['energy'])
with open('test_label.fdf', 'r') as f:
lines = f.readlines()
lines = [line.split() for line in lines]
assert ['%block', 'PAO.Basis'] in lines
assert ['%endblock', 'PAO.Basis'] in lines
from ase.calculators.siesta import Siesta
from ase.calculators.siesta.parameters import Species, PAOBasisBlock
from ase.optimize import QuasiNewton
from ase import Atoms
atoms = Atoms('3H', [(0.0, 0.0, 0.0), (0.0, 0.0, 0.5), (0.0, 0.0, 1.0)],
cell=[10, 10, 10])
basis_set = PAOBasisBlock("""1
0 2 S 0.2
0.0 0.0""")
atoms.set_tags([0, 1, 0])
siesta = Siesta(species=[
Species(symbol='H', tag=None, basis_set='SZ'),
Species(symbol='H', tag=1, basis_set=basis_set, ghost=True)
])
atoms.set_calculator(siesta)
dyn = QuasiNewton(atoms, trajectory='h.traj')
dyn.run(fmax=0.02)
