def test_turbomole_h2o():
from ase.calculators.turbomole import Turbomole
from ase.build import molecule
mol = molecule('H2O')
params = {
'title': 'water',
'task': 'geometry optimization',
'use redundant internals': True,
'basis set name': 'def2-SV(P)',
'total charge': 0,
'multiplicity': 1,
'use dft': True,
'density functional': 'b3-lyp',
'use resolution of identity': True,
'ri memory': 1000,
'force convergence': 0.001,
'geometry optimization iterations': 50,
'scf iterations': 100
}
calc = Turbomole(**params)
mol.calc = calc
calc.calculate(mol)
assert calc.converged
# use the get_property() method
print(calc.get_property('energy', mol, False))
print(calc.get_property('forces', mol, False))
print(calc.get_property('dipole', mol, False))
# use the get_results() method
results = calc.get_results()
print(results['molecular orbitals'])
# use the __getitem__() method
print(calc['results']['molecular orbitals'])
print(calc['results']['geometry optimization history'])
# perform a normal mode calculation with the optimized structure
params.update({
'task': 'normal mode analysis',
'density convergence': 1.0e-7
})
calc = Turbomole(**params)
mol.calc = calc
calc.calculate(mol)
print(calc['results']['vibrational spectrum'])
print(calc.todict(skip_default=False))
def test_turbomole_au13():
from ase.cluster.cubic import FaceCenteredCubic
from ase.calculators.turbomole import Turbomole
surfaces = [(1, 0, 0), (1, 1, 0), (1, 1, 1)]
layers = [1, 2, 1]
atoms = FaceCenteredCubic('Au', surfaces, layers, latticeconstant=4.08)
params = {
'title': 'Au13-',
'task': 'energy',
'basis set name': 'def2-SV(P)',
'total charge': -1,
'multiplicity': 1,
'use dft': True,
'density functional': 'pbe',
'use resolution of identity': True,
'ri memory': 1000,
'use fermi smearing': True,
'fermi initial temperature': 500,
'fermi final temperature': 100,
'fermi annealing factor': 0.9,
'fermi h**o-lumo gap criterion': 0.09,
'fermi stopping criterion': 0.002,
'scf energy convergence': 1.e-4,
'scf iterations': 250
}
calc = Turbomole(**params)
atoms.calc = calc
calc.calculate(atoms)
# use the get_property() method
print(calc.get_property('energy'))
print(calc.get_property('dipole'))
# test restart
params = {'task': 'gradient', 'scf energy convergence': 1.e-6}
calc = Turbomole(restart=True, **params)
assert calc.converged
calc.calculate()
print(calc.get_property('energy'))
print(calc.get_property('forces'))
print(calc.get_property('dipole'))
'ri memory': 1000,
'use fermi smearing': True,
'fermi initial temperature': 500,
'fermi final temperature': 100,
'fermi annealing factor': 0.9,
'fermi h**o-lumo gap criterion': 0.09,
'fermi stopping criterion': 0.002,
'scf energy convergence': 1.e-4,
'scf iterations': 250
}
calc = Turbomole(**params)
atoms.set_calculator(calc)
calc.calculate(atoms)
# use the get_property() method
print(calc.get_property('energy'))
print(calc.get_property('dipole'))
# test restart
params = {'task': 'gradient', 'scf energy convergence': 1.e-6}
calc = Turbomole(restart=True, **params)
assert calc.converged
calc.calculate()
print(calc.get_property('energy'))
print(calc.get_property('forces'))
print(calc.get_property('dipole'))
'use dft': True,
'density functional': 'b3-lyp',
'use resolution of identity': True,
'ri memory': 1000,
'force convergence': 0.001,
'geometry optimization iterations': 50,
'scf iterations': 100
}
calc = Turbomole(**params)
mol.set_calculator(calc)
calc.calculate(mol)
assert calc.converged
# use the get_property() method
print(calc.get_property('energy', mol, False))
print(calc.get_property('forces', mol, False))
print(calc.get_property('dipole', mol, False))
# use the get_results() method
results = calc.get_results()
print(results['molecular orbitals'])
# use the __getitem__() method
print(calc['results']['molecular orbitals'])
print(calc['results']['geometry optimization history'])
# perform a normal mode calculation with the optimized structure
params.update({'task': 'normal mode analysis', 'density convergence': 1.0e-7})