def parse(self, opts, args):
OptimizeTask.parse(self, opts, args)
if opts.srelax:
if len(opts.srelax.split(',')) > 1:
self.sfmax, self.soptimizer = opts.srelax.split(',')
else:
self.sfmax = opts.srelax
self.soptimizer = 'BFGS'
self.sfmax = float(self.sfmax)
if opts.srelaxsteps is not None:
self.ssteps = int(opts.srelaxsteps)
else:
# yes, the default number of ASE optimizer steps
# ase/optimize/optimize.py
self.ssteps = 100000000
if opts.fit:
points, strain = opts.fit.split(',')
if float(strain) > 0:
self.fit = (int(points), float(strain) * 0.01)
else:
self.fit = (int(points), float(strain))
self.eos = opts.eos
self.crystal_structure = opts.crystal_structure
self.lattice_constant = opts.lattice_constant
self.c_over_a = opts.c_over_a
self.orthorhombic = opts.orthorhombic
self.cubic = opts.cubic
self.repeat = opts.repeat
def parse(self, opts, args):
OptimizeTask.parse(self, opts, args)
if opts.srelax:
if len(opts.srelax.split(',')) > 1:
self.sfmax, self.soptimizer = opts.srelax.split(',')
else:
self.sfmax = opts.srelax
self.soptimizer = 'BFGS'
self.sfmax = float(self.sfmax)
if opts.srelaxsteps is not None:
self.ssteps = int(opts.srelaxsteps)
else:
# yes, the default number of ASE optimizer steps
# ase/optimize/optimize.py
self.ssteps = 100000000
if opts.fit:
points, strain = opts.fit.split(',')
if float(strain) > 0:
self.fit = (int(points), float(strain) * 0.01)
else:
self.fit = (int(points), float(strain))
self.eos = opts.eos
self.crystal_structure = opts.crystal_structure
self.lattice_constant = opts.lattice_constant
self.c_over_a = opts.c_over_a
self.orthorhombic = opts.orthorhombic
self.cubic = opts.cubic
self.repeat = opts.repeat
def __init__(self,
crystal_structure=None,
lattice_constant=None,
c_over_a=None,
cubic=False,
orthorhombic=False,
fit=None,
eos=None,
sfmax=None,
soptimizer='BFGS',
ssteps=100000000,
**kwargs):
"""Bulk task."""
self.crystal_structure = crystal_structure
self.lattice_constant = lattice_constant
self.c_over_a = c_over_a
self.cubic = cubic
self.orthorhombic = orthorhombic
self.eos = eos
self.fit = fit
self.sfmax = sfmax
self.soptimizer = soptimizer
self.ssteps = ssteps
self.repeat = None
OptimizeTask.__init__(self, **kwargs)
self.summary_keys = ['energy', 'fitted energy', 'volume', 'B']
def add_options(self, parser):
OptimizeTask.add_options(self, parser)
mol = optparse.OptionGroup(parser, 'Molecule')
mol.add_option('-v',
'--vacuum',
type='float',
default=3.0,
help='Amount of vacuum to add around isolated systems '
'(in Angstrom).')
mol.add_option('--unit-cell',
help='Unit cell. Examples: "10.0" or "9,10,11" ' +
'(in Angstrom).')
mol.add_option('--bond-length',
type='float',
help='Bond length of dimer in Angstrom.')
mol.add_option('-F',
'--fit',
metavar='N,x',
help='Find optimal bondlength and vibration ' +
'frequency using N points and displacements from ' +
'-x % to +x %.')
mol.add_option('--atomize',
action='store_true',
help='Calculate Atomization energies.')
parser.add_option_group(mol)
def add_options(self, parser):
OptimizeTask.add_options(self, parser)
bulk = optparse.OptionGroup(parser, 'Bulk')
bulk.add_option('-F',
'--fit',
metavar='N,x',
help='Find optimal volume and bulk modulus ' +
'using N points and variations of the lattice ' +
'constants from -x % to +x %.')
bulk.add_option('-x',
'--crystal-structure',
help='Crystal structure.',
choices=[
'sc', 'fcc', 'bcc', 'diamond', 'hcp', 'rocksalt',
'zincblende'
])
bulk.add_option('-a',
'--lattice-constant',
type='float',
help='Lattice constant in Angstrom.')
bulk.add_option('--c-over-a', type='float', help='c/a ratio.')
bulk.add_option('-O',
'--orthorhombic',
action='store_true',
help='Use orthorhombic unit cell.')
bulk.add_option('-C',
'--cubic',
action='store_true',
help='Use cubic unit cell.')
bulk.add_option('-r',
'--repeat',
help='Repeat unit cell. Use "-r 2" or "-r 2,3,1".')
parser.add_option_group(bulk)
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'distances' in data:
distances = data['distances']
energies = data['energies']
fit0 = np.poly1d(np.polyfit(1 / distances, energies, 3))
fit1 = np.polyder(fit0, 1)
fit2 = np.polyder(fit1, 1)
dmin = None
for t in np.roots(fit1):
if t > 0 and fit2(t) > 0:
dmin = 1 / t
break
if dmin is None:
raise ValueError('No minimum!')
if abs(dmin) < min(distances) or abs(dmin) > max(distances):
raise ValueError('Fit outside of range! ' + \
str(abs(dmin)) + ' not in ' + \
str(distances))
emin = fit0(t)
k = fit2(t) * t**4
m1, m2 = self.create_system(name).get_masses()
m = m1 * m2 / (m1 + m2)
hnu = units._hbar * 1e10 * sqrt(k / units._e / units._amu / m)
data['minimum energy'] = emin
self.results[name][1:] = [energies[2] - emin, dmin, 1000 * hnu]
else:
self.results[name].extend([None, None])
for name, data in self.data.items():
atoms = self.create_system(name)
if len(atoms) == 1:
self.results[name].extend([None, None])
continue
eatoms = 0.0
for symbol in atoms.get_chemical_symbols():
if symbol in self.data and symbol != name:
eatoms += self.data[symbol]['energy']
else:
eatoms = None
break
ea = None
ea0 = None
if eatoms is not None:
ea = eatoms - data['energy']
if 'minimum energy' in data:
ea0 = eatoms - data['minimum energy']
self.results[name].extend([ea, ea0])
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'distances' in data:
distances = data['distances']
energies = data['energies']
fit0 = np.poly1d(np.polyfit(1 / distances, energies, 3))
fit1 = np.polyder(fit0, 1)
fit2 = np.polyder(fit1, 1)
dmin = None
for t in np.roots(fit1):
if t > 0 and fit2(t) > 0:
dmin = 1 / t
break
if dmin is None:
raise ValueError('No minimum!')
if abs(dmin) < min(distances) or abs(dmin) > max(distances):
raise ValueError('Fit outside of range! ' + \
str(abs(dmin)) + ' not in ' + \
str(distances))
emin = fit0(t)
k = fit2(t) * t**4
m1, m2 = self.create_system(name).get_masses()
m = m1 * m2 / (m1 + m2)
hnu = units._hbar * 1e10 * sqrt(k / units._e / units._amu / m)
data['minimum energy'] = emin
self.results[name][1:] = [energies[2] - emin, dmin, 1000 * hnu]
else:
self.results[name].extend([None, None])
for name, data in self.data.items():
atoms = self.create_system(name)
if len(atoms) == 1:
self.results[name].extend([None, None])
continue
eatoms = 0.0
for symbol in atoms.get_chemical_symbols():
if symbol in self.data and symbol != name:
eatoms += self.data[symbol]['energy']
else:
eatoms = None
break
ea = None
ea0 = None
if eatoms is not None:
ea = eatoms - data['energy']
if 'minimum energy' in data:
ea0 = eatoms - data['minimum energy']
self.results[name].extend([ea, ea0])
def add_options(self, parser):
OptimizeTask.add_options(self, parser)
bulk = optparse.OptionGroup(parser, 'Bulk')
bulk.add_option(
'-F',
'--fit',
metavar='N,x',
help='Find optimal volume and bulk modulus ' +
'using odd N points and variations of the lattice ' +
'constant a from -x % to +x %, i.e. in the interval '
'<a - a * x * 100, ..., a, ..., a + a * x * 100>. ' +
'This method gives non-equidistant sampling of volume. ' +
'With x negative (in Angstrom**3) the sampling of ' +
'the cell volume (v) in the interval ' +
'<(1 + x /v), ..., 1, ..., (1 - x /v)> is used. ' +
'This method gives equidistant sampling of volume.')
bulk.add_option('--eos',
type='str',
metavar='eos',
help='Selects the type of eos.')
bulk.add_option(
'--srelax',
metavar='SFMAX[,SOPTIMIZER]',
help='Relax cell by minimizing stress using StranFilter '
'with SOPTIMIZER algorithm. The SOPTIMIZER keyword is '
'optional, and if omitted BFGS is used by default.')
bulk.add_option('--srelaxsteps',
type='int',
metavar='ssteps',
help='Limit the number of SF optimizer steps.')
bulk.add_option('-x',
'--crystal-structure',
help='Crystal structure.',
choices=[
'sc', 'fcc', 'bcc', 'hcp', 'diamond', 'zincblende',
'rocksalt', 'cesiumchloride', 'fluorite'
])
bulk.add_option('-a',
'--lattice-constant',
type='float',
help='Lattice constant in Angstrom.')
bulk.add_option('--c-over-a', type='float', help='c/a ratio.')
bulk.add_option('-O',
'--orthorhombic',
action='store_true',
help='Use orthorhombic unit cell.')
bulk.add_option('-C',
'--cubic',
action='store_true',
help='Use cubic unit cell.')
bulk.add_option('-r',
'--repeat',
help='Repeat unit cell. Use "-r 2" or "-r 2,3,1".')
parser.add_option_group(bulk)
def parse(self, opts, args):
OptimizeTask.parse(self, opts, args)
if opts.fit:
points, strain = opts.fit.split(',')
self.fit = (int(points), float(strain) * 0.01)
self.crystal_structure = opts.crystal_structure
self.lattice_constant = opts.lattice_constant
self.c_over_a = opts.c_over_a
self.orthorhombic = opts.orthorhombic
self.cubic = opts.cubic
self.repeat = opts.repeat
def parse(self, opts, args):
OptimizeTask.parse(self, opts, args)
if opts.fit:
points, strain = opts.fit.split(',')
self.fit = (int(points), float(strain) * 0.01)
self.crystal_structure = opts.crystal_structure
self.lattice_constant = opts.lattice_constant
self.c_over_a = opts.c_over_a
self.orthorhombic = opts.orthorhombic
self.cubic = opts.cubic
self.repeat = opts.repeat
def calculate(self, name, atoms):
if self.fit and len(atoms) == 2:
return self.fit_bond_length(name, atoms)
else:
data = OptimizeTask.calculate(self, name, atoms)
self.check_occupation_numbers(atoms)
return data
def parse(self, opts, args):
OptimizeTask.parse(self, opts, args)
self.vacuum = opts.vacuum
self.bond_length = opts.bond_length
self.atomize = opts.atomize
if opts.fit:
points, strain = opts.fit.split(',')
self.fit = (int(points), float(strain) * 0.01)
if opts.unit_cell:
if ',' in opts.unit_cell:
self.unit_cell = [float(x) for x in opts.unit_cell.split(',')]
else:
self.unit_cell = [float(opts.unit_cell)] * 3
def parse(self, opts, args):
OptimizeTask.parse(self, opts, args)
self.vacuum = opts.vacuum
self.bond_length = opts.bond_length
self.atomize = opts.atomize
if opts.fit:
points, strain = opts.fit.split(',')
self.fit = (int(points), float(strain) * 0.01)
if opts.unit_cell:
if ',' in opts.unit_cell:
self.unit_cell = [float(x) for x in opts.unit_cell.split(',')]
else:
self.unit_cell = [float(opts.unit_cell)] * 3
def run(self, names1):
names = []
atoms = set()
for name in names1:
if name.lower() == 'g2':
from ase.data.g2 import molecule_names
names.extend(molecule_names)
from ase.data.g2 import atom_names
if self.atomize:
atoms.update(atom_names)
elif name.lower() == 'g2-1':
from ase.data.g2_1 import molecule_names
names.extend(molecule_names)
from ase.data.g2_1 import atom_names
if self.atomize:
atoms.update(atom_names)
else:
names.append(name)
if self.atomize:
atoms.update(
self.build_system(name).get_chemical_symbols())
if self.atomize:
names.extend(atoms)
return OptimizeTask.run(self, names)
def calculate(self, name, atoms):
if self.fit and len(atoms) == 2:
return self.fit_bond_length(name, atoms)
else:
data = OptimizeTask.calculate(self, name, atoms)
self.check_occupation_numbers(atoms)
return data
def calculate(self, name, atoms):
data = OptimizeTask.calculate(self, name, atoms)
if self.fmax is not None and len(atoms) == 2:
data['distance'] = atoms.get_distance(0, 1)
self.check_occupation_numbers(atoms)
if self.fit and len(atoms) == 2:
self.fit_bond_length(name, atoms, data)
return data
def calculate(self, name, atoms):
data = OptimizeTask.calculate(self, name, atoms)
if self.fmax is not None and len(atoms) == 2:
data['distance'] = atoms.get_distance(0, 1)
self.check_occupation_numbers(atoms)
if self.fit and len(atoms) == 2:
self.fit_bond_length(name, atoms, data)
return data
def __init__(self, crystal_structure=None, lattice_constant=None,
c_over_a=None, cubic=False, orthorhombic=False, fit=None,
**kwargs):
"""Bulk task."""
self.crystal_structure = crystal_structure
self.lattice_constant = lattice_constant
self.c_over_a = c_over_a
self.cubic = cubic
self.orthorhombic = orthorhombic
self.fit = fit
self.repeat = None
OptimizeTask.__init__(self, **kwargs)
self.summary_keys = ['energy', 'fitted energy', 'volume', 'B']
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'strains' in data:
atoms = self.create_system(name)
volumes = data['strains']**3 * atoms.get_volume()
energies = data['energies']
eos = EquationOfState(volumes, energies)
try:
v, e, B = eos.fit()
except ValueError:
self.results[name].extend([None, None])
else:
self.results[name][1:] = [energies[2] - e, v,
B * 1e24 / units.kJ]
else:
self.results[name].extend([None, None])
def __init__(self, vacuum=3.0, cell=None, atomize=False,
bond_length=None, fit=None,
**kwargs):
"""Molecule task.
This task can calculate bond lengths and vibration frequencies
of dimer molecules."""
self.vacuum = vacuum
self.unit_cell = cell
self.atomize = atomize
self.bond_length = bond_length
self.fit = fit
OptimizeTask.__init__(self, **kwargs)
self.summary_keys = ['energy', 'relaxed energy', 'distance',
'frequency', 'atomic energy']
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'strains' in data:
atoms = self.create_system(name)
volumes = data['strains']**3 * atoms.get_volume()
energies = data['energies']
eos = EquationOfState(volumes, energies)
try:
v, e, B = eos.fit()
except ValueError:
self.results[name].extend([None, None])
else:
self.results[name][1:] = [
energies[2] - e, v, B * 1e24 / units.kJ
]
else:
self.results[name].extend([None, None])
def __init__(self, crystal_structure=None, lattice_constant=None,
c_over_a=None, cubic=False, orthorhombic=False, fit=None,
**kwargs):
"""Bulk task."""
self.crystal_structure = crystal_structure
self.lattice_constant = lattice_constant
self.c_over_a = c_over_a
self.cubic = cubic
self.orthorhombic = orthorhombic
self.fit = fit
self.repeat = None
OptimizeTask.__init__(self, **kwargs)
self.summary_header += [('V0', 'Ang^3'),
('B', 'GPa')]
def __init__(self, vacuum=3.0, cell=None, atomize=False,
bond_length=None, fit=None,
**kwargs):
"""Molecule task.
This task can calculate bond lengths and vibration frequencies
of dimer molecules."""
self.vacuum = vacuum
self.unit_cell = cell
self.atomize = atomize
self.bond_length = bond_length
self.fit = fit
OptimizeTask.__init__(self, **kwargs)
self.summary_keys = ['energy', 'relaxed energy', 'distance',
'frequency', 'atomic energy']
def calculate(self, name, atoms):
if self.fmax is not None:
# this performs relaxation of internal degrees of freedom
data = OptimizeTask.calculate(self, name, atoms)
data['distance'] = atoms.get_distance(0, -1)
else:
# no optimization
if self.fit is None or len(atoms) != 2:
# for dimers: only calculate single-point energy if no fit follows
data = OptimizeTask.calculate(self, name, atoms)
if self.fit is not None and len(atoms) == 2:
if self.fmax is not None:
# fit after optimization
self.fit_bond_length(name, atoms, data)
else:
# fit is the only task performed
data = self.fit_bond_length(name, atoms)
self.check_occupation_numbers(atoms)
return data
def calculate(self, name, atoms):
if self.fmax is not None:
# this performs relaxation of internal degrees of freedom
data = OptimizeTask.calculate(self, name, atoms)
data['distance'] = atoms.get_distance(0, -1)
else:
# no optimization
if self.fit is None or len(atoms) != 2:
# for dimers: only calculate single-point energy if no fit follows
data = OptimizeTask.calculate(self, name, atoms)
if self.fit is not None and len(atoms) == 2:
if self.fmax is not None:
# fit after optimization
self.fit_bond_length(name, atoms, data)
else:
# fit is the only task performed
data = self.fit_bond_length(name, atoms)
self.check_occupation_numbers(atoms)
return data
def add_options(self, parser):
OptimizeTask.add_options(self, parser)
mol = optparse.OptionGroup(parser, 'Molecule')
mol.add_option('-v', '--vacuum', type='float', default=3.0,
help='Amount of vacuum to add around isolated systems '
'(in Angstrom).')
mol.add_option('--unit-cell',
help='Unit cell. Examples: "10.0" or "9,10,11" ' +
'(in Angstrom).')
mol.add_option('--bond-length', type='float',
help='Bond length of dimer in Angstrom.')
mol.add_option('-F', '--fit', metavar='N,x',
help='Find optimal bondlength and vibration ' +
'frequency using N points and displacements from ' +
'-x % to +x %.')
mol.add_option('--atomize', action='store_true',
help='Calculate Atomization energies.')
parser.add_option_group(mol)
def __init__(self, vacuum=3.0, cell=None, atomize=False,
bond_length=None, fit=None,
**kwargs):
"""Molecule task.
This task can calculate bond lengths and vibration frequencies
of dimer molecules."""
self.vacuum = vacuum
self.unit_cell = cell
self.atomize = atomize
self.bond_length = bond_length
self.fit = fit
OptimizeTask.__init__(self, **kwargs)
self.summary_header += [('d0', 'Ang'),
('hnu', 'meV'),
('Ea', 'eV'),
('Ea0', 'eV')]
def add_options(self, parser):
OptimizeTask.add_options(self, parser)
bulk = optparse.OptionGroup(parser, 'Bulk')
bulk.add_option('-F', '--fit', metavar='N,x',
help='Find optimal volume and bulk modulus ' +
'using odd N points and variations of the lattice ' +
'constant a from -x % to +x %, i.e. in the interval '
'<a - a * x * 100, ..., a, ..., a + a * x * 100>. ' +
'This method gives non-equidistant sampling of volume. ' +
'With x negative (in Angstrom**3) the sampling of ' +
'the cell volume (v) in the interval ' +
'<(1 + x /v), ..., 1, ..., (1 - x /v)> is used. ' +
'This method gives equidistant sampling of volume.')
bulk.add_option('--eos', type='str',
metavar='eos',
help='Selects the type of eos.')
bulk.add_option('--srelax', metavar='SFMAX[,SOPTIMIZER]',
help='Relax cell by minimizing stress using StranFilter '
'with SOPTIMIZER algorithm. The SOPTIMIZER keyword is '
'optional, and if omitted BFGS is used by default.')
bulk.add_option('--srelaxsteps', type='int',
metavar='ssteps',
help='Limit the number of SF optimizer steps.')
bulk.add_option('-x', '--crystal-structure',
help='Crystal structure.',
choices=['sc', 'fcc', 'bcc', 'hcp', 'diamond',
'zincblende', 'rocksalt', 'cesiumchloride',
'fluorite'])
bulk.add_option('-a', '--lattice-constant', type='float',
help='Lattice constant in Angstrom.')
bulk.add_option('--c-over-a', type='float',
help='c/a ratio.')
bulk.add_option('-O', '--orthorhombic', action='store_true',
help='Use orthorhombic unit cell.')
bulk.add_option('-C', '--cubic', action='store_true',
help='Use cubic unit cell.')
bulk.add_option('-r', '--repeat',
help='Repeat unit cell. Use "-r 2" or "-r 2,3,1".')
parser.add_option_group(bulk)
def __init__(self,
crystal_structure=None,
lattice_constant=None,
c_over_a=None,
cubic=False,
orthorhombic=False,
fit=None,
**kwargs):
"""Bulk task."""
self.crystal_structure = crystal_structure
self.lattice_constant = lattice_constant
self.c_over_a = c_over_a
self.cubic = cubic
self.orthorhombic = orthorhombic
self.fit = fit
self.repeat = None
OptimizeTask.__init__(self, **kwargs)
self.summary_header += [('V0', 'Ang^3'), ('B', 'GPa')]
def __init__(self,
vacuum=3.0,
cell=None,
atomize=False,
bond_length=None,
fit=None,
**kwargs):
"""Molecule task.
This task can calculate bond lengths and vibration frequencies
of dimer molecules."""
self.vacuum = vacuum
self.unit_cell = cell
self.atomize = atomize
self.bond_length = bond_length
self.fit = fit
OptimizeTask.__init__(self, **kwargs)
self.summary_header += [('d0', 'Ang'), ('hnu', 'meV'), ('Ea', 'eV'),
('Ea0', 'eV')]
def __init__(self, crystal_structure=None, lattice_constant=None,
c_over_a=None, cubic=False, orthorhombic=False, fit=None,
eos=None, sfmax=None, soptimizer='BFGS', ssteps=100000000,
**kwargs):
"""Bulk task."""
self.crystal_structure = crystal_structure
self.lattice_constant = lattice_constant
self.c_over_a = c_over_a
self.cubic = cubic
self.orthorhombic = orthorhombic
self.eos = eos
self.fit = fit
self.sfmax = sfmax
self.soptimizer = soptimizer
self.ssteps = ssteps
self.repeat = None
OptimizeTask.__init__(self, **kwargs)
self.summary_keys = ['energy', 'fitted energy', 'volume', 'B']
def add_options(self, parser):
OptimizeTask.add_options(self, parser)
bulk = optparse.OptionGroup(parser, 'Bulk')
bulk.add_option('-F', '--fit', metavar='N,x',
help='Find optimal volume and bulk modulus ' +
'using N points and variations of the lattice ' +
'constants from -x % to +x %.')
bulk.add_option('-x', '--crystal-structure',
help='Crystal structure.',
choices=['sc', 'fcc', 'bcc', 'diamond', 'hcp',
'rocksalt', 'zincblende'])
bulk.add_option('-a', '--lattice-constant', type='float',
help='Lattice constant in Angstrom.')
bulk.add_option('--c-over-a', type='float',
help='c/a ratio.')
bulk.add_option('-O', '--orthorhombic', action='store_true',
help='Use orthorhombic unit cell.')
bulk.add_option('-C', '--cubic', action='store_true',
help='Use cubic unit cell.')
bulk.add_option('-r', '--repeat',
help='Repeat unit cell. Use "-r 2" or "-r 2,3,1".')
parser.add_option_group(bulk)
def expand(self, names):
"""Expand fcc, bcc, hcp and diamond.
The name fcc will be expanded to all the elements with the fcc
stucture and so on."""
names = OptimizeTask.expand(self, names)
newnames = []
for name in names:
if name in ['fcc', 'bcc', 'hcp', 'diamond']:
for Z in range(1, 95):
x = reference_states[Z]
if x is not None and x['symmetry'] == name:
newnames.append(chemical_symbols[Z])
else:
newnames.append(name)
return newnames
def expand(self, names):
"""Expand fcc, bcc, hcp and diamond.
The name fcc will be expanded to all the elements with the fcc
stucture and so on."""
names = OptimizeTask.expand(self, names)
newnames = []
for name in names:
if name in ['fcc', 'bcc', 'hcp', 'diamond']:
for Z in range(1, 95):
x = reference_states[Z]
if x is not None and x['symmetry'] == name:
newnames.append(chemical_symbols[Z])
else:
newnames.append(name)
return newnames
def run(self, names1):
names = []
atoms = set()
for name in names1:
if name.lower() == 'g2':
from ase.data.g2 import molecule_names
names.extend(molecule_names)
from ase.data.g2 import atom_names
if self.atomize:
atoms.update(atom_names)
elif name.lower() == 'g2-1':
from ase.data.g2_1 import molecule_names
names.extend(molecule_names)
from ase.data.g2_1 import atom_names
if self.atomize:
atoms.update(atom_names)
else:
names.append(name)
if self.atomize:
atoms.update(self.build_system(name).get_chemical_symbols())
if self.atomize:
names.extend(atoms)
return OptimizeTask.run(self, names)
def calculate(self, name, atoms):
#????
if self.sfmax is not None and self.fmax is not None:
# this performs first relaxation of internal degrees of freedom
data = OptimizeTask.calculate(self, name, atoms)
# writing traj from optimizer does not work for StrainFilter!
traj = PickleTrajectory(self.get_filename(name, 'traj'), 'a',
atoms)
sf = StrainFilter(atoms)
while not self.converged(atoms, sfmax=self.sfmax, fmax=self.fmax):
# take a step on the cell
self.soptimize(name, sf, data, trajectory=traj)
# relax internal degrees of freedom
OptimizeTask.optimize(self, name, atoms, data, trajectory=traj)
data['relaxed energy'] = atoms.get_potential_energy()
data['relaxed volume'] = atoms.get_volume()
elif self.sfmax is not None:
# this performs single-point energy calculation
data = OptimizeTask.calculate(self, name, atoms)
sf = StrainFilter(atoms)
# writing traj from optimizer does not work for StrainFilter!
traj = PickleTrajectory(self.get_filename(name, 'traj'), 'w',
atoms)
self.soptimize(name, sf, data, trajectory=traj)
data['relaxed energy'] = atoms.get_potential_energy()
data['relaxed volume'] = atoms.get_volume()
elif self.fmax is not None:
data = OptimizeTask.calculate(self, name, atoms)
else:
# no optimization
if self.fit is None:
# only calculate single-point energy if no fit follows
data = OptimizeTask.calculate(self, name, atoms)
if self.fit is not None:
if self.sfmax is not None or self.fmax is not None:
# fit after optimization
self.fit_volume(name, atoms, data)
else:
# fit is the only task performed
data = self.fit_volume(name, atoms)
return data
def calculate(self, name, atoms):
#????
if self.sfmax is not None and self.fmax is not None:
# this performs first relaxation of internal degrees of freedom
data = OptimizeTask.calculate(self, name, atoms)
# writing traj from optimizer does not work for StrainFilter!
traj = Trajectory(self.get_filename(name, 'traj'), 'a', atoms)
sf = StrainFilter(atoms)
while not self.converged(atoms, sfmax=self.sfmax, fmax=self.fmax):
# take a step on the cell
self.soptimize(name, sf, data, trajectory=traj)
# relax internal degrees of freedom
OptimizeTask.optimize(self, name, atoms, data, trajectory=traj)
data['relaxed energy'] = atoms.get_potential_energy()
data['relaxed volume'] = atoms.get_volume()
elif self.sfmax is not None:
# this performs single-point energy calculation
data = OptimizeTask.calculate(self, name, atoms)
sf = StrainFilter(atoms)
# writing traj from optimizer does not work for StrainFilter!
traj = Trajectory(self.get_filename(name, 'traj'), 'w', atoms)
self.soptimize(name, sf, data, trajectory=traj)
data['relaxed energy'] = atoms.get_potential_energy()
data['relaxed volume'] = atoms.get_volume()
elif self.fmax is not None:
data = OptimizeTask.calculate(self, name, atoms)
else:
# no optimization
if self.fit is None:
# only calculate single-point energy if no fit follows
data = OptimizeTask.calculate(self, name, atoms)
if self.fit is not None:
if self.sfmax is not None or self.fmax is not None:
# fit after optimization
self.fit_volume(name, atoms, data)
else:
# fit is the only task performed
data = self.fit_volume(name, atoms)
return data
def calculate(self, name, atoms):
#????
if self.fit:
return self.fit_volume(name, atoms)
else:
return OptimizeTask.calculate(self, name, atoms)
def calculate(self, name, atoms):
#????
if self.fit:
return self.fit_volume(name, atoms)
else:
return OptimizeTask.calculate(self, name, atoms)
