def minimize_seeds(nprocs=4):
seeds = []
seed_names = []
route_low = "! OPT B97-D3 def2-TZVP ECP{def2-TZVP} Grid7"
extra_section_low = ""
route_high = "! OPT PW6B95 def2-TZVP ECP{def2-TZVP} Grid7"
extra_section_high = ""
for seed in os.listdir("seed"):
seeds.append(files.read_cml("seed/%s" % seed, allow_errors=True, test_charges=False)[0])
seed_names.append(seed.split(".cml")[0])
jobs = []
for i,seed in enumerate(seeds):
charge = sum([a.type.charge for a in seed])
jobs.append( orca.job("seed_%d_low" % i, route_low, atoms=seed, extra_section=extra_section_low, charge=charge, grad=False, queue=QUEUE_TO_RUN_ON, procs=nprocs, sandbox=False) )
for j in jobs: j.wait()
for i,seed in enumerate(seeds):
charge = sum([a.type.charge for a in seed])
jobs.append( orca.job("seed_%d_high" % i, route_high, atoms=[], extra_section=extra_section_high, charge=charge, grad=False, queue=QUEUE_TO_RUN_ON, procs=nprocs, previous="seed_%d_low" % i, sandbox=False) )
for j in jobs: j.wait()
for i,seed in enumerate(seeds):
new_pos = orca.read("seed_%d_high" % i)
if not new_pos.converged:
print("Failed to optimize %s" % seed_names[i])
continue
new_pos = new_pos.atoms
cml_file = files.read_cml("seed/%s" % seed_names[i], allow_errors=True, test_charges=False, return_molecules=True)
j=0
for mol in cml_file:
for k,a in enumerate(mol.atoms):
b = new_pos[j]
a.x, a.y, a.z = b.x, b.y, b.z
j += 1
files.write_cml(cml_file, name="seed/%s_opt" % seed_names[i])
def run_low_level(training_sets_folder="training_set", procs=1, queue=None,
extra_parameters={}):
if not os.path.exists(training_sets_folder):
raise Exception("No training set folder to run.")
frange = [int(a.split('.cml')[0]) for a in os.listdir(training_sets_folder)
if a.endswith(".cml")]
frange.sort()
if len(frange) == 0:
raise Exception("No viable files in training sets folder.")
route = "! B97-D3 def2-TZVP GCP(DFT/TZ) ECP{def2-TZVP} Grid7"
extra_section = ""
running_jobs = []
for i in frange:
atoms = files.read_cml("%s/%d.cml" % (training_sets_folder, i),
allow_errors=True,
test_charges=False,
return_molecules=False,
extra_parameters=extra_parameters)[0]
charge = sum([a.type.charge for a in atoms])
running_jobs.append(orca.job("ts_%d" % i, route,
atoms=atoms,
extra_section=extra_section,
charge=charge,
grad=True,
queue=queue,
procs=procs,
sandbox=False))
return running_jobs
def __init__(self, atoms_or_filename_or_all, bonds=None, angles=None, dihedrals=None, parameter_file='oplsaa.prm', extra_parameters={}, check_charges=True): #set atoms, bonds, etc, or assume 'atoms' contains all those things if only one parameter is passed in
if type(atoms_or_filename_or_all)==type('string'):
self.filename = atoms_or_filename_or_all
atoms, bonds, angles, dihedrals = files.read_cml(self.filename, parameter_file=parameter_file, extra_parameters=extra_parameters, check_charges=check_charges)
elif not bonds:
atoms, bonds, angles, dihedrals = atoms_or_filename_or_all
else:
atoms = atoms_or_filename_or_all
self.atoms = atoms
self.bonds = bonds
self.angles = angles
self.dihedrals = dihedrals
def compile_training_set(path="./training_sets"):
if path.endswith("/"): path = path[:-1]
if not os.path.exists(path):
raise Exception("Unable to find training set directory")
frames = []
i = 0
while os.path.exists(path+"/"+str(i)+".cml"):
atoms = files.read_cml(path+"/"+str(i)+".cml", return_molecules=False, allow_errors=True, test_charges=False)[0]
frames.append(atoms)
i += 1
files.write_xyz(frames, "full_training_set")
def opls_options(molecule, parameter_file='oplsaa.prm'):
elements, atom_types, bond_types, angle_types, dihedral_types = files.read_opls_parameters(parameter_file)
elements_by_structure_indices = dict( [ (t.index2, elements_by_atomic_number[t.element] ) for t in atom_types ] )
elements_by_structure_indices[0] = 'X'
def add_to_list(dic,key,value):
if key in dic:
dic[key].append(value)
else:
dic[key] = [value]
dihedral_types_by_element={}
for d in dihedral_types:
structure_indices = d.index2s
elements = [ elements_by_structure_indices[i] for i in structure_indices]
add_to_list(dihedral_types_by_element, tuple(elements), d)
add_to_list(dihedral_types_by_element, tuple(reversed(elements)), d)
atoms, bonds, angles, dihedrals = files.read_cml(molecule, parameter_file=None)
for a in atoms:
a.index2_options = []
for d in dihedrals:
elements = tuple([ a.element for a in d.atoms ])
options = dihedral_types_by_element[elements]
options_by_i = [ [],[],[],[] ]
if elements in dihedral_types_by_element:
print elements
for a in d.atoms:
a.index2_options.append( set() )
for t in dihedral_types_by_element[elements]:
#print '\t', t.index2s
for i in range(4):
d.atoms[i].index2_options[-1].add( t.index2s[i] )
else:
print 'Error: dihedral', elements, 'does not exist in OPLS file', parameter_file
for a in atoms:
print a.element
for option in a.index2_options:
print '\t', option
options = a.index2_options[0]
for i in xrange(1,len(a.index2_options)):
options = options.intersection( a.index2_options[i] )
print '\t\t', options
def read_seed(path="./seed", extra_parameters={}):
"""
Read in all cml files from the seed directory.
**Parameters**
path: *str, optional*
A path to the seed directory.
extra_parameters: *dict, optional*
Additional parameters to add to OPLSAA.
**Returns**
molecules_A: *list, list, molecules*
A list of molecules from the seed directory
molecules_B: *list, molecules*
A list of molecules from the seed directory. In this case,
we merge child molecules into one.
"""
if path.endswith("/"):
path = path[:-1]
if not os.path.exists(path):
raise Exception("Unable to find seed directory")
molecules_A = []
for fptr in os.listdir(path):
if not fptr.endswith(".cml"):
continue
molecules_A.append(files.read_cml(path + "/" + fptr,
return_molecules=True,
allow_errors=True,
test_charges=False,
extra_parameters=extra_parameters))
if molecules_A == []:
raise Exception("Seed directory is empty")
molecules_B = []
for seed in molecules_A:
atoms, bonds, angles, dihedrals = [], [], [], []
for mol in seed:
atoms += mol.atoms
bonds += mol.bonds
angles += mol.angles
dihedrals += mol.dihedrals
molecules_B.append(structures.Molecule(atoms,
bonds,
angles,
dihedrals))
return molecules_A, molecules_B
def read_seed(path="./seed", extra_parameters={}):
"""
Read in all cml files from the seed directory.
**Parameters**
path: *str, optional*
A path to the seed directory.
extra_parameters: *dict, optional*
Additional parameters to add to OPLSAA.
**Returns**
molecules_A: *list, list, molecules*
A list of molecules from the seed directory
molecules_B: *list, molecules*
A list of molecules from the seed directory. In this case,
we merge child molecules into one.
"""
if path.endswith("/"):
path = path[:-1]
if not os.path.exists(path):
raise Exception("Unable to find seed directory")
molecules_A = []
for fptr in os.listdir(path):
if not fptr.endswith(".cml"):
continue
molecules_A.append(
files.read_cml(path + "/" + fptr,
return_molecules=True,
allow_errors=True,
test_charges=False,
extra_parameters=extra_parameters))
if molecules_A == []:
raise Exception("Seed directory is empty")
molecules_B = []
for seed in molecules_A:
atoms, bonds, angles, dihedrals = [], [], [], []
for mol in seed:
atoms += mol.atoms
bonds += mol.bonds
angles += mol.angles
dihedrals += mol.dihedrals
molecules_B.append(structures.Molecule(atoms, bonds, angles,
dihedrals))
return molecules_A, molecules_B
def compile_training_set(path="./training_set", extra_parameters={}):
if path.endswith("/"):
path = path[:-1]
if not os.path.exists(path):
raise Exception("Unable to find training set directory")
frames = []
i = 0
while os.path.exists(path + "/" + str(i) + ".cml"):
atoms = files.read_cml(path + "/" + str(i) + ".cml",
return_molecules=False,
allow_errors=True,
test_charges=False,
extra_parameters=extra_parameters)[0]
frames.append(atoms)
i += 1
files.write_xyz(frames, "full_training_set")
def run_high_level(training_sets_folder="training_set",
procs=1,
queue=None,
extra_parameters={}):
if not os.path.exists(training_sets_folder):
raise Exception("No training set folder to run.")
frange = [
int(a.split('.cml')[0]) for a in os.listdir(training_sets_folder)
if a.endswith(".cml")
]
frange.sort()
if len(frange) == 0:
raise Exception("No viable files in training sets folder.")
route = "! PW6B95 def2-TZVP GCP(DFT/TZ) ECP{def2-TZVP} Grid7"
extra_section = ""
running_jobs = []
previous_failed = []
for i in frange:
atoms = files.read_cml("%s/%d.cml" % (training_sets_folder, i),
allow_errors=True,
test_charges=False,
return_molecules=False,
extra_parameters=extra_parameters)[0]
charge = sum([a.type.charge for a in atoms])
prev_converged = orca.read("ts_%d" % i).converged
if prev_converged:
running_jobs.append(
orca.job("ts_%d_high" % i,
route,
atoms=[],
extra_section=extra_section,
charge=charge,
grad=True,
queue=queue,
procs=procs,
previous="ts_%d" % i,
sandbox=False))
else:
previous_failed.append(i)
return running_jobs, previous_failed
def run_high_level():
if not os.path.exists("training_sets"):
raise Exception("No training set folder to run.")
frange = [int(a.split('.cml')[0]) for a in os.listdir("training_sets") if a.endswith(".cml")]
frange.sort()
if len(frange) == 0:
raise Exception("No viable files in training sets folder.")
route = "! PW6B95 def2-TZVP GCP(DFT/TZ) ECP{def2-TZVP} Grid7"
extra_section = ""
running_jobs = []
previous_failed = []
for i in frange:
atoms = files.read_cml("training_sets/%d.cml" % i, allow_errors=True, test_charges=False, return_molecules=False)[0]
charge = sum([a.type.charge for a in atoms])
prev_converged = orca.read("ts_%d" % i).converged
if prev_converged:
running_jobs.append( orca.job("ts_%d_high" % i, route, atoms=[], extra_section=extra_section, charge=charge, grad=True, queue=QUEUE_TO_RUN_ON, procs=QUEUE_PROCS, previous="ts_%d" % i, sandbox=False) )
else:
previous_failed.append(i)
return running_jobs, previous_failed
def minimize_seeds(procs=4, queue=None, extra_parameters={}):
"""
A function to optimize the geometry of the supplied seeds in the seed
directory. Each optimized structure is then added to the seed directory
under the name "previous_name_opt.cml".
**Parameters**
procs: *int, optional*
How many processors to use for this.
queue: *str, optional*
What queue to run the simulation on.
"""
seeds = []
seed_names = []
route_low = "! OPT B97-D3 def2-TZVP ECP{def2-TZVP} Grid7"
extra_section_low = ""
route_high = "! OPT PW6B95 def2-TZVP ECP{def2-TZVP} Grid7"
extra_section_high = ""
for seed in os.listdir("seed"):
seeds.append(
files.read_cml("seed/%s" % seed,
allow_errors=True,
test_charges=False,
extra_parameters=extra_parameters)[0])
seed_names.append(seed.split(".cml")[0])
jobs = []
for i, seed in enumerate(seeds):
charge = sum([a.type.charge for a in seed])
jobs.append(
orca.job("seed_%d_low" % i,
route_low,
atoms=seed,
extra_section=extra_section_low,
charge=charge,
grad=False,
queue=queue,
procs=procs,
sandbox=False))
for j in jobs:
j.wait()
for i, seed in enumerate(seeds):
charge = sum([a.type.charge for a in seed])
jobs.append(
orca.job("seed_%d_high" % i,
route_high,
atoms=[],
extra_section=extra_section_high,
charge=charge,
grad=False,
queue=queue,
procs=procs,
previous="seed_%d_low" % i,
sandbox=False))
for j in jobs:
j.wait()
for i, seed in enumerate(seeds):
new_pos = orca.read("seed_%d_high" % i)
if not new_pos.converged:
print("Failed to optimize %s" % seed_names[i])
continue
new_pos = new_pos.atoms
cml_file = files.read_cml("seed/%s" % seed_names[i],
allow_errors=True,
test_charges=False,
return_molecules=True,
extra_parameters=extra_parameters)
j = 0
for mol in cml_file:
for k, a in enumerate(mol.atoms):
b = new_pos[j]
a.x, a.y, a.z = b.x, b.y, b.z
j += 1
files.write_cml(cml_file, name="seed/%s_opt" % seed_names[i])
def generate_lead_halide_cation(halide, cation, ion="Pb", run_opt=True):
cml_path = fpl_constants.cml_dir
# Check if system exists
fname = reduce_to_name(ion, halide, cation)
if not cml_path.endswith("/"):
cml_path += "/"
if os.path.exists(cml_path + fname + ".cml"):
print("Found system in cml folder, returning system")
system = structures.Molecule(
files.read_cml(cml_path + fname + ".cml",
test_charges=False,
allow_errors=True)[0])
return system
def vdw(y):
return PERIODIC_TABLE[units.elem_s2i(y)]['vdw_r']
# Get the PbX3 system
PbX3 = generate_lead_halide(halide, ion=ion)
# Get the cation from the cml file
atoms, bonds, _, _ = files.read_cml(cml_path + cation + ".cml",
test_charges=False,
allow_errors=True)
system = structures.Molecule(atoms)
# Align along X axis
system.atoms = geometry.align_centroid(system.atoms)[0]
# Rotate to Z axis
# NOTE! In case of FA, we want flat so only translate to origin instead
# NOTE! We have exactly 3 cations we observe: Cs, MA, FA. If 2 N, then FA
elems = [a.element for a in system.atoms]
if elems.count("N") == 2:
system.translate(system.get_center_of_mass())
else:
R = geometry.rotation_matrix([0, 1, 0], 90, units="deg")
system.rotate(R)
# If N and C in system, ensure N is below C (closer to Pb)
if "N" in elems and "C" in elems:
N_index = [i for i, a in enumerate(system.atoms)
if a.element == "N"][0]
C_index = [i for i, a in enumerate(system.atoms)
if a.element == "C"][0]
if system.atoms[N_index].z > system.atoms[C_index].z:
# Flip if needed
R = geometry.rotation_matrix([0, 1, 0], 180, units="deg")
system.rotate(R)
# Offset system so lowest point is at 0 in the z dir
z_offset = min([a.z for a in system.atoms]) * -1
system.translate([0, 0, z_offset])
# Add to the PbX3 system with an offset of vdw(Pb)
system.translate([0, 0, vdw(ion)])
system.atoms += PbX3.atoms
# Run a geometry optimization of this system
if run_opt:
PbXY = orca.job(fname,
fpl_constants.default_routes[0],
atoms=system.atoms,
extra_section=fpl_constants.extra_section,
queue="batch",
procs=2)
PbXY.wait()
new_pos = orca.read(fname).atoms
for a, b in zip(system.atoms, new_pos):
a.x, a.y, a.z = [b.x, b.y, b.z]
# Set OPLS types
for a in system.atoms:
if a.element in [ion, "Cl", "Br", "I"]:
a.type = fpl_constants.atom_types[a.element]
a.type_index = a.type["index"]
# Write cml file so we don't re-generate, and return system
files.write_cml(system, bonds=bonds, name=cml_path + fname + ".cml")
return system
def opt_opls(molecule, parameter_file='oplsaa.prm', taboo_time=100): elements, atom_types, bond_types, angle_types, dihedral_types = files.read_opls_parameters(parameter_file) atoms, bonds, angles, dihedrals = files.read_cml(molecule, parameter_file=None) bond_types_by_index2 = dict( [ (tuple(t.index2s),t) for t in bond_types ] + [ (tuple(reversed(t.index2s)),t) for t in bond_types ] ) angle_types_by_index2 = dict( [ (tuple(t.index2s),t) for t in angle_types ] + [ (tuple(reversed(t.index2s)),t) for t in angle_types ] ) dihedral_types_by_index2 = dict( [ (tuple(t.index2s),t) for t in dihedral_types ] + [ (tuple(reversed(t.index2s)),t) for t in dihedral_types ] ) charges_by_index = dict( [ (t.index,t.charge) for t in atom_types ] ) for a in atoms: a.possible_types = set() for t in atom_types: if elements_by_atomic_number[t.element] == a.element and t.bond_count==len(a.bonded): a.possible_types.add(t.index2) a.possible_types = list(a.possible_types) #a.possible_types.append(0) def count_conflicts(types): for i,a in enumerate(atoms): a.index2 = types[i] conflicts = 0 for b in bonds: index2s = (b.atoms[0].index2, b.atoms[1].index2) if not index2s in bond_types_by_index2: conflicts += 1 for a in angles: index2s = (a.atoms[0].index2, a.atoms[1].index2, a.atoms[2].index2) if not index2s in angle_types_by_index2: conflicts += 1 for d in dihedrals: index2s_0 = (d.atoms[0].index2, d.atoms[1].index2, d.atoms[2].index2, d.atoms[3].index2) index2s_1 = (0, d.atoms[1].index2, d.atoms[2].index2, d.atoms[3].index2) index2s_2 = (d.atoms[0].index2, d.atoms[1].index2, d.atoms[2].index2, 0) in0 = index2s_0 in dihedral_types_by_index2 in1 = index2s_1 in dihedral_types_by_index2 in2 = index2s_2 in dihedral_types_by_index2 if not in0 and not in1 and not in2: conflicts += 1 return conflicts import random types = [random.choice(a.possible_types) for a in atoms] taboo = [0 for a in atoms] best = count_conflicts(types) step = 0 for step in range(100000): i = random.randint( 0, len(types)-1 ) for guess in types: if taboo[i]>0: i = random.randint( 0, len(types)-1 ) else: break old_type = types[i] types[i] = random.choice(atoms[i].possible_types) conflicts = count_conflicts(types) if conflicts <= best: best = conflicts taboo[i] = taboo_time else: types[i] = old_type taboo = [t-1 if t>0 else 0 for t in taboo] if step % 10000 == 0: print best, conflicts, types step += 1 def types_from_index2(x): return [t for t in atom_types if t.index2==x and t.index<=440] for i,tt in enumerate( [ types_from_index2(x) for x in types] ): #print i, atoms[i].element atoms[i].index_options = [t.index for t in tt] #for t in tt: # print '\t', t.index, t.notes def net_charge(types): charge = 0.0 for t in types: charge += charges_by_index[t] return charge types = [random.choice(a.index_options) for a in atoms] taboo = [0 for a in atoms] best = net_charge(types) for step in range(100000): i = random.randint( 0, len(types)-1 ) for guess in types: if taboo[i]>0: i = random.randint( 0, len(types)-1 ) else: break old_type = types[i] types[i] = random.choice(atoms[i].index_options) charge = net_charge(types) if abs(charge) <= abs(best): best = charge taboo[i] = taboo_time else: types[i] = old_type taboo = [t-1 if t>0 else 0 for t in taboo] if step % 10000 == 0: print best, charge, types step += 1 for t in types: for t2 in atom_types: if t2.index==t: print t2.element, t2.notes
def minimize_seeds(procs=4, queue=None, extra_parameters={}):
"""
A function to optimize the geometry of the supplied seeds in the seed
directory. Each optimized structure is then added to the seed directory
under the name "previous_name_opt.cml".
**Parameters**
procs: *int, optional*
How many processors to use for this.
queue: *str, optional*
What queue to run the simulation on.
"""
seeds = []
seed_names = []
route_low = "! OPT B97-D3 def2-TZVP ECP{def2-TZVP} Grid7"
extra_section_low = ""
route_high = "! OPT PW6B95 def2-TZVP ECP{def2-TZVP} Grid7"
extra_section_high = ""
for seed in os.listdir("seed"):
seeds.append(files.read_cml("seed/%s" % seed,
allow_errors=True,
test_charges=False,
extra_parameters=extra_parameters)[0])
seed_names.append(seed.split(".cml")[0])
jobs = []
for i, seed in enumerate(seeds):
charge = sum([a.type.charge for a in seed])
jobs.append(orca.job("seed_%d_low" % i, route_low,
atoms=seed,
extra_section=extra_section_low,
charge=charge,
grad=False,
queue=queue,
procs=procs,
sandbox=False))
for j in jobs:
j.wait()
for i, seed in enumerate(seeds):
charge = sum([a.type.charge for a in seed])
jobs.append(orca.job("seed_%d_high" % i, route_high,
atoms=[],
extra_section=extra_section_high,
charge=charge,
grad=False,
queue=queue,
procs=procs,
previous="seed_%d_low" % i,
sandbox=False))
for j in jobs:
j.wait()
for i, seed in enumerate(seeds):
new_pos = orca.read("seed_%d_high" % i)
if not new_pos.converged:
print("Failed to optimize %s" % seed_names[i])
continue
new_pos = new_pos.atoms
cml_file = files.read_cml("seed/%s" % seed_names[i],
allow_errors=True,
test_charges=False,
return_molecules=True,
extra_parameters=extra_parameters)
j = 0
for mol in cml_file:
for k, a in enumerate(mol.atoms):
b = new_pos[j]
a.x, a.y, a.z = b.x, b.y, b.z
j += 1
files.write_cml(cml_file, name="seed/%s_opt" % seed_names[i])
