def qc_conf(self, species, geom, index=-1, ring=0):
"""
Creates a geometry optimization input for the conformational search and runs it.
qc: 'gauss' or 'nwchem'
wellorts: 0 for wells and 1 for saddle points
index: >=0 for sampling, each job will get numbered with index
"""
if index == -1:
job = 'conf/' + str(species.chemid) + '_well'
else:
r = ''
if ring: r = 'r'
if species.wellorts:
job = 'conf/' + species.name + '_' + r + str(index).zfill(
self.zf)
else:
job = 'conf/' + str(
species.chemid) + '_' + r + str(index).zfill(self.zf)
if species.wellorts:
kwargs = self.get_qc_arguments(job,
species.mult,
species.charge,
ts=1,
step=1,
max_step=1)
else:
kwargs = self.get_qc_arguments(job, species.mult, species.charge)
if self.qc == 'gauss':
kwargs['opt'] = 'CalcFC, Tight'
del kwargs['chk']
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom, species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom, ['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename(
'tpl', 'ase_{qc}_opt_well.py.tpl'.format(qc=self.qc))
template = open(template_file, 'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command)
f_out = open('{}.py'.format(job), 'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_ring_conf(self, species, geom, fix, change, conf_nr, scan_nr):
"""
Creates a constrained geometry optimization input for the
conformational search of cyclic structures and runs it.
Make use of the ASE optimizer PCOBFGS
qc: 'gauss' or 'nwchem'
scan: list of dihedrals to be scanned and their values
wellorts: 0 for wells and 1 for saddle points
conf_nr: number of the conformer in the conformer search
scan_nr: number of the scan for this conformer
"""
if species.wellorts:
job = 'conf/' + species.name + '_r' + str(conf_nr).zfill(
self.zf) + '_' + str(scan_nr).zfill(self.zf)
else:
job = 'conf/' + str(species.chemid) + '_r' + str(conf_nr).zfill(
self.zf) + '_' + str(scan_nr).zfill(self.zf)
kwargs = self.get_qc_arguments(job,
species.mult,
species.charge,
ts=species.wellorts,
step=1,
max_step=1,
hir=1)
del kwargs['opt']
del kwargs['chk']
kwargs['method'] = 'am1'
kwargs['basis'] = ''
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom, species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom, ['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename(
'tpl', 'ase_{qc}_ring_conf.py.tpl'.format(qc=self.qc))
template = open(template_file, 'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
fix=fix,
change=change,
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command)
f_out = open('{}.py'.format(job), 'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_hir(self, species, geom, rot_index, ang_index, fix, rigid):
"""
Creates a constrained geometry optimization input and runs it.
wellorts: 0 for wells and 1 for saddle points
rot_index: index of the rotor in the molecule
ang_index: index for the current size of the angle
fix: four atoms of the dihedral that is currently fixed
"""
if species.wellorts:
job = 'hir/' + species.name + '_hir_' + str(rot_index) + '_' + str(
ang_index).zfill(2)
else:
job = 'hir/' + str(species.chemid) + '_hir_' + str(
rot_index) + '_' + str(ang_index).zfill(2)
kwargs = self.get_qc_arguments(job,
species.mult,
species.charge,
ts=species.wellorts,
step=1,
max_step=1,
high_level=1,
hir=1,
rigid=rigid)
kwargs['fix'] = fix
del kwargs['chk']
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom, species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom, ['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename(
'tpl', 'ase_{qc}_hir.tpl.py'.format(qc=self.qc))
template = open(template_file, 'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job), 'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_conf(self,species, geom, index=-1, ring = 0):
"""
Creates a geometry optimization input for the conformational search and runs it.
qc: 'gauss' or 'nwchem'
wellorts: 0 for wells and 1 for saddle points
index: >=0 for sampling, each job will get numbered with index
"""
if index == -1:
job = 'conf/' + str(species.chemid) + '_well'
else:
r = ''
if ring: r = 'r'
if species.wellorts:
job = 'conf/' + species.name + '_' + r + str(index).zfill(self.zf)
else:
job = 'conf/' + str(species.chemid) + '_' + r + str(index).zfill(self.zf)
if species.wellorts:
kwargs = self.get_qc_arguments(job,species.mult,species.charge, ts = 1, step = 1, max_step = 1)
else:
kwargs = self.get_qc_arguments(job,species.mult,species.charge)
if self.qc == 'gauss':
kwargs['opt'] = 'CalcFC, Tight'
del kwargs['chk']
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom,species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom,['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename('tpl', 'ase_{qc}_opt_well.py.tpl'.format(qc = self.qc))
template = open(template_file,'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn = self.ppn,
dummy = dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job),'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_ring_conf(self,species, geom, fix, change, conf_nr, scan_nr):
"""
Creates a constrained geometry optimization input for the
conformational search of cyclic structures and runs it.
Make use of the ASE optimizer PCOBFGS
qc: 'gauss' or 'nwchem'
scan: list of dihedrals to be scanned and their values
wellorts: 0 for wells and 1 for saddle points
conf_nr: number of the conformer in the conformer search
scan_nr: number of the scan for this conformer
"""
if species.wellorts:
job = 'conf/' + species.name + '_r' + str(conf_nr).zfill(self.zf) + '_' + str(scan_nr).zfill(self.zf)
else:
job = 'conf/' + str(species.chemid) + '_r' + str(conf_nr).zfill(self.zf) + '_' + str(scan_nr).zfill(self.zf)
kwargs = self.get_qc_arguments(job,species.mult,species.charge, ts=species.wellorts, step=1, max_step=1, hir=1)
del kwargs['opt']
del kwargs['chk']
kwargs['method'] = 'am1'
kwargs['basis'] = ''
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom,species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom,['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename('tpl', 'ase_{qc}_ring_conf.py.tpl'.format(qc = self.qc))
template = open(template_file,'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
fix=fix,
change=change,
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job),'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_freq(self, species, geom, high_level=0):
"""
Creates a frequency input and runs it.
"""
job = str(species.chemid) + '_fr'
if high_level:
job = str(species.chemid) + '_fr_high'
kwargs = self.get_qc_arguments(job,
species.mult,
species.charge,
high_level=high_level)
if self.qc == 'gauss':
kwargs['freq'] = 'freq'
kwargs['ioplist'] = ['7/33=1']
elif self.qc == 'nwchem':
kwargs['task'] = 'frequencies'
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom, species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom, ['X'])
geom = np.concatenate((geom, [d]), axis=0)
# switch on the symmetry of gaussian
if 'NoSymm' in kwargs:
del kwargs['NoSymm']
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename(
'tpl', 'ase_{qc}_freq_well.tpl.py'.format(qc=self.qc))
template = open(template_file, 'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job), 'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_opt(self, species, geom, high_level=0, mp2=0):
"""
Creates a geometry optimization input and runs it.
"""
job = str(species.chemid) + '_well'
if high_level:
job = str(species.chemid) + '_well_high'
if mp2:
job = str(species.chemid) + '_well_mp2'
kwargs = self.get_qc_arguments(job,
species.mult,
species.charge,
high_level=high_level)
if self.qc == 'gauss':
kwargs['opt'] = 'CalcFC, Tight'
if mp2:
kwargs['method'] = 'mp2'
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom, species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom, ['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename(
'tpl', 'ase_{qc}_opt_well.py.tpl'.format(qc=self.qc))
template = open(template_file, 'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command)
f_out = open('{}.py'.format(job), 'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_freq(self, species, geom, high_level = 0):
"""
Creates a frequency input and runs it.
"""
job = str(species.chemid) + '_fr'
if high_level:
job = str(species.chemid) + '_fr_high'
kwargs = self.get_qc_arguments(job, species.mult, species.charge, high_level = high_level)
if self.qc == 'gauss':
kwargs['freq'] = 'freq'
kwargs['ioplist'] = ['7/33=1']
elif self.qc == 'nwchem':
kwargs['task'] = 'frequencies'
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom,species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom,['X'])
geom = np.concatenate((geom, [d]), axis=0)
#switch on the symmetry of gaussian
if 'NoSymm' in kwargs:
del kwargs['NoSymm']
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename('tpl', 'ase_{qc}_freq_well.py.tpl'.format(qc = self.qc))
template = open(template_file,'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job),'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def testLinear(self):
"""
Test the method to see if a geometry is linear
"""
np.random.seed(1)
a = np.random.uniform(size=3)
b = np.random.uniform(size=3)
d1 = np.random.rand()
d2 = np.random.rand()
c = a + b / np.linalg.norm(b) * d1
d = a + b / np.linalg.norm(b) * d2
geom = [a, c, d]
bond = [[0, 1, 0], [1, 0, 1], [0, 0, 1]]
dummy = geometry.is_linear(geom, bond)
exp = 1
cal = len(dummy)
warn = 'Linear molecule not well perceived.'
self.assertEqual(exp, cal, msg=warn)
def qc_opt(self, species, geom, high_level = 0, mp2 = 0):
"""
Creates a geometry optimization input and runs it.
"""
job = str(species.chemid) + '_well'
if high_level:
job = str(species.chemid) + '_well_high'
if mp2:
job = str(species.chemid) + '_well_mp2'
kwargs = self.get_qc_arguments(job, species.mult, species.charge, high_level = high_level)
if self.qc == 'gauss':
kwargs['opt'] = 'CalcFC, Tight'
if mp2:
kwargs['method'] = 'mp2'
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom,species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom,['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename('tpl', 'ase_{qc}_opt_well.py.tpl'.format(qc = self.qc))
template = open(template_file,'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy = dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job),'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_hir(self,species, geom, rot_index, ang_index,fix):
"""
Creates a constrained geometry optimization input and runs it.
wellorts: 0 for wells and 1 for saddle points
rot_index: index of the rotor in the molecule
ang_index: index for the current size of the angle
fix: four atoms of the dihedral that is currently fixed
"""
if species.wellorts:
job = 'hir/' + species.name + '_hir_' + str(rot_index) + '_' + str(ang_index).zfill(2)
else:
job = 'hir/' + str(species.chemid) + '_hir_' + str(rot_index) + '_' + str(ang_index).zfill(2)
kwargs = self.get_qc_arguments(job,species.mult,species.charge, ts = species.wellorts, step = 1, max_step = 1, high_level = 1, hir = 1)
kwargs['fix'] = fix
del kwargs['chk']
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom,species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom,['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename('tpl', 'ase_{qc}_hir.py.tpl'.format(qc = self.qc))
template = open(template_file,'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job),'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
def qc_opt(self, species, geom, high_level=0, mp2=0, bls=0):
"""
Creates a geometry optimization input and runs it.
"""
job = str(species.chemid) + '_well'
if high_level:
job = str(species.chemid) + '_well_high'
if mp2:
job = str(species.chemid) + '_well_mp2'
if bls:
job = str(species.chemid) + '_well_bls'
# TODO: Code exceptions into their own function/py script that opt can call.
# TODO: Fix symmetry numbers for calcs as well if needed
# O2
if species.chemid == "320320000000000000001":
mult = 3
# CH2
elif species.chemid == "140260020000000000001":
mult = 3
else:
mult = species.mult
kwargs = self.get_qc_arguments(job,
mult,
species.charge,
high_level=high_level)
if self.qc == 'gauss':
kwargs['opt'] = 'CalcFC, Tight'
if mp2:
kwargs['method'] = self.scan_method
kwargs['basis'] = self.scan_basis
if high_level:
if self.opt:
kwargs['opt'] = 'CalcFC, {}'.format(self.opt)
# the integral is set in the get_qc_arguments parts, bad design
atom = copy.deepcopy(species.atom)
dummy = geometry.is_linear(geom, species.bond)
if len(dummy) > 0: # add a dummy atom for each close to linear angle
for d in dummy:
atom = np.append(atom, ['X'])
geom = np.concatenate((geom, [d]), axis=0)
dummy = [d.tolist() for d in dummy]
template_file = pkg_resources.resource_filename(
'tpl', 'ase_{qc}_opt_well.tpl.py'.format(qc=self.qc))
template = open(template_file, 'r').read()
template = template.format(label=job,
kwargs=kwargs,
atom=list(atom),
geom=list([list(gi) for gi in geom]),
ppn=self.ppn,
dummy=dummy,
qc_command=self.qc_command,
working_dir=os.getcwd())
f_out = open('{}.py'.format(job), 'w')
f_out.write(template)
f_out.close()
self.submit_qc(job)
return 0
