def opt_with_frequency_flattener(
cls,
qchem_command,
multimode="openmp",
input_file="mol.qin",
output_file="mol.qout",
qclog_file="mol.qclog",
max_iterations=10,
max_molecule_perturb_scale=0.3,
check_connectivity=True,
linked=True,
transition_state=False,
freq_before_opt=False,
save_final_scratch=False,
**QCJob_kwargs,
):
"""
Optimize a structure and calculate vibrational frequencies to check if the
structure is in a true minima.
If there are an inappropriate number of imaginary frequencies (>0 for a
minimum-energy structure, >1 for a transition-state), attempt to re-calculate
using one of two methods:
- Perturb the geometry based on the imaginary frequencies and re-optimize
- Use the exact Hessian to inform a subsequent optimization
After each geometry optimization, the frequencies are re-calculated to
determine if a true minimum (or transition-state) has been found.
Note: Very small imaginary frequencies (-15cm^-1 < nu < 0) are allowed
if there is only one more than there should be. In other words, if there
is one very small imaginary frequency, it is still treated as a minimum,
and if there is one significant imaginary frequency and one very small
imaginary frequency, it is still treated as a transition-state.
Args:
qchem_command (str): Command to run QChem.
multimode (str): Parallelization scheme, either openmp or mpi.
input_file (str): Name of the QChem input file.
output_file (str): Name of the QChem output file.
max_iterations (int): Number of perturbation -> optimization -> frequency
iterations to perform. Defaults to 10.
max_molecule_perturb_scale (float): The maximum scaled perturbation that
can be applied to the molecule. Defaults to 0.3.
check_connectivity (bool): Whether to check differences in connectivity
introduced by structural perturbation. Defaults to True.
linked (bool): Whether or not to use the linked flattener. If set to True (default),
then the explicit Hessians from a vibrational frequency analysis will be used
as the initial Hessian of subsequent optimizations. In many cases, this can
significantly improve optimization efficiency.
transition_state (bool): If True (default False), use a ts
optimization (search for a saddle point instead of a minimum)
freq_before_opt (bool): If True (default False), run a frequency
calculation before any opt/ts searches to improve understanding
of the local potential energy surface.
save_final_scratch (bool): Whether to save full scratch directory contents
at the end of the flattening. Defaults to False.
**QCJob_kwargs: Passthrough kwargs to QCJob. See
:class:`custodian.qchem.jobs.QCJob`.
"""
if not os.path.exists(input_file):
raise AssertionError("Input file must be present!")
if transition_state:
opt_method = "ts"
perturb_index = 1
else:
opt_method = "opt"
perturb_index = 0
energy_diff_cutoff = 0.0000001
orig_input = QCInput.from_file(input_file)
freq_rem = copy.deepcopy(orig_input.rem)
freq_rem["job_type"] = "freq"
opt_rem = copy.deepcopy(orig_input.rem)
opt_rem["job_type"] = opt_method
first = True
energy_history = []
if freq_before_opt:
if not linked:
warnings.warn(
"WARNING: This first frequency calculation will not inform subsequent optimization!"
)
yield (QCJob(
qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_pre",
save_scratch=True,
backup=first,
**QCJob_kwargs,
))
if linked:
opt_rem["geom_opt_hessian"] = "read"
opt_rem["scf_guess_always"] = True
opt_QCInput = QCInput(
molecule=orig_input.molecule,
rem=opt_rem,
opt=orig_input.opt,
pcm=orig_input.pcm,
solvent=orig_input.solvent,
smx=orig_input.smx,
vdw_mode=orig_input.vdw_mode,
van_der_waals=orig_input.van_der_waals,
)
opt_QCInput.write_file(input_file)
first = False
if linked:
opt_rem["geom_opt_hessian"] = "read"
opt_rem["scf_guess_always"] = True
for ii in range(max_iterations):
yield (QCJob(
qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".{}_".format(opt_method) + str(ii),
save_scratch=True,
backup=first,
**QCJob_kwargs,
))
opt_outdata = QCOutput(output_file +
".{}_".format(opt_method) +
str(ii)).data
opt_indata = QCInput.from_file(input_file +
".{}_".format(opt_method) +
str(ii))
if opt_indata.rem["scf_algorithm"] != freq_rem["scf_algorithm"]:
freq_rem["scf_algorithm"] = opt_indata.rem["scf_algorithm"]
opt_rem["scf_algorithm"] = opt_indata.rem["scf_algorithm"]
first = False
if opt_outdata[
"structure_change"] == "unconnected_fragments" and not opt_outdata[
"completion"]:
if not transition_state:
warnings.warn(
"Unstable molecule broke into unconnected fragments which failed to optimize! Exiting..."
)
break
energy_history.append(opt_outdata.get("final_energy"))
freq_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=freq_rem,
opt=orig_input.opt,
pcm=orig_input.pcm,
solvent=orig_input.solvent,
smx=orig_input.smx,
vdw_mode=orig_input.vdw_mode,
van_der_waals=orig_input.van_der_waals,
)
freq_QCInput.write_file(input_file)
yield (QCJob(
qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_" + str(ii),
save_scratch=True,
backup=first,
**QCJob_kwargs,
))
outdata = QCOutput(output_file + ".freq_" + str(ii)).data
indata = QCInput.from_file(input_file + ".freq_" + str(ii))
if indata.rem["scf_algorithm"] != freq_rem["scf_algorithm"]:
freq_rem["scf_algorithm"] = indata.rem["scf_algorithm"]
opt_rem["scf_algorithm"] = indata.rem["scf_algorithm"]
errors = outdata.get("errors")
if len(errors) != 0:
raise AssertionError(
"No errors should be encountered while flattening frequencies!"
)
if not transition_state:
freq_0 = outdata.get("frequencies")[0]
freq_1 = outdata.get("frequencies")[1]
if freq_0 > 0.0:
warnings.warn("All frequencies positive!")
break
if abs(freq_0) < 15.0 and freq_1 > 0.0:
warnings.warn(
"One negative frequency smaller than 15.0 - not worth further flattening!"
)
break
if len(energy_history) > 1:
if abs(energy_history[-1] -
energy_history[-2]) < energy_diff_cutoff:
warnings.warn("Energy change below cutoff!")
break
opt_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=opt_rem,
opt=orig_input.opt,
pcm=orig_input.pcm,
solvent=orig_input.solvent,
smx=orig_input.smx,
vdw_mode=orig_input.vdw_mode,
van_der_waals=orig_input.van_der_waals,
)
opt_QCInput.write_file(input_file)
else:
freq_0 = outdata.get("frequencies")[0]
freq_1 = outdata.get("frequencies")[1]
freq_2 = outdata.get("frequencies")[2]
if freq_0 < 0.0 < freq_1:
warnings.warn("Saddle point found!")
break
if abs(freq_1) < 15.0 and freq_2 > 0.0:
warnings.warn(
"Second small imaginary frequency (smaller than 15.0) - not worth further flattening!"
)
break
opt_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=opt_rem,
opt=orig_input.opt,
pcm=orig_input.pcm,
solvent=orig_input.solvent,
smx=orig_input.smx,
vdw_mode=orig_input.vdw_mode,
van_der_waals=orig_input.van_der_waals,
)
opt_QCInput.write_file(input_file)
if not save_final_scratch:
shutil.rmtree(os.path.join(os.getcwd(), "scratch"))
else:
orig_opt_input = QCInput.from_file(input_file)
history = []
for ii in range(max_iterations):
yield (QCJob(
qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".{}_".format(opt_method) + str(ii),
backup=first,
**QCJob_kwargs,
))
opt_outdata = QCOutput(output_file +
".{}_".format(opt_method) +
str(ii)).data
if first:
orig_species = copy.deepcopy(opt_outdata.get("species"))
orig_charge = copy.deepcopy(opt_outdata.get("charge"))
orig_multiplicity = copy.deepcopy(
opt_outdata.get("multiplicity"))
orig_energy = copy.deepcopy(
opt_outdata.get("final_energy"))
first = False
if opt_outdata[
"structure_change"] == "unconnected_fragments" and not opt_outdata[
"completion"]:
if not transition_state:
warnings.warn(
"Unstable molecule broke into unconnected fragments which failed to optimize! Exiting..."
)
break
freq_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=freq_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent,
smx=orig_opt_input.smx,
vdw_mode=orig_opt_input.vdw_mode,
van_der_waals=orig_opt_input.van_der_waals,
)
freq_QCInput.write_file(input_file)
yield (QCJob(
qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_" + str(ii),
backup=first,
**QCJob_kwargs,
))
outdata = QCOutput(output_file + ".freq_" + str(ii)).data
errors = outdata.get("errors")
if len(errors) != 0:
raise AssertionError(
"No errors should be encountered while flattening frequencies!"
)
if not transition_state:
freq_0 = outdata.get("frequencies")[0]
freq_1 = outdata.get("frequencies")[1]
if freq_0 > 0.0:
warnings.warn("All frequencies positive!")
if opt_outdata.get("final_energy") > orig_energy:
warnings.warn(
"WARNING: Energy increased during frequency flattening!"
)
break
if abs(freq_0) < 15.0 and freq_1 > 0.0:
warnings.warn(
"One negative frequency smaller than 15.0 - not worth further flattening!"
)
break
if len(energy_history) > 1:
if abs(energy_history[-1] -
energy_history[-2]) < energy_diff_cutoff:
warnings.warn("Energy change below cutoff!")
break
else:
freq_0 = outdata.get("frequencies")[0]
freq_1 = outdata.get("frequencies")[1]
freq_2 = outdata.get("frequencies")[2]
if freq_0 < 0.0 < freq_1:
warnings.warn("Saddle point found!")
break
if abs(freq_1) < 15.0 and freq_2 > 0.0:
warnings.warn(
"Second small imaginary frequency (smaller than 15.0) - not worth further flattening!"
)
break
hist = {}
hist["molecule"] = copy.deepcopy(
outdata.get("initial_molecule"))
hist["geometry"] = copy.deepcopy(
outdata.get("initial_geometry"))
hist["frequencies"] = copy.deepcopy(outdata.get("frequencies"))
hist["frequency_mode_vectors"] = copy.deepcopy(
outdata.get("frequency_mode_vectors"))
hist["num_neg_freqs"] = sum(
1 for freq in outdata.get("frequencies") if freq < 0)
hist["energy"] = copy.deepcopy(opt_outdata.get("final_energy"))
hist["index"] = len(history)
hist["children"] = []
history.append(hist)
ref_mol = history[-1]["molecule"]
geom_to_perturb = history[-1]["geometry"]
negative_freq_vecs = history[-1]["frequency_mode_vectors"][
perturb_index]
reversed_direction = False
standard = True
# If we've found one or more negative frequencies in two consecutive iterations, let's dig in
# deeper:
if len(history) > 1:
# Start by finding the latest iteration's parent:
if history[-1]["index"] in history[-2]["children"]:
parent_hist = history[-2]
history[-1]["parent"] = parent_hist["index"]
elif history[-1]["index"] in history[-3]["children"]:
parent_hist = history[-3]
history[-1]["parent"] = parent_hist["index"]
else:
raise AssertionError(
"ERROR: your parent should always be one or two iterations behind you! Exiting..."
)
# if the number of negative frequencies has remained constant or increased from parent to
# child,
if history[-1]["num_neg_freqs"] >= parent_hist[
"num_neg_freqs"]:
# check to see if the parent only has one child, aka only the positive perturbation has
# been tried,
# in which case just try the negative perturbation from the same parent
if len(parent_hist["children"]) == 1:
ref_mol = parent_hist["molecule"]
geom_to_perturb = parent_hist["geometry"]
negative_freq_vecs = parent_hist[
"frequency_mode_vectors"][perturb_index]
reversed_direction = True
standard = False
parent_hist["children"].append(len(history))
# If the parent has two children, aka both directions have been tried, then we have to
# get creative:
elif len(parent_hist["children"]) == 2:
# If we're dealing with just one negative frequency,
if parent_hist["num_neg_freqs"] == 1:
if history[parent_hist["children"][0]][
"energy"] < history[-1]["energy"]:
good_child = copy.deepcopy(
history[parent_hist["children"][0]])
else:
good_child = copy.deepcopy(history[-1])
if good_child["num_neg_freqs"] > 1:
raise Exception(
"ERROR: Child with lower energy has more negative frequencies! "
"Exiting...")
if good_child["energy"] < parent_hist["energy"]:
make_good_child_next_parent = True
elif (vector_list_diff(
good_child["frequency_mode_vectors"]
[perturb_index],
parent_hist["frequency_mode_vectors"]
[perturb_index],
) > 0.2):
make_good_child_next_parent = True
else:
raise Exception(
"ERROR: Good child not good enough! Exiting..."
)
if make_good_child_next_parent:
good_child["index"] = len(history)
history.append(good_child)
ref_mol = history[-1]["molecule"]
geom_to_perturb = history[-1]["geometry"]
negative_freq_vecs = history[-1][
"frequency_mode_vectors"][
perturb_index]
else:
raise Exception(
"ERROR: Can't deal with multiple neg frequencies yet! Exiting..."
)
else:
raise AssertionError(
"ERROR: Parent cannot have more than two childen! Exiting..."
)
# Implicitly, if the number of negative frequencies decreased from parent to child,
# continue normally.
if standard:
history[-1]["children"].append(len(history))
min_molecule_perturb_scale = 0.1
scale_grid = 10
perturb_scale_grid = (max_molecule_perturb_scale -
min_molecule_perturb_scale) / scale_grid
structure_successfully_perturbed = False
for molecule_perturb_scale in np.arange(
max_molecule_perturb_scale,
min_molecule_perturb_scale,
-perturb_scale_grid,
):
new_coords = perturb_coordinates(
old_coords=geom_to_perturb,
negative_freq_vecs=negative_freq_vecs,
molecule_perturb_scale=molecule_perturb_scale,
reversed_direction=reversed_direction,
)
new_molecule = Molecule(
species=orig_species,
coords=new_coords,
charge=orig_charge,
spin_multiplicity=orig_multiplicity,
)
if check_connectivity and not transition_state:
structure_successfully_perturbed = (
check_for_structure_changes(
ref_mol, new_molecule) == "no_change")
if structure_successfully_perturbed:
break
if not structure_successfully_perturbed:
raise Exception(
"ERROR: Unable to perturb coordinates to remove negative frequency without changing "
"the connectivity! Exiting...")
new_opt_QCInput = QCInput(
molecule=new_molecule,
rem=opt_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent,
smx=orig_opt_input.smx,
vdw_mode=orig_opt_input.vdw_mode,
van_der_waals=orig_opt_input.van_der_waals,
)
new_opt_QCInput.write_file(input_file)
def setUpClass(cls):
cls.molecule = Molecule(["C", "O", "O"],
[[0, 0, 0], [-1, 0, 0], [1, 0, 0]])
cls.model = MEGNetModel.from_file(
os.path.join(
CWD, "../../../mvl_models/mp-2019.4.1/formation_energy.hdf5"))
def get_subgraphs_as_molecules(self, use_weights=False):
"""
Retrieve subgraphs as molecules, useful for extracting
molecules from periodic crystals.
Will only return unique molecules, not any duplicates
present in the crystal (a duplicate defined as an
isomorphic subgraph).
:param use_weights (bool): If True, only treat subgraphs
as isomorphic if edges have the same weights. Typically,
this means molecules will need to have the same bond
lengths to be defined as duplicates, otherwise bond
lengths can differ. This is a fairly robust approach,
but will treat e.g. enantiomers as being duplicates.
:return: list of unique Molecules in Structure
"""
# creating a supercell is an easy way to extract
# molecules (and not, e.g., layers of a 2D crystal)
# without adding extra logic
if getattr(self, '_supercell_sg', None) is None:
self._supercell_sg = supercell_sg = self*(3,3,3)
# make undirected to find connected subgraphs
supercell_sg.graph = nx.Graph(supercell_sg.graph)
# find subgraphs
all_subgraphs = list(nx.connected_component_subgraphs(supercell_sg.graph))
# discount subgraphs that lie across *supercell* boundaries
# these will subgraphs representing crystals
molecule_subgraphs = []
for subgraph in all_subgraphs:
intersects_boundary = any([d['to_jimage'] != (0, 0, 0)
for u, v, d in subgraph.edges(data=True)])
if not intersects_boundary:
molecule_subgraphs.append(subgraph)
# add specie names to graph to be able to test for isomorphism
for subgraph in molecule_subgraphs:
for n in subgraph:
subgraph.add_node(n, specie=str(supercell_sg.structure[n].specie))
# now define how we test for isomorphism
def node_match(n1, n2):
return n1['specie'] == n2['specie']
def edge_match(e1, e2):
if use_weights:
return e1['weight'] == e2['weight']
else:
return True
# prune duplicate subgraphs
unique_subgraphs = []
for subgraph in molecule_subgraphs:
already_present = [nx.is_isomorphic(subgraph, g,
node_match=node_match,
edge_match=edge_match)
for g in unique_subgraphs]
if not any(already_present):
unique_subgraphs.append(subgraph)
# get Molecule objects for each subgraph
molecules = []
for subgraph in unique_subgraphs:
coords = [supercell_sg.structure[n].coords for n
in subgraph.nodes()]
species = [supercell_sg.structure[n].specie for n
in subgraph.nodes()]
molecule = Molecule(species, coords)
# shift so origin is at center of mass
molecule = molecule.get_centered_molecule()
molecules.append(molecule)
return molecules
def _parse(self, filename):
start_patt = re.compile(" \(Enter \S+l101\.exe\)")
route_patt = re.compile(" #[pPnNtT]*.*")
link0_patt = re.compile("^\s(%.+)\s*=\s*(.+)")
charge_mul_patt = re.compile("Charge\s+=\s*([-\\d]+)\s+"
"Multiplicity\s+=\s*(\d+)")
num_basis_func_patt = re.compile("([0-9]+)\s+basis functions")
pcm_patt = re.compile("Polarizable Continuum Model")
stat_type_patt = re.compile("imaginary frequencies")
scf_patt = re.compile("E\(.*\)\s*=\s*([-\.\d]+)\s+")
mp2_patt = re.compile("EUMP2\s*=\s*(.*)")
oniom_patt = re.compile("ONIOM:\s+extrapolated energy\s*=\s*(.*)")
termination_patt = re.compile("(Normal|Error) termination")
error_patt = re.compile(
"(! Non-Optimized Parameters !|Convergence failure)")
mulliken_patt = re.compile(
"^\s*(Mulliken charges|Mulliken atomic charges)")
mulliken_charge_patt = re.compile('^\s+(\d+)\s+([A-Z][a-z]?)\s*(\S*)')
end_mulliken_patt = re.compile(
'(Sum of Mulliken )(.*)(charges)\s*=\s*(\D)')
std_orientation_patt = re.compile("Standard orientation")
end_patt = re.compile("--+")
orbital_patt = re.compile("Alpha\s*\S+\s*eigenvalues --(.*)")
thermo_patt = re.compile("(Zero-point|Thermal) correction(.*)="
"\s+([\d\.-]+)")
forces_on_patt = re.compile(
"Center\s+Atomic\s+Forces\s+\(Hartrees/Bohr\)")
forces_off_patt = re.compile("Cartesian\s+Forces:\s+Max.*RMS.*")
forces_patt = re.compile(
"\s+(\d+)\s+(\d+)\s+([0-9\.-]+)\s+([0-9\.-]+)\s+([0-9\.-]+)")
freq_on_patt = re.compile(
"Harmonic\sfrequencies\s+\(cm\*\*-1\),\sIR\sintensities.*Raman.*")
freq_patt = re.compile("Frequencies\s--\s+(.*)")
normal_mode_patt = re.compile(
"\s+(\d+)\s+(\d+)\s+([0-9\.-]{4,5})\s+([0-9\.-]{4,5}).*")
self.properly_terminated = False
self.is_pcm = False
self.stationary_type = "Minimum"
self.structures = []
self.corrections = {}
self.energies = []
self.pcm = None
self.errors = []
self.Mulliken_charges = {}
self.link0 = {}
self.cart_forces = []
self.frequencies = []
coord_txt = []
read_coord = 0
read_mulliken = False
orbitals_txt = []
parse_stage = 0
num_basis_found = False
terminated = False
parse_forces = False
forces = []
parse_freq = False
frequencies = []
with zopen(filename) as f:
for line in f:
if parse_stage == 0:
if start_patt.search(line):
parse_stage = 1
elif link0_patt.match(line):
m = link0_patt.match(line)
self.link0[m.group(1)] = m.group(2)
elif route_patt.search(line):
params = read_route_line(line)
self.functional = params[0]
self.basis_set = params[1]
self.route = params[2]
self.dieze_tag = params[3]
parse_stage = 1
elif parse_stage == 1:
if charge_mul_patt.search(line):
m = charge_mul_patt.search(line)
self.charge = int(m.group(1))
self.spin_mult = int(m.group(2))
parse_stage = 2
elif parse_stage == 2:
if self.is_pcm:
self._check_pcm(line)
if "FREQ" in self.route and thermo_patt.search(line):
m = thermo_patt.search(line)
if m.group(1) == "Zero-point":
self.corrections["Zero-point"] = float(m.group(3))
else:
key = m.group(2).strip(" to ")
self.corrections[key] = float(m.group(3))
if read_coord:
if not end_patt.search(line):
coord_txt.append(line)
else:
read_coord = (read_coord + 1) % 4
if not read_coord:
sp = []
coords = []
for l in coord_txt[2:]:
toks = l.split()
sp.append(Element.from_Z(int(toks[1])))
coords.append(
[float(i) for i in toks[3:6]])
self.structures.append(Molecule(sp, coords))
if parse_forces:
m = forces_patt.search(line)
if m:
forces.extend(
[float(_v) for _v in m.groups()[2:5]])
elif forces_off_patt.search(line):
self.cart_forces.append(forces)
forces = []
parse_forces = False
elif parse_freq:
m = freq_patt.search(line)
if m:
values = [
float(_v) for _v in m.groups()[0].split()
]
for value in values:
frequencies.append([value, []])
elif normal_mode_patt.search(line):
values = [float(_v) for _v in line.split()[2:]]
n = int(len(values) / 3)
for i in range(0, len(values), 3):
j = -n + int(i / 3)
frequencies[j][1].extend(values[i:i + 3])
elif line.find("-------------------") != -1:
parse_freq = False
self.frequencies.append(frequencies)
frequencies = []
elif termination_patt.search(line):
m = termination_patt.search(line)
if m.group(1) == "Normal":
self.properly_terminated = True
terminated = True
elif error_patt.search(line):
error_defs = {
"! Non-Optimized Parameters !": "Optimization "
"error",
"Convergence failure": "SCF convergence error"
}
m = error_patt.search(line)
self.errors.append(error_defs[m.group(1)])
elif (not num_basis_found) and \
num_basis_func_patt.search(line):
m = num_basis_func_patt.search(line)
self.num_basis_func = int(m.group(1))
num_basis_found = True
elif (not self.is_pcm) and pcm_patt.search(line):
self.is_pcm = True
self.pcm = {}
elif "FREQ" in self.route and "OPT" in self.route and \
stat_type_patt.search(line):
self.stationary_type = "Saddle"
elif mp2_patt.search(line):
m = mp2_patt.search(line)
self.energies.append(
float(m.group(1).replace("D", "E")))
elif oniom_patt.search(line):
m = oniom_patt.matcher(line)
self.energies.append(float(m.group(1)))
elif scf_patt.search(line):
m = scf_patt.search(line)
self.energies.append(float(m.group(1)))
elif std_orientation_patt.search(line):
coord_txt = []
read_coord = 1
elif orbital_patt.search(line):
orbitals_txt.append(line)
elif mulliken_patt.search(line):
mulliken_txt = []
read_mulliken = True
elif not parse_forces and forces_on_patt.search(line):
parse_forces = True
elif freq_on_patt.search(line):
parse_freq = True
if read_mulliken:
if not end_mulliken_patt.search(line):
mulliken_txt.append(line)
else:
m = end_mulliken_patt.search(line)
mulliken_charges = {}
for line in mulliken_txt:
if mulliken_charge_patt.search(line):
m = mulliken_charge_patt.search(line)
dict = {
int(m.group(1)):
[m.group(2),
float(m.group(3))]
}
mulliken_charges.update(dict)
read_mulliken = False
self.Mulliken_charges = mulliken_charges
if not terminated:
#raise IOError("Bad Gaussian output file.")
warnings.warn("\n" + self.filename + \
": Termination error or bad Gaussian output file !")
def test_split(self):
bonds = [(0, 1), (4, 5)]
alterations = {
(2, 3): {"weight": 1.0},
(0, 5): {"weight": 2.0},
(1, 2): {"weight": 2.0},
(3, 4): {"weight": 2.0},
}
# Perform retro-Diels-Alder reaction - turn product into reactants
reactants = self.cyclohexene.split_molecule_subgraphs(bonds, allow_reverse=True, alterations=alterations)
self.assertTrue(isinstance(reactants, list))
reactants = sorted(reactants, key=len)
# After alterations, reactants should be ethylene and butadiene
self.assertEqual(reactants[0], self.ethylene)
self.assertEqual(reactants[1], self.butadiene)
with self.assertRaises(MolGraphSplitError):
self.cyclohexene.split_molecule_subgraphs([(0, 1)])
# Test naive charge redistribution
hydroxide = Molecule(["O", "H"], [[0, 0, 0], [0.5, 0.5, 0.5]], charge=-1)
oh_mg = MoleculeGraph.with_empty_graph(hydroxide)
oh_mg.add_edge(0, 1)
new_mgs = oh_mg.split_molecule_subgraphs([(0, 1)])
for mg in new_mgs:
if str(mg.molecule[0].specie) == "O":
self.assertEqual(mg.molecule.charge, -1)
else:
self.assertEqual(mg.molecule.charge, 0)
# Trying to test to ensure that remapping of nodes to atoms works
diff_species = Molecule(
["C", "I", "Cl", "Br", "F"],
[
[0.8314, -0.2682, -0.9102],
[1.3076, 1.3425, -2.2038],
[-0.8429, -0.7410, -1.1554],
[1.9841, -1.7636, -1.2953],
[1.0098, 0.1231, 0.3916],
],
)
diff_spec_mg = MoleculeGraph.with_empty_graph(diff_species)
diff_spec_mg.add_edge(0, 1)
diff_spec_mg.add_edge(0, 2)
diff_spec_mg.add_edge(0, 3)
diff_spec_mg.add_edge(0, 4)
for i in range(1, 5):
bond = (0, i)
split_mgs = diff_spec_mg.split_molecule_subgraphs([bond])
for split_mg in split_mgs:
species = nx.get_node_attributes(split_mg.graph, "specie")
for j in range(len(split_mg.graph.nodes)):
atom = split_mg.molecule[j]
self.assertEqual(species[j], str(atom.specie))
def get_ase_mol(molname):
"""convert ase molecule to pymatgen style"""
ase_mol = molecule(molname)
pos = ase_mol.get_positions()
symbols = ase_mol.get_chemical_symbols()
return Molecule(symbols, pos)
def generate_doc(self, dir_name, qcinput_files, qcoutput_files, multirun):
try:
fullpath = os.path.abspath(dir_name)
d = jsanitize(self.additional_fields, strict=True)
d["schema"] = {
"code": "atomate",
"version": QChemDrone.__version__
}
d["dir_name"] = fullpath
# If a saved "orig" input file is present, parse it incase the error handler made changes
# to the initial input molecule or rem params, which we might want to filter for later
if len(qcinput_files) > len(qcoutput_files):
orig_input = QCInput.from_file(
os.path.join(dir_name, qcinput_files.pop("orig")))
d["orig"] = {}
d["orig"]["molecule"] = orig_input.molecule.as_dict()
d["orig"]["molecule"]["charge"] = int(
d["orig"]["molecule"]["charge"])
d["orig"]["rem"] = orig_input.rem
d["orig"]["opt"] = orig_input.opt
d["orig"]["pcm"] = orig_input.pcm
d["orig"]["solvent"] = orig_input.solvent
d["orig"]["smx"] = orig_input.smx
if multirun:
d["calcs_reversed"] = self.process_qchem_multirun(
dir_name, qcinput_files, qcoutput_files)
else:
d["calcs_reversed"] = [
self.process_qchemrun(dir_name, taskname,
qcinput_files.get(taskname),
output_filename)
for taskname, output_filename in qcoutput_files.items()
]
# reverse the calculations data order so newest calc is first
d["calcs_reversed"].reverse()
d["structure_change"] = []
d["warnings"] = {}
for entry in d["calcs_reversed"]:
if "structure_change" in entry and "structure_change" not in d[
"warnings"]:
if entry["structure_change"] != "no_change":
d["warnings"]["structure_change"] = True
if "structure_change" in entry:
d["structure_change"].append(entry["structure_change"])
for key in entry["warnings"]:
if key not in d["warnings"]:
d["warnings"][key] = True
d_calc_init = d["calcs_reversed"][-1]
d_calc_final = d["calcs_reversed"][0]
d["input"] = {
"initial_molecule": d_calc_init["initial_molecule"],
"job_type": d_calc_init["input"]["rem"]["job_type"]
}
d["output"] = {
"initial_molecule": d_calc_final["initial_molecule"],
"job_type": d_calc_final["input"]["rem"]["job_type"],
"mulliken": d_calc_final["Mulliken"][-1]
}
if "RESP" in d_calc_final:
d["output"]["resp"] = d_calc_final["RESP"][-1]
elif "ESP" in d_calc_final:
d["output"]["esp"] = d_calc_final["ESP"][-1]
if d["output"]["job_type"] == "opt" or d["output"][
"job_type"] == "optimization":
if "molecule_from_optimized_geometry" in d_calc_final:
d["output"]["optimized_molecule"] = d_calc_final[
"molecule_from_optimized_geometry"]
d["output"]["final_energy"] = d_calc_final["final_energy"]
else:
d["output"]["final_energy"] = "unstable"
if d_calc_final["opt_constraint"]:
d["output"]["constraint"] = [
d_calc_final["opt_constraint"][0],
float(d_calc_final["opt_constraint"][6])
]
if d["output"]["job_type"] == "freq" or d["output"][
"job_type"] == "frequency":
d["output"]["frequencies"] = d_calc_final["frequencies"]
d["output"]["enthalpy"] = d_calc_final["total_enthalpy"]
d["output"]["entropy"] = d_calc_final["total_entropy"]
if d["input"]["job_type"] == "opt" or d["input"][
"job_type"] == "optimization":
d["output"]["optimized_molecule"] = d_calc_final[
"initial_molecule"]
d["output"]["final_energy"] = d["calcs_reversed"][1][
"final_energy"]
opt_trajectory = []
calcs = copy.deepcopy(d["calcs_reversed"])
calcs.reverse()
for calc in calcs:
job_type = calc["input"]["rem"]["job_type"]
if job_type == "opt" or job_type == "optimization":
for ii, geom in enumerate(calc["geometries"]):
site_properties = {"Mulliken": calc["Mulliken"][ii]}
if "RESP" in calc:
site_properties["RESP"] = calc["RESP"][ii]
mol = Molecule(species=calc["species"],
coords=geom,
charge=calc["charge"],
spin_multiplicity=calc["multiplicity"],
site_properties=site_properties)
traj_entry = {"molecule": mol}
traj_entry["energy"] = calc["energy_trajectory"][ii]
opt_trajectory.append(traj_entry)
if opt_trajectory != []:
d["opt_trajectory"] = opt_trajectory
if "final_energy" not in d["output"]:
if d_calc_final["final_energy"] != None:
d["output"]["final_energy"] = d_calc_final["final_energy"]
else:
d["output"]["final_energy"] = d_calc_final["SCF"][-1][-1][
0]
if d_calc_final["completion"]:
total_cputime = 0.0
total_walltime = 0.0
for calc in d["calcs_reversed"]:
if calc["walltime"] is not None:
total_walltime += calc["walltime"]
if calc["cputime"] is not None:
total_cputime += calc["cputime"]
d["walltime"] = total_walltime
d["cputime"] = total_cputime
else:
d["walltime"] = None
d["cputime"] = None
comp = d["output"]["initial_molecule"].composition
d["formula_pretty"] = comp.reduced_formula
d["formula_anonymous"] = comp.anonymized_formula
d["formula_alphabetical"] = comp.alphabetical_formula
d["chemsys"] = "-".join(sorted(set(d_calc_final["species"])))
if d_calc_final["point_group"] != None:
d["pointgroup"] = d_calc_final["point_group"]
else:
try:
d["pointgroup"] = PointGroupAnalyzer(
d["output"]["initial_molecule"]).sch_symbol
except ValueError:
d["pointgroup"] = "PGA_error"
bb = BabelMolAdaptor(d["output"]["initial_molecule"])
pbmol = bb.pybel_mol
smiles = pbmol.write(str("smi")).split()[0]
d["smiles"] = smiles
d["state"] = "successful" if d_calc_final[
"completion"] else "unsuccessful"
if "special_run_type" in d:
if d["special_run_type"] == "frequency_flattener":
if d["state"] == "successful":
orig_num_neg_freq = sum(
1
for freq in d["calcs_reversed"][-2]["frequencies"]
if freq < 0)
orig_energy = d_calc_init["final_energy"]
final_num_neg_freq = sum(
1 for freq in d_calc_final["frequencies"]
if freq < 0)
final_energy = d["calcs_reversed"][1]["final_energy"]
d["num_frequencies_flattened"] = orig_num_neg_freq - final_num_neg_freq
if final_num_neg_freq > 0: # If a negative frequency remains,
# and it's too large to ignore,
if final_num_neg_freq > 1 or abs(
d["output"]["frequencies"][0]) >= 15.0:
d["state"] = "unsuccessful" # then the flattening was unsuccessful
if final_energy > orig_energy:
d["warnings"]["energy_increased"] = True
d["last_updated"] = datetime.datetime.utcnow()
return d
except Exception:
logger.error(traceback.format_exc())
logger.error("Error in " + os.path.abspath(dir_name) + ".\n" +
traceback.format_exc())
raise
def parse_coords(coord_lines):
"""
Helper method to parse coordinates.
"""
paras = {}
var_pattern = re.compile("^([A-Za-z]+\S*)[\s=,]+([\d\-\.]+)$")
for l in coord_lines:
m = var_pattern.match(l.strip())
if m:
paras[m.group(1)] = float(m.group(2))
species = []
coords = []
# Stores whether a Zmatrix format is detected. Once a zmatrix format
# is detected, it is assumed for the remaining of the parsing.
zmode = False
for l in coord_lines:
l = l.strip()
if not l:
break
if (not zmode) and GaussianInput.xyz_patt.match(l):
m = GaussianInput.xyz_patt.match(l)
species.append(m.group(1))
toks = re.split("[,\s]+", l.strip())
if len(toks) > 4:
coords.append([float(i) for i in toks[2:5]])
else:
coords.append([float(i) for i in toks[1:4]])
elif GaussianInput.zmat_patt.match(l):
zmode = True
toks = re.split("[,\s]+", l.strip())
species.append(toks[0])
toks.pop(0)
if len(toks) == 0:
coords.append(np.array([0, 0, 0]))
else:
nn = []
parameters = []
while len(toks) > 1:
ind = toks.pop(0)
data = toks.pop(0)
try:
nn.append(int(ind))
except ValueError:
nn.append(species.index(ind) + 1)
try:
val = float(data)
parameters.append(val)
except ValueError:
if data.startswith("-"):
parameters.append(-paras[data[1:]])
else:
parameters.append(paras[data])
if len(nn) == 1:
coords.append(np.array([0, 0, parameters[0]]))
elif len(nn) == 2:
coords1 = coords[nn[0] - 1]
coords2 = coords[nn[1] - 1]
bl = parameters[0]
angle = parameters[1]
axis = [0, 1, 0]
op = SymmOp.from_origin_axis_angle(
coords1, axis, angle, False)
coord = op.operate(coords2)
vec = coord - coords1
coord = vec * bl / np.linalg.norm(vec) + coords1
coords.append(coord)
elif len(nn) == 3:
coords1 = coords[nn[0] - 1]
coords2 = coords[nn[1] - 1]
coords3 = coords[nn[2] - 1]
bl = parameters[0]
angle = parameters[1]
dih = parameters[2]
v1 = coords3 - coords2
v2 = coords1 - coords2
axis = np.cross(v1, v2)
op = SymmOp.from_origin_axis_angle(
coords1, axis, angle, False)
coord = op.operate(coords2)
v1 = coord - coords1
v2 = coords1 - coords2
v3 = np.cross(v1, v2)
adj = get_angle(v3, axis)
axis = coords1 - coords2
op = SymmOp.from_origin_axis_angle(
coords1, axis, dih - adj, False)
coord = op.operate(coord)
vec = coord - coords1
coord = vec * bl / np.linalg.norm(vec) + coords1
coords.append(coord)
def parse_species(sp_str):
"""
The species specification can take many forms. E.g.,
simple integers representing atomic numbers ("8"),
actual species string ("C") or a labelled species ("C1").
Sometimes, the species string is also not properly capitalized,
e.g, ("c1"). This method should take care of these known formats.
"""
try:
return int(sp_str)
except ValueError:
sp = re.sub("\d", "", sp_str)
return sp.capitalize()
species = [parse_species(sp) for sp in species]
return Molecule(species, coords)
def get_subgraphs_as_molecules_all(sg, use_weights=False):
"""
Adapatation of
http://pymatgen.org/_modules/pymatgen/analysis/graphs.html#StructureGraph.get_subgraphs_as_molecules
for our needs
Args:
sg: structure graph
use_weights:
Returns:
list of molecules
"""
# creating a supercell is an easy way to extract
# molecules (and not, e.g., layers of a 2D crystal)
# without adding extra logic
supercell_sg = sg * (3, 3, 3)
# make undirected to find connected subgraphs
supercell_sg.graph = nx.Graph(supercell_sg.graph)
# find subgraphs
all_subgraphs = list(nx.connected_component_subgraphs(supercell_sg.graph))
# discount subgraphs that lie across *supercell* boundaries
# these will subgraphs representing crystals
molecule_subgraphs = []
for subgraph in all_subgraphs:
intersects_boundary = any(
[d["to_jimage"] != (0, 0, 0) for u, v, d in subgraph.edges(data=True)]
)
if not intersects_boundary:
molecule_subgraphs.append(subgraph)
# add specie names to graph to be able to test for isomorphism
for subgraph in molecule_subgraphs:
for n in subgraph:
subgraph.add_node(n, specie=str(supercell_sg.structure[n].specie))
# now define how we test for isomorphism
def node_match(n1, n2):
return n1["specie"] == n2["specie"]
def edge_match(e1, e2):
if use_weights:
return e1["weight"] == e2["weight"]
else:
return True
# get Molecule objects for each subgraph
molecules = []
for subgraph in molecule_subgraphs:
coords = [supercell_sg.structure[n].coords for n in subgraph.nodes()]
species = [supercell_sg.structure[n].specie for n in subgraph.nodes()]
molecule = Molecule(species, coords)
molecules.append(molecule)
return molecules
def cleave_operation():
struct = readstructure()
if isinstance(Structure, Molecule):
print("cleave operation is only supported for periodic structure")
return
print('your choice ?')
print('{} >>> {}'.format('1', 'cleave surface'))
print('{} >>> {}'.format('2', 'cleave sphere cluster'))
print('{} >>> {}'.format('3', 'cleave shell structure'))
wait_sep()
in_str = ""
while in_str == "":
in_str = input().strip()
choice = int(in_str)
if choice == 1:
print(
" input the miller index, minimum size in angstroms of layers containing atomssupercell"
)
print(
" and Minimize size in angstroms of layers containing vacuum like this:"
)
print(' 1 0 0 | 5 | 5')
print(' it means miller index is [1,0,0]')
print(" min_slab_size is 5 Ang ")
print(" min_vacum_size is 5 Ang ")
print(" or like this : ")
print(' 2 | 5 | 5')
print(' it will generate all slab with miller index less than 2')
def generate_selected_slab(in_str):
tmp_list = in_str.split('|')
miller_index = [int(x) for x in tmp_list[0].strip().split()]
min_slab_size = float(tmp_list[1])
min_vac_size = float(tmp_list[2])
slab = SlabGenerator(struct,
miller_index,
min_slab_size=min_slab_size,
min_vacuum_size=min_vac_size,
lll_reduce=True)
slab_struct = slab.get_slab()
slab_struct.sort()
miller_str = [str(i) for i in miller_index]
filename = '_'.join(miller_str) + '.vasp'
slab_struct.to(filename=filename, fmt='POSCAR')
def generate_all_slab(in_str):
tmp_list = in_str.split('|')
max_index = int(tmp_list[0])
min_slab_size = float(tmp_list[1])
min_vac_size = float(tmp_list[2])
slabs = generate_all_slabs(struct,
max_index=max_index,
min_slab_size=min_slab_size,
min_vacuum_size=min_vac_size,
lll_reduce=True)
for slab_struct in slabs:
slab_struct.sort()
miller_str = [str(i) for i in slab_struct.miller_index]
filename = '_'.join(miller_str) + '.vasp'
slab_struct.to(filename=filename, fmt='POSCAR')
wait_sep()
in_str = ""
while in_str == "":
in_str = input().strip()
len_para = len(in_str.split('|')[0].split())
#if in_str.strip().startswith('a'):
if len_para == 3:
generate_selected_slab(in_str)
#elif in_str.strip().startswith('b'):
elif len_para == 1:
generate_all_slab(in_str)
else:
print("unknow format")
os._exit()
elif choice == 2:
print(
" input the center atom index, sphere radius and vacuum layer thickness"
)
print(' 1 3.5 15')
print(
' it means the sphere will be selected according to the 1st atom')
print(
" with the radius equals 5Ang, and vacuum layer thickness is 15 Ang"
)
wait_sep()
in_str = ""
while in_str == "":
in_str = input().strip()
para = in_str.split()
center_atom = int(para[0]) - 1
radius = float(para[1])
vacuum = float(para[2])
center_coord = struct[center_atom].coords
sites = struct.get_neighbors_in_shell(center_coord, 0, radius)
coords = [site[0].coords for site in sites]
species = [site[0].specie for site in sites]
mol = Molecule(coords=coords, species=species)
max_dist = np.max(mol.distance_matrix)
a = b = c = max_dist + vacuum
box_struct = mol.get_boxed_structure(a, b, c)
file_name = "sphere.vasp"
box_struct.to(filename=file_name, fmt='poscar')
elif choice == 3:
print(
" input the center atom index, start radius, shell thickness and")
print(" vacuum layer thickness")
print(' 1 5 10 15')
print(
' it means the ball shell will be selected according to the 1st atom'
)
print(" with the 5< r <15Ang, and vacuum layer thickness is 15 Ang")
wait_sep()
in_str = ""
while in_str == "":
in_str = input().strip()
para = in_str.split()
center_atom = int(para[0]) - 1
radius = float(para[1])
shell = float(para[2])
vacuum = float(para[3])
center_coord = struct[center_atom].coords
sites = struct.get_neighbors_in_shell(center_coord, radius, shell)
coords = [site[0].coords for site in sites]
species = [site[0].specie for site in sites]
mol = Molecule(coords=coords, species=species)
max_dist = np.max(mol.distance_matrix)
a = b = c = max_dist + vacuum
box_struct = mol.get_boxed_structure(a, b, c)
file_name = "shell.vasp"
box_struct.to(filename=file_name, fmt='poscar')
else:
print("unkown choice")
return
def _parse(self, filename):
start_patt = re.compile(" \(Enter \S+l101\.exe\)")
route_patt = re.compile(" #[pPnNtT]*.*")
charge_mul_patt = re.compile("Charge\s+=\s*([-\\d]+)\s+"
"Multiplicity\s+=\s*(\d+)")
num_basis_func_patt = re.compile("([0-9]+)\s+basis functions")
pcm_patt = re.compile("Polarizable Continuum Model")
stat_type_patt = re.compile("imaginary frequencies")
scf_patt = re.compile("E\(.*\)\s*=\s*([-\.\d]+)\s+")
mp2_patt = re.compile("EUMP2\s*=\s*(.*)")
oniom_patt = re.compile("ONIOM:\s+extrapolated energy\s*=\s*(.*)")
termination_patt = re.compile("(Normal|Error) termination")
error_patt = re.compile(
"(! Non-Optimized Parameters !|Convergence failure)")
mulliken_patt = re.compile("^\s*Mulliken atomic charges")
mulliken_charge_patt = re.compile('^\s+(\d+)\s+([A-Z][a-z]?)\s*(\S*)')
end_mulliken_patt = re.compile(
'(Sum of Mulliken )(.*)(charges)\s*=\s*(\D)')
std_orientation_patt = re.compile("Standard orientation")
end_patt = re.compile("--+")
orbital_patt = re.compile("Alpha\s*\S+\s*eigenvalues --(.*)")
thermo_patt = re.compile("(Zero-point|Thermal) correction(.*)="
"\s+([\d\.-]+)")
self.properly_terminated = False
self.is_pcm = False
self.stationary_type = "Minimum"
self.structures = []
self.corrections = {}
self.energies = []
self.pcm = None
self.errors = []
self.Mulliken_charges = {}
coord_txt = []
read_coord = 0
read_mulliken = 0
orbitals_txt = []
parse_stage = 0
num_basis_found = False
terminated = False
with zopen(filename) as f:
for line in f:
if parse_stage == 0:
if start_patt.search(line):
parse_stage = 1
elif route_patt.search(line):
self.route = {}
for tok in line.split():
sub_tok = tok.strip().split("=")
key = sub_tok[0].upper()
self.route[key] = sub_tok[1].upper() \
if len(sub_tok) > 1 else ""
m = re.match("(\w+)/([^/]+)", key)
if m:
self.functional = m.group(1)
self.basis_set = m.group(2)
elif parse_stage == 1:
if charge_mul_patt.search(line):
m = charge_mul_patt.search(line)
self.charge = int(m.group(1))
self.spin_mult = int(m.group(2))
parse_stage = 2
elif parse_stage == 2:
if self.is_pcm:
self._check_pcm(line)
if "FREQ" in self.route and thermo_patt.search(line):
m = thermo_patt.search(line)
if m.group(1) == "Zero-point":
self.corrections["Zero-point"] = float(m.group(3))
else:
key = m.group(2).strip(" to ")
self.corrections[key] = float(m.group(3))
if read_mulliken:
if not end_mulliken_patt.search(line):
mulliken_txt.append(line)
else:
m = end_mulliken_patt.search(line)
mulliken_charges = {}
for line in mulliken_txt:
if mulliken_charge_patt.search(line):
m = mulliken_charge_patt.search(line)
dict = {
int(m.group(1)):
[m.group(2),
float(m.group(3))]
}
mulliken_charges.update(dict)
read_mulliken = 0
self.Mulliken_charges = mulliken_charges
if read_coord:
if not end_patt.search(line):
coord_txt.append(line)
else:
read_coord = (read_coord + 1) % 4
if not read_coord:
sp = []
coords = []
for l in coord_txt[2:]:
toks = l.split()
sp.append(Element.from_Z(int(toks[1])))
coords.append(
[float(i) for i in toks[3:6]])
self.structures.append(Molecule(sp, coords))
elif termination_patt.search(line):
m = termination_patt.search(line)
if m.group(1) == "Normal":
self.properly_terminated = True
terminated = True
elif error_patt.search(line):
error_defs = {
"! Non-Optimized Parameters !":
"Optimization error",
"Convergence failure": "SCF convergence error"
}
m = error_patt.search(line)
self.errors.append(error_defs[m.group(1)])
elif (not num_basis_found) and \
num_basis_func_patt.search(line):
m = num_basis_func_patt.search(line)
self.num_basis_func = int(m.group(1))
num_basis_found = True
elif (not self.is_pcm) and pcm_patt.search(line):
self.is_pcm = True
self.pcm = {}
elif "FREQ" in self.route and "OPT" in self.route and \
stat_type_patt.search(line):
self.stationary_type = "Saddle"
elif mp2_patt.search(line):
m = mp2_patt.search(line)
self.energies.append(
float(m.group(1).replace("D", "E")))
elif oniom_patt.search(line):
m = oniom_patt.matcher(line)
self.energies.append(float(m.group(1)))
elif scf_patt.search(line):
m = scf_patt.search(line)
self.energies.append(float(m.group(1)))
elif std_orientation_patt.search(line):
coord_txt = []
read_coord = 1
elif orbital_patt.search(line):
orbitals_txt.append(line)
elif mulliken_patt.search(line):
mulliken_txt = []
read_mulliken = 1
if not terminated:
raise IOError("Bad Gaussian output file.")
"""
Dummy test systems
"""
from pymatgen.core import Composition, Structure, Lattice, Molecule
from ._data_conversion import to_array
from ._inspect import get_param_types
DUMMY_OBJECTS = {
'str': 'H2O',
'composition': Composition('H2O'),
'structure': Structure(Lattice.cubic(3.167),
['Mo', 'Mo'],
[[0, 0, 0], [0.5, 0.5, 0.5]]),
'molecule': Molecule(['C', 'O'], [[0, 0, 0], [1, 0, 0]])
}
def get_describer_dummy_obj(instance):
"""
For a describers, get a dummy object for transform_one.
This relies on the type hint.
Args:
instance (BaseDescriber): describers instance
"""
obj_type = getattr(instance, "describer_type", None)
if obj_type is not None:
return DUMMY_OBJECTS[obj_type.lower()]
arg_types = get_param_types(instance.transform_one)
arg_type = list(arg_types.values())[0]
def _parse_job(self, output):
energy_patt = re.compile("Total \w+ energy\s+=\s+([\.\-\d]+)")
#In cosmo solvation results; gas phase energy = -152.5044774212
energy_gas_patt = re.compile("gas phase energy\s+=\s+([\.\-\d]+)")
#In cosmo solvation results; sol phase energy = -152.5044774212
energy_sol_patt = re.compile("sol phase energy\s+=\s+([\.\-\d]+)")
coord_patt = re.compile("\d+\s+(\w+)\s+[\.\-\d]+\s+([\.\-\d]+)\s+"
"([\.\-\d]+)\s+([\.\-\d]+)")
corrections_patt = re.compile("([\w\-]+ correction to \w+)\s+="
"\s+([\.\-\d]+)")
preamble_patt = re.compile("(No. of atoms|No. of electrons"
"|SCF calculation type|Charge|Spin "
"multiplicity)\s*:\s*(\S+)")
error_defs = {
"calculations not reaching convergence": "Bad convergence",
"Calculation failed to converge": "Bad convergence",
"geom_binvr: #indep variables incorrect": "autoz error",
"dft optimize failed": "Geometry optimization failed"
}
data = {}
energies = []
frequencies = None
corrections = {}
molecules = []
species = []
coords = []
errors = []
basis_set = {}
bset_header = []
parse_geom = False
parse_freq = False
parse_bset = False
job_type = ""
for l in output.split("\n"):
for e, v in error_defs.items():
if l.find(e) != -1:
errors.append(v)
if parse_geom:
if l.strip() == "Atomic Mass":
molecules.append(Molecule(species, coords))
species = []
coords = []
parse_geom = False
else:
m = coord_patt.search(l)
if m:
species.append(m.group(1).capitalize())
coords.append([
float(m.group(2)),
float(m.group(3)),
float(m.group(4))
])
if parse_freq:
if len(l.strip()) == 0:
if len(frequencies[-1][1]) == 0:
continue
else:
parse_freq = False
else:
vibs = [float(vib) for vib in l.strip().split()[1:]]
num_vibs = len(vibs)
for mode, dis in zip(frequencies[-num_vibs:], vibs):
mode[1].append(dis)
elif parse_bset:
if l.strip() == "":
parse_bset = False
else:
toks = l.split()
if toks[0] != "Tag" and not re.match("\-+", toks[0]):
basis_set[toks[0]] = dict(
zip(bset_header[1:], toks[1:]))
elif toks[0] == "Tag":
bset_header = toks
bset_header.pop(4)
bset_header = [h.lower() for h in bset_header]
else:
m = energy_patt.search(l)
if m:
energies.append(Energy(m.group(1), "Ha").to("eV"))
continue
m = energy_gas_patt.search(l)
if m:
cosmo_scf_energy = energies[-1]
energies[-1] = dict()
energies[-1].update({"cosmo scf": cosmo_scf_energy})
energies[-1].update(
{"gas phase": Energy(m.group(1), "Ha").to("eV")})
m = energy_sol_patt.search(l)
if m:
energies[-1].update(
{"sol phase": Energy(m.group(1), "Ha").to("eV")})
m = preamble_patt.search(l)
if m:
try:
val = int(m.group(2))
except ValueError:
val = m.group(2)
k = m.group(1).replace("No. of ", "n").replace(" ", "_")
data[k.lower()] = val
elif l.find("Geometry \"geometry\"") != -1:
parse_geom = True
elif l.find("Summary of \"ao basis\"") != -1:
parse_bset = True
elif l.find("P.Frequency") != -1:
parse_freq = True
if not frequencies:
frequencies = []
frequencies.extend([(float(freq), [])
for freq in l.strip().split()[1:]])
elif job_type == "" and l.strip().startswith("NWChem"):
job_type = l.strip()
if job_type == "NWChem DFT Module" and \
"COSMO solvation results" in output:
job_type += " COSMO"
else:
m = corrections_patt.search(l)
if m:
corrections[m.group(1)] = FloatWithUnit(
m.group(2), "kJ mol^-1").to("eV atom^-1")
if frequencies:
for freq, mode in frequencies:
mode[:] = zip(*[iter(mode)] * 3)
data.update({
"job_type": job_type,
"energies": energies,
"corrections": corrections,
"molecules": molecules,
"basis_set": basis_set,
"errors": errors,
"has_error": len(errors) > 0,
"frequencies": frequencies
})
return data
def from_string(cls, string_input):
"""
Read an NwInput from a string. Currently tested to work with
files generated from this class itself.
Args:
string_input: string_input to parse.
Returns:
NwInput object
"""
directives = []
tasks = []
charge = None
spin_multiplicity = None
title = None
basis_set = None
theory_directives = {}
geom_options = None
symmetry_options = None
memory_options = None
lines = string_input.strip().split("\n")
while len(lines) > 0:
l = lines.pop(0).strip()
if l == "":
continue
toks = l.split()
if toks[0].lower() == "geometry":
geom_options = toks[1:]
l = lines.pop(0).strip()
toks = l.split()
if toks[0].lower() == "symmetry":
symmetry_options = toks[1:]
l = lines.pop(0).strip()
#Parse geometry
species = []
coords = []
while l.lower() != "end":
toks = l.split()
species.append(toks[0])
coords.append([float(i) for i in toks[1:]])
l = lines.pop(0).strip()
mol = Molecule(species, coords)
elif toks[0].lower() == "charge":
charge = int(toks[1])
elif toks[0].lower() == "title":
title = l[5:].strip().strip("\"")
elif toks[0].lower() == "basis":
#Parse basis sets
l = lines.pop(0).strip()
basis_set = {}
while l.lower() != "end":
toks = l.split()
basis_set[toks[0]] = toks[-1].strip("\"")
l = lines.pop(0).strip()
elif toks[0].lower() in NwTask.theories:
#Parse theory directives.
theory = toks[0].lower()
l = lines.pop(0).strip()
theory_directives[theory] = {}
while l.lower() != "end":
toks = l.split()
theory_directives[theory][toks[0]] = toks[-1]
if toks[0] == "mult":
spin_multiplicity = float(toks[1])
l = lines.pop(0).strip()
elif toks[0].lower() == "task":
tasks.append(
NwTask(charge=charge,
spin_multiplicity=spin_multiplicity,
title=title,
theory=toks[1],
operation=toks[2],
basis_set=basis_set,
theory_directives=theory_directives.get(toks[1])))
elif toks[0].lower() == "memory":
memory_options = ' '.join(toks[1:])
else:
directives.append(l.strip().split())
return NwInput(mol,
tasks=tasks,
directives=directives,
geometry_options=geom_options,
symmetry_options=symmetry_options,
memory_options=memory_options)
def opt_with_frequency_flattener(cls,
qchem_command,
multimode="openmp",
input_file="mol.qin",
output_file="mol.qout",
qclog_file="mol.qclog",
max_iterations=10,
max_molecule_perturb_scale=0.3,
check_connectivity=True,
**QCJob_kwargs):
"""
Optimize a structure and calculate vibrational frequencies to check if the
structure is in a true minima. If a frequency is negative, iteratively
perturbe the geometry, optimize, and recalculate frequencies until all are
positive, aka a true minima has been found.
Args:
qchem_command (str): Command to run QChem.
multimode (str): Parallelization scheme, either openmp or mpi.
input_file (str): Name of the QChem input file.
output_file (str): Name of the QChem output file.
max_iterations (int): Number of perturbation -> optimization -> frequency
iterations to perform. Defaults to 10.
max_molecule_perturb_scale (float): The maximum scaled perturbation that
can be applied to the molecule. Defaults to 0.3.
check_connectivity (bool): Whether to check differences in connectivity
introduced by structural perturbation. Defaults to True.
**QCJob_kwargs: Passthrough kwargs to QCJob. See
:class:`custodian.qchem.jobs.QCJob`.
"""
min_molecule_perturb_scale = 0.1
scale_grid = 10
perturb_scale_grid = (max_molecule_perturb_scale -
min_molecule_perturb_scale) / scale_grid
if not os.path.exists(input_file):
raise AssertionError('Input file must be present!')
orig_opt_input = QCInput.from_file(input_file)
orig_opt_rem = copy.deepcopy(orig_opt_input.rem)
orig_freq_rem = copy.deepcopy(orig_opt_input.rem)
orig_freq_rem["job_type"] = "freq"
first = True
reversed_direction = False
num_neg_freqs = []
for ii in range(max_iterations):
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".opt_" + str(ii),
backup=first,
**QCJob_kwargs))
first = False
opt_outdata = QCOutput(output_file + ".opt_" + str(ii)).data
if opt_outdata[
"structure_change"] == "unconnected_fragments" and not opt_outdata[
"completion"]:
print(
"Unstable molecule broke into unconnected fragments which failed to optimize! Exiting..."
)
break
else:
freq_QCInput = QCInput(molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=orig_freq_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent)
freq_QCInput.write_file(input_file)
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_" + str(ii),
backup=first,
**QCJob_kwargs))
outdata = QCOutput(output_file + ".freq_" + str(ii)).data
errors = outdata.get("errors")
if len(errors) != 0:
raise AssertionError(
'No errors should be encountered while flattening frequencies!'
)
if outdata.get('frequencies')[0] > 0.0:
print("All frequencies positive!")
break
else:
num_neg_freqs += [
sum(1 for freq in outdata.get('frequencies')
if freq < 0)
]
if len(num_neg_freqs) > 1:
if num_neg_freqs[-1] == num_neg_freqs[
-2] and not reversed_direction:
reversed_direction = True
elif num_neg_freqs[-1] == num_neg_freqs[
-2] and reversed_direction:
if len(num_neg_freqs) < 3:
raise AssertionError(
"ERROR: This should only be possible after at least three frequency flattening iterations! Exiting..."
)
else:
raise Exception(
"ERROR: Reversing the perturbation direction still could not flatten any frequencies. Exiting..."
)
elif num_neg_freqs[-1] != num_neg_freqs[
-2] and reversed_direction:
reversed_direction = False
negative_freq_vecs = outdata.get(
"frequency_mode_vectors")[0]
structure_successfully_perturbed = False
for molecule_perturb_scale in np.arange(
max_molecule_perturb_scale,
min_molecule_perturb_scale, -perturb_scale_grid):
new_coords = perturb_coordinates(
old_coords=outdata.get("initial_geometry"),
negative_freq_vecs=negative_freq_vecs,
molecule_perturb_scale=molecule_perturb_scale,
reversed_direction=reversed_direction)
new_molecule = Molecule(
species=outdata.get('species'),
coords=new_coords,
charge=outdata.get('charge'),
spin_multiplicity=outdata.get('multiplicity'))
if check_connectivity:
old_molgraph = MoleculeGraph.with_local_env_strategy(
outdata.get("initial_molecule"),
OpenBabelNN(),
reorder=False,
extend_structure=False)
new_molgraph = MoleculeGraph.with_local_env_strategy(
new_molecule,
OpenBabelNN(),
reorder=False,
extend_structure=False)
if old_molgraph.isomorphic_to(new_molgraph):
structure_successfully_perturbed = True
break
if not structure_successfully_perturbed:
raise Exception(
"ERROR: Unable to perturb coordinates to remove negative frequency without changing the connectivity! Exiting..."
)
new_opt_QCInput = QCInput(molecule=new_molecule,
rem=orig_opt_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent)
new_opt_QCInput.write_file(input_file)
def opt_with_frequency_flattener(cls,
qchem_command,
multimode="openmp",
input_file="mol.qin",
output_file="mol.qout",
qclog_file="mol.qclog",
max_iterations=10,
max_molecule_perturb_scale=0.3,
reversed_direction=False,
ignore_connectivity=False,
**QCJob_kwargs):
"""
Optimize a structure and calculate vibrational frequencies to check if the
structure is in a true minima. If a frequency is negative, iteratively
perturbe the geometry, optimize, and recalculate frequencies until all are
positive, aka a true minima has been found.
Args:
qchem_command (str): Command to run QChem.
multimode (str): Parallelization scheme, either openmp or mpi.
input_file (str): Name of the QChem input file.
output_file (str): Name of the QChem output file.
max_iterations (int): Number of perturbation -> optimization -> frequency
iterations to perform. Defaults to 10.
max_molecule_perturb_scale (float): The maximum scaled perturbation that
can be applied to the molecule. Defaults to 0.3.
reversed_direction (bool): Whether to reverse the direction of the
vibrational frequency vectors. Defaults to False.
ignore_connectivity (bool): Whether to ignore differences in connectivity
introduced by structural perturbation. Defaults to False.
**QCJob_kwargs: Passthrough kwargs to QCJob. See
:class:`custodian.qchem.jobs.QCJob`.
"""
min_molecule_perturb_scale = 0.1
scale_grid = 10
perturb_scale_grid = (max_molecule_perturb_scale -
min_molecule_perturb_scale) / scale_grid
msc = MoleculeStructureComparator()
if not os.path.exists(input_file):
raise AssertionError('Input file must be present!')
orig_opt_input = QCInput.from_file(input_file)
orig_opt_rem = copy.deepcopy(orig_opt_input.rem)
orig_freq_rem = copy.deepcopy(orig_opt_input.rem)
orig_freq_rem["job_type"] = "freq"
first = True
for ii in range(max_iterations):
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".opt_" + str(ii),
backup=first,
**QCJob_kwargs))
first = False
opt_outdata = QCOutput(output_file + ".opt_" + str(ii)).data
if opt_outdata["structure_change"] == "unconnected_fragments":
print(
"Unstable molecule broke into unconnected fragments! Exiting..."
)
break
else:
freq_QCInput = QCInput(molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=orig_freq_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent)
freq_QCInput.write_file(input_file)
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_" + str(ii),
backup=first,
**QCJob_kwargs))
outdata = QCOutput(output_file + ".freq_" + str(ii)).data
errors = outdata.get("errors")
if len(errors) != 0:
raise AssertionError(
'No errors should be encountered while flattening frequencies!'
)
if outdata.get('frequencies')[0] > 0.0:
print("All frequencies positive!")
break
else:
negative_freq_vecs = outdata.get(
"frequency_mode_vectors")[0]
old_coords = outdata.get("initial_geometry")
old_molecule = outdata.get("initial_molecule")
structure_successfully_perturbed = False
for molecule_perturb_scale in np.arange(
max_molecule_perturb_scale,
min_molecule_perturb_scale, -perturb_scale_grid):
new_coords = perturb_coordinates(
old_coords=old_coords,
negative_freq_vecs=negative_freq_vecs,
molecule_perturb_scale=molecule_perturb_scale,
reversed_direction=reversed_direction)
new_molecule = Molecule(
species=outdata.get('species'),
coords=new_coords,
charge=outdata.get('charge'),
spin_multiplicity=outdata.get('multiplicity'))
if msc.are_equal(old_molecule,
new_molecule) or ignore_connectivity:
structure_successfully_perturbed = True
break
if not structure_successfully_perturbed:
raise Exception(
"Unable to perturb coordinates to remove negative frequency without changing the bonding structure"
)
new_opt_QCInput = QCInput(molecule=new_molecule,
rem=orig_opt_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent)
new_opt_QCInput.write_file(input_file)
def opt_with_frequency_flattener(cls,
qchem_command,
multimode="openmp",
input_file="mol.qin",
output_file="mol.qout",
qclog_file="mol.qclog",
max_iterations=10,
max_molecule_perturb_scale=0.3,
check_connectivity=True,
linked=True,
**QCJob_kwargs):
"""
Optimize a structure and calculate vibrational frequencies to check if the
structure is in a true minima. If a frequency is negative, iteratively
perturbe the geometry, optimize, and recalculate frequencies until all are
positive, aka a true minima has been found.
Args:
qchem_command (str): Command to run QChem.
multimode (str): Parallelization scheme, either openmp or mpi.
input_file (str): Name of the QChem input file.
output_file (str): Name of the QChem output file.
max_iterations (int): Number of perturbation -> optimization -> frequency
iterations to perform. Defaults to 10.
max_molecule_perturb_scale (float): The maximum scaled perturbation that
can be applied to the molecule. Defaults to 0.3.
check_connectivity (bool): Whether to check differences in connectivity
introduced by structural perturbation. Defaults to True.
**QCJob_kwargs: Passthrough kwargs to QCJob. See
:class:`custodian.qchem.jobs.QCJob`.
"""
if not os.path.exists(input_file):
raise AssertionError("Input file must be present!")
if linked:
energy_diff_cutoff = 0.0000001
orig_input = QCInput.from_file(input_file)
freq_rem = copy.deepcopy(orig_input.rem)
freq_rem["job_type"] = "freq"
opt_rem = copy.deepcopy(orig_input.rem)
opt_rem["geom_opt_hessian"] = "read"
opt_rem["scf_guess_always"] = True
first = True
energy_history = []
for ii in range(max_iterations):
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".opt_" + str(ii),
scratch_dir=os.getcwd(),
save_scratch=True,
save_name="chain_scratch",
backup=first,
**QCJob_kwargs))
opt_outdata = QCOutput(output_file + ".opt_" + str(ii)).data
first = False
if (opt_outdata["structure_change"] == "unconnected_fragments"
and not opt_outdata["completion"]):
print(
"Unstable molecule broke into unconnected fragments which failed to optimize! Exiting..."
)
break
else:
energy_history.append(opt_outdata.get("final_energy"))
freq_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=freq_rem,
opt=orig_input.opt,
pcm=orig_input.pcm,
solvent=orig_input.solvent,
smx=orig_input.smx,
)
freq_QCInput.write_file(input_file)
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_" + str(ii),
scratch_dir=os.getcwd(),
save_scratch=True,
save_name="chain_scratch",
backup=first,
**QCJob_kwargs))
outdata = QCOutput(output_file + ".freq_" + str(ii)).data
errors = outdata.get("errors")
if len(errors) != 0:
raise AssertionError(
"No errors should be encountered while flattening frequencies!"
)
if outdata.get("frequencies")[0] > 0.0:
print("All frequencies positive!")
break
elif (abs(outdata.get("frequencies")[0]) < 15.0
and outdata.get("frequencies")[1] > 0.0):
print(
"One negative frequency smaller than 15.0 - not worth further flattening!"
)
break
else:
if len(energy_history) > 1:
if (abs(energy_history[-1] - energy_history[-2]) <
energy_diff_cutoff):
print("Energy change below cutoff!")
break
opt_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=opt_rem,
opt=orig_input.opt,
pcm=orig_input.pcm,
solvent=orig_input.solvent,
smx=orig_input.smx,
)
opt_QCInput.write_file(input_file)
if os.path.exists(os.path.join(os.getcwd(), "chain_scratch")):
shutil.rmtree(os.path.join(os.getcwd(), "chain_scratch"))
else:
if not os.path.exists(input_file):
raise AssertionError("Input file must be present!")
orig_opt_input = QCInput.from_file(input_file)
orig_opt_rem = copy.deepcopy(orig_opt_input.rem)
orig_freq_rem = copy.deepcopy(orig_opt_input.rem)
orig_freq_rem["job_type"] = "freq"
first = True
history = []
for ii in range(max_iterations):
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".opt_" + str(ii),
backup=first,
**QCJob_kwargs))
opt_outdata = QCOutput(output_file + ".opt_" + str(ii)).data
if first:
orig_species = copy.deepcopy(opt_outdata.get("species"))
orig_charge = copy.deepcopy(opt_outdata.get("charge"))
orig_multiplicity = copy.deepcopy(
opt_outdata.get("multiplicity"))
orig_energy = copy.deepcopy(
opt_outdata.get("final_energy"))
first = False
if (opt_outdata["structure_change"] == "unconnected_fragments"
and not opt_outdata["completion"]):
print(
"Unstable molecule broke into unconnected fragments which failed to optimize! Exiting..."
)
break
else:
freq_QCInput = QCInput(
molecule=opt_outdata.get(
"molecule_from_optimized_geometry"),
rem=orig_freq_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent,
smx=orig_opt_input.smx,
)
freq_QCInput.write_file(input_file)
yield (QCJob(qchem_command=qchem_command,
multimode=multimode,
input_file=input_file,
output_file=output_file,
qclog_file=qclog_file,
suffix=".freq_" + str(ii),
backup=first,
**QCJob_kwargs))
outdata = QCOutput(output_file + ".freq_" + str(ii)).data
errors = outdata.get("errors")
if len(errors) != 0:
raise AssertionError(
"No errors should be encountered while flattening frequencies!"
)
if outdata.get("frequencies")[0] > 0.0:
print("All frequencies positive!")
if opt_outdata.get("final_energy") > orig_energy:
print(
"WARNING: Energy increased during frequency flattening!"
)
break
else:
hist = {}
hist["molecule"] = copy.deepcopy(
outdata.get("initial_molecule"))
hist["geometry"] = copy.deepcopy(
outdata.get("initial_geometry"))
hist["frequencies"] = copy.deepcopy(
outdata.get("frequencies"))
hist["frequency_mode_vectors"] = copy.deepcopy(
outdata.get("frequency_mode_vectors"))
hist["num_neg_freqs"] = sum(
1 for freq in outdata.get("frequencies")
if freq < 0)
hist["energy"] = copy.deepcopy(
opt_outdata.get("final_energy"))
hist["index"] = len(history)
hist["children"] = []
history.append(hist)
ref_mol = history[-1]["molecule"]
geom_to_perturb = history[-1]["geometry"]
negative_freq_vecs = history[-1][
"frequency_mode_vectors"][0]
reversed_direction = False
standard = True
# If we've found one or more negative frequencies in two consecutive iterations, let's dig in
# deeper:
if len(history) > 1:
# Start by finding the latest iteration's parent:
if history[-1]["index"] in history[-2]["children"]:
parent_hist = history[-2]
history[-1]["parent"] = parent_hist["index"]
elif history[-1]["index"] in history[-3][
"children"]:
parent_hist = history[-3]
history[-1]["parent"] = parent_hist["index"]
else:
raise AssertionError(
"ERROR: your parent should always be one or two iterations behind you! Exiting..."
)
# if the number of negative frequencies has remained constant or increased from parent to
# child,
if (history[-1]["num_neg_freqs"] >=
parent_hist["num_neg_freqs"]):
# check to see if the parent only has one child, aka only the positive perturbation has
# been tried,
# in which case just try the negative perturbation from the same parent
if len(parent_hist["children"]) == 1:
ref_mol = parent_hist["molecule"]
geom_to_perturb = parent_hist["geometry"]
negative_freq_vecs = parent_hist[
"frequency_mode_vectors"][0]
reversed_direction = True
standard = False
parent_hist["children"].append(
len(history))
# If the parent has two children, aka both directions have been tried, then we have to
# get creative:
elif len(parent_hist["children"]) == 2:
# If we're dealing with just one negative frequency,
if parent_hist["num_neg_freqs"] == 1:
make_good_child_next_parent = False
if (history[parent_hist["children"]
[0]]["energy"] <
history[-1]["energy"]):
good_child = copy.deepcopy(history[
parent_hist["children"][0]])
else:
good_child = copy.deepcopy(
history[-1])
if good_child["num_neg_freqs"] > 1:
raise Exception(
"ERROR: Child with lower energy has more negative frequencies! "
"Exiting...")
elif (good_child["energy"] <
parent_hist["energy"]):
make_good_child_next_parent = True
elif (vector_list_diff(
good_child[
"frequency_mode_vectors"]
[0],
parent_hist[
"frequency_mode_vectors"]
[0],
) > 0.2):
make_good_child_next_parent = True
else:
raise Exception(
"ERROR: Good child not good enough! Exiting..."
)
if make_good_child_next_parent:
good_child["index"] = len(history)
history.append(good_child)
ref_mol = history[-1]["molecule"]
geom_to_perturb = history[-1][
"geometry"]
negative_freq_vecs = history[-1][
"frequency_mode_vectors"][0]
else:
raise Exception(
"ERROR: Can't deal with multiple neg frequencies yet! Exiting..."
)
else:
raise AssertionError(
"ERROR: Parent cannot have more than two childen! Exiting..."
)
# Implicitly, if the number of negative frequencies decreased from parent to child,
# continue normally.
if standard:
history[-1]["children"].append(len(history))
min_molecule_perturb_scale = 0.1
scale_grid = 10
perturb_scale_grid = (
max_molecule_perturb_scale -
min_molecule_perturb_scale) / scale_grid
structure_successfully_perturbed = False
for molecule_perturb_scale in np.arange(
max_molecule_perturb_scale,
min_molecule_perturb_scale,
-perturb_scale_grid,
):
new_coords = perturb_coordinates(
old_coords=geom_to_perturb,
negative_freq_vecs=negative_freq_vecs,
molecule_perturb_scale=molecule_perturb_scale,
reversed_direction=reversed_direction,
)
new_molecule = Molecule(
species=orig_species,
coords=new_coords,
charge=orig_charge,
spin_multiplicity=orig_multiplicity,
)
if check_connectivity:
structure_successfully_perturbed = (
check_for_structure_changes(
ref_mol, new_molecule) == "no_change")
if structure_successfully_perturbed:
break
if not structure_successfully_perturbed:
raise Exception(
"ERROR: Unable to perturb coordinates to remove negative frequency without changing "
"the connectivity! Exiting...")
new_opt_QCInput = QCInput(
molecule=new_molecule,
rem=orig_opt_rem,
opt=orig_opt_input.opt,
pcm=orig_opt_input.pcm,
solvent=orig_opt_input.solvent,
smx=orig_opt_input.smx,
)
new_opt_QCInput.write_file(input_file)
def _get_SiteEnvironments(struct: PymatgenStructure,
cutoff: float,
PBC: List[bool],
get_permutations: bool = True,
eigen_tol: float = 1e-5) -> List[Dict[str, Any]]:
"""
Used to extract information about both primitive cells and data points.
Extract local environments from Structure object by calculating neighbors
based on gaussian distance. For primitive cell, Different permutations of the
neighbors are calculated and will be later will mapped for data point in the
_SiteEnvironment.get_mapping() function.
site types ,
Parameters
----------
struct: PymatgenStructure
Pymatgen Structure object of the primitive cell used for calculating
neighbors from lattice transformations.It also requires site_properties
attribute with "Sitetypes"(Active or spectator site).
cutoff : float
cutoff distance in angstrom for collecting local
environment.
pbc : np.ndarray
Periodic boundary condition
get_permutations : bool (default True)
Whether to find permuted neighbor list or not.
eigen_tol : float (default 1e-5)
Tolerance for eigenanalysis of point group analysis in
pymatgen.
Returns
------
site_envs : List[Dict[str, Any]]
list of local_env class
"""
try:
from pymatgen.core import Molecule
from pymatgen.symmetry.analyzer import PointGroupAnalyzer
except:
raise ImportError("This class requires pymatgen to be installed.")
pbc = np.array(PBC)
structure = struct
neighbors = structure.get_all_neighbors(cutoff, include_index=True)
symbols = structure.species
site_idxs = [
i for i, sitetype in enumerate(structure.site_properties['SiteTypes'])
if sitetype == 'A1'
]
site_sym_map = {}
sym_site_map = {}
for i, new_ele in enumerate(structure.species):
sym_site_map[new_ele] = structure.site_properties['SiteTypes'][i]
site_sym_map[structure.site_properties['SiteTypes'][i]] = new_ele
site_envs = []
for site_idx in site_idxs:
local_env_sym = [symbols[site_idx]]
local_env_xyz = [structure[site_idx].coords]
local_env_dist = [0.0]
local_env_sitemap = [site_idx]
for n in neighbors[site_idx]:
# if PBC condition is fulfilled..
c = np.around(n[0].frac_coords, 10)
withinPBC = np.logical_and(0 <= c, c < 1)
if np.all(withinPBC[~pbc]):
local_env_xyz.append(n[0].coords)
local_env_sym.append(n[0].specie)
local_env_dist.append(n[1])
local_env_sitemap.append(n[2])
local_env_xyz = np.subtract(local_env_xyz, np.mean(local_env_xyz, 0))
perm = []
if get_permutations:
finder = PointGroupAnalyzer(Molecule(local_env_sym, local_env_xyz),
eigen_tolerance=eigen_tol)
pg = finder.get_pointgroup()
for i, op in enumerate(pg):
newpos = op.operate_multi(local_env_xyz)
perm.append(
np.argmin(cdist(local_env_xyz, newpos), axis=1).tolist())
site_env = {
'pos': local_env_xyz,
'sitetypes': [sym_site_map[s] for s in local_env_sym],
'env2config': local_env_sitemap,
'permutations': perm,
'dist': local_env_dist
}
site_envs.append(site_env)
return site_envs
def parse_symmetry(pos):
mol = Molecule(["C"] * len(pos), pos)
pga = PointGroupAnalyzer(mol)
return pga.sch_symbol
def extract_molecule(self, indices: List[int]) -> Molecule:
struct = self.get_molecular_structure_from_indices(indices)
sgraph = self.get_structure_graph(struct)
coords = self.walk_graph_and_get_coords(sgraph)
return Molecule(species=struct.species, coords=coords)
from pymatgen.io.vaspio.vasp_input import Incar, Poscar, Potcar, Kpoints
from pymatgen.core import Structure, Molecule
from mpinterfaces.interface import Interface, Ligand
#create ligand, interface and slab from the starting POSCARs
strt= Structure.from_file("POSCAR_PbS_bulk_with_vdw") #using POSCAR of vdW relaxed PbS
mol_struct= Structure.from_file("POSCAR_DMF") #using POSCAR of vdW relaxed PbS
mol= Molecule(mol_struct.species, mol_struct.cart_coords)
DMF= Ligand([mol]) #create Ligand DMF
supercell = [1,1,1]
# slab thickness and vacuum set manual for now to converged values, surface coverage fixed at 0.014 ligand/sq.Angstrom
#for consistency, best ligand spacing at the coverage
min_thick= 19
min_vac= 12
surface_coverage= 0.014
#hkl of facet to reproduce
hkl= [1,0,0]
# specify the species on slab to adsorb over
slab_species= 'Pb'
# specify the species onb ligand serving as the bridge atom
adatom_on_ligand= 'O'
#initial adsorption distance in angstrom
ads_distance = 3.0
def from_string(cls, string_input):
"""
Read an FiestaInput from a string. Currently tested to work with
files generated from this class itself.
Args:
string_input: string_input to parse.
Returns:
FiestaInput object
"""
correlation_grid = {}
Exc_DFT_option = {}
COHSEX_options = {}
GW_options= {}
BSE_TDDFT_options = {}
lines = string_input.strip().split("\n")
#number of atoms and species
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
nat = toks[0]
nsp = toks[1]
# number of valence bands
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
nvbands = toks[0]
# correlation_grid
# number of points and spacing in eV for correlation grid
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
correlation_grid['n_grid'] = toks[0]
correlation_grid['dE_grid'] = toks[1]
# Exc DFT
# relire=1 ou recalculer=0 Exc DFT
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
Exc_DFT_option['rdVxcpsi'] = toks[0]
# COHSEX
# number of COHSEX corrected occp and unoccp bands: C=COHSEX H=HF
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
COHSEX_options['nv_cohsex'] = toks[0]
COHSEX_options['nc_cohsex'] = toks[1]
COHSEX_options['eigMethod'] = toks[2]
# number of COHSEX iter, scf on wfns, mixing coeff; V=RI-V I=RI-D
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
COHSEX_options['nit_cohsex'] = toks[0]
COHSEX_options['resMethod'] = toks[1]
COHSEX_options['scf_cohsex_wf'] = toks[2]
COHSEX_options['mix_cohsex'] = toks[3]
# GW
# number of GW corrected occp and unoccp bands
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
GW_options['nv_corr'] = toks[0]
GW_options['nc_corr'] = toks[1]
# number of GW iterations
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
GW_options['nit_gw'] = toks[0]
# BSE
# dumping for BSE and TDDFT
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
BSE_TDDFT_options['do_bse'] = toks[0]
BSE_TDDFT_options['do_tddft'] = toks[1]
# number of occp. and virtual bands fo BSE: nocore and up to 40 eVs
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
BSE_TDDFT_options['nv_bse'] = toks[0]
BSE_TDDFT_options['nc_bse'] = toks[1]
# number of excitations needed and number of iterations
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
BSE_TDDFT_options['npsi_bse'] = toks[0]
BSE_TDDFT_options['nit_bse'] = toks[1]
# Molecule
# list of symbols in order
lines.pop(0)
atname = []
i = int(nsp)
while i != 0:
l = lines.pop(0).strip()
toks = l.split()
atname.append(toks[0])
i -= 1
# scaling factor
lines.pop(0)
l = lines.pop(0).strip()
toks = l.split()
scale = toks[0]
# atoms x,y,z cartesian .. will be multiplied by scale
lines.pop(0)
#Parse geometry
species = []
coords = []
i = int(nat)
while i != 0:
l = lines.pop(0).strip()
toks = l.split()
coords.append([float(j) for j in toks[0:3]])
species.append(atname[int(toks[3])-1])
i -= 1
mol = Molecule(species, coords)
return FiestaInput(mol=mol, correlation_grid=correlation_grid, Exc_DFT_option=Exc_DFT_option, COHSEX_options=COHSEX_options,
GW_options=GW_options, BSE_TDDFT_options=BSE_TDDFT_options)
