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 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 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 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
