def test_structural_change(self):
t1 = Molecule.from_file(os.path.join(PymatgenTest.TEST_FILES_DIR, "molecules", "structural_change", "t1.xyz"))
t2 = Molecule.from_file(os.path.join(PymatgenTest.TEST_FILES_DIR, "molecules", "structural_change", "t2.xyz"))
t3 = Molecule.from_file(os.path.join(PymatgenTest.TEST_FILES_DIR, "molecules", "structural_change", "t3.xyz"))
thio_1 = Molecule.from_file(
os.path.join(PymatgenTest.TEST_FILES_DIR, "molecules", "structural_change", "thiophene1.xyz")
)
thio_2 = Molecule.from_file(
os.path.join(PymatgenTest.TEST_FILES_DIR, "molecules", "structural_change", "thiophene2.xyz")
)
frag_1 = Molecule.from_file(
os.path.join(
PymatgenTest.TEST_FILES_DIR, "molecules", "new_qchem_files", "test_structure_change", "frag_1.xyz"
)
)
frag_2 = Molecule.from_file(
os.path.join(
PymatgenTest.TEST_FILES_DIR, "molecules", "new_qchem_files", "test_structure_change", "frag_2.xyz"
)
)
self.assertEqual(check_for_structure_changes(t1, t1), "no_change")
self.assertEqual(check_for_structure_changes(t2, t3), "no_change")
self.assertEqual(check_for_structure_changes(t1, t2), "fewer_bonds")
self.assertEqual(check_for_structure_changes(t2, t1), "more_bonds")
self.assertEqual(check_for_structure_changes(thio_1, thio_2), "unconnected_fragments")
self.assertEqual(check_for_structure_changes(frag_1, frag_2), "bond_change")
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 = list()
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,
)
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,
)
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,
)
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,
)
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 = list()
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,
)
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"] = list()
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,
)
new_opt_QCInput.write_file(input_file)
def test_all(self):
expected_cmd_options = {"g": "H2O", "c": "2"}
expected_energies = [
[
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
"-13.66580",
],
[
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
"-13.66479",
],
]
expected_sorted_structures = [[], []]
for f in os.listdir(expected_output_dir):
if f.endswith("xyz") and "_r" in f:
n_conf = int(f.split("_")[0][-1])
n_rot = int(f.split("_")[1].split(".")[0][-1])
m = Molecule.from_file(os.path.join(expected_output_dir, f))
expected_sorted_structures[n_conf].insert(n_rot, m)
cout = CRESTOutput(output_filename="crest_out.out", path=test_dir)
exp_best = Molecule.from_file(
os.path.join(expected_output_dir, "expected_crest_best.xyz"))
for i, c in enumerate(cout.sorted_structures_energies):
for j, r in enumerate(c):
if have_babel:
self.assertEqual(
check_for_structure_changes(
r[0], expected_sorted_structures[i][j]),
"no_change")
self.assertAlmostEqual(float(r[1]),
float(expected_energies[i][j]), 4)
self.assertEqual(cout.properly_terminated, True)
if have_babel:
self.assertEqual(
check_for_structure_changes(cout.lowest_energy_structure,
exp_best), "no_change")
self.assertDictEqual(cout.cmd_options, expected_cmd_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,
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 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"]
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]
# else:
# print(d_calc_final)
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":
opt_traj = []
for entry in d["calcs_reversed"]:
if entry["input"]["rem"]["job_type"] == "opt" or entry["input"]["rem"]["job_type"] == "optimization":
doc = {"initial": {}, "final": {}}
doc["initial"]["molecule"] = entry["initial_molecule"]
doc["final"]["molecule"] = entry["molecule_from_last_geometry"]
doc["initial"]["total_energy"] = entry["energy_trajectory"][0]
doc["final"]["total_energy"] = entry["energy_trajectory"][-1]
doc["initial"]["scf_energy"] = entry["SCF"][0][-1][0]
doc["final"]["scf_energy"] = entry["SCF"][-1][-1][0]
doc["structure_change"] = entry["structure_change"]
opt_traj.append(doc)
opt_traj.reverse()
opt_trajectory = {"trajectory": opt_traj, "structure_change": [[ii, entry["structure_change"]] for ii,entry in enumerate(opt_traj)], "energy_increase": []}
for ii, entry in enumerate(opt_traj):
if entry["final"]["total_energy"] > entry["initial"]["total_energy"]:
opt_trajectory["energy_increase"].append([ii, entry["final"]["total_energy"]-entry["initial"]["total_energy"]])
if ii != 0:
if entry["final"]["total_energy"] > opt_traj[ii-1]["final"]["total_energy"]:
opt_trajectory["energy_increase"].append([ii-1, ii, entry["final"]["total_energy"]-opt_traj[ii-1]["final"]["total_energy"]])
struct_change = check_for_structure_changes(opt_traj[ii-1]["final"]["molecule"], entry["final"]["molecule"])
if struct_change != entry["structure_change"]:
opt_trajectory["structure_change"].append([ii-1, ii, struct_change])
d["warnings"]["between_iteration_structure_change"] = True
if "linked" in d:
if d["linked"] == True:
opt_trajectory["discontinuity"] = {"structure": [], "scf_energy": [], "total_energy": []}
for ii, entry in enumerate(opt_traj):
if ii != 0:
if entry["initial"]["molecule"] != opt_traj[ii-1]["final"]["molecule"]:
opt_trajectory["discontinuity"]["structure"].append([ii-1,ii])
d["warnings"]["linked_structure_discontinuity"] = True
if entry["initial"]["total_energy"] != opt_traj[ii-1]["final"]["total_energy"]:
opt_trajectory["discontinuity"]["total_energy"].append([ii-1,ii])
if entry["initial"]["scf_energy"] != opt_traj[ii-1]["final"]["scf_energy"]:
opt_trajectory["discontinuity"]["scf_energy"].append([ii-1,ii])
d["opt_trajectory"] = opt_trajectory
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