def example_1():
# In this example, we will generate a smooth CNH-HCN isomerization
# guessed pathway
# Step 1 - Generate the bad initial guess
print("Step 1 - Generate the bad initial guess...")
H_coords = [(2, 0), (2, 1), (1, 1), (0, 1), (-1, 1), (-1, 0)]
CNH_frames = [[
structures.Atom("C", 0, 0, 0),
structures.Atom("N", 1, 0, 0),
structures.Atom("H", x, y, 0)
] for x, y in H_coords]
# Further, randomly rotate the atoms
CNH_frames = [
geometry.perturbate(frame, dx=0.0, dr=360) for frame in CNH_frames
]
files.write_xyz(CNH_frames, "rotated_pathway.xyz")
# Step 2 - Use procrustes to remove rotations
print("Step 2 - Use Procrustes to remove rotations...")
geometry.procrustes(CNH_frames)
files.write_xyz(CNH_frames, "procrustes_pathway.xyz")
# Step 3 - Smooth out the band by minimizing the RMS atomic motion between
# consecutive frames until it is below 0.1 (with a max of 50 frames).
print("Step 3 - Smooth out the band...")
CNH_frames = geometry.smooth_xyz(CNH_frames,
R_max=0.1,
F_max=50,
use_procrustes=True)
# Save smoothed band
files.write_xyz(CNH_frames, "smoothed_pathway.xyz")
def _generate_ion_halide(halide, ion="Pb"):
"""
A function to generate atomic coordinates of an ion and three halides for
a perovskite monomer.
**Parameters**
halide: *list, str or str*
The halides to use.
ion: *str, optional*
What ion to use.
**Returns**
ionX3: *Molecule*
A molecule object holding the perovskite ion + halide atoms.
"""
ionX3 = structures.Molecule([structures.Atom(ion, 0, 0, 0)])
if type(halide) is str:
halide = [halide, halide, halide]
def vdw(y):
return constants.PERIODIC_TABLE[units.elem_s2i(y)]['vdw_r']
for x in halide:
v = vdw(x)
ionX3.atoms.append(structures.Atom(x, v, 0, 0.5 * v))
R = geometry.rotation_matrix([0, 0, 1], 120, units="deg")
ionX3.rotate(R)
return ionX3
def isolate_atom_types(types, dump_obj, mol_len):
count = 0
atoms_list = []
frames = []
for line in dump_obj:
words = line.split()
if count == mol_len:
frames.append(atoms_list)
count = 0
atoms_list = []
try:
if int(words[0]) in types:
atom_type = types[int(words[0])]
x = float(words[1])
y = float(words[2])
z = float(words[3])
count = count + 1
atoms_list.append(
structures.Atom(atom_type, x, y, z, index=count))
except ValueError:
pass
return frames
def _test_generate_ion_halide_cation():
obj = generate_ion_halide_cation(["Cl", "Br", "Br"],
"Cs",
"CsPbBrBrCl_0",
WORLD_CONSTS.default_routes[0],
WORLD_CONSTS.extra_section,
ion="Pb",
run_opt=False).atoms
saved = [
structures.Atom("Cs", 0.0, 0.0, 2.5299999999999998),
structures.Atom("Pb", 0.0, 0.0, 0.0),
structures.Atom("Cl", 2.0499999999999998, -3.3306690738754696e-15,
1.0249999999999999),
structures.Atom("Br", -1.050000000000002, -1.8186533479473201, 1.05),
structures.Atom("Br", -1.0499999999999989, 1.8186533479473219, 1.05),
]
TOLERANCE = 1E-6
return geometry.motion_per_frame([obj, saved])[1] < TOLERANCE
def parse_scan(input_file):
contents = open(input_file).read()
if 'Normal termination of Gaussian 09' not in contents:
return None
scan_steps = contents.split('on scan point')
energy_list = []
atoms_list = []
scan_steps = [
scan_steps[i] for i in range(1,
len(scan_steps) - 1)
if scan_steps[i][:10].split()[0] != scan_steps[i + 1][:10].split()[0]
]
for scan_step in scan_steps:
a = scan_step.rindex('SCF Done')
energy_line = scan_step[a:scan_step.index('\n', a)]
energy = float(
re.search('SCF Done: +\S+ += +(\S+)', energy_line).group(1))
last_coordinates = scan_step.rindex('Coordinates (Angstroms)')
start = scan_step.index('---\n', last_coordinates) + 4
end = scan_step.index('\n ---', start)
atoms = []
for line in scan_step[start:end].splitlines():
columns = line.split()
x, y, z = [float(s) for s in columns[3:6]]
atoms.append(
structures.Atom(element=constants.PERIODIC_TABLE[int(
columns[1])]['sym'],
x=x,
y=y,
z=z))
energy_list.append(energy)
atoms_list.append(atoms)
return energy_list, atoms_list
def read(input_file, atom_units="Ang"):
'''
General read in of all possible data from a JDFTx output file.
**Parameters**
input_file: *str*
JDFTx output file to be parsed.
atom_units: *str, optional*
What units you want coordinates to be converted to.
**Returns**
data: :class:`results.DFT_out`
Generic DFT output object containing all parsed results.
'''
raise Exception("NEEDS TO BE DONE!")
# Check file exists, and open
# Allow absolute paths as filenames
if not input_file.startswith('/') and not os.path.isfile(input_file):
input_path = 'jdftx/%s/%s.out' % (input_file, input_file)
else:
input_path = input_file
if not os.path.isfile(input_path):
raise IOError('Expected JDFTx output file does not exist at %s'
% (input_path))
sys.exit()
data = open(input_path, 'r').read()
data_lines = data.splitlines()
# Get coordinates
section, frames, gradients = data, [], []
s = "# Ionic positions in cartesian coordinates:"
ss = "# Forces in Cartesian coordinates:"
while s in section:
section = section[section.find(s) + len(s):]
atom_block = section[:section.find('\n\n')].split('\n')[1:]
frame, gradient = [], []
for i, line in enumerate(atom_block):
a = line.split()
frame.append(structures.Atom(
a[1],
units.convert_dist("Bohr", atom_units, float(a[2])),
units.convert_dist("Bohr", atom_units, float(a[3])),
units.convert_dist("Bohr", atom_units, float(a[4])),
index=i))
frames.append(frame)
# If we also have forces, read those in
if ss in section:
section = section[section.find(ss) + len(ss):]
force_block = section[:section.find('\n\n')].split('\n')[1:]
for i, line in enumerate(force_block):
a = line.split()
frames[-1][i].fx = units.convert(
"Ha/Bohr", "Ha/%s" % atom_units, float(a[2]))
frames[-1][i].fy = units.convert(
"Ha/Bohr", "Ha/%s" % atom_units, float(a[3]))
frames[-1][i].fz = units.convert(
"Ha/Bohr", "Ha/%s" % atom_units, float(a[4]))
gradient.append(
[frames[-1][i].fx, frames[-1][i].fy, frames[-1][i].fz])
gradients.append(gradient)
atoms = None
if frames:
atoms = frames[-1]
section, energies = data, []
s = "IonicMinimize: Iter:"
while s in section:
section = section[section.find(s) + len(s):]
energy = float(section.split("\n")[0].strip().split()[2])
grad_k = float(section.split("\n")[0].strip().split()[4])
energies.append(energy)
convergence = None
if len(energies) > 2:
section = data[data.find("ionic-minimize"):]
de_criteria = float(section[
section.find("energyDiffThreshold"):].split("\n")[0].strip().split()[1])
k_criteria = float(section[
section.find("knormThreshold"):].split("\n")[0].strip().split()[1])
de1 = abs(energies[-2] - energies[-3])
de2 = abs(energies[-1] - energies[-2])
convergence = [
["Change in Energy 1",
"%.2e" % de1,
de_criteria,
["NO", "YES"][de_criteria > de1]],
["Change in Energy 2",
"%.2e" % de2,
de_criteria,
["NO", "YES"][de_criteria > de2]],
["K Norm",
"%.2e" % abs(grad_k),
k_criteria,
["NO", "YES"][k_criteria > abs(grad_k)]]
]
energy = None
if energies:
energy = energies[-1]
converged = None
finished = "Done!" in data
if "IonicMinimize: Converged" in data:
converged = True
elif finished:
converged = False
time = None
if "Duration:" in data:
time = data[data.find("Duration:"):].split("\n")[0].split("Duration:")[-1].strip()[:-1].split(":")
# Time should be x-x:yy:zz.zz, thus: [x-x, yy, zz.zz]
time = float(time[2]) + 60.0 * float(time[1]) + 3600.0 * float(time[0].split("-")[-1])
data = results.DFT_out(input_file, 'jdftx')
# data.route = route
# data.extra_section = extra_section
# data.charge_and_multiplicity = charge_and_multiplicity.strip()
data.frames = frames
data.atoms = atoms
data.gradients = gradients
data.energies = energies
data.energy = energy
# data.charges_MULLIKEN = charges_MULLIKEN
# data.charges_LOEWDIN = charges_LOEWDIN
# data.charges_CHELPG = charges_CHELPG
# data.charges = copy.deepcopy(charges_MULLIKEN)
# data.MBO = MBO
data.convergence = convergence
data.converged = converged
data.time = time
# data.bandgaps = bandgaps
# data.bandgap = bandgap
# data.orbitals = orbitals
data.finished = finished
# data.warnings = warnings
return data
from squid import orca
from squid import files
from squid import geometry
from squid.calcs import NEB
from squid import structures
if __name__ == "__main__":
# In this example we will generate the full CNH-HCN isomerization using
# only squid. Then we optimize the endpoints in DFT, smooth the frames,
# and subsequently run NEB
# Step 1 - Generate the bad initial guess
print("Step 1 - Generate the bad initial guess...")
H_coords = [(2, 0), (2, 0.5), (1, 1), (0, 1), (-1, 0.5), (-1, 0)]
CNH_frames = [[
structures.Atom("C", 0, 0, 0),
structures.Atom("N", 1, 0, 0),
structures.Atom("H", x, y, 0)
] for x, y in H_coords]
# Save initial frames
files.write_xyz(CNH_frames, "bad_guess.xyz")
# Step 2 - Optimize the endpoints
print("Step 2 - Optimize endpoints...")
frame_start_job = orca.job("frame_start",
"! HF-3c Opt",
atoms=CNH_frames[0],
queue=None)
frame_last_job = orca.job("frame_last",
"! HF-3c Opt",
atoms=CNH_frames[-1],
def parse_atoms(input_file,
get_atoms=True,
get_energy=True,
check_convergence=True,
get_time=False,
counterpoise=False,
parse_all=False):
# @input_file [str] : string name of log file
# Returns: (? energy, ? atoms, ? time) | None
# @energy [float] : If get_energy or parse_all, otherwise return omitted.
# @atoms |[atom list] : Iff parse_all, returns atom list list.
# |[atom list list] : Iff not parse_all and get_atoms, atom list.
# Otherwise omitted.
# @time [float] : If get_time returns float (seconds). Otherwise, return
# omitted.
# Note that None may be returned in the event that Gaussian did not
# terminate normally (see 7 lines down).
if input_file[-4:] != '.log':
input_file = input_file + '.log'
if not os.path.exists(input_file):
input_file = "gaussian/" + input_file
contents = open(input_file).read()
time = None
if (check_convergence and get_energy and not parse_all
and 'Normal termination of Gaussian 09' not in contents):
return None
if (('Normal termination of Gaussian 09' in contents)
and (get_time | parse_all)):
m = re.search(
'Job cpu time: +(\S+) +days +(\S+) +hours \
+(\S+) +minutes +(\S+) +seconds', contents)
try:
time = float(m.group(1)) * 24 * 60 * 60 +\
float(m.group(2)) * 60 * 60 +\
float(m.group(3)) * 60 +\
float(m.group(4))
except:
pass
if ('Summary of Optimized Potential Surface Scan' in contents
and not parse_all):
index = contents.rindex('Summary of Optimized Potential Surface Scan')
end_section = contents[index:]
energy_lines = re.findall('Eigenvalues -- ([^\\n]+)', end_section)
energy = [
float(s) for line in energy_lines
for s in re.findall('-[\d]+\.[\d]+', line)
]
minima = re.split('Stationary point found', contents)
atoms = []
for m in minima[1:]:
coordinates = m.index('Coordinates (Angstroms)')
start = m.index('---\n', coordinates) + 4
end = m.index('\n ---', start)
atoms.append([])
for line in m[start:end].splitlines():
columns = line.split()
element = columns[1]
x, y, z = [float(s) for s in columns[3:6]]
atoms[-1].append(
structures.Atom(element=constants.PERIODIC_TABLE[int(
columns[1])]['sym'],
x=x,
y=y,
z=z,
index=len(atoms[-1]) + 1))
if get_energy:
return energy, atoms
elif get_energy and not parse_all:
if ' MP2/' in contents:
# MP2 files don't have just SCF energy
energy = float(
re.findall('EUMP2 = +(\S+)', contents)[-1].replace('D', 'e'))
elif ' CCSD/' in contents:
energy = float(re.findall('E\(CORR\)= +(\S+)', contents)[-1])
else:
if not counterpoise:
try:
a = contents.rindex('SCF Done')
energy_line = contents[a:contents.index('\n', a)]
except ValueError:
raise Exception('No SCF for ' + input_file)
energy = float(
re.search('SCF Done: +\S+ += +(\S+)',
energy_line).group(1))
else:
energy = float(
re.findall('Counterpoise: corrected energy = +(\S+)',
contents)[-1])
if parse_all:
energies = []
atom_frames = []
start = 0
orientation = 'Input orientation:'
while True:
try:
# Match energy
input_orientation = contents.find(orientation, start)
if input_orientation == -1:
orientation = 'Standard orientation'
input_orientation = contents.find(orientation, start)
if input_orientation >= 0:
start = input_orientation
next_coordinates = contents.index('Coordinates (Angstroms)',
start)
start = contents.index('SCF Done', start)
energies.append(
float(
re.search('SCF Done: +\S+ += +(\S+)',
contents[start:]).group(1)))
except:
break
start = contents.index('---\n', next_coordinates) + 4
end = contents.index('\n ---', start)
lines = contents[start:end].splitlines()
start = end
atoms = []
for line in lines:
columns = line.split()
element = columns[1]
x, y, z = columns[3:6]
atoms.append(
structures.Atom(element=element,
x=float(x),
y=float(y),
z=float(z)))
atom_frames.append(atoms)
return energies, atom_frames, time
if get_energy and not get_atoms:
if get_time:
return energy, time
else:
return energy
try:
# Get coordinates
last_coordinates = contents.rindex('Input orientation:')
last_coordinates = contents.index('Coordinates (Angstroms)',
last_coordinates)
except ValueError:
last_coordinates = contents.rindex('Coordinates (Angstroms)')
start = contents.index('---\n', last_coordinates) + 4
end = contents.index('\n ---', start)
atoms = []
for line in contents[start:end].splitlines():
columns = line.split()
element = columns[1]
x, y, z = [float(s) for s in columns[3:6]]
atoms.append(
structures.Atom(element=constants.PERIODIC_TABLE[int(
columns[1])]['sym'],
x=x,
y=y,
z=z,
index=len(atoms) + 1))
if 'Forces (Hartrees/Bohr)' in contents:
# Get forces
last_forces = contents.rindex('Forces (Hartrees/Bohr)')
start = contents.index('---\n', last_forces) + 4
end = contents.index('\n ---', start)
for i, line in enumerate(contents[start:end].splitlines()):
columns = line.split()
read_force = [float(s) for s in columns[2:5]]
atoms[i].fx, atoms[i].fy, atoms[i].fz = read_force
# Return the appropriate values
if get_time:
if get_atoms:
return energy, atoms, time
else:
return energy, time
if get_energy:
return energy, atoms
else:
return atoms