def test_selected_atoms_with_orca():
mol = molkit.from_smiles('CO')
s = Settings()
s.selected_atoms = ["H"]
expected_settings = Settings({
'selected_atoms': ['H'],
'specific': {
'orca': {
'geom': {
'Constraints': {
'_end': '{ C 0 C }{ C 1 C }'
}
}
}
}
})
assert str(orca.generic2specific(s, mol)) == str(expected_settings)
s.selected_atoms = [3, 4, 5, 6]
expected_settings = Settings({
'selected_atoms': [3, 4, 5, 6],
'specific': {
'orca': {
'geom': {
'Constraints': {
'_end': '{ C 0 C }{ C 1 C }'
}
}
}
}
})
assert str(orca.generic2specific(s, mol)) == str(expected_settings)
def test_freeze_with_adf():
mol = molkit.from_smiles('CO')
s = Settings()
s.freeze = ["C", "O"]
expected_settings = Settings({
'freeze': ["C", "O"],
'specific': {
'adf': {
'constraints': {
'atom 1': '',
'atom 2': ''
},
'geometry': {
'optim': 'cartesian'
}
}
}
})
assert str(adf.generic2specific(s, mol)) == str(expected_settings)
s.freeze = [1, 2]
expected_settings = Settings({
'freeze': [1, 2],
'specific': {
'adf': {
'constraints': {
'atom 1': '',
'atom 2': ''
},
'geometry': {
'optim': 'cartesian'
}
}
}
})
assert str(adf.generic2specific(s, mol)) == str(expected_settings)
def test_selected_atoms_with_adf():
mol = molkit.from_smiles('CO')
s = Settings()
s.selected_atoms = ["H"]
expected_settings = Settings({
'selected_atoms': ['H'],
'specific': {
'adf': {
'constraints': {
'atom 1': '',
'atom 2': ''
},
'geometry': {
'optim': 'cartesian'
}
}
}
})
assert str(adf.generic2specific(s, mol)) == str(expected_settings)
s.selected_atoms = [3, 4, 5, 6]
expected_settings = Settings({
'selected_atoms': [3, 4, 5, 6],
'specific': {
'adf': {
'constraints': {
'atom 1': '',
'atom 2': ''
},
'geometry': {
'optim': 'cartesian'
}
}
}
})
assert str(adf.generic2specific(s, mol)) == str(expected_settings)
def test_freeze_with_orca():
mol = molkit.from_smiles('CO')
s = Settings()
s.freeze = ["C", "O"]
expected_settings = Settings({
'freeze': ['C', 'O'],
'specific': {
'orca': {
'geom': {
'Constraints': {
'_end': '{ C 0 C }{ C 1 C }'
}
}
}
}
})
assert str(orca.generic2specific(s, mol)) == str(expected_settings)
s.freeze = [1, 2]
expected_settings = Settings({
'freeze': [1, 2],
'specific': {
'orca': {
'geom': {
'Constraints': {
'_end': '{ C 0 C }{ C 1 C }'
}
}
}
}
})
assert str(orca.generic2specific(s, mol)) == str(expected_settings)
def test_dftb_props():
"""
Get properties from DFTB freq calc
"""
mol = molkit.from_smiles('F[H]')
result = run(dftb(templates.freq, mol, job_name='dftb_FH'))
expected_energy = -4.76
assert abs(result.energy - expected_energy) < 0.01
assert len(result.dipole) == 3
expected_frequency = 3460.92
assert abs(result.frequencies - expected_frequency) < 0.1
assert len(result.charges) == 2
assert abs(result.charges[0] + result.charges[1]) < 1e-6
def test_adf_props():
"""
Get properties from ADF freq calc
"""
mol = molkit.from_smiles('F[H]')
result = run(adf(templates.freq, mol, job_name='adf_FH'))
expected_energy = -0.30
assert abs(result.energy - expected_energy) < 0.01
assert len(result.dipole) == 3
expected_frequency = 3480.90
assert abs(result.frequencies[1] - expected_frequency) < 0.1
assert len(result.charges) == 2
assert abs(result.charges[0] + result.charges[1]) < 1e-6
def example_generic_constraints():
"""
This examples illustrates that, by using generic keywords, it is possible
to call different packages interchangeably with the same Settings
"""
# build hydrogen fluoride molecule
hydrogen_fluoride = molkit.from_smiles('F[H]')
# loop over distances
jobs = []
for distance in [1.0, 1.1, 1.2]:
s = Settings()
s.constraint['dist 1 2'] = distance
# loop over packages
for package in [dftb, adf, orca]:
job_name = package.pkg_name + '_' + str(distance)
constraint_opt = package(templates.geometry.overlay(s),
hydrogen_fluoride, job_name)
jobs.append(constraint_opt)
# run the jobs
results = run(gather(*jobs))
energies = [r.energy for r in results]
names = [r.job_name for r in results]
# put resulting energies into a dictionary
table = {'dftb': {}, 'adf': {}, 'orca': {}}
for r in results:
package, distance = r.job_name.split('_')
table[package][distance] = round(r.energy, 6)
# print table
hartree_to_kcalpermol = 627.094
for package in ['dftb', 'adf', 'orca']:
row = [package]
for distance in ['1.0', '1.1', '1.2']:
val = table[package][distance] - table[package]['1.0']
row.append(round(val * hartree_to_kcalpermol, 2))
print('{:10s} {:10.2f} {:10.2f} {:10.2f}'.format(*row))
return names, energies
def example_freqs():
"""
This examples illustrates the possibility to use different packages interchangeably.
Analytical frequencies are not available for B3LYP in ADF
This workflow captures the resulting error and submits the same job to ORCA.
"""
# Generate water molecule
water = molkit.from_smiles('[OH2]', forcefield='mmff')
# Pre-optimize the water molecule
opt_water = dftb(templates.geometry, water, job_name="dftb_geometry")
jobs = []
# Generate freq jobs for 3 functionals
for functional in ['pbe', 'b3lyp', 'blyp']:
s = Settings()
s.basis = 'DZ'
s.functional = functional
# Try to perform the jobs with adf or orca, take result from first successful calculation
freqjob = find_first_job(is_successful, [adf, orca],
templates.freq.overlay(s),
opt_water.molecule,
job_name=functional)
jobs.append(freqjob)
# Run workflow
results = run(gather(*jobs), n_processes=1)
# extrac results
freqs = [r.frequencies[-3:] for r in results]
functionals = ['pbe', 'b3lyp', 'blyp']
# Print the result
table = [
"{:10s}{:10.3f}{:10.3f}{:10.3f}\n".format(fun, *fs)
for fun, fs in zip(functionals, freqs)
]
print(table)
return freqs
def test_hessian_transfer():
"""
Test DFTB -> Orca hessian transfer
"""
h2o = molkit.from_smiles('O')
h2o.properties.symmetry = 'C1'
h2o_freq = dftb(templates.freq, h2o, job_name="freq").hessian
s = Settings()
s.inithess = h2o_freq
h2o_opt = orca(templates.geometry.overlay(s), h2o, job_name="opt")
energy = h2o_opt.energy
dipole = h2o_opt.dipole
wf = gather(energy, dipole)
# draw_workflow('wf.svg', wf._workflow)
print(run(wf))
def example_partial_geometry_opt():
"""
Performa partial optimization freezing the Hydrogen atoms
"""
methanol = molkit.from_smiles('CO')
# optimize only H atoms
s = Settings()
s.freeze = [1, 2]
geom_job1 = adf(templates.geometry.overlay(s),
methanol,
job_name='geom_job1').molecule
# optimize only H atoms
s = Settings()
s.selected_atoms = ['H']
geom_job2 = adf(templates.geometry.overlay(s),
methanol,
job_name='geom_job2').molecule
geom1, geom2 = run(gather(geom_job1, geom_job2), n_processes=1)
return geom1, geom2
def test_freeze_with_gamess():
mol = molkit.from_smiles('CO')
s = Settings()
s.freeze = ["C", "O"]
expected_settings = Settings({
'freeze': ['C', 'O'],
'specific': {
'gamess': {
'statpt': 'IFREEZ(1)=1,2,3,4,5,6'
}
}
})
assert str(gamess.generic2specific(s, mol)) == str(expected_settings)
s = Settings()
s.freeze = [1, 2]
expected_settings = Settings({
'freeze': [1, 2],
'specific': {
'gamess': {
'statpt': 'IFREEZ(1)=1,2,3,4,5,6'
}
}
})
assert str(gamess.generic2specific(s, mol)) == str(expected_settings)
def test_selected_atoms_with_gamess():
mol = molkit.from_smiles('CO')
s = Settings()
s.selected_atoms = ["H"]
expected_settings = Settings({
'selected_atoms': ['H'],
'specific': {
'gamess': {
'statpt': 'IFREEZ(1)=1,2,3,4,5,6'
}
}
})
assert str(gamess.generic2specific(s, mol)) == str(expected_settings)
s = Settings()
s.selected_atoms = [3, 4, 5, 6]
expected_settings = Settings({
'selected_atoms': [3, 4, 5, 6],
'specific': {
'gamess': {
'statpt': 'IFREEZ(1)=1,2,3,4,5,6'
}
}
})
assert str(gamess.generic2specific(s, mol)) == str(expected_settings)
def test_SerMolecule():
mol = molkit.from_smiles("c1ccccc1CC")
registry = packages.registry()
encoded_molecule = registry.deep_encode(mol)
decoded_molecule = registry.deep_decode(encoded_molecule)
assert len(mol) == len(decoded_molecule)
"""get the base job name"""
name = job.job_name
return name.split('_')[1]
# List of Molecules to simulate
smiles = {
'naphthalene': 'C1=CC2=C(C=C1)C=CC=C2',
'azulene': 'C1=CC2=CC=CC=CC2=C1',
'cycloheptatriene': 'C1=CC=C(C=C1)OC1=CC=CC=C1',
'furaldehyde': 'O=CC1=COC=C1',
'methylthiophene': 'CC1=C(C=CS1)C#C'
}
# transform the string into a format understandable by Qmflows
molecules = {name: from_smiles(smile) for name, smile in smiles.items()}
# Used DFTB to optimize the geometries
dftb_jobs = {
name: dftb(templates.geometry, mol, job_name='dftb_{}'.format(name))
for name, mol in molecules.items()
}
optimized_mols = {name: job.molecule for name, job in dftb_jobs.items()}
# Settings for ADF SAOP single point calculation
s = Settings()
s.basis = 'DZP'
s.specific.adf.basis.core = None
s.specific.adf.xc.model = 'saop'
s.specific.adf.scf.converge = 1e-6
s.specific.adf.symmetry = 'nosym'
bond1 = Distance(1, 0)
bond2 = Distance(4, 3)
# User define Settings
settings = Settings()
settings.functional = "pbe"
settings.basis = "TZ2P"
settings.specific.dftb.dftb.scc
job_list = []
# Loop over all reactions
for name, reactant1, reactant2, product in reactions[:1]:
# Prepare reactant1 job
r1mol = molkit.from_smiles(reactant1)
r1job = adf(
templates.geometry.overlay(settings),
dftb(templates.geometry.overlay(settings), r1mol,
job_name=name + "_r1_DFTB").molecule,
job_name=name + "_r1")
# Prepare reactant2 job
r2mol = molkit.from_smiles(reactant2)
r2job = adf(
templates.geometry.overlay(settings),
dftb(templates.geometry.overlay(settings), r2mol,
job_name=name + "_r2_DFTB").molecule,
job_name=name + "_r2")
# Prepare product job
settings = Settings()
settings.functional = "b3lyp"
settings.specific.orca.basis.basis = "_6_31G"
settings.specific.orca.basis.pol = "_d"
settings.specific.orca.basis.diff = "_p"
settings.specific.dftb.dftb.scc.ndiis = 4
settings.specific.dftb.dftb.scc.Mixing = 0.1
settings.specific.dftb.dftb.scc.iterations = 300
job_list = []
# Loop over all reactions
for name, r1_smiles, r2_smiles, p_smiles in reactions:
# Prepare reactant1 job
r1_mol = molkit.from_smiles(r1_smiles)
r1_dftb = dftb(templates.geometry.overlay(settings), r1_mol, job_name=name + "_r1_DFTB")
r1 = adf(templates.geometry.overlay(settings), r1_dftb.molecule, job_name=name + "_r1")
r1_freq = adf(templates.freq.overlay(settings), r1.molecule, job_name=name + "_r1_freq")
# Prepare reactant2 job
r2s_mol = molkit.from_smiles(r2_smiles, nconfs=10, forcefield='mmff', rms=0.1)
r2s_dftb = [dftb(templates.geometry.overlay(settings), r2_mol, job_name=name + "_r2_DFTB") for r2_mol in r2s_mol]
r2_dftb = select_min(gather(*r2s_dftb), 'energy')
r2 = adf(templates.geometry.overlay(settings), r2_dftb.molecule, job_name=name + "_r2")
r2_freq = adf(templates.freq.overlay(settings), r2.molecule, job_name=name + "_r2_freq")
# Prepare product job
ps_mol = molkit.from_smiles(p_smiles, nconfs=10, forcefield='mmff', name=name, rms=0.1)
ps_dftb = [dftb(templates.geometry.overlay(settings), p_mol, job_name=name + "_ps_DFTB") for p_mol in ps_mol]
p_dftb = select_min(gather(*ps_dftb), 'energy')
from qmflows.molkit import from_smiles # List of Molecules to simulate smiles = ['C1=CC2=C(C=C1)C=CC=C2', 'C1CC2(CCCCC2)C=C1'] # Transform the smiles into a format understandable by QMFlows molecules = [from_smiles(s) for s in smiles]
