def testBondFilter(self):
"""
Test the molecular formula creator, which is
basically a RDKit feature, but used in KinBot
for postprocessing
"""
smi = 'CCC=CCC'
motif = ['C', 'C', 'C', 'C']
bondpattern = [2, 'X', 'X']
exp = 2
st_pt = StationaryPoint(smi, 0, 1, smiles=smi)
st_pt.characterize()
bond = st_pt.bond
natom = st_pt.natom
atom = st_pt.atom
# do not use any equivalencies
eqv = [[k] for k in range(natom)]
hits = find_motif.start_motif(motif, natom, bond, atom, -1, eqv)
count = 0
for hit in hits:
if find_motif.bondfilter(hit, bond, bondpattern) == 0:
count += 1
warn = 'Unexpected number of motif hits for '
warn += '{}, expected {}, calculated {}'.format(smi, exp, count)
self.assertEqual(exp, count, warn)
def irc2stationary_pt(self):
"""
Read the irc files
There are three possible scenarios:
1. One of the ircs leads the initial well and
the other to another well or bimolecular product
2. Neither of the ircs lead to the inital well,
transition state structure is not the one
kinbot was looking for
3. Both the ircs lead to the initial well,
KinBot found either an identical reaction
or the ts is not correct
"""
instance_name = self.rxn.instance_name
directions = ['Forward', 'Reverse']
ini_well_hits = 0
prod_hit = -1
st_pts = [-1, -1]
for i, direction in enumerate(directions):
irc_name = '{}_IRC_{}_prod'.format(instance_name, direction[0])
err, geom = self.rxn.qc.get_qc_geom(irc_name,
self.rxn.species.natom,
allow_error=1)
if err == -1:
return 0
if self.problem_in_geom(geom):
# this happens seldomly that all the atoms are
# very close to one another (problem in Gaussian)
logging.info('\tProblem with product geometry for {}'.format(
instance_name))
return 0
temp = StationaryPoint(irc_name,
self.rxn.species.charge,
self.rxn.species.mult,
atom=self.rxn.species.atom,
geom=geom)
temp.calc_chemid()
st_pts[i] = temp
if temp.chemid == self.rxn.species.chemid:
ini_well_hits += 1
else:
prod_hit = i
if ini_well_hits == 0:
logging.info(
'\tNeither IRC leads to the well for {}'.format(instance_name))
return 0
elif ini_well_hits == 2:
logging.info(
'\tBoth IRCs lead to the well, identical reaction found: {}'.
format(instance_name))
return 0
else:
# ircs OK: well and product found
logging.info('\tIRCs successful for {}'.format(instance_name))
return st_pts[prod_hit]
def testNumberOfHitsWithStartAtom(self):
"""
Test the molecular formula creator, which is
basically a RDKit feature, but used in KinBot
for postprocessing
"""
# the data contains the smiles as key and as value:
# 1. the multiplicity
# 2. start atom
# 3. the motif which will be searched for
# 4. the expected number of hits of the motif
data = {'CCCO[O]': [2, 0, ['C', 'C', 'C', 'O', 'O'], 1],
'C=C': [1, 0, ['X', 'X', 'X'], 2],
'S=S': [1, 0, ['S', 'S'], 1],
}
for smi in data:
mult = data[smi][0]
start = data[smi][1]
motif = data[smi][2]
exp = data[smi][3]
st_pt = StationaryPoint(smi, 0, mult, smiles=smi)
st_pt.characterize()
bond = st_pt.bond
natom = st_pt.natom
atom = st_pt.atom
# do not use any equivalencies
eqv = [[k] for k in range(natom)]
hits = find_motif.start_motif(motif, natom, bond, atom, start, eqv)
cal = len(hits)
warn = 'Unexpected number of motif hits for '
warn += '{}, expected {}, calculated {}'.format(smi, exp, cal)
self.assertEqual(exp, cal, warn)
def testAll(self):
data = {
'CC': [1, 0],
'CCC': [2, 0],
'CCCC': [3, 1],
'C=C': [0, 0],
'C=CC': [1, 0],
'C=C[CH2]': [0, 0],
'CC=C[CH2]': [1, 0],
'C1CCCC1': [0, 0],
'CO': [1, 0],
'C=CO': [1, 1],
}
for name in data:
par = Parameters()
qc = QuantumChemistry(par)
mol = StationaryPoint(name, 0, 1, smiles=name)
mol.characterize()
hir_exp = data[name][0]
conf_exp = data[name][1]
hir_calc = len(mol.dihed)
conf_calc = len(mol.conf_dihed)
self.assertEqual(
hir_exp, hir_calc,
name + ': HIR, expected: {}, calculated: {}'.format(
hir_exp, hir_calc))
self.assertEqual(
conf_exp, conf_calc,
name + ': CONF, expected: {}, calculated: {}'.format(
conf_exp, conf_calc))
def main():
smi = '[CH2]CC'
mult = 1
charge = 0
mol = StationaryPoint('well0', charge, mult, smiles=smi)
mol.characterize()
reactions = generate_all_product_bond_matrices(mol)
def testBondFilter(self):
"""
Test the molecular formula creator, which is
basically a RDKit feature, but used in KinBot
for postprocessing
"""
smi = 'CCC=CCC'
motif = ['C', 'C', 'C', 'C']
bondpattern = [2, 'X', 'X']
exp = 2
st_pt = StationaryPoint(smi, 0, 1, smiles=smi)
st_pt.characterize()
bond = st_pt.bond
natom = st_pt.natom
atom = st_pt.atom
# do not use any equivalencies
eqv = [[k] for k in range(natom)]
hits = find_motif.start_motif(motif, natom, bond, atom, -1, eqv)
count = 0
for hit in hits:
if find_motif.bondfilter(hit, bond, bondpattern) == 0:
count += 1
warn = 'Unexpected number of motif hits for '
warn += '{}, expected {}, calculated {}'.format(smi, exp, count)
self.assertEqual(exp, count, warn)
def test_conformer(self, conf):
"""
Test whether a conformer has the same bond matrix as the original structure.
Returns the conformer object and -1 if not yet finished, 0 if same, and 1 if not.
"""
add = ''
if self.semi_emp:
add = 'semi_emp_'
job = self.get_job_name(conf, add=add)
status, geom = self.qc.get_qc_geom(job, self.species.natom)
if status == 1: # still running
return np.zeros((self.species.natom, 3)), -1
elif status == -1: # conformer search failed
return np.zeros((self.species.natom, 3)), 1
else:
# check if all the bond lenghts are withing 10% of the original bond lengths
temp = StationaryPoint('temp',
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=geom)
temp.bond_mx()
if geometry.equal_geom(self.species, temp, 0.10):
return geom, 0
else:
return np.zeros((self.species.natom, 3)), 1
def testNumberOfHitsWithStartAtom(self):
"""
Test the molecular formula creator, which is
basically a RDKit feature, but used in KinBot
for postprocessing
"""
# the data contains the smiles as key and as value:
# 1. the multiplicity
# 2. start atom
# 3. the motif which will be searched for
# 4. the expected number of hits of the motif
data = {
'CCCO[O]': [2, 0, ['C', 'C', 'C', 'O', 'O'], 1],
'C=C': [1, 0, ['X', 'X', 'X'], 2],
'S=S': [1, 0, ['S', 'S'], 1],
}
for smi in data:
mult = data[smi][0]
start = data[smi][1]
motif = data[smi][2]
exp = data[smi][3]
st_pt = StationaryPoint(smi, 0, mult, smiles=smi)
st_pt.characterize()
bond = st_pt.bond
natom = st_pt.natom
atom = st_pt.atom
# do not use any equivalencies
eqv = [[k] for k in range(natom)]
hits = find_motif.start_motif(motif, natom, bond, atom, start, eqv)
cal = len(hits)
warn = 'Unexpected number of motif hits for '
warn += '{}, expected {}, calculated {}'.format(smi, exp, cal)
self.assertEqual(exp, cal, warn)
def testDihedralChangeHeptyl(self):
"""
The generation of ring conformers requires qc calculations and is therefore slow!
"""
if not os.path.exists('conf/'):
os.mkdir('conf/')
par = Parameters()
qc = QuantumChemistry(par)
smi = '[CH2]CCCCC'
mol = StationaryPoint(smi, 0, 2, smiles=smi)
mol.characterize()
changes = [
[0, 3, 4, 5, 25.],
[3, 4, 5, 6, 25.],
[4, 5, 6, 7, 25.],
[5, 6, 7, 16, 25.],
]
name = 'hexyl_dihedral'
success, new_geom = kinbot.modify_geom.modify_coordinates(
mol,
name,
mol.geom,
changes,
mol.bond,
write_files=self.write_files)
def testNewRingDihedrals(self):
"""
Test the calculation of new dihedrals necessary for the update
"""
smi = 'CCCC'
mol = StationaryPoint(smi, 0, 1, smiles=smi)
mol.characterize()
ins = [0, 1, 2, 3] # change the C-C-C-C dihedral
step_nr = 0 # This corresponds to the first dihedral update
# there are 12 steps done in total, this means that the dihedral
# angle should be changed by 1/10 of the total change
total_nr_of_steps = 10
# final dihedral value we are shooting for after 10 updates
# this is a default value for a instance of 5 or less atoms
final_val = 15.
# initial dihedral
ini = geometry.calc_dihedral(mol.geom[ins[0]],
mol.geom[ins[1]],
mol.geom[ins[2]],
mol.geom[ins[3]])[0]
# this is the new dihedral angle after one step
update = geometry.new_ring_dihedrals(mol, ins, step_nr,
total_nr_of_steps)
exp = ini - (ini - final_val) / 10
cal = update[0]
warn = 'Dihedral update is not correct: '
warn += 'expected {}, calculated {}'.format(exp, cal)
self.assertEqual(exp, cal, msg=warn)
def testAll(self):
data = {
'CC':[1,0],
'CCC':[2,0],
'CCCC':[3,1],
'C=C':[0,0],
'C=CC':[1,0],
'C=C[CH2]':[0,0],
'CC=C[CH2]':[1,0],
'C1CCCC1':[0,0],
'CO':[1,0],
'C=CO':[1,1],
}
for name in data:
par = Parameters()
qc = QuantumChemistry(par)
mol = StationaryPoint(name,0,1,smiles = name)
mol.characterize()
hir_exp = data[name][0]
conf_exp = data[name][1]
hir_calc = len(mol.dihed)
conf_calc = len(mol.conf_dihed)
self.assertEqual(hir_exp ,hir_calc ,name + ': HIR, expected: {}, calculated: {}'.format(hir_exp,hir_calc))
self.assertEqual(conf_exp ,conf_calc ,name + ': CONF, expected: {}, calculated: {}'.format(conf_exp,conf_calc))
def testAll(self):
with open('symmetry_data.json') as f:
data = json.load(f)
messages = [
'Expected external symmetry: {}, calculated: {}',
'Expected internal symmetry: {}, calculated: {}',
'Expected number of single events symmetry: {}, calculated: {}',
]
for name in data:
par = Parameters()
qc = QuantumChemistry(par)
mol = StationaryPoint(name, 0, 1, smiles=name)
mol.characterize()
kinbot.symmetry.calculate_symmetry(mol)
sigma_int = 1
for row in mol.sigma_int:
for at in row:
sigma_int *= at
calc = [mol.sigma_ext, sigma_int, mol.nopt]
for i in range(3):
cal = calc[i]
exp = data[name]['expected_values'][i]
self.assertEqual(exp, cal,
name + ': ' + messages[i].format(exp, cal))
def main():
smi = '[CH2]CC'
mult = 1
charge = 0
mol = StationaryPoint('well0', charge, mult, smiles=smi)
mol.characterize()
reactions = generate_all_product_bond_matrices(mol)
print(len(reactions))
def test_hir(self):
for rotor in range(len(self.species.dihed)):
for ai in range(self.nrotation):
success = None
if self.hir_status[rotor][ai] == -1:
if self.species.wellorts:
job = 'hir/' + self.species.name + '_hir_' + str(
rotor) + '_' + str(ai).zfill(2)
else:
job = 'hir/' + str(
self.species.chemid) + '_hir_' + str(
rotor) + '_' + str(ai).zfill(2)
err, geom = self.qc.get_qc_geom(job, self.species.natom)
if err == 1: # still running
continue
elif err == -1: # failed
success = -1
else:
# check if all the bond lenghts are within
# 15% or the original bond lengths
temp = StationaryPoint('temp',
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=geom)
temp.bond_mx()
if geometry.equal_geom(self.species, temp, 0.15):
err, energy = self.qc.get_qc_energy(job)
if ai == 0:
success = 1
# cut off barriers above 20 kcal/mol to prevent the Fourier fit to oscillate
elif (energy - self.hir_energies[rotor][0]
) < 20. / constants.AUtoKCAL:
success = 1
else:
success = -1
else:
success = -1
if success == 1:
err, energy = self.qc.get_qc_energy(job)
self.hir_status[rotor][ai] = 0
self.hir_energies[rotor][ai] = energy
self.hir_geoms[rotor][ai] = geom
elif success == -1:
logging.warning(
"Hindered rotor optimization not successful for {}".
format(job))
self.hir_status[rotor][ai] = 1
self.hir_energies[rotor][ai] = -1
self.hir_geoms[rotor][ai] = geom
return 0
def testAll(self):
with open('multimolecular_data.json') as f:
data = json.load(f)
for name in data:
print name
par = Parameters()
qc = QuantumChemistry(par)
structure = data[name]['structure']
mol = StationaryPoint(name,0,1,structure = structure)
mol.characterize()
mols = mol.start_multi_molecular()
calculated = len(mols)
expected = data[name]['expected_value']
self.assertEqual(calculated,expected, name + ': expected: {}, calculated: {}'.format(expected,calculated))
def testAll(self):
with open('multimolecular_data.json') as f:
data = json.load(f)
for name in data:
print(name)
par = Parameters()
qc = QuantumChemistry(par)
structure = data[name]['structure']
mol = StationaryPoint(name,0,1,structure = structure)
mol.characterize()
mols = mol.start_multi_molecular()
calculated = len(mols)
expected = data[name]['expected_value']
self.assertEqual(calculated,expected, name + ': expected: {}, calculated: {}'.format(expected,calculated))
def create_geometries(self):
"""
Cut the species in two parts and save the geometries of both parts into
new stationary point objects
"""
# copy the reactant stationary point
atom = copy.deepcopy(self.species.atom)
geom = copy.deepcopy(self.species.geom)
temp = StationaryPoint('temp', self.species.charge,
self.species.mult, atom=atom, geom=geom)
temp.characterize()
# set the bond order of the breaking bond to 0
temp.bond[self.bond[0]][self.bond[1]] = 0
temp.bond[self.bond[1]][self.bond[0]] = 0
self.products, maps = temp.start_multi_molecular()
def create_geometries(self):
"""
Cut the species in two parts and save the geometries of both parts into
new stationary point objects
"""
# copy the reactant stationary point
atom = copy.deepcopy(self.species.atom)
geom = copy.deepcopy(self.species.geom)
temp = StationaryPoint('temp', self.species.charge,
self.species.mult, atom=atom, geom=geom)
temp.characterize()
# set the bond order of the breaking bond to 0
temp.bond[self.bond[0]][self.bond[1]] = 0
temp.bond[self.bond[1]][self.bond[0]] = 0
self.products, maps = temp.start_multi_molecular()
def testAll(self):
data = {"C1=CC=CC=C1":2,
"C1=CC=C(C)C=C1":2,
"C=C[CH2]":2,
"C=C=C":1,
"C#C[CH2]":2,
"S=S":1,
"O=S=C":1,
"O=S(C)[CH2]":3,
"C1CC=CC=C1":1
}
for name in data:
par = Parameters()
qc = QuantumChemistry(par)
mol = StationaryPoint(name,0,1,smiles = name)
mol.characterize()
cal = len(mol.bonds)
exp = data[name]
self.assertEqual(exp ,cal ,name + ': expected: {}, calculated: {}'.format(exp,cal))
def testBondChangeEthane(self):
"""
The generation of a longer C-C bond in ethane
"""
if not os.path.exists('conf/'):
os.mkdir('conf/')
par = Parameters()
qc = QuantumChemistry(par)
smi = 'CC'
mol = StationaryPoint(smi, 0, 1, smiles=smi)
mol.characterize()
changes = [
[0, 1, 1.8],
]
name = 'ethane_bond_length_test'
success, new_geom = kinbot.modify_geom.modify_coordinates(
mol,
name,
mol.geom,
changes,
mol.bond,
write_files=self.write_files)
def testNewBondLength(self):
"""
Test the calculation of new bond length necessary for the update
"""
smi = 'CCCC'
mol = StationaryPoint(smi, 0, 1, smiles=smi)
mol.characterize()
ati = 0
atj = 1
step_nr = 1 # This corresponds to the first bond length update
# there are 12 steps done in total, this means that the bond
# length should be changed by 1/10 of the total change
total_nr_of_steps = 10
# final bond length we are shooting for after 10 updates
final_val = 2.0
# initial bond length
ini = np.linalg.norm(mol.geom[ati] - mol.geom[atj])
# this is the new bond length one step
cal = geometry.new_bond_length(mol, ati, atj, step_nr,
total_nr_of_steps, final_val)
exp = ini + (final_val - ini) / 10
warn = 'Dihedral update is not correct: '
warn += 'expected {}, calculated {}'.format(exp, cal)
self.assertEqual(exp, cal, msg=warn)
def testAll(self):
with open('symmetry_data.json') as f:
data = json.load(f)
messages = [
'Expected external symmetry: {}, calculated: {}',
'Expected internal symmetry: {}, calculated: {}',
'Expected number of single events symmetry: {}, calculated: {}',]
for name in data:
par = Parameters()
qc = QuantumChemistry(par)
mol = StationaryPoint(name,0,1,smiles = name)
mol.characterize()
kinbot.symmetry.calculate_symmetry(mol)
sigma_int = 1
for row in mol.sigma_int:
for at in row:
sigma_int *= at
calc = [mol.sigma_ext, sigma_int, mol.nopt]
for i in range(3):
cal = calc[i]
exp = data[name]['expected_values'][i]
self.assertEqual(exp ,cal ,name + ': ' + messages[i].format(exp,cal))
def test_ring_conformer(self, index):
"""
Test whether a conformer has the same bond matrix as the original structure.
Returns the conformer object and -1 if not yet finished, 0 if same, and 1 if not.
"""
job = self.get_job_name(index, cyc=1)
status, geom = self.qc.get_qc_geom(job, self.species.natom)
if status == 1: # still running
return np.zeros((self.species.natom, 3)), -1
elif status == -1: # conformer search failed
logging.debug(
'Conformer search failed for scan point {}'.format(job))
return np.zeros((self.species.natom, 3)), 1
else:
if self.start_ring_conformer_search(index, geom):
logging.debug(
'Running the next dihedral for conformer {}'.format(job))
return geom, -1
else:
# check if all the bond lenghts are withing 10% or the original bond lengths
temp = StationaryPoint('temp',
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=geom)
temp.bond_mx()
if geometry.equal_geom(self.species, temp, 0.10):
logging.debug(
'Successfullly finished conformer {}'.format(job))
return geom, 0
else:
logging.debug(
'Conformer too far from original structure {}'.format(
job))
return np.zeros((self.species.natom, 3)), 1
def generate(self):
"""
Creates the input for each reaction, runs them, and tests for success.
If successful, it creates the barrier and product objects.
It also then does the conformational search, and finally, the hindered rotor scans.
To make the code the most efficient, all of these happen in parallel, in a sense that
the jobs are not waiting for each other. E.g., one reaction can still be in the stage
of TS search, while the other can be already at the hindered rotor scan. This way,
all cores are occupied efficiently.
The switching between the various stages are done via the reac_ts_done variable.
0: initiate the TS search
1: check barrier height and errors in TS, and initiates normal mode displacement test, start the irc calculations
2: submit product optimization
3: submit the frequency calculation
4: do the optimization of the ts and the products
5: follow up on the optimizations
6: finalize calculations, check for wrong number of negative frequencies
If at any times the calculation fails, reac_ts_done is set to -999.
If all steps are successful, reac_ts_done is set to -1.
"""
deleted = []
if len(self.species.reac_inst) > 0:
alldone = 1
else:
alldone = 0
# status to see of kinbot needs to wait for the product optimizations
# from another kinbot run, to avoid duplication of calculations
products_waiting_status = [[] for i in self.species.reac_inst]
count = 0
for i in self.species.reac_inst:
count = count + 1
frag_unique = []
while alldone:
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
# START REACTION SEARCH
if self.species.reac_ts_done[
index] == 0 and self.species.reac_step[index] == 0:
# verify after restart if search has failed in previous kinbot run
status = self.qc.check_qc(obj.instance_name)
if status == 'error' or status == 'killed':
logging.info(
'\tRxn search failed (error or killed) for {}'.
format(obj.instance_name))
self.species.reac_ts_done[index] = -999
if self.species.reac_type[
index] == 'hom_sci' and self.species.reac_ts_done[
index] == 0: # no matter what, set to 2
# somewhat messy manipulation to force the new bond matrix for hom_sci
obj.products = copy.deepcopy(obj.species)
obj.products.bonds = copy.deepcopy(
obj.species.bond) # plural/non plural!
obj.products.bonds[obj.instance[0]][
obj.instance[1]] = 0 # delete bond
obj.products.bonds[obj.instance[1]][
obj.instance[0]] = 0 # delete bond
obj.products.bond[obj.instance[0]][
obj.instance[1]] = 0 # delete bond
obj.products.bond[obj.instance[1]][
obj.instance[0]] = 0 # delete bond
obj.product_bonds = copy.deepcopy(
obj.species.bonds[0]) # the first resonance structure
obj.product_bonds[obj.instance[0]][
obj.instance[1]] = 0 # delete bond
obj.product_bonds[obj.instance[1]][
obj.instance[0]] = 0 # delete bond
self.species.reac_ts_done[index] = 2
if self.species.reac_ts_done[
index] == 0: # ts search is ongoing
if obj.scan == 0: # don't do a scan of a bond
if self.species.reac_step[index] == obj.max_step + 1:
status, freq = self.qc.get_qc_freq(
obj.instance_name, self.species.natom)
if status == 0 and freq[0] < 0. and freq[1] > 0.:
self.species.reac_ts_done[index] = 1
elif status == 0 and freq[0] > 0.:
logging.info(
'\tRxn search failed for {}, no imaginary freq.'
.format(obj.instance_name))
self.species.reac_ts_done[index] = -999
elif status == 0 and freq[1] < 0.:
logging.info(
'\tRxn search failed for {}, more than one imaginary freq.'
.format(obj.instance_name))
self.species.reac_ts_done[index] = -999
elif status == -1:
logging.info(
'\tRxn search failed for {}'.format(
obj.instance_name))
self.species.reac_ts_done[index] = -999
else:
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj, self.species.reac_step[index],
self.par['qc_command'])
else: # do a bond scan
if self.species.reac_step[
index] == self.par['scan_step'] + 1:
status, freq = self.qc.get_qc_freq(
obj.instance_name, self.species.natom)
if status == 0 and freq[0] < 0. and freq[1] > 0.:
self.species.reac_ts_done[index] = 1
elif status == 0 and freq[0] > 0.:
logging.info(
'\tRxn search failed for {}, no imaginary freq.'
.format(obj.instance_name))
self.species.reac_ts_done[index] = -999
elif status == 0 and freq[1] < 0.:
logging.info(
'\tRxn search failed for {}, more than one imaginary freq.'
.format(obj.instance_name))
self.species.reac_ts_done[index] = -999
elif status == -1:
logging.info(
'\tRxn search using scan failed for {} in TS optimization stage.'
.format(obj.instance_name))
self.species.reac_ts_done[index] = -999
else:
if self.species.reac_step[index] == 0:
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj, self.species.reac_step[index],
self.par['qc_command'])
elif self.species.reac_step[index] < self.par[
'scan_step']:
status = self.qc.check_qc(obj.instance_name)
if status == 'error' or status == 'killed':
logging.info(
'\tRxn search using scan failed for {} in step {}'
.format(obj.instance_name,
self.species.reac_step[index]))
self.species.reac_ts_done[index] = -999
else:
err, energy = self.qc.get_qc_energy(
obj.instance_name)
if err == 0:
self.species.reac_scan_energy[
index].append(energy)
# need at least 3 points for a maximum
if len(self.species.
reac_scan_energy[index]) >= 3:
ediff = np.diff(
self.species.
reac_scan_energy[index])
if ediff[-1] < 0 and ediff[
-2] > 0: # max
self.species.reac_step[
index] = self.par[
'scan_step'] # ending the scan
if len(ediff) >= 3:
if 10. * (
ediff[-3] / ediff[-2]
) < (
ediff[-2] / ediff[-1]
): # sudden change in slope
self.species.reac_step[
index] = self.par[
'scan_step'] # ending the scan
logging.info(
'\tCurrent raw scan energy for {}: {} Hartree.'
.format(
obj.instance_name,
self.species.
reac_scan_energy[index][-1]))
# scan continues, and if reached scan_step, then goes for full optimization
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj,
self.species.reac_step[index],
self.par['qc_command'])
else: # the last step was reached, and no max or inflection was found
logging.info(
'\tRxn search using scan failed for {}, no saddle guess found.'
.format(obj.instance_name))
db = connect('{}/kinbot.db'.format(
os.getcwd()))
rows = db.select(name=obj.instance_name)
for row in self.reversed_iterator(rows):
row.data['status'] = 'error'
break # only write error to the last calculation
self.species.reac_ts_done[index] = -999
elif self.species.reac_ts_done[index] == 1:
status = self.qc.check_qc(obj.instance_name)
if status == 'running':
continue
elif status == 'error':
logging.info(
'\tRxn search failed (gaussian error) for {}'.
format(obj.instance_name))
self.species.reac_ts_done[index] = -999
else:
# check the barrier height:
ts_energy = self.qc.get_qc_energy(obj.instance_name)[1]
ts_zpe = self.qc.get_qc_zpe(obj.instance_name)[1]
if self.species.reac_type[
index] == 'R_Addition_MultipleBond':
ending = 'well_mp2'
elif self.species.reac_type[
index] == 'barrierless_saddle':
ending = 'well_bls'
else:
ending = 'well'
sp_energy = self.qc.get_qc_energy('{}_{}'.format(
str(self.species.chemid), ending))[1]
sp_zpe = self.qc.get_qc_zpe('{}_{}'.format(
str(self.species.chemid), ending))[1]
try:
barrier = (ts_energy + ts_zpe - sp_energy -
sp_zpe) * constants.AUtoKCAL
except TypeError:
logging.error(
f'Faulty calculations, check or delete files for {obj.instance_name}.'
)
sys.exit(-1)
if barrier > self.par['barrier_threshold']:
logging.info(
'\tRxn barrier too high ({0:.2f} kcal/mol) for {1}'
.format(barrier, obj.instance_name))
self.species.reac_ts_done[index] = -999
else:
obj.irc = IRC(
obj, self.par
) # TODO: this doesn't seem like a good design
irc_status = obj.irc.check_irc()
if 0 in irc_status:
logging.info(
'\tRxn barrier is {0:.2f} kcal/mol for {1}'
.format(barrier, obj.instance_name))
# No IRC started yet, start the IRC now
logging.info(
'\tStarting IRC calculations for {}'.
format(obj.instance_name))
obj.irc.do_irc_calculations()
elif irc_status[0] == 'running' or irc_status[
1] == 'running':
continue
else:
# IRC's have successfully finished, have an error or were killed, in any case
# read the geometries and try to make products out of them
# verify which of the ircs leads back to the reactant, if any
prod = obj.irc.irc2stationary_pt()
if prod == 0:
logging.info(
'\t\tNo product found for {}'.format(
obj.instance_name))
self.species.reac_ts_done[index] = -999
else:
obj.products = prod
obj.product_bonds = prod.bond
self.species.reac_ts_done[index] = 2
elif self.species.reac_ts_done[index] == 2:
if len(products_waiting_status[index]) == 0:
# identify bimolecular products and wells
fragments, maps = obj.products.start_multi_molecular()
obj.products = []
a = []
for frag in fragments:
a.append(frag)
if len(frag_unique) == 0:
frag_unique.append(frag)
elif len(frag_unique) > 0:
new = 1
for fragb in frag_unique:
if frag.chemid == fragb.chemid:
e, geom2 = self.qc.get_qc_geom(
str(fragb.chemid) + '_well',
fragb.natom)
if e == 0:
a.pop()
frag = fragb
a.append(frag)
new = 0
break
if new:
frag_unique.append(frag)
obj.products_final = []
for frag in a:
self.qc.qc_opt(frag, frag.geom)
e, geom2 = self.qc.get_qc_geom(
str(frag.chemid) + '_well', frag.natom)
obj.products_final.append(frag)
# check products make sure they are the same
for i, st_pt_i in enumerate(obj.products_final):
for j, st_pt_j in enumerate(obj.products_final):
if st_pt_i.chemid == st_pt_j.chemid and i < j:
obj.products_final[j] = obj.products_final[
i]
# print products generated by IRC
products = []
for i, st_pt in enumerate(obj.products_final):
products.append(st_pt.chemid)
products.extend([' ', ' ', ' '])
logging.info(
'\tReaction {} leads to products {} {} {}'.format(
obj.instance_name, products[0], products[1],
products[2]))
for i, st_pt in enumerate(obj.products_final):
chemid = st_pt.chemid
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
logging.info(
'\tProduct optimization failed for {}, product {}'
.format(obj.instance_name, st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
elif e != 0:
err = -1
else:
e2, st_pt.energy = self.qc.get_qc_energy(
str(st_pt.chemid) + '_well')
e2, st_pt.zpe = self.qc.get_qc_zpe(
str(st_pt.chemid) + '_well')
st_pt.characterize(
dimer=0
) # not allowed to use the dimer option here
if chemid != st_pt.chemid:
obj.products_final.pop(i)
newfrags, newmaps = st_pt.start_multi_molecular(
) # newfrags is list of stpt obj
products_waiting_status[index] = [
0 for frag in newfrags
]
frag_chemid = []
for i, newfr in enumerate(newfrags):
newfr.characterize(dimer=0)
for prod in frag_unique:
if newfr.chemid == prod.chemid:
newfrags.pop(i)
newfr = prod
j = i - 1
newfrags.insert(j, newfr)
j = i - 1
obj.products_final.insert(j, newfr)
self.qc.qc_opt(newfr, newfr.geom, 0)
frag_chemid.append(newfr.chemid)
if len(frag_chemid) == 1:
frag_chemid.append(" ")
for i, frag in enumerate(newfrags):
products_waiting_status[index][i] = 1
logging.info(
'\ta) Product optimized to other structure for {}'
', product {} to {} {}'.format(
obj.instance_name, chemid,
frag_chemid[0], frag_chemid[1]))
obj.products = []
for prod in obj.products_final:
obj.products.append(prod)
obj.products_final = []
if all([pi == 1 for pi in products_waiting_status[index]]):
self.species.reac_ts_done[index] = 3
elif self.species.reac_ts_done[index] == 3:
# wait for the optimization to finish
# if two st_pt are the same in the products, we make them exactly identical otherwise
# the different ordering of the atoms causes the chemid of the second to be seemingly wrong
for i, st_pt_i in enumerate(obj.products):
for j, st_pt_j in enumerate(obj.products):
if st_pt_i.chemid == st_pt_j.chemid and i < j:
obj.products[j] = obj.products[i]
err = 0
for st_pt in obj.products:
chemid = st_pt.chemid
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
logging.info(
'\tProduct optimization failed for {}, product {}'
.format(obj.instance_name, st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
elif e != 0:
err = -1
else:
e2, st_pt.energy = self.qc.get_qc_energy(
str(st_pt.chemid) + '_well')
e2, st_pt.zpe = self.qc.get_qc_zpe(
str(st_pt.chemid) + '_well')
st_pt.characterize(
dimer=0
) # not allowed to use the dimer option here
if chemid != st_pt.chemid:
# product was optimized to another structure, give warning but don't remove reaction
logging.info(
'\tb) Product optimized to other structure for {}'
', product {} to {}'.format(
obj.instance_name, chemid,
st_pt.chemid))
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
err = -1
if err == 0:
self.species.reac_ts_done[index] = 4
elif self.species.reac_ts_done[index] == 4:
# Do the TS and product optimization
# make a stationary point object of the ts
bond_mx = np.zeros(
(self.species.natom, self.species.natom), dtype=int)
for i in range(self.species.natom):
for j in range(self.species.natom):
bond_mx[i][j] = max(self.species.bond[i][j],
obj.product_bonds[i][j])
if self.species.reac_type[index] != 'hom_sci':
err, geom = self.qc.get_qc_geom(
obj.instance_name, self.species.natom)
ts = StationaryPoint(obj.instance_name,
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=geom,
wellorts=1)
err, ts.energy = self.qc.get_qc_energy(
obj.instance_name)
err, ts.zpe = self.qc.get_qc_zpe(
obj.instance_name) # NEW STOPS HERE
err, ts.freq = self.qc.get_qc_freq(
obj.instance_name, self.species.natom)
ts.distance_mx()
ts.bond = bond_mx
ts.find_cycle()
ts.find_conf_dihedral()
obj.ts = ts
# do the ts optimization
obj.ts_opt = Optimize(obj.ts, self.par, self.qc)
obj.ts_opt.do_optimization()
else:
obj.ts = copy.deepcopy(
obj.species
) # the TS will be for now the species itself
obj.ts.wellorts = 1
# do the products optimizations
for st_pt in obj.products:
# do the products optimizations
# check for products of other reactions that are the same as this product
# in the case such products are found, use the same Optimize object for both
for i, inst_i in enumerate(self.species.reac_inst):
new = 1
if not i == index:
obj_i = self.species.reac_obj[i]
if self.species.reac_ts_done[i] > 3:
for j, st_pt_i in enumerate(
obj_i.products):
if st_pt_i.chemid == st_pt.chemid:
if len(obj_i.prod_opt) > j:
prod_opt = obj_i.prod_opt[j]
new = 0
break
if new:
prod_opt = Optimize(st_pt, self.par, self.qc)
prod_opt.do_optimization()
obj.prod_opt.append(prod_opt)
for st_pt in obj.products:
# section where comparing products in same reaction occurs
if len(obj.prod_opt) > 0:
for j, st_pt_opt in enumerate(obj.prod_opt):
if st_pt.chemid == st_pt_opt.species.chemid:
if len(obj.prod_opt) > j:
prod_opt = obj.prod_opt[j]
break
self.species.reac_ts_done[index] = 5
elif self.species.reac_ts_done[index] == 5:
# check up on the TS and product optimizations
opts_done = 1
fails = 0
# check if ts is done
if self.species.reac_type[index] != 'hom_sci':
if not obj.ts_opt.shir == 1: # last stage in optimize
opts_done = 0
obj.ts_opt.do_optimization()
if obj.ts_opt.shigh == -999:
logging.info(
"Reaction {} ts_opt_shigh failure".format(
obj.instance_name))
fails = 1
for pr_opt in obj.prod_opt:
if not pr_opt.shir == 1:
opts_done = 0
pr_opt.do_optimization()
if pr_opt.shigh == -999:
logging.info(
"Reaction {} pr_opt_shigh failure".format(
obj.instance_name))
fails = 1
break
if fails:
self.species.reac_ts_done[index] = -999
elif opts_done:
self.species.reac_ts_done[index] = 6
elif self.species.reac_ts_done[index] == 6:
# Finilize the calculations
# continue to PES search in case a new well was found
if self.par['pes']:
# verify if product is monomolecular, and if it is new
if len(obj.products) == 1:
st_pt = obj.prod_opt[0].species
chemid = st_pt.chemid
new_barrier_threshold = self.par[
'barrier_threshold'] - (
st_pt.energy -
self.species.energy) * constants.AUtoKCAL
dirwell = os.path.dirname(os.getcwd())
jobs = open(dirwell + '/chemids',
'r').read().split('\n')
jobs = [ji for ji in jobs]
if not str(chemid) in jobs:
# this well is new, add it to the jobs
while 1:
try:
# try to open the file and write to it
pes.write_input(
self.inp, obj.products[0],
new_barrier_threshold, dirwell)
with open(dirwell + '/chemids',
'a') as f:
f.write('{}\n'.format(chemid))
break
except IOError:
# wait a second and try again
time.sleep(1)
pass
# copy the files of the species to an upper directory
frags = obj.products
for frag in frags:
filecopying.copy_to_database_folder(
self.species.chemid, frag.chemid, self.qc)
# check for wrong number of negative frequencies
neg_freq = 0
for st_pt in obj.products:
if any([fi < 0. for fi in st_pt.reduced_freqs]):
neg_freq = 1
if any([fi < 0. for fi in obj.ts.reduced_freqs[1:]]):
neg_freq = 1
if neg_freq:
logging.info('\tFound negative frequency for ' +
obj.instance_name)
self.species.reac_ts_done[index] = -999
else:
# the reaction search is finished
self.species.reac_ts_done[
index] = -1 # this is the success code
# write a temporary pes input file
# remove old xval and im_extent files
if os.path.exists('{}_xval.txt'.format(
self.species.chemid)):
os.remove('{}_xval.txt'.format(
self.species.chemid))
if os.path.exists('{}_im_extent.txt'.format(
self.species.chemid)):
os.remove('{}_im_extent.txt'.format(
self.species.chemid))
postprocess.createPESViewerInput(
self.species, self.qc, self.par)
elif self.species.reac_ts_done[index] == -999:
if self.par['delete_intermediate_files'] == 1:
if not self.species.reac_obj[
index].instance_name in deleted:
self.delete_files(
self.species.reac_obj[index].instance_name)
deleted.append(
self.species.reac_obj[index].instance_name)
alldone = 1
for index, instance in enumerate(self.species.reac_inst):
if any(self.species.reac_ts_done[i] >= 0
for i in range(len(self.species.reac_inst))):
alldone = 1
break
else:
alldone = 0
# write a small summary while running
with open('kinbot_monitor.out', 'w') as f_out:
for index, instance in enumerate(self.species.reac_inst):
if self.species.reac_ts_done[index] == -1:
prodstring = []
for pp in self.species.reac_obj[index].products:
prodstring.append(str(pp.chemid))
f_out.write('{}\t{}\t{}\t{}\n'.format(
self.species.reac_ts_done[index],
self.species.reac_step[index],
self.species.reac_obj[index].instance_name,
' '.join(prodstring)))
else:
f_out.write('{}\t{}\t{}\n'.format(
self.species.reac_ts_done[index],
self.species.reac_step[index],
self.species.reac_obj[index].instance_name))
time.sleep(1)
# Create molpro file for the BLS products
for index, instance in enumerate(self.species.reac_inst):
if self.species.reac_type[index] == 'barrierless_saddle':
obj = self.species.reac_obj[index]
if len(obj.products) == 2:
blsprodatom = np.append(obj.products[0].atom,
obj.products[1].atom)
# vector connecting the centers
# assumed: the two structures are naturally separated at the end of the IRC
x0 = 0
y0 = 0
z0 = 0
for g in obj.products[0].geom:
x0 += g[0]
y0 += g[1]
z0 += g[1]
x0 /= obj.products[0].natom
y0 /= obj.products[0].natom
z0 /= obj.products[0].natom
x1 = 0
y1 = 0
z1 = 0
for g in obj.products[1].geom:
x1 += g[0]
y1 += g[1]
z1 += g[1]
x1 /= obj.products[1].natom
y1 /= obj.products[1].natom
z1 /= obj.products[1].natom
center_vec = [x1 - x0, y1 - y0, z1 - z0]
blsprodgeom = np.append(obj.products[0].geom,
obj.products[1].geom)
blsprodgeom = np.reshape(blsprodgeom, (-1, 3))
bps = list(zip(blsprodatom, blsprodgeom))
bps1 = [item for sublist in bps for item in sublist]
blsprodstruct = [
item for sublist in bps1 for item in sublist
]
blsprod = StationaryPoint('blsprod',
self.par['charge'],
self.par['mult'],
structure=blsprodstruct)
blsprod.characterize()
molp = Molpro(blsprod, self.par)
molpname = '{}_prod'.format(obj.instance_name)
molp.create_molpro_input(bls=1,
name=molpname,
shift_vec=center_vec,
natom1=len(obj.products[1].geom))
molp.create_molpro_submit(name=molpname)
s = []
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
# Write a summary on the combinatorial exploration
if 'combinatorial' in obj.instance_name:
s.append('NAME\t' + obj.instance_name)
# Write the bonds that were broken and formed
s.append('BROKEN_BONDS\t' +
'\t'.join('[{}, {}]'.format(re[0], re[1])
for re in obj.reac))
s.append('FORMED_BONDS\t' +
'\t'.join('[{}, {}]'.format(pr[0], pr[1])
for pr in obj.prod))
# Populate the ts_bond_lengths dict with the values
# of this reaction
if self.species.reac_ts_done[index] == -1:
for i in range(self.species.natom - 1):
for j in range(i + 1, self.species.natom):
if self.species.bond[i][j] != obj.product_bonds[i][
j]:
if (self.species.bond[i][j] == 0
or obj.product_bonds[i][j] == 0):
syms = []
syms.append(self.species.atom[i])
syms.append(self.species.atom[j])
syms = ''.join(sorted(syms))
dist = np.linalg.norm(obj.ts.geom[i] -
obj.ts.geom[j])
s.append('TS_BOND_LENGTHS\t{}\t{}'.format(
syms, dist))
# write the expected inchis
s.append('EXPECTED_INCHIS\t' +
'\t'.join(inchi for inchi in obj.prod_inchi))
# get the inchis the reaction found
if self.species.reac_ts_done[index] == -1:
inchis = obj.get_final_inchis()
s.append('FOUND_INCHIS\t' + '\t'.join(inchis))
s.append('\n')
with open('combinatorial.txt', 'w') as f:
f.write('\n'.join(s) + '\n')
logging.info("Reaction generation done!")
def main():
try:
input_file = sys.argv[1]
except IndexError:
print('To use the pes script, supply one argument being the input file!')
sys.exit(-1)
# TODO: write information about the arguments
# change this to nice argument parsers with
# dashes etc.
no_kinbot = 0
task = 'all'
names = []
if len(sys.argv) > 2:
if sys.argv[2] == 'no-kinbot':
no_kinbot = 1
if len(sys.argv) > 3:
# possible tasks are:
# 1. all: This is the default showing all pathways
# 2. lowestpath: show the lowest path between the species
# corresponding to the names
# 3. allpaths: show all paths between the species
# corresponding to the names
# 4. wells: show all reactions of one wells
# corresponding to the names
task = sys.argv[3]
names = sys.argv[4:]
# print the license message to the console
print(license_message.message)
# initialize the parameters
par = Parameters(input_file)
# set up the logging environment
logging.basicConfig(filename='pes.log', level=logging.INFO)
logging.info(license_message.message)
msg = 'Starting the PES search at {}'.format(datetime.datetime.now())
logging.info(msg)
if par.par['pes'] and par.par['specific_reaction']:
logging.error('Specific reaction cannot be searched in PES mode.')
return
well0 = StationaryPoint('well0',
par.par['charge'],
par.par['mult'],
smiles=par.par['smiles'],
structure=par.par['structure'])
well0.characterize(par.par['dimer'])
write_input(par, well0, par.par['barrier_threshold'], os.getcwd())
# add the initial well to the chemids
with open('chemids', 'w') as f:
f.write(str(well0.chemid) + '\n')
# create a directory for the L3 single point calculations
# direcotry has the name of the code, e.g., molpro
try:
os.mkdir(par.par['single_point_qc'])
except OSError:
pass
# maximum number of kinbot jobs that run simultaneously
max_running = par.par['simultaneous_kinbot']
# jobs that are running
running = []
# jobs that are finished
finished = []
# list of all jobs
jobs = []
# dict of the pid's for all jobs
pids = {}
while 1:
j = len(jobs)
with open('chemids', 'r') as f:
jobs = f.read().split('\n')
jobs = [ji for ji in jobs if ji != '']
if len(jobs) > j:
logging.info('\tPicked up new jobs: ' + ' '.join(jobs[j:]))
if len(finished) == len(jobs):
break
while (len(running) < max_running and
len(running) + len(finished) < len(jobs)):
# start a new job
job = jobs[len(running) + len(finished)]
pid = 0
if not no_kinbot:
pid = submit_job(job, par) # kinbot is submitted here
else:
get_wells(job)
pids[job] = pid
t = datetime.datetime.now()
logging.info('\tStarted job {} at {}'.format(job, t))
running.append(job)
# check if a thread is done
for job in running:
if not check_status(job, pids[job]):
t = datetime.datetime.now()
logging.info('\tFinished job {} at {}'.format(job, t))
finished.append(job)
if not no_kinbot:
# write a temporary pes file
# remove old xval and im_extent files
try:
os.remove('{}_xval.txt'.format(par.par['title']))
except OSError:
pass
try:
os.remove('{}_im_extent.txt'.format(par.par['title']))
except OSError:
pass
postprocess(par, jobs, task, names)
# remove the finished threads
for job in finished:
if job in running:
running.remove(job)
if not no_kinbot:
# write a summary of what is running and finished
summary_lines = []
summary_lines.append('Total\t\t{}'.format(len(jobs)))
summary_lines.append('Running\t\t{}'.format(len(running)))
summary_lines.append('Finished\t{}'.format(len(finished)))
summary_lines.append('')
summary_lines.append('Running:')
for job in running:
summary_lines.append('\t{}'.format(job))
summary_lines.append('')
summary_lines.append('Finished:')
for job in finished:
summary_lines.append('\t{}'.format(job))
with open('pes_summary.txt', 'w') as f:
f.write('\n'.join(summary_lines))
time.sleep(1)
postprocess(par, jobs, task, names)
# make molpro inputs for all keys above
# place submission script in the directory for offline submission
# read in the molpro energies for the keys in the above three dicts
# for key in newdict.keys():
# print(key)
# if all energies are there
# do something like postprocess, but with new energies
# postprocess_L3(saddle_zpe, well_zpe, prod_zpe, saddle_energy, well_energy, prod_energyi, conn)
# Notify user the search is done
logging.info('PES search done!')
print('PES search done!')
def testFrequencies(self):
"""
Test the frequency calculator based on the hessian
by projecting out the translations, external rotations
and internal rotations
"""
# smiles of the speciesdata
smi = 'CCCC'
# geometry of the species
geom = [[0.91879892, -0.04331223, 0.12849174],
[0.40326992, 1.40563299, 0.15982756],
[0.89480303, 2.27635373, -1.01612317],
[0.37978671, 1.82281002, -2.39283836],
[0.56484506, -0.58104398, -0.75857814],
[0.58013979, -0.60154787, 1.00923604],
[2.01620768, -0.06868878, 0.11831706],
[-0.69710354, 1.40285362, 0.16867873],
[0.71679336, 1.87619548, 1.10252902],
[0.58079101, 3.31524962, -0.84143554],
[1.99516746, 2.28446562, -1.02104071],
[-0.71762425, 1.80824531, -2.41526122],
[0.72083567, 2.50053168, -3.18429864],
[0.73195359, 0.81628395, -2.64600957]]
geom = np.array(geom)
# hessian matrix
hess = [
[
0.557479281000, 0.036110582900, -0.001644922990,
-0.094996623900, 0.033793056500, 0.001227901360,
0.004435637720, -0.010301447300, -0.000714141524,
-0.000402278745, 0.000207775781, 0.000217081305,
-0.079155177000, -0.041443873100, -0.069861527200,
-0.075582477100, -0.041043274800, 0.066112977000,
-0.304832510000, 0.000618737475, 0.001591972160,
-0.011587172100, 0.035782319500, 0.001443997110,
0.003566576500, -0.009402669700, -0.000876604471,
-0.000457712074, -0.002463678640, 0.002595980330,
0.000811779351, -0.001430576500, 0.000032881273,
0.000129362928, -0.000033252298, 0.000111660638,
0.000157802640, -0.000276891050, 0.000074700177,
0.000433511158, -0.000116808738, -0.000311955226
],
[
0.036110582900, 0.481460445000, -0.001497124110,
0.032529383300, -0.168787802000, -0.006867396700,
0.006092748400, -0.023937567300, 0.003730351660,
-0.000262084184, 0.001441647380, 0.000316178114,
-0.041989591900, -0.107986603000, -0.100880979000,
-0.041503733500, -0.111783765000, 0.104729927000,
0.001080107490, -0.048474562500, -0.000241263827,
-0.000265123753, -0.003449223290, -0.000371820596,
0.007618533730, -0.018717902300, 0.000837312337,
-0.000555734072, -0.000078154904, 0.001999374050,
0.000646690960, 0.001260483370, -0.001360417570,
0.000088413780, -0.000085577196, 0.000164034911,
-0.000076331932, -0.000238612455, -0.000622223468,
0.000486138870, -0.000622805294, 0.000064046666
],
[
-0.001644922990, -0.001497124110, 0.575851102000,
0.001964210840, -0.005551021620, -0.083609101600,
-0.009286228620, 0.027728004900, 0.001373639180,
-0.000137246872, 0.000910607647, 0.001051929890,
-0.069899107500, -0.100194460000, -0.221142958000,
0.066308541300, 0.103907569000, -0.219256026000,
0.001583703380, -0.000080359730, -0.050839140900,
-0.000325149456, 0.000352021138, 0.000321544325,
0.010807393200, -0.028918001700, -0.000253208170,
0.001475456680, 0.004079204780, -0.004016767400,
-0.000771004699, -0.000846365220, 0.001019809380,
-0.000285363620, 0.000091087635, -0.000075059137,
-0.000051358279, -0.000192607794, 0.000123367411,
0.000261076583, 0.000211445408, -0.000549130393
],
[
-0.094996623900, 0.032529383300, 0.001964210840,
0.570648686000, 0.013726467600, 0.029803019800,
-0.093894628800, -0.017540296600, 0.025240052300,
0.004430547620, 0.007089239030, -0.008470974800,
0.003985664600, -0.011160808500, -0.000344880024,
0.003352509560, -0.011319168200, -0.000141509338,
-0.012312369900, 0.035615059900, 0.001341132350,
-0.296633947000, -0.002814851200, -0.001857303680,
-0.074252517800, -0.032055118900, -0.063305094900,
0.003016262880, 0.007165737430, -0.008147262180,
-0.013171250400, -0.021050691300, 0.028389638100,
0.000515556167, 0.000833930927, -0.001531483210,
-0.000733957654, -0.000834470892, -0.003543541800,
0.000046069520, -0.000184412716, 0.000603996490
],
[
0.033793056500, -0.168787802000, -0.005551021620,
0.013726467600, 0.471256701000, 0.045550224600,
-0.019055559200, -0.110456357000, 0.036788918500,
0.003690246720, 0.008042833690, -0.005946132620,
0.007450531200, -0.016991805700, 0.000566738577,
0.007662075620, -0.017473385800, -0.003434017560,
-0.000361925575, -0.003081819410, 0.000169600942,
-0.002141608270, -0.048419727000, -0.001543381080,
-0.032001313400, -0.092648760200, -0.091748171900,
-0.012965805500, -0.019490702300, 0.027544257800,
0.000385145313, -0.001607085100, 0.002202751810,
0.000097042391, 0.000342026851, -0.000085156640,
-0.000935724242, -0.000536578498, -0.003946142400,
0.000657370855, -0.000147538049, -0.000568468443
],
[
0.001227901360, -0.006867396700, -0.083609101600,
0.029803019800, 0.045550224600, 0.530185827000, 0.024067639100,
0.038254723800, -0.133557501000, 0.009656895150,
0.018083768400, -0.030339996500, 0.011176997800,
-0.027940521700, 0.000197610447, -0.010718813500,
0.027683119400, 0.001610715360, -0.000005380242,
0.000353950668, -0.000058645732, -0.001385963250,
-0.001490240310, -0.050945021400, -0.064025490200,
-0.091980721200, -0.231554605000, -0.000068702298,
-0.003001675120, 0.001414867140, 0.000375944632,
0.002432713030, -0.002619663450, 0.000967738003,
0.000578480710, 0.002131790000, -0.001665661510,
-0.002059701750, -0.003910908180, 0.000593875240,
0.000403276118, 0.001054632250
],
[
0.004435637720, 0.006092748400, -0.009286228620,
-0.093894628800, -0.019055559200, 0.024067639100,
0.570900497000, -0.032595232800, -0.004232877500,
-0.095277421800, -0.011406581600, -0.030607826800,
0.000043159915, -0.000514838503, 0.000373962854,
-0.000738247952, 0.003637838290, -0.000237140037,
0.000536641625, 0.001192182950, -0.001206594120,
-0.013027642100, -0.020980119300, 0.028551488200,
0.003004879320, 0.005695987090, -0.009218334250,
-0.074383197800, 0.070019040800, 0.012170299900,
-0.296727831000, 0.002527681320, 0.001997681770,
-0.012251640800, -0.011694206300, -0.033682439000,
0.003373175160, 0.003467893310, 0.010833941000, 0.004006619800,
0.003613165550, 0.010476427500
],
[
-0.010301447300, -0.023937567300, 0.027728004900,
-0.017540296600, -0.110456357000, 0.038254723800,
-0.032595232800, 0.550390617000, 0.021569897200,
-0.011111359900, -0.094250354100, -0.029239287500,
-0.000711297419, 0.000972151769, 0.000156692325,
0.001860692500, -0.005293631350, -0.000606360776,
-0.000954457648, 0.002153894160, 0.000019371190,
-0.000397838529, -0.001228430010, 0.002336752460,
0.003859667870, 0.006484974130, -0.010940182100,
0.070740800200, -0.270987312000, -0.037645349500,
0.001896728730, -0.051464188100, -0.000592835086,
0.000107061175, -0.000144181197, -0.000504021609,
0.007980792090, 0.006761052700, 0.020233362800,
-0.012833812400, -0.009000668610, -0.030770768100
],
[
-0.000714141524, 0.003730351660, 0.001373639180,
0.025240052300, 0.036788918500, -0.133557501000,
-0.004232877500, 0.021569897200, 0.451154731000,
-0.032090962200, -0.028197579400, -0.158290659000,
-0.000492084585, -0.000980152816, 0.000034862639,
0.000407801351, -0.002484344690, 0.000853888771,
0.000202037882, -0.000630194064, 0.000364010457,
-0.000276661410, 0.002092844910, -0.002923760270,
0.012375619200, 0.019611685600, -0.024571878400,
0.011914527900, -0.037319661200, -0.053342229400,
0.001505776140, -0.000642112942, -0.047826423400,
0.000374606934, -0.000789448823, -0.002922212160,
-0.010438695000, -0.010818867900, -0.022572722700,
-0.003774999570, -0.001931336020, -0.007773745060
],
[
-0.000402278745, -0.000262084184, -0.000137246872,
0.004430547620, 0.003690246720, 0.009656895150,
-0.095277421800, -0.011111359900, -0.032090962200,
0.558209213000, -0.009125619780, -0.034709539800,
0.000415340383, 0.000358171193, 0.000000905828, 0.000159128422,
0.000009821173, 0.000284856041, 0.000131990037,
-0.000097828362, 0.000069311231, 0.000786266725,
0.000375904422, 0.001427105050, -0.000456009865,
-0.001763774920, 0.003115461590, 0.003579063430,
0.003594264790, 0.008763686330, -0.011547639900,
-0.011824677300, -0.033753097500, -0.305000167000,
-0.001072859680, -0.000565352953, -0.076013943900,
-0.051443542800, 0.058887756100, -0.079014088600,
0.078673338600, 0.019050222000
],
[
0.000207775781, 0.001441647380, 0.000910607647, 0.007089239030,
0.008042833690, 0.018083768400, -0.011406581600,
-0.094250354100, -0.028197579400, -0.009125619780,
0.566853551000, -0.028314924300, -0.000405813331,
-0.000316988707, 0.000087265018, 0.000073046030,
-0.000135428465, -0.000227947707, 0.000240096907,
-0.000003210597, 0.000052913364, 0.000562715807,
0.000386702658, -0.000586865840, -0.001249462100,
-0.001984311450, 0.004661147410, -0.012569365100,
-0.009659129560, -0.031620324000, 0.000388942723,
-0.000027569785, -0.000685633791, -0.001195716590,
-0.050831902500, 0.000607484566, -0.051498752000,
-0.151306241000, 0.115957274000, 0.078889494300,
-0.268209599000, -0.050727185300
],
[
0.000217081305, 0.000316178114, 0.001051929890,
-0.008470974800, -0.005946132620, -0.030339996500,
-0.030607826800, -0.029239287500, -0.158290659000,
-0.034709539800, -0.028314924300, 0.489796505000,
-0.000391829054, 0.000142067560, -0.000688267160,
0.000058546643, -0.000657571583, 0.000021899914,
-0.000169145634, 0.000141233266, -0.000156069524,
-0.000870684066, -0.001083763300, 0.001824089140,
0.000966571121, 0.002585026930, -0.002110201450,
-0.004138394740, -0.001918643950, -0.009243037780,
0.000150249840, -0.001426379210, -0.003164852780,
-0.000834451580, 0.000361231775, -0.048518990600,
0.059364188600, 0.116743826000, -0.179560342000,
0.019436209100, -0.051702861200, -0.060622006300
],
[
-0.079155177000, -0.041989591900, -0.069899107500,
0.003985664600, 0.007450531200, 0.011176997800, 0.000043159915,
-0.000711297419, -0.000492084585, 0.000415340383,
-0.000405813331, -0.000391829054, 0.082170568700,
0.043883239500, 0.078044112600, 0.003495229520, 0.005401929110,
0.009954809490, -0.011350612800, -0.016458449400,
-0.029453532500, 0.000665737135, -0.000625010895,
0.000511858440, 0.000059291070, 0.003007766240, 0.000159750828,
-0.000060170797, -0.000108422372, 0.000205866966,
0.000026998545, -0.000052468999, -0.000060908234,
-0.000223489041, 0.000140719611, -0.000038210872,
-0.000144869281, 0.000150427844, 0.000065183444,
0.000072328995, 0.000316440754, 0.000217093184
],
[
-0.041443873100, -0.107986603000, -0.100194460000,
-0.011160808500, -0.016991805700, -0.027940521700,
-0.000514838503, 0.000972151769, -0.000980152816,
0.000358171193, -0.000316988707, 0.000142067560,
0.043883239500, 0.119840850000, 0.111814261000, 0.005906654280,
0.008066891770, 0.014503839500, 0.000290968064,
-0.000150240230, 0.000002063185, 0.000111922593,
0.001514337600, 0.000276003926, 0.002702457800,
-0.005775080730, 0.000445401249, 0.000111932829,
0.000347388012, 0.000177052371, -0.000006786294,
-0.000145911482, 0.000108411307, 0.000058268378,
-0.000061770157, 0.000096427007, -0.000010851744,
0.000535119260, 0.000099909733, -0.000286456542,
0.000151661591, 0.001449697830
],
[
-0.069861527200, -0.100880979000, -0.221142958000,
-0.000344880024, 0.000566738577, 0.000197610447,
0.000373962854, 0.000156692325, 0.000034862639, 0.000000905828,
0.000087265018, -0.000688267160, 0.078044112600,
0.111814261000, 0.242817300000, -0.010493946400,
-0.013936543100, -0.022850703000, 0.001401288830,
0.000811652521, 0.000387534637, 0.000611035395,
-0.000326880526, 0.000202559149, 0.000448151560,
0.000430071969, 0.001316161050, -0.000015506148,
-0.000226147940, 0.000124908040, 0.000053325603,
0.000066745555, -0.000005338059, 0.000029804814,
-0.000043582456, -0.000002800832, 0.000036585620,
0.000161681863, 0.000229922299, -0.000283313413,
0.001319024070, -0.000620791338
],
[
-0.075582477100, -0.041503733500, 0.066308541300,
0.003352509560, 0.007662075620, -0.010718813500,
-0.000738247952, 0.001860692500, 0.000407801351,
0.000159128422, 0.000073046030, 0.000058546643, 0.003495229520,
0.005906654280, -0.010493946400, 0.078647598400,
0.043929663400, -0.073931943100, -0.010722735000,
-0.017236570000, 0.029041201600, 0.000394715293,
-0.000537710690, -0.000362268343, 0.000241854679,
-0.000788101770, 0.000060932686, 0.000467445735,
0.000365561370, -0.000302900477, 0.000423918964,
0.000300000013, -0.000134663171, 0.000006452519,
-0.000023320384, 0.000023532240, -0.000000863259,
0.000025535986, 0.000019870589, -0.000144529814,
-0.000033792868, 0.000024108483
],
[
-0.041043274800, -0.111783765000, 0.103907569000,
-0.011319168200, -0.017473385800, 0.027683119400,
0.003637838290, -0.005293631350, -0.002484344690,
0.000009821173, -0.000135428465, -0.000657571583,
0.005401929110, 0.008066891770, -0.013936543100,
0.043929663400, 0.124958213000, -0.115530393000,
0.000325281332, -0.000125007354, -0.000129258723,
0.000384143980, 0.001259101570, 0.000001306272,
-0.000870123215, 0.001042362100, -0.000426380057,
-0.000426090514, -0.001300320690, 0.001591298870,
0.000048406439, 0.000287275840, -0.000388354266,
0.000055737630, 0.000018935809, 0.000059096657,
-0.000026671850, 0.000148561268, 0.000149502306,
-0.000107492816, 0.000330197117, 0.000160952609
],
[
0.066112977000, 0.104729927000, -0.219256026000,
-0.000141509338, -0.003434017560, 0.001610715360,
-0.000237140037, -0.000606360776, 0.000853888771,
0.000284856041, -0.000227947707, 0.000021899914,
0.009954809490, 0.014503839500, -0.022850703000,
-0.073931943100, -0.115530393000, 0.238426713000,
-0.001172378760, -0.000473740575, -0.000085975425,
-0.000598006922, 0.000347859264, -0.000022101354,
-0.000104883229, 0.000359811438, 0.000893666466,
0.000047613768, -0.000039210588, -0.000025405264,
0.000008736166, -0.000126420940, -0.000048007771,
-0.000081103354, 0.000034200394, 0.000014479678,
-0.000018292858, 0.000237094498, 0.000030248075,
-0.000123734870, 0.000225358158, 0.000436607680
],
[
-0.304832510000, 0.001080107490, 0.001583703380,
-0.012312369900, -0.000361925575, -0.000005380242,
0.000536641625, -0.000954457648, 0.000202037882,
0.000131990037, 0.000240096907, -0.000169145634,
-0.011350612800, 0.000290968064, 0.001401288830,
-0.010722735000, 0.000325281332, -0.001172378760,
0.335968234000, -0.003796974450, -0.002196966940,
0.001014683130, 0.002301249850, 0.000503015956, 0.000603465592,
0.000253289215, 0.000532380180, 0.000412280046,
-0.000005518885, -0.000002154967, 0.000632261852,
0.000727256027, -0.000699875116, 0.000142228293,
-0.000106601886, 0.000148869356, 0.000006913742,
0.000060554518, -0.000076997589, -0.000230470931,
-0.000053324956, -0.000048396325
],
[
0.000618737475, -0.048474562500, -0.000080359730,
0.035615059900, -0.003081819410, 0.000353950668,
0.001192182950, 0.002153894160, -0.000630194064,
-0.000097828362, -0.000003210597, 0.000141233266,
-0.016458449400, -0.000150240230, 0.000811652521,
-0.017236570000, -0.000125007354, -0.000473740575,
-0.003796974450, 0.054324381900, 0.000328044615,
0.002236943710, -0.006269973860, -0.000090077542,
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0.000217093184, 0.001449697830, -0.000620791338,
0.000024108483, 0.000160952609, 0.000436607680,
-0.000048396325, 0.000051313421, -0.000045791452,
0.000021980223, 0.000034002544, -0.000161425910,
-0.000113295827, 0.000240416270, 0.000346357198,
-0.002453862220, -0.001611656380, -0.005414335160,
0.000067580103, 0.000648275660, 0.001417002250, 0.000095977887,
-0.000132316779, -0.000404681882, -0.008677730390,
0.023139046100, 0.005232700240, -0.018952145900,
0.058022644000, 0.067104607900
]
]
hess = np.array(hess)
st_pt = StationaryPoint(smi, 0, 1, smiles=smi)
st_pt.characterize()
natom = st_pt.natom
freq, reduced_freqs = frequencies.get_frequencies(st_pt, hess, geom)
# check the number of frequencies when neglecting internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(
smi, 3 * natom - 6, len(freq))
self.assertEqual(len(freq), 3 * natom - 6, warn)
# check the number of freauencies when including internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(
smi, 3 * natom - 6 - 3, len(reduced_freqs))
self.assertEqual(len(reduced_freqs), 3 * natom - 6 - 3, warn)
# check if the frequencies are as expected
exps = [
114.00308362581993, 217.27775429467988, 258.3569949257438,
324.95225959788496, 444.19934314923285, 775.6200599866132,
809.834778642176, 851.31690919327, 978.7594097713534,
1001.6985870040352, 1016.7427610310635, 1110.2892683008433,
1182.5149554411457, 1226.4874456219302, 1318.400582167711,
1338.054870101307, 1396.7770994771845, 1417.5721206176297,
1452.5015242293932, 1460.9939823348343, 1533.7500760993864,
1534.471579819868, 1544.1270899676583, 1549.977599644375,
1552.1602547884133, 1556.1540474667054, 3018.3546194275327,
3020.597688051164, 3036.615370205335, 3039.4844797216215,
3056.030309062557, 3060.954764774267, 3108.0034857725796,
3110.0837061265997, 3114.029978361547, 3121.6966999165047
]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, freq[i])
self.assertAlmostEqual(exp, freq[i], places=2, msg=warn)
# check if the reduced frequencies are as expected
exps = [
323.2231139328416, 443.06424130022316, 670.1999625377555,
765.4302138965138, 805.4817609384712, 923.6453995768678,
998.4208652995756, 1010.1550075636384, 1026.2590199716992,
1035.9563299669474, 1110.728140649036, 1237.3899533374413,
1297.0701335112522, 1319.639259548847, 1338.4248295874343,
1402.476769356911, 1417.8484347981505, 1476.7358681560938,
1487.8976160436305, 1525.2008164401302, 1544.0032443689747,
1547.12404969892, 1549.9807205743023, 2426.2794467384574,
2507.323795982035, 2864.813636413937, 3024.7798090881265,
3034.7484086579275, 3037.1066584298032, 3052.278595042064,
3107.809971415559, 3113.170533468414, 3113.8054919074493
]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, reduced_freqs[i])
self.assertAlmostEqual(exp, reduced_freqs[i], places=2, msg=warn)
def create_mess_input(par, wells, products, reactions, barrierless,
well_energies, prod_energies, parent):
"""
When calculating a full pes, the files from the separate wells
are read and concatenated into one file
Two things per file need to be updated
1. the names of all the wells, bimolecular products and ts's
2. all the zpe corrected energies
"""
# generate short names for all startionary points
well_short, pr_short, fr_short, ts_short, nobar_short = create_short_names(
wells, products, reactions, barrierless)
# list of the strings to write to mess input file
s = []
#create mess0 label for mess header
well0 = StationaryPoint('well0',
par.par['charge'],
par.par['mult'],
smiles=par.par['smiles'],
structure=par.par['structure'])
well0.characterize(dimer=par.par['dimer'])
"""
Create the header block for MESS
"""
# Read the header template
header_file = pkg_resources.resource_filename('tpl', 'mess_header.tpl')
with open(header_file) as f:
tpl = f.read()
header = tpl.format(
TemperatureList=' '.join(
[str(ti) for ti in par.par['TemperatureList']]),
PressureList=' '.join([str(pi) for pi in par.par['PressureList']]),
EnergyStepOverTemperature=par.par['EnergyStepOverTemperature'],
ExcessEnergyOverTemperature=par.par['ExcessEnergyOverTemperature'],
ModelEnergyLimit=par.par['ModelEnergyLimit'],
CalculationMethod=par.par['CalculationMethod'],
ChemicalEigenvalueMax=par.par['ChemicalEigenvalueMax'],
Reactant=well_short[wells[0]],
EnergyRelaxationFactor=par.par['EnergyRelaxationFactor'],
EnergyRelaxationPower=par.par['EnergyRelaxationPower'],
EnergyRelaxationExponentCutoff=par.
par['EnergyRelaxationExponentCutoff'],
e_coll=constants.epsilon[par.par['collider']],
s_coll=constants.sigma[par.par['collider']],
m_coll=constants.mass[par.par['collider']],
e_well=par.par['epsilon'],
s_well=par.par['sigma'],
m_well=well0.mass,
)
# write the wells
s.append('######################')
s.append('# WELLS')
s.append('######################')
for well in wells:
name = well_short[well] + ' ! ' + well
energy = well_energies[well]
with open(parent[well] + '/' + well + '.mess') as f:
s.append(f.read().format(name=name, zeroenergy=energy))
s.append('!****************************************')
# write the products
s.append('######################')
s.append('# BIMOLECULAR PRODUCTS')
s.append('######################')
a = 0
for prod in products:
name = pr_short[prod] + ' ! ' + prod
energy = prod_energies[prod]
fr_names = {}
for fr in prod.split('_'):
key = 'fr_name_{}'.format(fr)
value = fr_short[fr] + ' ! ' + fr
fr_names[key] = value
with open(parent[prod] + '/' + prod + '.mess') as f:
s.append(f.read().format(name=name,
ground_energy=energy,
**fr_names))
a = a + 1
s.append('!****************************************')
# write the barrier
s.append('######################')
s.append('# BARRIERS')
s.append('######################')
for rxn in reactions:
name = [ts_short[rxn[1]]]
name.append(well_short[rxn[0]])
if len(rxn[2]) == 1:
name.append(well_short[rxn[2][0]])
else:
name.append(pr_short['_'.join(sorted(rxn[2]))])
name.append('!')
name.append(rxn[1])
energy = rxn[3]
try:
with open(rxn[0] + '/' + rxn[1] + '.mess') as f:
s.append(f.read().format(name=' '.join(name),
zeroenergy=energy))
s.append('!****************************************')
except:
logging.info(('{0} {1} {2} {3}'.format(rxn[0], "/", rxn[1],
".mess not found\n")))
# add last end statement
s.append('!****************************************')
for rxn in barrierless:
name = [nobar_short]
name.append(rxn[0])
if len(rxn[2]) > 1:
name.append(pr_short['_'.join(sorted(rxn[2]))])
else:
name.append(rxn[2])
energy = rxn[3]
s.append('! BARRIERLESS REACTIONS !')
try:
with open(rxn[0] + '/' + rxn[2] + '.mess') as f:
s.append(f.read().format(name=' '.join(name),
zeroenergy=energy))
s.append('!****************************************')
except:
logging.info('{0} {1} {2} {3}'.format(rxn[0], "/", rxn[2],
".mess not found\n"))
# add last end statement
s.append('!****************************************')
s.append('End ! end kinetics\n')
if not os.path.exists('me'):
os.mkdir('me')
# write everything to a file
with open('me/mess.inp', 'w') as f:
f.write(header)
f.write('\n'.join(s))
dummy = StationaryPoint('dummy',
par.par['charge'],
par.par['mult'],
smiles=par.par['smiles'],
structure=par.par['structure'])
well0.characterize(dimer=par.par['dimer'])
def create_mess_input(par, wells, products, reactions, well_energies,
prod_energies, parent):
"""
When calculating a full pes, the files from the separate wells
are read and concatenated into one file
Two things per file need to be updated
1. the names of all the wells, bimolecular products and ts's
2. all the zpe corrected energies
"""
# generate short names for all startionary points
well_short, pr_short, fr_short, ts_short = create_short_names(
wells, products, reactions)
# list of the strings to write to mess input file
s = []
# write the header
s.append(write_header(par, well_short[wells[0]]))
# write the wells
s.append('######################')
s.append('# WELLS')
s.append('######################')
for well in wells:
name = well_short[well] + ' ! ' + well
energy = well_energies[well]
with open(parent[well] + '/' + well + '.mess') as f:
s.append(f.read().format(name=name, zeroenergy=energy))
s.append('!****************************************')
# write the products
s.append('######################')
s.append('# BIMOLECULAR PRODUCTS')
s.append('######################')
for prod in products:
name = pr_short[prod] + ' ! ' + prod
energy = prod_energies[prod]
fr_names = {}
for fr in prod.split('_'):
key = 'fr_name_{}'.format(fr)
value = fr_short[fr] + ' ! ' + fr
fr_names[key] = value
with open(parent[prod] + '/' + prod + '.mess') as f:
s.append(f.read().format(name=name,
ground_energy=energy,
**fr_names))
s.append('!****************************************')
# write the barrier
s.append('######################')
s.append('# BARRIERS')
s.append('######################')
for rxn in reactions:
name = [ts_short[rxn[1]]]
name.append(well_short[rxn[0]])
if len(rxn[2]) == 1:
name.append(well_short[rxn[2][0]])
else:
name.append(pr_short['_'.join(sorted(rxn[2]))])
name.append('!')
name.append(rxn[1])
energy = rxn[3]
with open(rxn[0] + '/' + rxn[1] + '.mess') as f:
s.append(f.read().format(name=' '.join(name), zeroenergy=energy))
s.append('!****************************************')
# add last end statement
s.append('!****************************************')
s.append('End ! end kinetics\n')
if not os.path.exists('me'):
os.mkdir('me')
# write everything to a file
with open('me/mess.inp', 'w') as f:
f.write('\n'.join(s))
dummy = StationaryPoint('dummy',
par.par['charge'],
par.par['mult'],
smiles=par.par['smiles'],
structure=par.par['structure'])
mess = MESS(par, dummy)
if par.par['me']:
mess.run()
def testFrequencies(self):
"""
Test the frequency calculator based on the hessian
by projecting out the translations, external rotations
and internal rotations
"""
# smiles of the speciesdata
smi = 'CCCC'
# geometry of the species
geom = [[ 0.91879892, -0.04331223, 0.12849174],
[ 0.40326992, 1.40563299, 0.15982756],
[ 0.89480303, 2.27635373, -1.01612317],
[ 0.37978671, 1.82281002, -2.39283836],
[ 0.56484506, -0.58104398, -0.75857814],
[ 0.58013979, -0.60154787, 1.00923604],
[ 2.01620768, -0.06868878, 0.11831706],
[-0.69710354, 1.40285362, 0.16867873],
[ 0.71679336, 1.87619548, 1.10252902],
[ 0.58079101, 3.31524962, -0.84143554],
[ 1.99516746, 2.28446562, -1.02104071],
[-0.71762425, 1.80824531, -2.41526122],
[ 0.72083567, 2.50053168, -3.18429864],
[ 0.73195359, 0.81628395, -2.64600957]]
geom = np.array(geom)
# hessian matrix
hess = [[ 0.557479281000, 0.036110582900, -0.001644922990, -0.094996623900, 0.033793056500, 0.001227901360, 0.004435637720, -0.010301447300, -0.000714141524, -0.000402278745, 0.000207775781, 0.000217081305, -0.079155177000, -0.041443873100, -0.069861527200, -0.075582477100, -0.041043274800, 0.066112977000, -0.304832510000, 0.000618737475, 0.001591972160, -0.011587172100, 0.035782319500, 0.001443997110, 0.003566576500, -0.009402669700, -0.000876604471, -0.000457712074, -0.002463678640, 0.002595980330, 0.000811779351, -0.001430576500, 0.000032881273, 0.000129362928, -0.000033252298, 0.000111660638, 0.000157802640, -0.000276891050, 0.000074700177, 0.000433511158, -0.000116808738, -0.000311955226],
[ 0.036110582900, 0.481460445000, -0.001497124110, 0.032529383300, -0.168787802000, -0.006867396700, 0.006092748400, -0.023937567300, 0.003730351660, -0.000262084184, 0.001441647380, 0.000316178114, -0.041989591900, -0.107986603000, -0.100880979000, -0.041503733500, -0.111783765000, 0.104729927000, 0.001080107490, -0.048474562500, -0.000241263827, -0.000265123753, -0.003449223290, -0.000371820596, 0.007618533730, -0.018717902300, 0.000837312337, -0.000555734072, -0.000078154904, 0.001999374050, 0.000646690960, 0.001260483370, -0.001360417570, 0.000088413780, -0.000085577196, 0.000164034911, -0.000076331932, -0.000238612455, -0.000622223468, 0.000486138870, -0.000622805294, 0.000064046666],
[-0.001644922990, -0.001497124110, 0.575851102000, 0.001964210840, -0.005551021620, -0.083609101600, -0.009286228620, 0.027728004900, 0.001373639180, -0.000137246872, 0.000910607647, 0.001051929890, -0.069899107500, -0.100194460000, -0.221142958000, 0.066308541300, 0.103907569000, -0.219256026000, 0.001583703380, -0.000080359730, -0.050839140900, -0.000325149456, 0.000352021138, 0.000321544325, 0.010807393200, -0.028918001700, -0.000253208170, 0.001475456680, 0.004079204780, -0.004016767400, -0.000771004699, -0.000846365220, 0.001019809380, -0.000285363620, 0.000091087635, -0.000075059137, -0.000051358279, -0.000192607794, 0.000123367411, 0.000261076583, 0.000211445408, -0.000549130393],
[-0.094996623900, 0.032529383300, 0.001964210840, 0.570648686000, 0.013726467600, 0.029803019800, -0.093894628800, -0.017540296600, 0.025240052300, 0.004430547620, 0.007089239030, -0.008470974800, 0.003985664600, -0.011160808500, -0.000344880024, 0.003352509560, -0.011319168200, -0.000141509338, -0.012312369900, 0.035615059900, 0.001341132350, -0.296633947000, -0.002814851200, -0.001857303680, -0.074252517800, -0.032055118900, -0.063305094900, 0.003016262880, 0.007165737430, -0.008147262180, -0.013171250400, -0.021050691300, 0.028389638100, 0.000515556167, 0.000833930927, -0.001531483210, -0.000733957654, -0.000834470892, -0.003543541800, 0.000046069520, -0.000184412716, 0.000603996490],
[ 0.033793056500, -0.168787802000, -0.005551021620, 0.013726467600, 0.471256701000, 0.045550224600, -0.019055559200, -0.110456357000, 0.036788918500, 0.003690246720, 0.008042833690, -0.005946132620, 0.007450531200, -0.016991805700, 0.000566738577, 0.007662075620, -0.017473385800, -0.003434017560, -0.000361925575, -0.003081819410, 0.000169600942, -0.002141608270, -0.048419727000, -0.001543381080, -0.032001313400, -0.092648760200, -0.091748171900, -0.012965805500, -0.019490702300, 0.027544257800, 0.000385145313, -0.001607085100, 0.002202751810, 0.000097042391, 0.000342026851, -0.000085156640, -0.000935724242, -0.000536578498, -0.003946142400, 0.000657370855, -0.000147538049, -0.000568468443],
[ 0.001227901360, -0.006867396700, -0.083609101600, 0.029803019800, 0.045550224600, 0.530185827000, 0.024067639100, 0.038254723800, -0.133557501000, 0.009656895150, 0.018083768400, -0.030339996500, 0.011176997800, -0.027940521700, 0.000197610447, -0.010718813500, 0.027683119400, 0.001610715360, -0.000005380242, 0.000353950668, -0.000058645732, -0.001385963250, -0.001490240310, -0.050945021400, -0.064025490200, -0.091980721200, -0.231554605000, -0.000068702298, -0.003001675120, 0.001414867140, 0.000375944632, 0.002432713030, -0.002619663450, 0.000967738003, 0.000578480710, 0.002131790000, -0.001665661510, -0.002059701750, -0.003910908180, 0.000593875240, 0.000403276118, 0.001054632250],
[ 0.004435637720, 0.006092748400, -0.009286228620, -0.093894628800, -0.019055559200, 0.024067639100, 0.570900497000, -0.032595232800, -0.004232877500, -0.095277421800, -0.011406581600, -0.030607826800, 0.000043159915, -0.000514838503, 0.000373962854, -0.000738247952, 0.003637838290, -0.000237140037, 0.000536641625, 0.001192182950, -0.001206594120, -0.013027642100, -0.020980119300, 0.028551488200, 0.003004879320, 0.005695987090, -0.009218334250, -0.074383197800, 0.070019040800, 0.012170299900, -0.296727831000, 0.002527681320, 0.001997681770, -0.012251640800, -0.011694206300, -0.033682439000, 0.003373175160, 0.003467893310, 0.010833941000, 0.004006619800, 0.003613165550, 0.010476427500],
[-0.010301447300, -0.023937567300, 0.027728004900, -0.017540296600, -0.110456357000, 0.038254723800, -0.032595232800, 0.550390617000, 0.021569897200, -0.011111359900, -0.094250354100, -0.029239287500, -0.000711297419, 0.000972151769, 0.000156692325, 0.001860692500, -0.005293631350, -0.000606360776, -0.000954457648, 0.002153894160, 0.000019371190, -0.000397838529, -0.001228430010, 0.002336752460, 0.003859667870, 0.006484974130, -0.010940182100, 0.070740800200, -0.270987312000, -0.037645349500, 0.001896728730, -0.051464188100, -0.000592835086, 0.000107061175, -0.000144181197, -0.000504021609, 0.007980792090, 0.006761052700, 0.020233362800, -0.012833812400, -0.009000668610, -0.030770768100],
[-0.000714141524, 0.003730351660, 0.001373639180, 0.025240052300, 0.036788918500, -0.133557501000, -0.004232877500, 0.021569897200, 0.451154731000, -0.032090962200, -0.028197579400, -0.158290659000, -0.000492084585, -0.000980152816, 0.000034862639, 0.000407801351, -0.002484344690, 0.000853888771, 0.000202037882, -0.000630194064, 0.000364010457, -0.000276661410, 0.002092844910, -0.002923760270, 0.012375619200, 0.019611685600, -0.024571878400, 0.011914527900, -0.037319661200, -0.053342229400, 0.001505776140, -0.000642112942, -0.047826423400, 0.000374606934, -0.000789448823, -0.002922212160, -0.010438695000, -0.010818867900, -0.022572722700, -0.003774999570, -0.001931336020, -0.007773745060],
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[ 0.000032881273, -0.001360417570, 0.001019809380, 0.028389638100, 0.002202751810, -0.002619663450, 0.001997681770, -0.000592835086, -0.047826423400, -0.033753097500, -0.000685633791, -0.003164852780, -0.000060908234, 0.000108411307, -0.000005338059, -0.000134663171, -0.000388354266, -0.000048007771, -0.000699875116, 0.000271078925, 0.000154467411, 0.001598510130, 0.003357378420, -0.003659014380, -0.000262541603, -0.000746827584, 0.000708239491, 0.005531828340, -0.000114897060, -0.000005613339, -0.001747096940, -0.000605423301, 0.058534733700, -0.002049213140, -0.002165722420, -0.005580464400, 0.000418020512, 0.000671917929, 0.001075125330, 0.000738835582, 0.000048572694, 0.001417002250],
[ 0.000129362928, 0.000088413780, -0.000285363620, 0.000515556167, 0.000097042391, 0.000967738003, -0.012251640800, 0.000107061175, 0.000374606934, -0.305000167000, -0.001195716590, -0.000834451580, -0.000223489041, 0.000058268378, 0.000029804814, 0.000006452519, 0.000055737630, -0.000081103354, 0.000142228293, -0.000111873714, 0.000145393609, 0.000698874397, 0.000462630311, -0.000911429020, 0.000410586032, 0.000002929675, 0.000002552083, 0.000601743047, -0.000586260703, -0.000083374773, 0.001019228930, -0.001149055730, -0.002049213140, 0.335981374000, 0.002561958750, 0.003297591000, -0.010801126300, 0.001048090670, -0.000668728837, -0.011228983200, -0.001439226010, 0.000095977887],
[-0.000033252298, -0.000085577196, 0.000091087635, 0.000833930927, 0.000342026851, 0.000578480710, -0.011694206300, -0.000144181197, -0.000789448823, -0.001072859680, -0.050831902500, 0.000361231775, 0.000140719611, -0.000061770157, -0.000043582456, -0.000023320384, 0.000018935809, 0.000034200394, -0.000106601886, -0.000047084158, -0.000107573025, 0.000144489938, 0.000228833858, -0.000039664076, -0.000025447089, -0.000191975080, 0.000110082904, -0.000294036321, 0.000093614097, 0.000585667105, -0.000665600374, 0.000174592071, -0.002165722420, 0.002561958750, 0.050149008600, 0.001623164620, -0.022680777200, -0.000201125886, -0.000105607558, 0.032915002300, 0.000556604861, -0.000132316779],
[ 0.000111660638, 0.000164034911, -0.000075059137, -0.001531483210, -0.000085156640, 0.002131790000, -0.033682439000, -0.000504021609, -0.002922212160, -0.000565352953, 0.000607484566, -0.048518990600, -0.000038210872, 0.000096427007, -0.000002800832, 0.000023532240, 0.000059096657, 0.000014479678, 0.000148869356, -0.000192239409, 0.000044588785, 0.001199897110, 0.000196170553, -0.000139936846, 0.000355884856, -0.000225290437, 0.000462709809, 0.000670043314, 0.000252036708, 0.001289783720, -0.002131478270, -0.002056008930, -0.005580464400, 0.003297591000, 0.001623164620, 0.053631786900, 0.024986835900, -0.000480266327, 0.000069007009, 0.007154649910, 0.000544568335, -0.000404681882],
[ 0.000157802640, -0.000076331932, -0.000051358279, -0.000733957654, -0.000935724242, -0.001665661510, 0.003373175160, 0.007980792090, -0.010438695000, -0.076013943900, -0.051498752000, 0.059364188600, -0.000144869281, -0.000010851744, 0.000036585620, -0.000000863259, -0.000026671850, -0.000018292858, 0.000006913742, -0.000015838536, 0.000030151342, 0.000425109666, 0.000041609570, -0.000326542275, 0.000467119865, 0.000183020208, -0.000439580294, 0.000236669508, 0.000174692057, 0.000768879885, 0.000410888095, 0.000508508927, 0.000418020512, -0.010801126300, -0.022680777200, 0.024986835900, 0.079112534200, 0.058103695900, -0.063986801200, 0.003504547540, 0.008252628830, -0.008677730390],
[-0.000276891050, -0.000238612455, -0.000192607794, -0.000834470892, -0.000536578498, -0.002059701750, 0.003467893310, 0.006761052700, -0.010818867900, -0.051443542800, -0.151306241000, 0.116743826000, 0.000150427844, 0.000535119260, 0.000161681863, 0.000025535986, 0.000148561268, 0.000237094498, 0.000060554518, 0.000041274254, 0.000025660583, -0.000023343949, -0.000164615301, 0.000011716933, 0.000078313296, 0.000300248641, -0.000527497416, 0.000359711857, 0.000878659823, 0.000408824903, 0.000459443519, 0.000184012806, 0.000671917929, 0.001048090670, -0.000201125886, -0.000480266327, 0.058103695900, 0.163615200000, -0.127320828000, -0.011175418200, -0.020016956200, 0.023139046100],
[ 0.000074700177, -0.000622223468, 0.000123367411, -0.003543541800, -0.003946142400, -0.003910908180, 0.010833941000, 0.020233362800, -0.022572722700, 0.058887756100, 0.115957274000, -0.179560342000, 0.000065183444, 0.000099909733, 0.000229922299, 0.000019870589, 0.000149502306, 0.000030248075, -0.000076997589, 0.000051844795, -0.000005990485, -0.000045138440, -0.000252036680, 0.000402783060, 0.000422103376, 0.001103529870, -0.001625311350, 0.000799593785, -0.000383102689, 0.001044358070, -0.000527224047, 0.000337657158, 0.001075125330, -0.000668728837, -0.000105607558, 0.000069007009, -0.063986801200, -0.127320828000, 0.199467764000, -0.002254716580, -0.005303140270, 0.005232700240],
[ 0.000433511158, 0.000486138870, 0.000261076583, 0.000046069520, 0.000657370855, 0.000593875240, 0.004006619800, -0.012833812400, -0.003774999570, -0.079014088600, 0.078889494300, 0.019436209100, 0.000072328995, -0.000286456542, -0.000283313413, -0.000144529814, -0.000107492816, -0.000123734870, -0.000230470931, -0.000007975487, -0.000144934523, 0.000030532015, 0.000080159392, 0.000006043879, -0.000061535147, -0.000164903323, 0.000164720405, 0.000063586822, -0.001026058750, -0.002821565820, 0.000650645014, -0.000305373851, 0.000738835582, -0.011228983200, 0.032915002300, 0.007154649910, 0.003504547540, -0.011175418200, -0.002254716580, 0.081871766900, -0.087120674300, -0.018952145900],
[-0.000116808738, -0.000622805294, 0.000211445408, -0.000184412716, -0.000147538049, 0.000403276118, 0.003613165550, -0.009000668610, -0.001931336020, 0.078673338600, -0.268209599000, -0.051702861200, 0.000316440754, 0.000151661591, 0.001319024070, -0.000033792868, 0.000330197117, 0.000225358158, -0.000053324956, 0.000015565803, -0.000084799847, -0.000057961271, 0.000038523433, 0.000025108980, -0.000019191454, 0.000129961184, -0.000158353984, -0.001218387310, 0.000957816966, -0.001619506420, -0.000611794096, 0.000327669725, 0.000048572694, -0.001439226010, 0.000556604861, 0.000544568335, 0.008252628830, -0.020016956200, -0.005303140270, -0.087120674300, 0.295489566000, 0.058022644000],
[-0.000311955226, 0.000064046666, -0.000549130393, 0.000603996490, -0.000568468443, 0.001054632250, 0.010476427500, -0.030770768100, -0.007773745060, 0.019050222000, -0.050727185300, -0.060622006300, 0.000217093184, 0.001449697830, -0.000620791338, 0.000024108483, 0.000160952609, 0.000436607680, -0.000048396325, 0.000051313421, -0.000045791452, 0.000021980223, 0.000034002544, -0.000161425910, -0.000113295827, 0.000240416270, 0.000346357198, -0.002453862220, -0.001611656380, -0.005414335160, 0.000067580103, 0.000648275660, 0.001417002250, 0.000095977887, -0.000132316779, -0.000404681882, -0.008677730390, 0.023139046100, 0.005232700240, -0.018952145900, 0.058022644000, 0.067104607900]]
hess = np.array(hess)
st_pt = StationaryPoint(smi, 0, 1, smiles=smi)
st_pt.characterize()
natom = st_pt.natom
freq, reduced_freqs = frequencies.get_frequencies(st_pt, hess, geom)
# check the number of frequencies when neglecting internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(smi, 3 * natom - 6, len(freq))
self.assertEqual(len(freq), 3 * natom - 6, warn)
# check the number of freauencies when including internal rotors
warn = 'Wrong number of frequencies for '
warn += '{}, expected {}, calculated {}'.format(smi, 3 * natom - 6 - 3, len(reduced_freqs))
self.assertEqual(len(reduced_freqs), 3 * natom - 6 - 3, warn)
# check if the frequencies are as expected
exps = [114.00308362581993, 217.27775429467988, 258.3569949257438, 324.95225959788496, 444.19934314923285, 775.6200599866132, 809.834778642176, 851.31690919327, 978.7594097713534, 1001.6985870040352, 1016.7427610310635, 1110.2892683008433, 1182.5149554411457, 1226.4874456219302, 1318.400582167711, 1338.054870101307, 1396.7770994771845, 1417.5721206176297, 1452.5015242293932, 1460.9939823348343, 1533.7500760993864, 1534.471579819868, 1544.1270899676583, 1549.977599644375, 1552.1602547884133, 1556.1540474667054, 3018.3546194275327, 3020.597688051164, 3036.615370205335, 3039.4844797216215, 3056.030309062557, 3060.954764774267, 3108.0034857725796, 3110.0837061265997, 3114.029978361547, 3121.6966999165047]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, freq[i])
self.assertAlmostEqual(exp, freq[i], places=2, msg=warn)
# check if the reduced frequencies are as expected
exps = [323.2231139328416, 443.06424130022316, 670.1999625377555, 765.4302138965138, 805.4817609384712, 923.6453995768678, 998.4208652995756, 1010.1550075636384, 1026.2590199716992, 1035.9563299669474, 1110.728140649036, 1237.3899533374413, 1297.0701335112522, 1319.639259548847, 1338.4248295874343, 1402.476769356911, 1417.8484347981505, 1476.7358681560938, 1487.8976160436305, 1525.2008164401302, 1544.0032443689747, 1547.12404969892, 1549.9807205743023, 2426.2794467384574, 2507.323795982035, 2864.813636413937, 3024.7798090881265, 3034.7484086579275, 3037.1066584298032, 3052.278595042064, 3107.809971415559, 3113.170533468414, 3113.8054919074493]
for i, exp in enumerate(exps):
warn = 'Frequency values have wrong number: '
warn += 'expected {}, calculated {}'.format(exp, reduced_freqs[i])
self.assertAlmostEqual(exp, reduced_freqs[i], places=2, msg=warn)
def generate(self):
"""
Creates the input for each reaction, runs them, and tests for success.
If successful, it creates the barrier and product objects.
It also then does the conformational search, and finally, the hindered rotor scans.
To make the code the most efficient, all of these happen in parallel, in a sense that
the jobs are not waiting for each other. E.g., one reaction can still be in the stage
of TS search, while the other can be already at the hindered rotor scan. This way,
all cores are occupied efficiently.
The switching between the various stages are done via the reac_ts_done variable.
0: initiate the TS search
1: check barrier height and errors in TS, and initiates normal mode displacement test, start the irc calculations
2: submit product optimization
3: submit the frequency calculation
4: do the optimization of the ts and the products
5: follow up on the optimizations
6: finalize calculations, check for wrong number of negative frequencies
If at any times the calculation fails, reac_ts_done is set to -999.
If all steps are successful, reac_ts_done is set to -1.
"""
deleted = []
if len(self.species.reac_inst) > 0:
alldone = 1
else:
alldone = 0
while alldone:
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
instance_name = obj.instance_name
# START REATION SEARCH
if self.species.reac_ts_done[index] == 0 and self.species.reac_step[index] == 0:
#verify after restart if search has failed in previous kinbot run
status = self.qc.check_qc(instance_name)
if status == 'error' or status == 'killed':
logging.info('\tRxn search failed (error or killed) for {}'.format(instance_name))
self.species.reac_ts_done[index] = -999
if self.species.reac_ts_done[index] == 0: # ts search is ongoing
if obj.scan == 0: #don't do a scan of a bond
if self.species.reac_step[index] == obj.max_step + 1:
status = self.qc.get_qc_freq(instance_name, self.species.natom)[0]
if status == 0:
self.species.reac_ts_done[index] = 1
elif status == -1:
logging.info('\tRxn search failed for {}'.format(instance_name))
self.species.reac_ts_done[index] = -999
else:
self.species.reac_step[index] = reac_family.carry_out_reaction(obj, self.species.reac_step[index], self.par.par['qc_command'])
else: # do a bond scan
if self.species.reac_step[index] == self.par.par['scan_step'] + 1:
status = self.qc.get_qc_freq(instance_name, self.species.natom)[0]
if status == 0:
self.species.reac_ts_done[index] = 1
elif status == -1:
logging.info('\tRxn search failed for {}'.format(instance_name))
self.species.reac_ts_done[index] = -999
else:
if self.species.reac_step[index] == 0:
self.species.reac_step[index] = reac_family.carry_out_reaction(obj, self.species.reac_step[index], self.par.par['qc_command'])
elif self.species.reac_step[index] > 0:
status = self.qc.check_qc(instance_name)
if status == 'error' or status == 'killed':
logging.info('\tRxn search failed for {}'.format(instance_name))
self.species.reac_ts_done[index] = -999
else:
err, energy = self.qc.get_qc_energy(instance_name)
if err == 0:
self.species.reac_scan_energy[index].append(energy)
if len(self.species.reac_scan_energy[index]) > 1:
if self.species.reac_scan_energy[index][-1] < self.species.reac_scan_energy[index][-2]:
self.species.reac_step[index] = self.par.par['scan_step']
self.species.reac_step[index] = reac_family.carry_out_reaction(obj, self.species.reac_step[index], self.par.par['qc_command'])
elif self.species.reac_ts_done[index] == 1:
status = self.qc.check_qc(instance_name)
if status == 'running': continue
elif status == 'error':
logging.info('\tRxn search failed (gaussian error) for {}'.format(instance_name))
self.species.reac_ts_done[index] = -999
else:
#check the barrier height:
if self.species.reac_type[index] == 'R_Addition_MultipleBond':
sp_energy = self.qc.get_qc_energy(str(self.species.chemid) + '_well_mp2')[1]
barrier = (self.qc.get_qc_energy(instance_name)[1] - sp_energy) * constants.AUtoKCAL
else:
sp_energy = self.qc.get_qc_energy(str(self.species.chemid) + '_well')[1]
barrier = (self.qc.get_qc_energy(instance_name)[1] - sp_energy) * constants.AUtoKCAL
if barrier > self.par.par['barrier_threshold']:
logging.info('\tRxn barrier too high ({val}) for {name}'.format(val=barrier,name=instance_name))
self.species.reac_ts_done[index] = -999
else:
obj.irc = IRC(obj, self.par) #TODO: this doesn't seem like a good design
irc_status = obj.irc.check_irc()
if 0 in irc_status:
# No IRC started yet, start the IRC now
logging.info('\tStarting IRC calculations for {}'.format(instance_name))
obj.irc.do_irc_calculations()
elif irc_status[0] == 'running' or irc_status[1] == 'running':
continue
else:
#IRC's have succesfully finished, have an error or were killed, in any case
#read the geometries and try to make products out of them
#verify which of the ircs leads back to the reactant, if any
prod = obj.irc.irc2stationary_pt()
if prod == 0:
logging.info('\t\tNo product found for {}'.format(instance_name))
self.species.reac_ts_done[index] = -999
else:
#IRC's are done
obj.products = prod
obj.product_bonds = prod.bond
self.species.reac_ts_done[index] = 2
elif self.species.reac_ts_done[index] == 2:
#identify bimolecular products and wells
fragments, maps = obj.products.start_multi_molecular()
obj.products = []
for i, frag in enumerate(fragments):
obj.products.append(frag)
self.qc.qc_opt(frag, frag.geom)
self.species.reac_ts_done[index] = 3
elif self.species.reac_ts_done[index] == 3:
#wait for the optimization to finish
err = 0
for st_pt in obj.products:
chemid = st_pt.chemid
orig_geom = copy.deepcopy(st_pt.geom)
e, st_pt.geom = self.qc.get_qc_geom(str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
logging.info('\tProduct optimization failed for {}, product {}'.format(instance_name,st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
elif e != 0:
err = -1
else:
e2, st_pt.energy = self.qc.get_qc_energy(str(st_pt.chemid) + '_well')
e2, st_pt.zpe = self.qc.get_qc_zpe(str(st_pt.chemid) + '_well')
st_pt.bond_mx()
st_pt.characterize(0) # not allowed to use the dimer option here
st_pt.calc_chemid()
if chemid != st_pt.chemid:
# product was optimized to another structure, give warning and remove this reaction
logging.info('\tProduct optimizatied to other structure for {}, product {} to {}'.format(instance_name,chemid,st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
if err == 0:
self.species.reac_ts_done[index] = 4
elif self.species.reac_ts_done[index] == 4:
# Do the TS and product optimization
#make a stationary point object of the ts
bond_mx = np.zeros((self.species.natom, self.species.natom), dtype=int)
for i in range(self.species.natom):
for j in range(self.species.natom):
bond_mx[i][j] = max(self.species.bond[i][j],obj.product_bonds[i][j])
err, geom = self.qc.get_qc_geom(instance_name, self.species.natom)
ts = StationaryPoint( instance_name, self.species.charge, self.species.mult,
atom = self.species.atom, geom = geom, wellorts = 1)
err, ts.energy = self.qc.get_qc_energy(instance_name)
err, ts.zpe = self.qc.get_qc_zpe(instance_name)
ts.bond = bond_mx
ts.find_cycle()
ts.find_conf_dihedral()
obj.ts = ts
#do the ts optimization
obj.ts_opt = Optimize(obj.ts,self.par,self.qc)
obj.ts_opt.do_optimization()
#do the products optimizations
for st_pt in obj.products:
#check for products of other reactions that are the same as this product
#in the case such products are found, use the same Optimize object for both
new = 1
for i, inst_i in enumerate(self.species.reac_inst):
if not i == index:
obj_i = self.species.reac_obj[i]
if self.species.reac_ts_done[i] > 3:
for j,st_pt_i in enumerate(obj_i.products):
if st_pt_i.chemid == st_pt.chemid:
if len(obj_i.prod_opt) > j:
prod_opt = obj_i.prod_opt[j]
new = 0
break
if new:
prod_opt = Optimize(st_pt,self.par,self.qc)
prod_opt.do_optimization()
obj.prod_opt.append(prod_opt)
self.species.reac_ts_done[index] = 5
elif self.species.reac_ts_done[index] == 5:
#check up on the TS and product optimizations
opts_done = 1
fails = 0
#check if ts is done
if not obj.ts_opt.shir == 1:
opts_done = 0
obj.ts_opt.do_optimization()
if obj.ts_opt.shigh == -999:
fails = 1
for pr_opt in obj.prod_opt:
if not pr_opt.shir == 1:
opts_done = 0
pr_opt.do_optimization()
if pr_opt.shigh == -999:
fails = 1
if fails:
self.species.reac_ts_done[index] = -999
elif opts_done:
self.species.reac_ts_done[index] = 6
elif self.species.reac_ts_done[index] == 6:
#Finilize the calculations
#continue to PES search in case a new well was found
if self.par.par['pes']:
#verify if product is monomolecular, and if it is new
if len(obj.products) ==1:
st_pt = obj.prod_opt[0].species
chemid = st_pt.chemid
energy = st_pt.energy
well_energy = self.species.energy
new_barrier_threshold = self.par.par['barrier_threshold'] - (energy-well_energy)*constants.AUtoKCAL
dir = os.path.dirname(os.getcwd())
jobs = open(dir+'/chemids','r').read().split('\n')
jobs = [ji for ji in jobs]
if not str(chemid) in jobs:
#this well is new, add it to the jobs
while 1:
try:
#try to open the file and write to it
pes.write_input(self.par,obj.products[0],new_barrier_threshold,dir)
f = open(dir+'/chemids','a')
f.write('{}\n'.format(chemid))
f.close()
break
except IOError:
#wait a second and try again
time.sleep(1)
pass
#check for wrong number of negative frequencies
neg_freq = 0
for st_pt in obj.products:
if any([fi < 0. for fi in st_pt.reduced_freqs]):
neg_freq = 1
if any([fi < 0. for fi in obj.ts.reduced_freqs[1:]]):
neg_freq = 1
if neg_freq:
logging.info('\tFound negative frequency for ' + instance_name)
self.species.reac_ts_done[index] = -999
else:
#the reaction search is finished
self.species.reac_ts_done[index] = -1 # this is the success code
# write a temporary pes input file
# remove old xval and im_extent files
if os.path.exists('{}_xval.txt'.format(self.species.chemid)):
os.remove('{}_xval.txt'.format(self.species.chemid))
if os.path.exists('{}_im_extent.txt'.format(self.species.chemid)):
os.remove('{}_im_extent.txt'.format(self.species.chemid))
postprocess.createPESViewerInput(self.species, self.qc, self.par)
elif self.species.reac_ts_done[index] == -999:
if not self.species.reac_obj[index].instance_name in deleted:
self.delete_files(self.species.reac_obj[index].instance_name)
deleted.append(self.species.reac_obj[index].instance_name)
alldone = 1
for index, instance in enumerate(self.species.reac_inst):
if any(self.species.reac_ts_done[i] >= 0 for i in range(len(self.species.reac_inst))):
alldone = 1
break
else:
alldone = 0
# write a small summary while running
wr = 1
if wr:
f_out = open('kinbot_monitor.out','w')
for index, instance in enumerate(self.species.reac_inst):
f_out.write('{}\t{}\t{}\n'.format(self.species.reac_ts_done[index],self.species.reac_step[index],self.species.reac_obj[index].instance_name))
f_out.close()
time.sleep(1)
s = []
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
instance_name = obj.instance_name
# Write a summary on the combinatorial exploration
if 'combinatorial' in instance_name:
s.append('NAME\t' + instance_name)
# Write the bonds that were broken and formed
s.append('BROKEN_BONDS\t' + '\t'.join('[{}, {}]'.format(re[0], re[1]) for re in obj.reac))
s.append('FORMED_BONDS\t' + '\t'.join('[{}, {}]'.format(pr[0], pr[1]) for pr in obj.prod))
# Populate the ts_bond_lengths dict with the values
# of this reaction
if self.species.reac_ts_done[index] == -1:
for i in range(self.species.natom - 1):
for j in range(i + 1, self.species.natom):
if self.species.bond[i][j] != obj.product_bonds[i][j]:
if (self.species.bond[i][j] == 0 or
obj.product_bonds[i][j] == 0):
syms = []
syms.append(self.species.atom[i])
syms.append(self.species.atom[j])
syms = ''.join(sorted(syms))
dist = np.linalg.norm(obj.ts.geom[i] - obj.ts.geom[j])
s.append('TS_BOND_LENGTHS\t{}\t{}'.format(syms, dist))
# write the expected inchis
s.append('EXPECTED_INCHIS\t' + '\t'.join(inchi for inchi in obj.prod_inchi))
# get the inchis the reaction found
if self.species.reac_ts_done[index] == -1:
inchis = obj.get_final_inchis()
s.append('FOUND_INCHIS\t' + '\t'.join(inchis))
s.append('\n')
with open('combinatorial.txt', 'w') as f:
f.write('\n'.join(s) + '\n')
logging.info("Reaction generation done!")
def generate(self):
"""
Creates the input for each reaction, runs them, and tests for success.
If successful, it creates the barrier and product objects.
It also then does the conformational search, and finally, the hindered rotor scans.
To make the code the most efficient, all of these happen in parallel, in a sense that
the jobs are not waiting for each other. E.g., one reaction can still be in the stage
of TS search, while the other can be already at the hindered rotor scan. This way,
all cores are occupied efficiently.
The switching between the various stages are done via the reac_ts_done variable.
0: initiate the TS search
1: check barrier height and errors in TS, and initiates normal mode displacement test, start the irc calculations
2: submit product optimization
3: submit the frequency calculation
4: do the optimization of the ts and the products
5: follow up on the optimizations
6: finalize calculations, check for wrong number of negative frequencies
If at any times the calculation fails, reac_ts_done is set to -999.
If all steps are successful, reac_ts_done is set to -1.
"""
deleted = []
if len(self.species.reac_inst) > 0:
alldone = 1
else:
alldone = 0
# status to see of kinbot needs to wait for the product optimizations
# from another kinbot run, to avoid duplication of calculations
products_waiting_status = [[] for i in self.species.reac_inst]
count = 0
for i in self.species.reac_inst:
count = count + 1
all_unique_prod = []
frag_unique = []
nameUnique = []
stpt_inchis = []
while alldone:
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
instance_name = obj.instance_name
# START REACTION SEARCH
if self.species.reac_ts_done[
index] == 0 and self.species.reac_step[index] == 0:
#verify after restart if search has failed in previous kinbot run
status = self.qc.check_qc(instance_name)
if status == 'error' or status == 'killed':
logging.info(
'\tRxn search failed (error or killed) for {}'.
format(instance_name))
self.species.reac_ts_done[index] = -999
if self.species.reac_ts_done[
index] == 0: # ts search is ongoing
if obj.scan == 0: #don't do a scan of a bond
if self.species.reac_step[index] == obj.max_step + 1:
status = self.qc.get_qc_freq(
instance_name, self.species.natom)[0]
if status == 0:
self.species.reac_ts_done[index] = 1
elif status == -1:
logging.info(
'\tRxn search failed for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj, self.species.reac_step[index],
self.par.par['qc_command'])
else: # do a bond scan
if self.species.reac_step[
index] == self.par.par['scan_step'] + 1:
status = self.qc.get_qc_freq(
instance_name, self.species.natom)[0]
if status == 0:
self.species.reac_ts_done[index] = 1
elif status == -1:
logging.info(
'\tRxn search failed for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
if self.species.reac_step[index] == 0:
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj, self.species.reac_step[index],
self.par.par['qc_command'])
elif self.species.reac_step[index] > 0:
status = self.qc.check_qc(instance_name)
if status == 'error' or status == 'killed':
logging.info(
'\tRxn search failed for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
err, energy = self.qc.get_qc_energy(
instance_name)
if err == 0:
self.species.reac_scan_energy[
index].append(energy)
if len(self.species.
reac_scan_energy[index]) > 1:
if self.species.reac_scan_energy[
index][
-1] < self.species.reac_scan_energy[
index][-2]:
self.species.reac_step[
index] = self.par.par[
'scan_step']
#ts search restarted w/ next line?
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj,
self.species.reac_step[index],
self.par.par['qc_command'])
elif self.species.reac_ts_done[index] == 1:
status = self.qc.check_qc(instance_name)
if status == 'running': continue
elif status == 'error':
logging.info(
'\tRxn search failed (gaussian error) for {}'.
format(instance_name))
self.species.reac_ts_done[index] = -999
else:
#check the barrier height:
if self.species.reac_type[
index] == 'R_Addition_MultipleBond':
sp_energy = self.qc.get_qc_energy(
str(self.species.chemid) + '_well_mp2')[1]
barrier = (self.qc.get_qc_energy(instance_name)[1]
- sp_energy) * constants.AUtoKCAL
else:
sp_energy = self.qc.get_qc_energy(
str(self.species.chemid) + '_well')[1]
barrier = (self.qc.get_qc_energy(instance_name)[1]
- sp_energy) * constants.AUtoKCAL
if barrier > self.par.par['barrier_threshold']:
logging.info(
'\tRxn barrier too high ({0:.2f} kcal/mol) for {1}'
.format(barrier, instance_name))
self.species.reac_ts_done[index] = -999
else:
obj.irc = IRC(
obj, self.par
) #TODO: this doesn't seem like a good design
irc_status = obj.irc.check_irc()
if 0 in irc_status:
# No IRC started yet, start the IRC now
logging.info(
'\tStarting IRC calculations for {}'.
format(instance_name))
obj.irc.do_irc_calculations()
elif irc_status[0] == 'running' or irc_status[
1] == 'running':
continue
else:
#IRC's have successfully finished, have an error or were killed, in any case
#read the geometries and try to make products out of them
#verify which of the ircs leads back to the reactant, if any
prod = obj.irc.irc2stationary_pt()
if prod == 0:
logging.info(
'\t\tNo product found for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
obj.products = prod
obj.product_bonds = prod.bond
self.species.reac_ts_done[index] = 2
elif self.species.reac_ts_done[index] == 2:
if len(products_waiting_status[index]) == 0:
#identify bimolecular products and wells
fragments, maps = obj.products.start_multi_molecular()
obj.products = []
a = []
for frag in fragments:
a.append(frag)
if len(frag_unique) == 0:
frag_unique.append(frag)
elif len(frag_unique) > 0:
new = 1
for fragb in frag_unique:
if frag.chemid == fragb.chemid:
e, geom2 = self.qc.get_qc_geom(
str(fragb.chemid) + '_well',
fragb.natom)
if e == 0:
a.pop()
frag = fragb
a.append(frag)
new = 0
break
if new:
frag_unique.append(frag)
obj.products_final = []
for frag in a:
self.qc.qc_opt(frag, frag.geom)
e, geom2 = self.qc.get_qc_geom(
str(frag.chemid) + '_well', frag.natom)
obj.products_final.append(frag)
#check products make sure they are the same
for i, st_pt_i in enumerate(obj.products_final):
for j, st_pt_j in enumerate(obj.products_final):
if st_pt_i.chemid == st_pt_j.chemid and i < j:
obj.products_final[j] = obj.products_final[
i]
#print products generated by IRC
products = []
for i, st_pt in enumerate(obj.products_final):
products.append(st_pt.chemid)
products.extend([' ', ' ', ' '])
barrier = (self.qc.get_qc_energy(instance_name)[1] -
sp_energy) * constants.AUtoKCAL
logging.info(
'\tReaction {0} has a barrier of {1:.2f} kcal/mol and leads to products {2} {3} {4}'
.format(instance_name, barrier, products[0],
products[1], products[2]))
for i, st_pt in enumerate(obj.products_final):
chemid = st_pt.chemid
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
logging.info(
'\tProduct optimization failed for {}, product {}'
.format(instance_name, st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
elif e != 0:
err = -1
else:
e2, st_pt.energy = self.qc.get_qc_energy(
str(st_pt.chemid) + '_well')
e2, st_pt.zpe = self.qc.get_qc_zpe(
str(st_pt.chemid) + '_well')
st_pt.characterize(
dimer=0
) # not allowed to use the dimer option here
if chemid != st_pt.chemid:
obj.products_final.pop(i)
newfrags, newmaps = st_pt.start_multi_molecular(
) # newfrags is list of stpt obj
products_waiting_status[index] = [
0 for frag in newfrags
]
fragChemid = []
for i, newfr in enumerate(newfrags):
for prod in frag_unique:
if newfr.chemid == prod.chemid:
newfrags.pop(i)
newfr = prod
j = i - 1
newfrags.insert(j, newfr)
#add new frag to frag_unique somehow?
j = i - 1
obj.products_final.insert(j, newfr)
self.qc.qc_opt(newfr, newfr.geom, 0)
fragChemid.append(newfr.chemid)
if len(fragChemid) == 1:
fragChemid.append(" ")
for i, frag in enumerate(newfrags):
products_waiting_status[index][i] = 1
logging.info(
'\ta) Product optimized to other structure for {}, product {} to {} {}'
.format(instance_name, chemid,
fragChemid[0], fragChemid[1]))
obj.products = []
for prod in obj.products_final:
obj.products.append(prod)
obj.products_final = []
if all([pi == 1 for pi in products_waiting_status[index]]):
self.species.reac_ts_done[index] = 3
elif self.species.reac_ts_done[index] == 3:
# wait for the optimization to finish
# if two st_pt are the same in the products, we make them exactly identical otherwise
# the different ordering of the atoms causes the chemid of the second to be seemingly wrong
for i, st_pt_i in enumerate(obj.products):
for j, st_pt_j in enumerate(obj.products):
if st_pt_i.chemid == st_pt_j.chemid and i < j:
obj.products[j] = obj.products[i]
'''
# generate and compare inchis
if len(stpt_inchis) == 0:
well0_inchi = cheminfo.create_inchi_from_geom(self.species.atom,self.species.geom)
well0_chemicalFormula = well0_inchi.split('S/')[1].split('/')[0]
well0_stereochem = ''
if "/t" in str(well0_inchi):
well0_stereochem = well0_inchi.split('/t')[1].split('/')[0]
well0_info = [self.species.chemid, well0_chemicalFormula, well0_inchi, well0_stereochem]
stpt_inchis.append(well0_info)
for st_pt in obj.products:
prod_chemid = st_pt.chemid
prod_inchi = cheminfo.create_inchi_from_geom(st_pt.atom,st_pt.geom)
prod_chemicalFormula = prod_inchi.split('S/')[1].split('/')[0]
prod_stereochem = ''
if "/t" in str(prod_inchi):
prod_stereochem = prod_inchi.split('/t')[1].split('/')[0]
prod_info = [prod_chemid, prod_chemicalFormula, prod_inchi, prod_stereochem]
stpt_inchis.append(prod_info)
inchiFile = open('inchis.log','w')
well0_chemid = stpt_inchis[0][0]
well0_chemicalFormula = stpt_inchis[0][1]
well0_stereochem = stpt_inchis[0][3]
for inchi in stpt_inchis:
inchiFile.write("{}\t|{}\t|{}\t|{}\n".format(inchi[0],inchi[1],inchi[2],inchi[3]))
prod_chemid = inchi[0]
prod_stereochem = inchi[3]
prod_chemicalFormula = inchi[1]
if well0_chemicalFormula == prod_chemicalFormula:
if str(well0_stereochem) != str(prod_stereochem):
logging.warning("\t\t!WARNING! Stereochemistry for product {} differs from the initial well ({}) for reaction {}".format(prod_chemid, well0_chemid, instance_name))
inchiFile.close()
'''
err = 0
for st_pt in obj.products:
chemid = st_pt.chemid
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
logging.info(
'\tProduct optimization failed for {}, product {}'
.format(instance_name, st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
elif e != 0:
err = -1
else:
e2, st_pt.energy = self.qc.get_qc_energy(
str(st_pt.chemid) + '_well')
e2, st_pt.zpe = self.qc.get_qc_zpe(
str(st_pt.chemid) + '_well')
st_pt.characterize(
dimer=0
) # not allowed to use the dimer option here
if chemid != st_pt.chemid:
# product was optimized to another structure, give warning but don't remove reaction
logging.info(
'\tb) Product optimized to other structure for {}, product {} to {}'
.format(instance_name, chemid,
st_pt.chemid))
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
err = -1
if err == 0:
self.species.reac_ts_done[index] = 4
elif self.species.reac_ts_done[index] == 4:
# Do the TS and product optimization
# make a stationary point object of the ts
bond_mx = np.zeros(
(self.species.natom, self.species.natom))
for i in range(self.species.natom):
for j in range(self.species.natom):
bond_mx[i][j] = max(self.species.bond[i][j],
obj.product_bonds[i][j])
err, geom = self.qc.get_qc_geom(instance_name,
self.species.natom)
ts = StationaryPoint(instance_name,
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=geom,
wellorts=1)
err, ts.energy = self.qc.get_qc_energy(instance_name)
err, ts.zpe = self.qc.get_qc_zpe(
instance_name) # NEW STOPS HERE
ts.bond = bond_mx
ts.find_cycle()
ts.find_conf_dihedral()
obj.ts = ts
#do the ts optimization
obj.ts_opt = Optimize(obj.ts, self.par, self.qc)
obj.ts_opt.do_optimization()
#do the products optimizations
for st_pt in obj.products:
#do the products optimizations
#check for products of other reactions that are the same as this product
#in the case such products are found, use the same Optimize object for both
for i, inst_i in enumerate(self.species.reac_inst):
new = 1
if not i == index:
obj_i = self.species.reac_obj[i]
if self.species.reac_ts_done[i] > 3:
for j, st_pt_i in enumerate(
obj_i.products):
if st_pt_i.chemid == st_pt.chemid:
if len(obj_i.prod_opt) > j:
prod_opt = obj_i.prod_opt[j]
new = 0
break
if new:
prod_opt = Optimize(st_pt, self.par, self.qc)
prod_opt.do_optimization()
obj.prod_opt.append(prod_opt)
for st_pt in obj.products:
#section where comparing products in same reaction occurs
if len(obj.prod_opt) > 0:
for j, st_pt_opt in enumerate(obj.prod_opt):
if st_pt.chemid == st_pt_opt.species.chemid:
if len(obj.prod_opt) > j:
prod_opt = obj.prod_opt[j]
break
elog = open("energy.log", 'a')
for prod_opt in obj.prod_opt:
elog.write("prod_opt: {} |\tenergy: {}\n".format(
prod_opt.species.chemid, prod_opt.species.energy))
elog.close()
self.species.reac_ts_done[index] = 5
elif self.species.reac_ts_done[index] == 5:
#check up on the TS and product optimizations
opts_done = 1
fails = 0
#check if ts is done
if not obj.ts_opt.shir == 1:
opts_done = 0
obj.ts_opt.do_optimization()
if obj.ts_opt.shigh == -999:
logging.info("Reaction {} ts_opt_shigh failure".format(
instance_name))
fails = 1
for pr_opt in obj.prod_opt:
if not pr_opt.shir == 1:
opts_done = 0
pr_opt.do_optimization()
if pr_opt.shigh == -999:
logging.info(
"Reaction {} pr_opt_shigh failure".format(
instance_name))
fails = 1
if fails:
self.species.reac_ts_done[index] = -999
elif opts_done:
self.species.reac_ts_done[index] = 6
elif self.species.reac_ts_done[index] == 6:
#Finilize the calculations
#continue to PES search in case a new well was found
if self.par.par['pes']:
#verify if product is monomolecular, and if it is new
if len(obj.products) == 1:
st_pt = obj.prod_opt[0].species
chemid = st_pt.chemid
energy = st_pt.energy
well_energy = self.species.energy
new_barrier_threshold = self.par.par[
'barrier_threshold'] - (
energy - well_energy) * constants.AUtoKCAL
dir = os.path.dirname(os.getcwd())
jobs = open(dir + '/chemids',
'r').read().split('\n')
jobs = [ji for ji in jobs]
if not str(chemid) in jobs:
#this well is new, add it to the jobs
while 1:
try:
#try to open the file and write to it
pes.write_input(
self.par, obj.products[0],
new_barrier_threshold, dir)
f = open(dir + '/chemids', 'a')
f.write('{}\n'.format(chemid))
f.close()
break
except IOError:
#wait a second and try again
time.sleep(1)
pass
# copy the files of the species to an upper directory
frags = obj.products
for frag in frags:
filecopying.copy_to_database_folder(
self.species.chemid, frag.chemid, self.qc)
#check for wrong number of negative frequencies
neg_freq = 0
for st_pt in obj.products:
if any([fi < 0. for fi in st_pt.reduced_freqs]):
neg_freq = 1
if any([fi < 0. for fi in obj.ts.reduced_freqs[1:]]):
neg_freq = 1
if neg_freq:
logging.info('\tFound negative frequency for ' +
instance_name)
self.species.reac_ts_done[index] = -999
else:
#the reaction search is finished
self.species.reac_ts_done[
index] = -1 # this is the success code
# write a temporary pes input file
# remove old xval and im_extent files
if os.path.exists('{}_xval.txt'.format(
self.species.chemid)):
os.remove('{}_xval.txt'.format(
self.species.chemid))
if os.path.exists('{}_im_extent.txt'.format(
self.species.chemid)):
os.remove('{}_im_extent.txt'.format(
self.species.chemid))
postprocess.createPESViewerInput(
self.species, self.qc, self.par)
elif self.species.reac_ts_done[index] == -999:
if self.par.par['delete_intermediate_files'] == 1:
if not self.species.reac_obj[
index].instance_name in deleted:
self.delete_files(
self.species.reac_obj[index].instance_name)
deleted.append(
self.species.reac_obj[index].instance_name)
alldone = 1
for index, instance in enumerate(self.species.reac_inst):
if any(self.species.reac_ts_done[i] >= 0
for i in range(len(self.species.reac_inst))):
alldone = 1
break
else:
alldone = 0
# write a small summary while running
wr = 1
if wr:
f_out = open('kinbot_monitor.out', 'w')
for index, instance in enumerate(self.species.reac_inst):
f_out.write('{}\t{}\t{}\n'.format(
self.species.reac_ts_done[index],
self.species.reac_step[index],
self.species.reac_obj[index].instance_name))
f_out.close()
time.sleep(1)
s = []
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
instance_name = obj.instance_name
# Write a summary on the combinatorial exploration
if 'combinatorial' in instance_name:
s.append('NAME\t' + instance_name)
# Write the bonds that were broken and formed
s.append('BROKEN_BONDS\t' +
'\t'.join('[{}, {}]'.format(re[0], re[1])
for re in obj.reac))
s.append('FORMED_BONDS\t' +
'\t'.join('[{}, {}]'.format(pr[0], pr[1])
for pr in obj.prod))
# Populate the ts_bond_lengths dict with the values
# of this reaction
if self.species.reac_ts_done[index] == -1:
for i in range(self.species.natom - 1):
for j in range(i + 1, self.species.natom):
if self.species.bond[i][j] != obj.product_bonds[i][
j]:
if (self.species.bond[i][j] == 0
or obj.product_bonds[i][j] == 0):
syms = []
syms.append(self.species.atom[i])
syms.append(self.species.atom[j])
syms = ''.join(sorted(syms))
dist = np.linalg.norm(obj.ts.geom[i] -
obj.ts.geom[j])
s.append('TS_BOND_LENGTHS\t{}\t{}'.format(
syms, dist))
# write the expected inchis
s.append('EXPECTED_INCHIS\t' +
'\t'.join(inchi for inchi in obj.prod_inchi))
# get the inchis the reaction found
if self.species.reac_ts_done[index] == -1:
inchis = obj.get_final_inchis()
s.append('FOUND_INCHIS\t' + '\t'.join(inchis))
s.append('\n')
with open('combinatorial.txt', 'w') as f:
f.write('\n'.join(s) + '\n')
logging.info("Reaction generation done!")
def generate(self):
"""
Creates the input for each reaction, runs them, and tests for success.
If successful, it creates the barrier and product objects.
It also then does the conformational search, and finally, the hindered rotor scans.
To make the code the most efficient, all of these happen in parallel, in a sense that
the jobs are not waiting for each other. E.g., one reaction can still be in the stage
of TS search, while the other can be already at the hindered rotor scan. This way,
all cores are occupied efficiently.
The switching between the various stages are done via the reac_ts_done variable.
0: initiate the TS search
1: check barrier height and errors in TS, and initiates normal mode displacement test, start the irc calculations
2: submit product optimization
3: submit the frequency calculation
4: do the optimization of the ts and the products
5: follow up on the optimizations
6: finalize calculations, check for wrong number of negative frequencies
If at any times the calculation fails, reac_ts_done is set to -999.
If all steps are successful, reac_ts_done is set to -1.
"""
if len(self.species.reac_inst) > 0:
alldone = 1
else:
alldone = 0
while alldone:
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
instance_name = obj.instance_name
# START REATION SEARCH
if self.species.reac_ts_done[
index] == 0 and self.species.reac_step[index] == 0:
#verify after restart if search has failed in previous kinbot run
status = self.qc.check_qc(instance_name)
if status == 'error' or status == 'killed':
logging.info(
'\tRxn search failed (error or killed) for {}'.
format(instance_name))
self.species.reac_ts_done[index] = -999
if self.species.reac_ts_done[
index] == 0: # ts search is ongoing
if obj.scan == 0: #don't do a scan of a bond
if self.species.reac_step[index] == obj.max_step + 1:
status = self.qc.get_qc_freq(
instance_name, self.species.natom)[0]
if status == 0:
self.species.reac_ts_done[index] = 1
elif status == -1:
logging.info(
'\tRxn search failed for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj, self.species.reac_step[index],
self.par.par['qc_command'])
else: # do a bond scan
if self.species.reac_step[
index] == self.par.par['scan_step'] + 1:
status = self.qc.get_qc_freq(
instance_name, self.species.natom)[0]
if status == 0:
self.species.reac_ts_done[index] = 1
elif status == -1:
logging.info(
'\tRxn search failed for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
if self.species.reac_step[index] == 0:
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj, self.species.reac_step[index],
self.par.par['qc_command'])
elif self.species.reac_step[index] > 0:
status = self.qc.check_qc(instance_name)
if status == 'error' or status == 'killed':
logging.info(
'\tRxn search failed for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
err, energy = self.qc.get_qc_energy(
instance_name)
if err == 0:
self.species.reac_scan_energy[
index].append(energy)
if len(self.species.
reac_scan_energy[index]) > 1:
if self.species.reac_scan_energy[
index][
-1] < self.species.reac_scan_energy[
index][-2]:
self.species.reac_step[
index] = self.par.par[
'scan_step']
self.species.reac_step[
index] = reac_family.carry_out_reaction(
obj,
self.species.reac_step[index],
self.par.par['qc_command'])
elif self.species.reac_ts_done[index] == 1:
status = self.qc.check_qc(instance_name)
if status == 'running': continue
elif status == 'error':
logging.info(
'\tRxn search failed (gaussian error) for {}'.
format(instance_name))
self.species.reac_ts_done[index] = -999
else:
#check the barrier height:
if self.species.reac_type[
index] == 'R_Addition_MultipleBond':
sp_energy = self.qc.get_qc_energy(
str(self.species.chemid) + '_well_mp2')[1]
barrier = (self.qc.get_qc_energy(instance_name)[1]
- sp_energy) * constants.AUtoKCAL
else:
sp_energy = self.qc.get_qc_energy(
str(self.species.chemid) + '_well')[1]
barrier = (self.qc.get_qc_energy(instance_name)[1]
- sp_energy) * constants.AUtoKCAL
if barrier > self.par.par['barrier_threshold']:
logging.info(
'\tRxn barrier too high ({val}) for {name}'.
format(val=barrier, name=instance_name))
self.species.reac_ts_done[index] = -999
else:
obj.irc = IRC(
obj, self.par
) #TODO: this doesn't seem like a good design
irc_status = obj.irc.check_irc()
if 0 in irc_status:
# No IRC started yet, start the IRC now
logging.info(
'\tStarting IRC calculations for {}'.
format(instance_name))
obj.irc.do_irc_calculations()
elif irc_status[0] == 'running' or irc_status[
1] == 'running':
continue
else:
#IRC's have succesfully finished, have an error or were killed, in any case
#read the geometries and try to make products out of them
#verify which of the ircs leads back to the reactant, if any
prod = obj.irc.irc2stationary_pt()
if prod == 0:
logging.info(
'\t\tNo product found for {}'.format(
instance_name))
self.species.reac_ts_done[index] = -999
else:
#IRC's are done
obj.products = prod
obj.product_bonds = prod.bond
self.species.reac_ts_done[index] = 2
elif self.species.reac_ts_done[index] == 2:
#identify bimolecular products and wells
fragments, maps = obj.products.start_multi_molecular()
obj.products = []
for i, frag in enumerate(fragments):
obj.products.append(frag)
self.qc.qc_opt(frag, frag.geom)
self.species.reac_ts_done[index] = 3
elif self.species.reac_ts_done[index] == 3:
#wait for the optimization to finish
err = 0
for st_pt in obj.products:
chemid = st_pt.chemid
orig_geom = copy.deepcopy(st_pt.geom)
e, st_pt.geom = self.qc.get_qc_geom(
str(st_pt.chemid) + '_well', st_pt.natom)
if e < 0:
logging.info(
'\tProduct optimization failed for {}, product {}'
.format(instance_name, st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
elif e != 0:
err = -1
else:
e2, st_pt.energy = self.qc.get_qc_energy(
str(st_pt.chemid) + '_well')
e2, st_pt.zpe = self.qc.get_qc_zpe(
str(st_pt.chemid) + '_well')
st_pt.bond_mx()
st_pt.characterize(
0) # not allowed to use the dimer option here
st_pt.calc_chemid()
if chemid != st_pt.chemid:
# product was optimized to another structure, give warning and remove this reaction
logging.info(
'\tProduct optimizatied to other structure for {}, product {} to {}'
.format(instance_name, chemid,
st_pt.chemid))
self.species.reac_ts_done[index] = -999
err = -1
if err == 0:
self.species.reac_ts_done[index] = 4
elif self.species.reac_ts_done[index] == 4:
# Do the TS and product optimization
#make a stationary point object of the ts
bond_mx = np.zeros(
(self.species.natom, self.species.natom), dtype=int)
for i in range(self.species.natom):
for j in range(self.species.natom):
bond_mx[i][j] = max(self.species.bond[i][j],
obj.product_bonds[i][j])
err, geom = self.qc.get_qc_geom(instance_name,
self.species.natom)
ts = StationaryPoint(instance_name,
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=geom,
wellorts=1)
err, ts.energy = self.qc.get_qc_energy(instance_name)
err, ts.zpe = self.qc.get_qc_zpe(instance_name)
ts.bond = bond_mx
ts.find_cycle()
ts.find_conf_dihedral()
obj.ts = ts
#do the ts optimization
obj.ts_opt = Optimize(obj.ts, self.par, self.qc)
obj.ts_opt.do_optimization()
#do the products optimizations
for st_pt in obj.products:
#check for products of other reactions that are the same as this product
#in the case such products are found, use the same Optimize object for both
new = 1
for i, inst_i in enumerate(self.species.reac_inst):
if not i == index:
obj_i = self.species.reac_obj[i]
if self.species.reac_ts_done[i] > 3:
for j, st_pt_i in enumerate(
obj_i.products):
if st_pt_i.chemid == st_pt.chemid:
if len(obj_i.prod_opt) > j:
prod_opt = obj_i.prod_opt[j]
new = 0
break
if new:
prod_opt = Optimize(st_pt, self.par, self.qc)
prod_opt.do_optimization()
obj.prod_opt.append(prod_opt)
self.species.reac_ts_done[index] = 5
elif self.species.reac_ts_done[index] == 5:
#check up on the TS and product optimizations
opts_done = 1
fails = 0
#check if ts is done
if not obj.ts_opt.shir == 1:
opts_done = 0
obj.ts_opt.do_optimization()
if obj.ts_opt.shigh == -999:
fails = 1
for pr_opt in obj.prod_opt:
if not pr_opt.shir == 1:
opts_done = 0
pr_opt.do_optimization()
if pr_opt.shigh == -999:
fails = 1
if fails:
self.species.reac_ts_done[index] = -999
elif opts_done:
self.species.reac_ts_done[index] = 6
elif self.species.reac_ts_done[index] == 6:
#Finilize the calculations
#continue to PES search in case a new well was found
if self.par.par['pes']:
#verify if product is monomolecular, and if it is new
if len(obj.products) == 1:
st_pt = obj.prod_opt[0].species
chemid = st_pt.chemid
energy = st_pt.energy
well_energy = self.species.energy
new_barrier_threshold = self.par.par[
'barrier_threshold'] - (
energy - well_energy) * constants.AUtoKCAL
dir = os.path.dirname(os.getcwd())
jobs = open(dir + '/chemids',
'r').read().split('\n')
jobs = [ji for ji in jobs]
if not str(chemid) in jobs:
#this well is new, add it to the jobs
while 1:
try:
#try to open the file and write to it
pes.write_input(
self.par, obj.products[0],
new_barrier_threshold, dir)
f = open(dir + '/chemids', 'a')
f.write('{}\n'.format(chemid))
f.close()
break
except IOError:
#wait a second and try again
time.sleep(1)
pass
#check for wrong number of negative frequencies
neg_freq = 0
for st_pt in obj.products:
if any([fi < 0. for fi in st_pt.reduced_freqs]):
neg_freq = 1
if any([fi < 0. for fi in obj.ts.reduced_freqs[1:]]):
neg_freq = 1
if neg_freq:
logging.info('\tFound negative frequency for ' +
instance_name)
self.species.reac_ts_done[index] = -999
else:
#the reaction search is finished
self.species.reac_ts_done[
index] = -1 # this is the success code
# write a temporary pes input file
# remove old xval and im_extent files
if os.path.exists('{}_xval.txt'.format(
self.species.chemid)):
os.remove('{}_xval.txt'.format(
self.species.chemid))
if os.path.exists('{}_im_extent.txt'.format(
self.species.chemid)):
os.remove('{}_im_extent.txt'.format(
self.species.chemid))
postprocess.createPESViewerInput(
self.species, self.qc, self.par)
alldone = 1
for index, instance in enumerate(self.species.reac_inst):
if any(self.species.reac_ts_done[i] >= 0
for i in range(len(self.species.reac_inst))):
alldone = 1
break
else:
alldone = 0
# write a small summary while running
wr = 1
if wr:
f_out = open('kinbot_monitor.out', 'w')
for index, instance in enumerate(self.species.reac_inst):
f_out.write('{}\t{}\t{}\n'.format(
self.species.reac_ts_done[index],
self.species.reac_step[index],
self.species.reac_obj[index].instance_name))
f_out.close()
time.sleep(1)
s = []
for index, instance in enumerate(self.species.reac_inst):
obj = self.species.reac_obj[index]
instance_name = obj.instance_name
# Write a summary on the combinatorial exploration
if 'combinatorial' in instance_name:
s.append('NAME\t' + instance_name)
# Write the bonds that were broken and formed
s.append('BROKEN_BONDS\t' +
'\t'.join('[{}, {}]'.format(re[0], re[1])
for re in obj.reac))
s.append('FORMED_BONDS\t' +
'\t'.join('[{}, {}]'.format(pr[0], pr[1])
for pr in obj.prod))
# Populate the ts_bond_lengths dict with the values
# of this reaction
if self.species.reac_ts_done[index] == -1:
for i in range(self.species.natom - 1):
for j in range(i + 1, self.species.natom):
if self.species.bond[i][j] != obj.product_bonds[i][
j]:
if (self.species.bond[i][j] == 0
or obj.product_bonds[i][j] == 0):
syms = []
syms.append(self.species.atom[i])
syms.append(self.species.atom[j])
syms = ''.join(sorted(syms))
dist = np.linalg.norm(obj.ts.geom[i] -
obj.ts.geom[j])
s.append('TS_BOND_LENGTHS\t{}\t{}'.format(
syms, dist))
# write the expected inchis
s.append('EXPECTED_INCHIS\t' +
'\t'.join(inchi for inchi in obj.prod_inchi))
# get the inchis the reaction found
if self.species.reac_ts_done[index] == -1:
inchis = obj.get_final_inchis()
s.append('FOUND_INCHIS\t' + '\t'.join(inchis))
s.append('\n')
with open('combinatorial.txt', 'w') as f:
f.write('\n'.join(s) + '\n')
logging.info("Reaction generation done!")
def main():
try:
input_file = sys.argv[1]
except IndexError:
print(
'To use the pes script, supply one argument being the input file!')
sys.exit(-1)
# TODO: write information about the arguments
# change this to nice argument parsers with
# dashes etc.
no_kinbot = 0
task = 'all'
names = []
if len(sys.argv) > 2:
if sys.argv[2] == 'no-kinbot':
no_kinbot = 1
if len(sys.argv) > 3:
# possible tasks are:
# 1. all: This is the default showing all pathways
# 2. lowestpath: show the lowest path between the species
# corresponding to the names
# 3. allpaths: show all paths between the species
# corresponding to the names
# 4. wells: show all reactions of one wells
# corresponding to the names
task = sys.argv[3]
names = sys.argv[4:]
# print the license message to the console
print(license_message.message)
# initialize the parameters
par = Parameters(input_file)
# set up the logging environment
logging.basicConfig(filename='pes.log', level=logging.INFO)
logging.info(license_message.message)
msg = 'Starting the PES search at {}'.format(datetime.datetime.now())
logging.info(msg)
if par.par['pes'] and par.par['specific_reaction']:
logging.error('Specific reaction cannot be searched in PES mode.')
return
well0 = StationaryPoint('well0',
par.par['charge'],
par.par['mult'],
smiles=par.par['smiles'],
structure=par.par['structure'])
well0.characterize(dimer=par.par['dimer'])
write_input(par, well0, par.par['barrier_threshold'], os.getcwd())
# add the initial well to the chemids
with open('chemids', 'w') as f:
f.write(str(well0.chemid) + '\n')
# create a directory for the L3 single point calculations
# directory has the name of the code, e.g., molpro
try:
os.mkdir(par.par['single_point_qc'])
except OSError:
pass
#List of chemids to skip KinBot submissions for.
skipChemids = par.par['skip_chemids']
# maximum number of kinbot jobs that run simultaneously
max_running = par.par['simultaneous_kinbot']
# jobs that are running
running = []
# jobs that are finished
finished = []
# list of all jobs
jobs = []
# dict of the pid's for all jobs
pids = {}
a = 0
b = 0
c = 0
while 1:
j = len(jobs)
if j != a:
logging.info('{0} {1} {2}'.format("len(jobs): ", j, "\n"))
a = j
with open('chemids', 'r') as f:
jobs = f.read().split('\n')
jobs = [ji for ji in jobs if ji != '']
if len(jobs) > j:
logging.info('\tPicked up new jobs: ' + ' '.join(jobs[j:]))
k = len(running)
l = len(finished)
if b != k:
logging.info('{0} {1} {2}'.format("len(running): ", len(running),
"\n"))
b = k
if c != l:
logging.info('{0} {1} {2}'.format("len(finished): ", len(finished),
"\n"))
c = l
if len(finished) == len(jobs):
time.sleep(2)
if len(finished) == len(jobs):
break
while (len(running) < max_running
and len(running) + len(finished) < len(jobs)):
# start a new job
job = jobs[len(running) + len(finished)]
kb = 1
logging.info('Job: {}'.format(job))
if 'none' in skipChemids:
logging.info('No KinBot runs to be skipped')
else:
if job in skipChemids:
kb = 0
logging.info('kb: {}'.format(kb))
if kb == 1:
pid = 0
if not no_kinbot:
pid = submit_job(job, par) # kinbot is submitted here
else:
get_wells(job)
pids[job] = pid
t = datetime.datetime.now()
logging.info('\tStarted job {} at {}'.format(job, t))
running.append(job)
elif kb == 0:
logging.info('Skipping Kinbot for {}'.format(job))
finished.append(job)
else:
logging.info('kb value not 0 or 1')
# check if a thread is done
for job in running:
if not check_status(job, pids[job]):
t = datetime.datetime.now()
logging.info('\tFinished job {} at {}'.format(job, t))
finished.append(job)
if not no_kinbot:
# write a temporary pes file
# remove old xval and im_extent files
try:
os.remove('{}_xval.txt'.format(par.par['title']))
except OSError:
pass
try:
os.remove('{}_im_extent.txt'.format(par.par['title']))
except OSError:
pass
# remove the finished threads
for job in finished:
if job in running:
running.remove(job)
if not no_kinbot:
# write a summary of what is running and finished
summary_lines = []
summary_lines.append('Total\t\t{}'.format(len(jobs)))
summary_lines.append('Running\t\t{}'.format(len(running)))
summary_lines.append('Finished\t{}'.format(len(finished)))
summary_lines.append('')
summary_lines.append('Running:')
for job in running:
summary_lines.append('\t{}'.format(job))
summary_lines.append('')
summary_lines.append('Finished:')
for job in finished:
summary_lines.append('\t{}'.format(job))
with open('pes_summary.txt', 'w') as f:
f.write('\n'.join(summary_lines))
time.sleep(1)
# delete skipped jobs from the jobs before sending to postprocess
for skip in skipChemids:
try:
jobs.pop(jobs.index(skip))
except ValueError:
pass
postprocess(par, jobs, task, names)
# make molpro inputs for all keys above
# place submission script in the directory for offline submission
# read in the molpro energies for the keys in the above three dicts
# for key in newdict.keys():
# print(key)
# if all energies are there
# do something like postprocess, but with new energies
# postprocess_L3(saddle_zpe, well_zpe, prod_zpe, saddle_energy, well_energy, prod_energyi, conn)
# Notify user the search is done
logging.info('PES search done!')
print('PES search done!')
def do_optimization(self):
while 1:
# do the conformational search
if self.par['conformer_search'] == 1:
if self.scycconf == -1 and self.sconf == -1:
logging.info('\tStarting conformational search of {}'.format(self.name))
self.species.confs = Conformers(self.species, self.par, self.qc)
# first do the cyclic part of the molecule
if self.scycconf == -1:
# start the ring conf search
if len(self.species.cycle_chain) > 0:
# there are rings in the molecule, do a search
self.species.confs.generate_ring_conformers(copy.deepcopy(self.species.geom))
# set the cyclic conf status to running
self.scycconf = 0
else:
# there are no rings in the molecule, continue from the current geometry
self.species.confs.cyc_conf_geoms = [copy.deepcopy(self.species.geom)]
# no ring conf search has to be done, set status to finished
self.scycconf = 1
if self.scycconf == 0:
# ring conf search is running, check if finished
status, self.species.confs.cyc_conf_geoms = self.species.confs.check_ring_conformers()
if status:
# ring conf search is finished
self.scycconf = 1
# first do an semi empirical optimization if requested by the user
if self.par['semi_emp_conformer_search'] == 1:
logging.info('semi empirical conformer search is starting for {}'.format(self.name))
if self.ssemi_empconf == -1:
# semi empirical part has not started yet
self.species.semi_emp_confs = Conformers(self.species, self.par, self.qc, semi_emp=1)
for geom in self.species.confs.cyc_conf_geoms:
# take all the geometries from the cyclic part
# generate the conformers for the current geometry
self.species.semi_emp_confs.generate_conformers(0, geom)
# set conf status to running
self.ssemi_empconf = 0
if self.ssemi_empconf == 0:
# semi empirical conformational search is running
# check if the conformational search is done
status, lowest_conf, geom, self.semi_emp_low_energy, self.semi_emp_conformers, self.semi_emp_energies = self.species.semi_emp_confs.check_conformers(wait=self.wait)
if status == 1:
logging.info("semi empirical lowest energy conformer for species {} is number {}".format(self.name, lowest_conf))
# set conf status to finished
self.ssemi_empconf = 1
else:
self.ssemi_empconf = 1
if self.ssemi_empconf == 1 and self.scycconf == 1:
# do open chain part if cyclic (if there were any) and semi empirical (if requested) parts are done
if self.sconf == -1:
# open chain part has not started yet
# if semi empirical conformer were searched for, start from those,
# else start from cyclic conformers
if self.par['semi_emp_conformer_search'] == 1:
self.species.confs.nconfs = 1
for i, geom in enumerate(self.semi_emp_conformers):
if (self.semi_emp_energies[i] - self.semi_emp_low_energy) * constants.AUtoKCAL < self.par['semi_emp_confomer_threshold']:
self.species.confs.generate_conformers(-999, geom)
logging.info("There are {} structures below the {} kcal/mol threshold for species {} in the semiempirical search.". \
format(i, self.par['semi_emp_confomer_threshold'], self.name))
else:
print_warning = True
for geom in self.species.confs.cyc_conf_geoms:
# take all the geometries from the cyclic part
# generate the conformers for the current geometry
self.skip_conf_check = self.species.confs.generate_conformers(0, geom, print_warning=print_warning)
print_warning = False
# set conf status to running
self.sconf = 0
if self.sconf == 0:
# conformational search is running
# check if the conformational search is done
if self.skip_conf_check == 0:
status, lowest_conf, geom, low_energy, conformers, energies = self.species.confs.check_conformers(wait=self.wait)
if status == 1:
logging.info("lowest energy conformer for species: {} is number {}".format(self.name, lowest_conf))
# save lowest energy conformer as species geometry
self.species.geom = geom
# save lowest energy conformer energy
self.species.energy = low_energy
# set conf status to finished
self.sconf = 1
elif self.skip_conf_check == 1:
self.species.geom, self.species.energy = self.species.confs.lowest_conf_info()
logging.info('Conformers are not checked for {} to speed up calculations.'.format(self.name))
logging.info('They seem to have been done in a previous run.')
logging.info('Energy and geometry updated based on conf/{}_low file.'.format(self.name))
self.sconf = 1
else:
# no conf search necessary, set status to finished
self.sconf = 1
if self.sconf == 1: # conf search is finished
# if the conformers were already done in a previous run
# not clear what the purpose of these lines were
if self.par['conformer_search'] == 1:
status, lowest_conf, geom, low_energy, conformers, energies = self.species.confs.check_conformers(wait=self.wait)
while self.restart <= self.max_restart:
# do the high level calculations
if self.par['high_level'] == 1:
if self.shigh == -1:
if self.species.wellorts:
name = self.species.name
else:
name = self.species.chemid
# high level calculation did not start yet
logging.info('\tStarting high level optimization of {}'.format(name))
if self.species.wellorts:
# do the high level optimization of a ts
self.qc.qc_opt_ts(self.species, self.species.geom, high_level=1)
else:
# do the high level optimization of a well
self.qc.qc_opt(self.species, self.species.geom, high_level=1)
self.shigh = 0 # set the high status to running
if self.shigh == 0:
# high level calculation is running
# check if it is finished
status = self.qc.check_qc(self.job_high)
if status == 'error':
# found an error
logging.info('\tHigh level optimization failed for {}'.format(self.name))
self.shigh = -999
elif status == 'normal':
# finished successfully
err, new_geom = self.qc.get_qc_geom(self.job_high, self.species.natom, wait=self.wait)
temp = StationaryPoint('temp',
self.species.charge,
self.species.mult,
atom=self.species.atom,
geom=new_geom)
temp.bond_mx()
if self.species.wellorts: # for TS we need reasonable geometry agreement and normal mode correlation
if self.par['conformer_search'] == 0:
fr_file = self.fr_file_name(0) # name of the original TS file
else:
if self.skip_conf_check == 0:
fr_file = 'conf/{}_{}'.format(self.fr_file_name(0), lowest_conf)
else:
fr_file = 'conf/{}_low'.format(self.fr_file_name(0))
if self.qc.qc == 'gauss':
imagmode = reader_gauss.read_imag_mode(fr_file, self.species.natom)
fr_file = self.fr_file_name(1)
if self.qc.qc == 'gauss':
imagmode_high = reader_gauss.read_imag_mode(fr_file, self.species.natom)
# either geom is roughly same with closely matching imaginary modes, or geometry is very close
# maybe we need to do IRC at the high level as well...
same_geom = ((geometry.matrix_corr(imagmode, imagmode_high) > 0.9) and \
(geometry.equal_geom(self.species, temp, 0.3))) \
or (geometry.equal_geom(self.species, temp, 0.15))
else:
same_geom = geometry.equal_geom(self.species, temp, 0.1)
err, fr = self.qc.get_qc_freq(self.job_high, self.species.natom)
if self.species.natom == 1:
freq_ok = 1
elif len(fr) == 1 and fr[0] == 0:
freq_ok = 0
elif self.species.wellorts == 0 and fr[0] > 0.:
freq_ok = 1
elif self.species.wellorts == 1 and fr[0] < 0. and fr[1] > 0.:
freq_ok = 1
else:
freq_ok = 0
if same_geom and freq_ok:
# geometry is as expected and normal modes are the same for TS
err, self.species.geom = self.qc.get_qc_geom(self.job_high, self.species.natom)
err, self.species.energy = self.qc.get_qc_energy(self.job_high)
err, self.species.freq = self.qc.get_qc_freq(self.job_high, self.species.natom)
err, self.species.zpe = self.qc.get_qc_zpe(self.job_high)
self.shigh = 1
else:
# geometry diverged to other structure
if not same_geom:
logging.info('\tHigh level optimization converged to different structure for {}, related channels are deleted.'.format(self.name))
if not freq_ok:
logging.info('\tWrong number of imaginary frequencies for {}, related channels are deleted.'.format(self.name))
self.shigh = -999
else:
# no high-level calculations necessary, set status to finished
self.shigh = 1
if self.shigh == 1:
# do the HIR calculation
if self.par['rotor_scan'] == 1:
if self.shir == -1:
# hir not stated yet
logging.info('\tStarting hindered rotor calculations of {}'.format(self.name))
self.species.hir = HIR(self.species, self.qc, self.par)
self.species.hir.generate_hir_geoms(copy.deepcopy(self.species.geom), self.par['rigid_hir'])
self.shir = 0
if self.shir == 0:
# hir is running
# check if it is done:
status = self.species.hir.check_hir(wait=self.wait)
if status:
if len(self.species.hir.hir_energies) > 0:
# check if along the hir potential a structure was found with a lower energy
min = self.species.hir.hir_energies[0][0]
min_rotor = -1
min_ai = -1
for rotor in range(len(self.species.dihed)):
for ai in range(self.species.hir.nrotation):
# use a 0.1kcal/mol cutoff for numerical noise
if self.species.hir.hir_energies[rotor][ai] < min - 1.6E-4:
min = self.species.hir.hir_energies[rotor][ai]
min_rotor = rotor
min_ai = ai
if min_rotor > -1:
self.restart += 1
if self.restart < self.max_restart:
# lower energy structure found
logging.info('\t\tLower energy found during hindered rotor scan for {}'.format(self.name))
logging.info('\t\tRestart number: ' + str(self.restart))
logging.info('\t\tRotor: ' + str(min_rotor))
logging.info('\t\tScan point: ' + str(min_ai))
job = self.job_hir + str(min_rotor) + '_' + str(min_ai).zfill(2)
err, self.species.geom = self.qc.get_qc_geom(job, self.species.natom)
# delete the high_level log file and the hir log files
if os.path.exists(self.job_high + '.log'):
# logging.info("\t\t\tRemoving file " + self.job_high + '.log')
os.remove(self.job_high + '.log')
for rotor in range(len(self.species.dihed)):
for ai in range(self.species.hir.nrotation):
if os.path.exists(self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log'):
# logging.info("\t\t\tRemoving file " + self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
os.remove(self.job_hir + str(rotor) + '_' + str(ai).zfill(2) + '.log')
# set the status of high and hir back to not started
self.shigh = -1
self.shir = -1
else:
logging.info('\t\tLower energy found, but readched max restart for {}'.format(self.name))
self.shir = 1
else:
self.shir = 1
else:
self.shir = 1
else:
# no hir calculations necessary, set status to finished
self.shir = 1
if not self.wait or self.shir == 1 or self.shigh == -999:
# break the loop if no waiting is required or
# if the hir calcs are done or
# if the high level calc failed
break
else:
time.sleep(1)
if self.shir == 1:
# finalize if no waiting is required or if the hir calcs are done
# calculate the symmetry numbers
symmetry.calculate_symmetry(self.species)
# calculate the new frequencies with the internal rotations projected out
fr_file = self.name
if not self.species.wellorts:
fr_file += '_well'
if self.par['high_level']:
fr_file += '_high'
fr_file = self.fr_file_name(self.par['high_level'])
hess = self.qc.read_qc_hess(fr_file, self.species.natom)
self.species.kinbot_freqs, self.species.reduced_freqs = frequencies.get_frequencies(self.species, hess, self.species.geom)
# write the molpro input and read the molpro energy, if available
if self.par['L3_calc'] == 1:
if self.par['single_point_qc'] == 'molpro':
molp = Molpro(self.species, self.par)
if 'barrierless_saddle' in self.name:
key = self.par['barrierless_saddle_single_point_key']
molp.create_molpro_input(bls=1)
else:
key = self.par['single_point_key']
molp.create_molpro_input()
molp.create_molpro_submit()
status, molpro_energy = molp.get_molpro_energy(key)
# FIXME this might be wrong here:
if status:
self.species.energy = molpro_energy
self.delete_files()
if self.wait:
if self.shir == 1 or self.shigh == -999:
return 0
time.sleep(1)
else:
return 0
def main():
try:
input_file = sys.argv[1]
except IndexError:
print('To use KinBot, supply one argument being the input file!')
sys.exit(-1)
# print the license message to the console
print(license_message.message)
# initialize the parameters for this run
masterpar = Parameters(input_file)
par = masterpar.par
input_file = masterpar.input_file
# set up the logging environment
if par['verbose']:
logging.basicConfig(filename='kinbot.log', level=logging.DEBUG)
else:
logging.basicConfig(filename='kinbot.log', level=logging.INFO)
# write the license message to the log file
logging.info(license_message.message)
logging.info('Input parameters')
for param in par:
logging.info('{} {}'.format(param, par[param]))
# time stamp of the KinBot start
logging.info('Starting KinBot at {}'.format(datetime.datetime.now()))
# Make the necessary directories
if not os.path.exists('perm'):
os.makedirs('perm')
if not os.path.exists('scratch'):
os.makedirs('scratch')
if not os.path.exists(par['single_point_qc']):
os.mkdir(par['single_point_qc'])
if par['rotor_scan'] == 1:
if not os.path.exists('hir'):
os.mkdir('hir')
if not os.path.exists('hir_profiles'):
os.mkdir('hir_profiles')
if not os.path.exists('perm/hir/'):
os.makedirs('perm/hir/')
if par['conformer_search'] == 1:
if not os.path.exists('conf'):
os.mkdir('conf')
if not os.path.exists('perm/conf'):
os.makedirs('perm/conf')
if not os.path.exists('me'):
os.mkdir('me')
# initialize the reactant
well0 = StationaryPoint('well0',
par['charge'],
par['mult'],
smiles=par['smiles'],
structure=par['structure'])
well0.short_name = 'w1'
# write the initial reactant geometry to a file for visualization
geom_out = open('geometry.xyz', 'w')
geom_out.write('{}\n\n'.format(well0.natom))
for i, at in enumerate(well0.atom):
x, y, z = well0.geom[i]
geom_out.write('{} {:.6f} {:.6f} {:.6f}\n'.format(at, x, y, z))
geom_out.write('\n\n')
geom_out.close()
# characterize the initial reactant
well0.characterize(dimer=par['dimer'])
well0.name = str(well0.chemid)
start_name = well0.name
# initialize the qc instance
qc = QuantumChemistry(par)
# only run filecopying if PES is turned on
# if par['pes']:
# check if this well was calcualted before in another directory
# this flag indicates that this kinbot run
# should wait for the information from another
# kinbot run to become available and copy the necessary information
# wait_for_well = 1
# while wait_for_well:
# wait_for_well = filecopying.copy_from_database_folder(well0.chemid, well0.chemid, qc)
# if wait_for_well:
# time.sleep(1)
# start the initial optimization of the reactant
logging.info('Starting optimization of intial well')
qc.qc_opt(well0, well0.geom)
err, well0.geom = qc.get_qc_geom(str(well0.chemid) + '_well',
well0.natom,
wait=1)
err, well0.freq = qc.get_qc_freq(str(well0.chemid) + '_well',
well0.natom,
wait=1)
if err < 0:
logging.error('Error with initial structure optimization.')
return
if any(well0.freq[i] <= 0 for i in range(len(well0.freq))):
logging.error('Found imaginary frequency for initial structure.')
return
# characterize again and look for differences
well0.characterize(dimer=par['dimer'])
well0.name = str(well0.chemid)
if well0.name != start_name:
logging.error(
'The first well optimized to a structure different from the input.'
)
return
# do an MP2 optimization of the reactant,
# to compare some scan barrier heigths to
if par['families'] == ['all'] or \
'birad_recombination_R' in par['families'] or \
'r12_cycloaddition' in par['families'] or \
'r14_birad_scission' in par['families'] or \
'R_Addition_MultipleBond' in par['families'] or \
(par['skip_families'] != ['none'] and \
('birad_recombination_R' not in par['skip_families'] or \
'r12_cycloaddition' not in par['skip_families'] or \
'r14_birad_scission' not in par['skip_families'] or \
'R_Addition_MultipleBond' not in par['skip_families'])) or \
par['reaction_search'] == 0:
logging.info('Starting MP2 optimization of intial well')
qc.qc_opt(well0, well0.geom, mp2=1)
err, geom = qc.get_qc_geom(
str(well0.chemid) + '_well_mp2', well0.natom, 1)
# comparison for barrierless scan
if par['barrierless_saddle']:
logging.info(
'Optimization of intial well for barrierless at {}/{}'.format(
par['barrierless_saddle_method'],
par['barrierless_saddle_basis']))
qc.qc_opt(well0, well0.geom, bls=1)
err, geom = qc.get_qc_geom(
str(well0.chemid) + '_well_bls', well0.natom, 1)
# characterize again and look for differences
well0.characterize(dimer=par['dimer'])
well0.name = str(well0.chemid)
err, well0.energy = qc.get_qc_energy(str(well0.chemid) + '_well', 1)
err, well0.zpe = qc.get_qc_zpe(str(well0.chemid) + '_well', 1)
well_opt = Optimize(well0, par, qc, wait=1)
well_opt.do_optimization()
if well_opt.shigh == -999:
logging.error(
'Error with high level optimization of initial structure.')
return
if par['pes']:
filecopying.copy_to_database_folder(well0.chemid, well0.chemid, qc)
if par['reaction_search'] == 1:
logging.info('Starting reaction searches of intial well')
rf = ReactionFinder(well0, par, qc)
rf.find_reactions()
rg = ReactionGenerator(well0, par, qc, input_file)
rg.generate()
if par['homolytic_scissions'] == 1:
logging.info('Starting the search for homolytic scission products')
well0.homolytic_scissions = HomolyticScissions(well0, par, qc)
well0.homolytic_scissions.find_homolytic_scissions()
if par['me'] > 0: # it will be 2 for kinbots when the mess file is needed but not run
mess = MESS(par, well0)
mess.write_input(qc)
if par['me'] == 1:
logging.info('Starting Master Equation calculations')
if par['me_code'] == 'mess':
mess.run()
postprocess.createSummaryFile(well0, qc, par)
postprocess.createPESViewerInput(well0, qc, par)
postprocess.creatMLInput(well0, qc, par)
logging.info('Finished KinBot at {}'.format(datetime.datetime.now()))
print("Done!")
def main():
try:
input_file = sys.argv[1]
except IndexError:
print('To use KinBot, supply one argument being the input file!')
sys.exit(-1)
# print the license message to the console
print(license_message.message)
# initialize the parameters for this run
par = Parameters(input_file)
# set up the logging environment
if par.par['verbose']:
logging.basicConfig(filename='kinbot.log', level=logging.DEBUG)
else:
logging.basicConfig(filename='kinbot.log', level=logging.INFO)
# write the license message to the log file
logging.info(license_message.message)
# time stamp of the KinBot start
logging.info('Starting KinBot at {}'.format(datetime.datetime.now()))
# Make the necessary directories
if not os.path.exists('perm'):
os.makedirs('perm')
if not os.path.exists('scratch'):
os.makedirs('scratch')
if not os.path.exists(par.par['single_point_qc']):
os.mkdir(par.par['single_point_qc'])
if par.par['rotor_scan'] == 1:
if not os.path.exists('hir'):
os.mkdir('hir')
if not os.path.exists('hir_profiles'):
os.mkdir('hir_profiles')
if not os.path.exists('perm/hir/'):
os.makedirs('perm/hir/')
if par.par['conformer_search'] == 1:
if not os.path.exists('conf'):
os.mkdir('conf')
if not os.path.exists('perm/conf'):
os.makedirs('perm/conf')
if not os.path.exists('me'):
os.mkdir('me')
if par.par['pes'] and par.par['specific_reaction']:
logging.error('Specific reaction cannot be searched in PES mode.')
return
# initialize the reactant
well0 = StationaryPoint('well0',
par.par['charge'],
par.par['mult'],
smiles=par.par['smiles'],
structure=par.par['structure'])
well0.short_name = 'w1'
# wrtie the initial reactant geometry to a file for visualization
geom_out = open('geometry.xyz', 'w')
geom_out.write('{}\n\n'.format(well0.natom))
for i, at in enumerate(well0.atom):
x, y, z = well0.geom[i]
geom_out.write('{} {:.6f} {:.6f} {:.6f}\n'.format(at, x, y, z))
geom_out.write('\n\n')
geom_out.close()
# characterize the initial reactant
well0.characterize(par.par['dimer'])
well0.name = str(well0.chemid)
start_name = well0.name
# initialize the qc instance
qc = QuantumChemistry(par)
# start the initial optimization of the reactant
logging.info('Starting optimization of intial well')
qc.qc_opt(well0, well0.geom)
err, well0.geom = qc.get_qc_geom(str(well0.chemid) + '_well',
well0.natom, wait=1)
err, well0.freq = qc.get_qc_freq(str(well0.chemid) + '_well',
well0.natom, wait=1)
if err < 0:
logging.error('Error with initial structure optimization.')
return
if any(well0.freq[i] <= 0 for i in range(len(well0.freq))):
logging.error('Found imaginary frequency for initial structure.')
return
# characterize again and look for differences
well0.characterize(par.par['dimer'])
well0.name = str(well0.chemid)
if well0.name != start_name:
logging.error('The first well optimized to a structure different from the input.')
return
# do an MP2 optimization of the reactant,
# to compare Beta scission barrier heigths to
logging.info('Starting MP2 optimization of intial well')
qc.qc_opt(well0, well0.geom, mp2=1)
err, geom = qc.get_qc_geom(str(well0.chemid) + '_well_mp2', well0.natom, 1)
# characterize again and look for differences
well0.characterize(par.par['dimer'])
well0.name = str(well0.chemid)
# read the energy and the zpe corrected energy
err, well0.energy = qc.get_qc_energy(str(well0.chemid) + '_well', 1)
err, well0.zpe = qc.get_qc_zpe(str(well0.chemid) + '_well', 1)
well_opt = Optimize(well0, par, qc, wait=1)
well_opt.do_optimization()
if well_opt.shigh == -999:
logging.error('Error with high level optimization of initial structure.')
return
# do the reaction search using heuristics
if par.par['reaction_search'] == 1:
logging.info('Starting reaction searches of intial well')
rf = ReactionFinder(well0, par, qc)
rf.find_reactions()
rg = ReactionGenerator(well0, par, qc)
rg.generate()
# do the homolytic scission products search
if par.par['homolytic_scissions'] == 1:
logging.info('Starting the search for homolytic scission products')
well0.homolytic_scissions = HomolyticScissions(well0, par, qc)
well0.homolytic_scissions.find_homolytic_scissions()
# initialize the master equation instance
mess = MESS(par, well0)
mess.write_input()
mesmer = MESMER(par, well0)
mesmer.write_input()
if par.par['me'] == 1:
logging.info('Starting Master Equation calculations')
if par.par['me_code'] == 'mess':
mess.run()
elif par.par['me_code'] == 'mesmer':
mesmer.run()
else:
logging.error('Cannot recognize me code {}'.format(par.par['me_code']))
# postprocess the calculations
postprocess.createSummaryFile(well0, qc, par)
postprocess.createPESViewerInput(well0, qc, par)
postprocess.creatMLInput(well0, qc, par)
logging.info('Finished KinBot at {}'.format(datetime.datetime.now()))
print("Done!")
