def prepare_options_for_set_options():
"""Capture current state of C++ psi4.core.Options information for reloading by `psi4.set_options()`.
Returns
-------
dict
Dictionary where keys are option names to be set globally or module__option
mangled names to be set locally. Values are option values.
"""
flat_options = {}
has_changed_snapshot = {module: core.options_to_python(module) for module in _modules}
for opt in core.get_global_option_list():
handled_locally = False
ghoc = core.has_global_option_changed(opt)
opt_snapshot = {k: v[opt] for k, v in has_changed_snapshot.items() if opt in v}
for module, (lhoc, ohoc) in opt_snapshot.items():
if ohoc:
if lhoc:
key = module + '__' + opt
val = core.get_local_option(module, opt)
else:
key = opt
val = core.get_global_option(opt)
handled_locally = True
flat_options[key] = val
if ghoc and not handled_locally:
# some options are globals section (not level) so not in any module
flat_options[opt] = core.get_global_option(opt)
return flat_options
def __init__(self, option: str, module: Optional[str] = None):
self.option = option.upper()
if module:
self.module = module.upper()
else:
self.module = None
self.value_global = core.get_global_option(option)
self.haschanged_global = core.has_global_option_changed(option)
if self.module:
self.value_local = core.get_local_option(self.module, option)
self.haschanged_local = core.has_local_option_changed(self.module, option)
self.value_used = core.get_option(self.module, option)
self.haschanged_used = core.has_option_changed(self.module, option)
else:
self.value_local = None
self.haschanged_local = None
self.value_used = None
self.haschanged_used = None
def __init__(self, option, module=None):
self.option = option.upper()
if module:
self.module = module.upper()
else:
self.module = None
self.value_global = core.get_global_option(option)
self.haschanged_global = core.has_global_option_changed(option)
if self.module:
self.value_local = core.get_local_option(self.module, option)
self.haschanged_local = core.has_local_option_changed(self.module, option)
self.value_used = core.get_option(self.module, option)
self.haschanged_used = core.has_option_changed(self.module, option)
else:
self.value_local = None
self.haschanged_local = None
self.value_used = None
self.haschanged_used = None
def frac_traverse(name, **kwargs):
"""Scan electron occupancy from +1 electron to -1.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
cation_mult : int, optional
Multiplicity of cation, if not neutral multiplicity + 1.
anion_mult : int, optional
Multiplicity of anion, if not neutral multiplicity + 1.
frac_start : int, optional
Iteration at which to start frac procedure when not reading previous
guess. Defaults to 25.
HOMO_occs : list, optional
Occupations to step through for cation, by default `[1 - 0.1 * x for x in range(11)]`.
LUMO_occs : list, optional
Occupations to step through for anion, by default `[1 - 0.1 * x for x in range(11)]`.
H**O : int, optional
Index of H**O.
LUMO : int, optional
Index of LUMO.
frac_diis : bool, optional
Do use DIIS for non-1.0-occupied points?
neutral_guess : bool, optional
Do use neutral orbitals as guess for the anion?
hf_guess: bool, optional
Do use UHF guess before UKS?
continuous_guess : bool, optional
Do carry along guess rather than reguessing at each occupation?
filename : str, optional
Result filename, if not name of molecule.
Returns
-------
dict
Dictionary associating SCF energies with occupations.
"""
optstash = p4util.OptionsState(
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
['SCF', 'REFERENCE'],
["SCF", "FRAC_START"],
["SCF", "FRAC_RENORMALIZE"],
#["SCF", "FRAC_LOAD"],
["SCF", "FRAC_OCC"],
["SCF", "FRAC_VAL"],
["SCF", "FRAC_DIIS"])
kwargs = p4util.kwargs_lower(kwargs)
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError(
"""frac_traverse requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(
""" Requested procedure `frac_traverse` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
chargep = charge0 + 1
chargem = charge0 - 1
multp = kwargs.get('cation_mult', mult0 + 1)
multm = kwargs.get('anion_mult', mult0 + 1)
# By default, we start the frac procedure on the 25th iteration
# when not reading a previous guess
frac_start = kwargs.get('frac_start', 25)
# By default, we occupy by tenths of electrons
HOMO_occs = kwargs.get(
'HOMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
LUMO_occs = kwargs.get(
'LUMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
# By default, H**O and LUMO are both in alpha
Z = 0
for A in range(molecule.natom()):
Z += molecule.Z(A)
Z -= charge0
H**O = kwargs.get('H**O', (Z / 2 + 1 if (Z % 2) else Z / 2))
LUMO = kwargs.get('LUMO', H**O + 1)
# By default, DIIS in FRAC (1.0 occupation is always DIIS'd)
frac_diis = kwargs.get('frac_diis', True)
# By default, use the neutral orbitals as a guess for the anion
neutral_guess = kwargs.get('neutral_guess', True)
# By default, burn-in with UHF first, if UKS
hf_guess = False
if core.get_local_option('SCF', 'REFERENCE') == 'UKS':
hf_guess = kwargs.get('hf_guess', True)
# By default, re-guess at each N
continuous_guess = kwargs.get('continuous_guess', False)
# By default, drop the files to the molecule's name
root = kwargs.get('filename', molecule.name())
traverse_filename = root + '.traverse.dat'
# => Traverse <= #
occs = []
energies = []
potentials = []
convs = []
# => Run the neutral for its orbitals, if requested <= #
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
old_guess = core.get_local_option("SCF", "GUESS")
if (neutral_guess):
if (hf_guess):
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the anion first <= #
molecule.set_molecular_charge(chargem)
molecule.set_multiplicity(multm)
# => Burn the anion in with hf, if requested <= #
if hf_guess:
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
for occ in LUMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [LUMO])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
**kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(LUMO) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(LUMO) - 1))
occs.append(occ)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
#core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the neutral next <= #
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
# Burn the neutral in with hf, if requested <= #
if not continuous_guess:
core.set_local_option("SCF", "GUESS", old_guess)
if hf_guess:
core.set_local_option("SCF", "FRAC_START", 0)
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf',
dft_functional=name,
molecule=molecule,
**kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
for occ in HOMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [H**O])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
**kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(H**O) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(H**O) - 1))
occs.append(occ - 1.0)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
# NYI core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Print the results out <= #
E = {}
core.print_out(
"""\n ==> Fractional Occupation Traverse Results <==\n\n""")
core.print_out(""" %-11s %-24s %-24s %11s\n""" %
('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
core.print_out(""" %11.3E %24.16E %24.16E %11d\n""" %
(occs[k], energies[k], potentials[k], convs[k]))
E[occs[k]] = energies[k]
core.print_out("""
You trying to be a hero Watkins?
Just trying to kill some bugs sir!
-Starship Troopers""")
# Drop the files out
with open(traverse_filename, 'w') as fh:
fh.write(""" %-11s %-24s %-24s %11s\n""" %
('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
fh.write(""" %11.3E %24.16E %24.16E %11d\n""" %
(occs[k], energies[k], potentials[k], convs[k]))
optstash.restore()
return E
def mcscf_solver(ref_wfn):
# Build CIWavefunction
core.prepare_options_for_module("DETCI")
ciwfn = core.CIWavefunction(ref_wfn)
# Hush a lot of CI output
ciwfn.set_print(0)
# Begin with a normal two-step
step_type = 'Initial CI'
total_step = core.Matrix("Total step", ciwfn.get_dimension('OA'), ciwfn.get_dimension('AV'))
start_orbs = ciwfn.get_orbitals("ROT").clone()
ciwfn.set_orbitals("ROT", start_orbs)
# Grab da options
mcscf_orb_grad_conv = core.get_option("DETCI", "MCSCF_R_CONVERGENCE")
mcscf_e_conv = core.get_option("DETCI", "MCSCF_E_CONVERGENCE")
mcscf_max_macroiteration = core.get_option("DETCI", "MCSCF_MAXITER")
mcscf_type = core.get_option("DETCI", "MCSCF_TYPE")
mcscf_d_file = core.get_option("DETCI", "CI_FILE_START") + 3
mcscf_nroots = core.get_option("DETCI", "NUM_ROOTS")
mcscf_wavefunction_type = core.get_option("DETCI", "WFN")
mcscf_ndet = ciwfn.ndet()
mcscf_nuclear_energy = ciwfn.molecule().nuclear_repulsion_energy()
mcscf_steplimit = core.get_option("DETCI", "MCSCF_MAX_ROT")
mcscf_rotate = core.get_option("DETCI", "MCSCF_ROTATE")
# DIIS info
mcscf_diis_start = core.get_option("DETCI", "MCSCF_DIIS_START")
mcscf_diis_freq = core.get_option("DETCI", "MCSCF_DIIS_FREQ")
mcscf_diis_error_type = core.get_option("DETCI", "MCSCF_DIIS_ERROR_TYPE")
mcscf_diis_max_vecs = core.get_option("DETCI", "MCSCF_DIIS_MAX_VECS")
# One-step info
mcscf_target_conv_type = core.get_option("DETCI", "MCSCF_ALGORITHM")
mcscf_so_start_grad = core.get_option("DETCI", "MCSCF_SO_START_GRAD")
mcscf_so_start_e = core.get_option("DETCI", "MCSCF_SO_START_E")
mcscf_current_step_type = 'Initial CI'
# Start with SCF energy and other params
scf_energy = core.get_variable("HF TOTAL ENERGY")
eold = scf_energy
norb_iter = 1
converged = False
ah_step = False
qc_step = False
approx_integrals_only = True
# Fake info to start with the inital diagonalization
ediff = 1.e-4
orb_grad_rms = 1.e-3
# Grab needed objects
diis_obj = solvers.DIIS(mcscf_diis_max_vecs)
mcscf_obj = ciwfn.mcscf_object()
# Execute the rotate command
for rot in mcscf_rotate:
if len(rot) != 4:
raise p4util.PsiException("Each element of the MCSCF rotate command requires 4 arguements (irrep, orb1, orb2, theta).")
irrep, orb1, orb2, theta = rot
if irrep > ciwfn.Ca().nirrep():
raise p4util.PsiException("MCSCF_ROTATE: Expression %s irrep number is larger than the number of irreps" %
(str(rot)))
if max(orb1, orb2) > ciwfn.Ca().coldim()[irrep]:
raise p4util.PsiException("MCSCF_ROTATE: Expression %s orbital number exceeds number of orbitals in irrep" %
(str(rot)))
theta = np.deg2rad(theta)
x = ciwfn.Ca().nph[irrep][:, orb1].copy()
y = ciwfn.Ca().nph[irrep][:, orb2].copy()
xp = np.cos(theta) * x - np.sin(theta) * y
yp = np.sin(theta) * x + np.cos(theta) * y
ciwfn.Ca().nph[irrep][:, orb1] = xp
ciwfn.Ca().nph[irrep][:, orb2] = yp
# Limited RAS functionality
if core.get_local_option("DETCI", "WFN") == "RASSCF" and mcscf_target_conv_type != "TS":
core.print_out("\n Warning! Only the TS algorithm for RASSCF wavefunction is currently supported.\n")
core.print_out(" Switching to the TS algorithm.\n\n")
mcscf_target_conv_type = "TS"
# Print out headers
if mcscf_type == "CONV":
mtype = " @MCSCF"
core.print_out("\n ==> Starting MCSCF iterations <==\n\n")
core.print_out(" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n")
elif mcscf_type == "DF":
mtype = " @DF-MCSCF"
core.print_out("\n ==> Starting DF-MCSCF iterations <==\n\n")
core.print_out(" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n")
else:
mtype = " @AO-MCSCF"
core.print_out("\n ==> Starting AO-MCSCF iterations <==\n\n")
core.print_out(" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n")
# Iterate !
for mcscf_iter in range(1, mcscf_max_macroiteration + 1):
# Transform integrals, diagonalize H
ciwfn.transform_mcscf_integrals(approx_integrals_only)
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
# After the first diag we need to switch to READ
ciwfn.set_ci_guess("DFILE")
ciwfn.form_opdm()
ciwfn.form_tpdm()
ci_grad_rms = core.get_variable("DETCI AVG DVEC NORM")
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
current_energy = core.get_variable("MCSCF TOTAL ENERGY")
orb_grad_rms = mcscf_obj.gradient_rms()
ediff = current_energy - eold
# Print iterations
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms, ci_grad_rms,
nci_iter, norb_iter, mcscf_current_step_type)
eold = current_energy
if mcscf_current_step_type == 'Initial CI':
mcscf_current_step_type = 'TS'
# Check convergence
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) < abs(mcscf_e_conv)) and\
(mcscf_iter > 3) and not qc_step:
core.print_out("\n %s has converged!\n\n" % mtype);
converged = True
break
# Which orbital convergence are we doing?
if ah_step:
converged, norb_iter, step = ah_iteration(mcscf_obj, print_micro=False)
norb_iter += 1
if converged:
mcscf_current_step_type = 'AH'
else:
core.print_out(" !Warning. Augmented Hessian did not converge. Taking an approx step.\n")
step = mcscf_obj.approx_solve()
mcscf_current_step_type = 'TS, AH failure'
else:
step = mcscf_obj.approx_solve()
step_type = 'TS'
maxstep = step.absmax()
if maxstep > mcscf_steplimit:
core.print_out(' Warning! Maxstep = %4.2f, scaling to %4.2f\n' % (maxstep, mcscf_steplimit))
step.scale(mcscf_steplimit / maxstep)
xstep = total_step.clone()
total_step.add(step)
# Do or add DIIS
if (mcscf_iter >= mcscf_diis_start) and ("TS" in mcscf_current_step_type):
# Figure out DIIS error vector
if mcscf_diis_error_type == "GRAD":
error = core.Matrix.triplet(ciwfn.get_orbitals("OA"),
mcscf_obj.gradient(),
ciwfn.get_orbitals("AV"),
False, False, True)
else:
error = step
diis_obj.add(total_step, error)
if not (mcscf_iter % mcscf_diis_freq):
total_step = diis_obj.extrapolate()
mcscf_current_step_type = 'TS, DIIS'
# Build the rotation by continuous updates
if mcscf_iter == 1:
totalU = mcscf_obj.form_rotation_matrix(total_step)
else:
xstep.axpy(-1.0, total_step)
xstep.scale(-1.0)
Ustep = mcscf_obj.form_rotation_matrix(xstep)
totalU = core.Matrix.doublet(totalU, Ustep, False, False)
# Build the rotation directly (not recommended)
# orbs_mat = mcscf_obj.Ck(start_orbs, total_step)
# Finally rotate and set orbitals
orbs_mat = core.Matrix.doublet(start_orbs, totalU, False, False)
ciwfn.set_orbitals("ROT", orbs_mat)
# Figure out what the next step should be
if (orb_grad_rms < mcscf_so_start_grad) and (abs(ediff) < abs(mcscf_so_start_e)) and\
(mcscf_iter >= 2):
if mcscf_target_conv_type == 'AH':
approx_integrals_only = False
ah_step = True
elif mcscf_target_conv_type == 'OS':
approx_integrals_only = False
mcscf_current_step_type = 'OS, Prep'
break
else:
continue
#raise p4util.PsiException("")
# If we converged do not do onestep
if converged or (mcscf_target_conv_type != 'OS'):
one_step_iters = []
# If we are not converged load in Dvec and build iters array
else:
one_step_iters = range(mcscf_iter + 1, mcscf_max_macroiteration + 1)
dvec = ciwfn.D_vector()
dvec.init_io_files(True)
dvec.read(0, 0)
dvec.symnormalize(1.0, 0)
ci_grad = ciwfn.new_civector(1, mcscf_d_file + 1, True, True)
ci_grad.set_nvec(1)
ci_grad.init_io_files(True)
# Loop for onestep
for mcscf_iter in one_step_iters:
# Transform integrals and update the MCSCF object
ciwfn.transform_mcscf_integrals(ciwfn.H(), False)
ciwfn.form_opdm()
ciwfn.form_tpdm()
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
orb_grad_rms = mcscf_obj.gradient_rms()
# Warning! Does not work for SA-MCSCF
current_energy = mcscf_obj.current_total_energy()
current_energy += mcscf_nuclear_energy
core.set_variable("CI ROOT %d TOTAL ENERGY" % 1, current_energy)
core.set_variable("CURRENT ENERGY", current_energy)
docc_energy = mcscf_obj.current_docc_energy()
ci_energy = mcscf_obj.current_ci_energy()
# Compute CI gradient
ciwfn.sigma(dvec, ci_grad, 0, 0)
ci_grad.scale(2.0, 0)
ci_grad.axpy(-2.0 * ci_energy, dvec, 0, 0)
ci_grad_rms = ci_grad.norm(0)
orb_grad_rms = mcscf_obj.gradient().rms()
ediff = current_energy - eold
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms, ci_grad_rms,
nci_iter, norb_iter, mcscf_current_step_type)
mcscf_current_step_type = 'OS'
eold = current_energy
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) < abs(mcscf_e_conv)):
core.print_out("\n %s has converged!\n\n" % mtype);
converged = True
break
# Take a step
converged, norb_iter, nci_iter, step = qc_iteration(dvec, ci_grad, ciwfn, mcscf_obj)
# Rotate integrals to new frame
total_step.add(step)
orbs_mat = mcscf_obj.Ck(ciwfn.get_orbitals("ROT"), step)
ciwfn.set_orbitals("ROT", orbs_mat)
core.print_out(mtype + " Final Energy: %20.15f\n" % current_energy)
# Die if we did not converge
if (not converged):
if core.get_global_option("DIE_IF_NOT_CONVERGED"):
raise p4util.PsiException("MCSCF: Iterations did not converge!")
else:
core.print_out("\nWarning! MCSCF iterations did not converge!\n\n")
# Print out CI vector information
if mcscf_target_conv_type == 'OS':
dvec.close_io_files()
ci_grad.close_io_files()
# For orbital invariant methods we transform the orbitals to the natural or
# semicanonical basis. Frozen doubly occupied and virtual orbitals are not
# modified.
if core.get_option("DETCI", "WFN") == "CASSCF":
# Do we diagonalize the opdm?
if core.get_option("DETCI", "NAT_ORBS"):
ciwfn.ci_nat_orbs()
else:
ciwfn.semicanonical_orbs()
# Retransform intragrals and update CI coeffs., OPDM, and TPDM
ciwfn.transform_mcscf_integrals(approx_integrals_only)
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
ciwfn.set_ci_guess("DFILE")
ciwfn.form_opdm()
ciwfn.form_tpdm()
proc_util.print_ci_results(ciwfn, "MCSCF", scf_energy, current_energy, print_opdm_no=True)
# Set final energy
core.set_variable("CURRENT ENERGY", core.get_variable("MCSCF TOTAL ENERGY"))
# What do we need to cleanup?
if core.get_option("DETCI", "MCSCF_CI_CLEANUP"):
ciwfn.cleanup_ci()
if core.get_option("DETCI", "MCSCF_DPD_CLEANUP"):
ciwfn.cleanup_dpd()
del diis_obj
del mcscf_obj
return ciwfn
def run_roa(name, **kwargs):
"""
Main driver for managing Raman Optical activity computations with
CC response theory.
Uses distributed finite differences approach -->
1. Sets up a database to keep track of running/finished/waiting
computations.
2. Generates separate input files for displaced geometries.
3. When all displacements are run, collects the necessary information
from each displaced computation, and computes final result.
"""
# Get list of omega values -> Make sure we only have one wavelength
# Catch this now before any real work gets done
omega = core.get_option('CCRESPONSE', 'OMEGA')
if len(omega) > 2:
raise Exception('ROA scattering can only be performed for one wavelength.')
else:
pass
core.print_out(
'Running ROA computation. Subdirectories for each '
'required displaced geometry have been created.\n\n')
dbno = 0
# Initialize database
db = shelve.open('database', writeback=True)
# Check if final result is in here
# ->if we have already computed roa, back up the dict
# ->copy it setting this flag to false and continue
if ('roa_computed' in db) and ( db['roa_computed'] ):
db2 = shelve.open('.database.bak{}'.format(dbno), writeback=True)
dbno += 1
for key,value in db.items():
db2[key]=value
db2.close()
db['roa_computed'] = False
else:
db['roa_computed'] = False
if 'inputs_generated' not in db:
findif_response_utils.initialize_database(db,name,"roa", ["roa_tensor"])
# Generate input files
if not db['inputs_generated']:
findif_response_utils.generate_inputs(db,name)
# handled by helper db['inputs_generated'] = True
# Check job status
if db['inputs_generated'] and not db['jobs_complete']:
print('Checking status')
findif_response_utils.stat(db)
for job, status in db['job_status'].items():
print("{} --> {}".format(job, status))
# Compute ROA Scattering
if db['jobs_complete']:
mygauge = core.get_option('CCRESPONSE', 'GAUGE')
consider_gauge = {
'LENGTH': ['Length Gauge'],
'VELOCITY': ['Modified Velocity Gauge'],
'BOTH': ['Length Gauge', 'Modified Velocity Gauge']
}
gauge_list = ["{} Results".format(x) for x in consider_gauge[mygauge]]
# Gather data
dip_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, 'Dipole Polarizability', 3)
opt_rot_list = [
x for x in (
findif_response_utils.collect_displaced_matrix_data(
db,
"Optical Rotation Tensor ({})".format(gauge),
3
)
for gauge in consider_gauge[mygauge]
)
]
dip_quad_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, "Electric-Dipole/Quadrupole Polarizability", 9)
# Compute Scattering
# Run new function (src/bin/ccresponse/scatter.cc)
core.print_out('Running scatter function')
step = core.get_local_option('FINDIF', 'DISP_SIZE')
for g_idx, gauge in enumerate(opt_rot_list):
print('\n\n----------------------------------------------------------------------')
print('\t%%%%%%%%%% {} %%%%%%%%%%'.format(gauge_list[g_idx]))
print('----------------------------------------------------------------------\n\n')
core.print_out('\n\n----------------------------------------------------------------------\n')
core.print_out('\t%%%%%%%%%% {} %%%%%%%%%%\n'.format(gauge_list[g_idx]))
core.print_out('----------------------------------------------------------------------\n\n')
print('roa.py:85 I am not being passed a molecule, grabbing from global :(')
core.scatter(core.get_active_molecule(), step, dip_polar_list, gauge, dip_quad_polar_list)
db['roa_computed'] = True
db.close()
def scf_initialize(self):
"""Specialized initialization, compute integrals and does everything to prepare for iterations"""
# Figure out memory distributions
# Get memory in terms of doubles
total_memory = (core.get_memory() /
8) * core.get_global_option("SCF_MEM_SAFETY_FACTOR")
# Figure out how large the DFT collocation matrices are
vbase = self.V_potential()
if vbase:
collocation_size = vbase.grid().collocation_size()
if vbase.functional().ansatz() == 1:
collocation_size *= 4 # First derivs
elif vbase.functional().ansatz() == 2:
collocation_size *= 10 # Second derivs
else:
collocation_size = 0
# Change allocation for collocation matrices based on DFT type
jk = _build_jk(self, total_memory)
jk_size = jk.memory_estimate()
# Give remaining to collocation
if total_memory > jk_size:
collocation_memory = total_memory - jk_size
# Give up to 10% to collocation
elif (total_memory * 0.1) > collocation_size:
collocation_memory = collocation_size
else:
collocation_memory = total_memory * 0.1
if collocation_memory > collocation_size:
collocation_memory = collocation_size
# Set constants
self.iteration_ = 0
self.memory_jk_ = int(total_memory - collocation_memory)
self.memory_collocation_ = int(collocation_memory)
if self.get_print():
core.print_out(" ==> Integral Setup <==\n\n")
# Initialize EFP
efp_enabled = hasattr(self.molecule(), 'EFP')
if efp_enabled:
# EFP: Set QM system, options, and callback. Display efp geom in [A]
efpobj = self.molecule().EFP
core.print_out(efpobj.banner())
core.print_out(
efpobj.geometry_summary(units_to_bohr=constants.bohr2angstroms))
efpptc, efpcoords, efpopts = get_qm_atoms_opts(self.molecule())
efpobj.set_point_charges(efpptc, efpcoords)
efpobj.set_opts(efpopts, label='psi', append='psi')
efpobj.set_electron_density_field_fn(efp_field_fn)
# Initialize all integrals and perform the first guess
if self.attempt_number_ == 1:
mints = core.MintsHelper(self.basisset())
self.initialize_jk(self.memory_jk_, jk=jk)
if self.V_potential():
self.V_potential().build_collocation_cache(
self.memory_collocation_)
core.timer_on("HF: Form core H")
self.form_H()
core.timer_off("HF: Form core H")
if efp_enabled:
# EFP: Add in permanent moment contribution and cache
core.timer_on("HF: Form Vefp")
verbose = core.get_option('SCF', "PRINT")
Vefp = modify_Fock_permanent(self.molecule(),
mints,
verbose=verbose - 1)
Vefp = core.Matrix.from_array(Vefp)
self.H().add(Vefp)
Horig = self.H().clone()
self.Horig = Horig
core.print_out(
" QM/EFP: iterating Total Energy including QM/EFP Induction\n"
)
core.timer_off("HF: Form Vefp")
core.timer_on("HF: Form S/X")
self.form_Shalf()
core.timer_off("HF: Form S/X")
core.print_out("\n ==> Pre-Iterations <==\n\n")
core.timer_on("HF: Guess")
self.guess()
core.timer_off("HF: Guess")
# Print out initial docc/socc/etc data
if self.get_print():
lack_occupancy = core.get_local_option('SCF', 'GUESS') in ['SAD']
if core.get_global_option('GUESS') in ['SAD']:
lack_occupancy = core.get_local_option('SCF',
'GUESS') in ['AUTO']
self.print_preiterations(small=lack_occupancy)
else:
self.print_preiterations(small=lack_occupancy)
else:
# We're reading the orbitals from the previous set of iterations.
self.form_D()
self.set_energies("Total Energy", self.compute_initial_E())
# turn off VV10 for iterations
if core.get_option(
'SCF', "DFT_VV10_POSTSCF") and self.functional().vv10_b() > 0.0:
core.print_out(" VV10: post-SCF option active \n \n")
self.functional().set_lock(False)
self.functional().set_do_vv10(False)
self.functional().set_lock(True)
# Print iteration header
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
diis_rms = core.get_option('SCF', 'DIIS_RMS_ERROR')
core.print_out(" ==> Iterations <==\n\n")
core.print_out(
"%s Total Energy Delta E %s |[F,P]|\n\n"
% (" " if is_dfjk else "", "RMS" if diis_rms else "MAX"))
def mcscf_solver(ref_wfn):
# Build CIWavefunction
core.prepare_options_for_module("DETCI")
ciwfn = core.CIWavefunction(ref_wfn)
# Hush a lot of CI output
ciwfn.set_print(0)
# Begin with a normal two-step
step_type = 'Initial CI'
total_step = core.Matrix("Total step", ciwfn.get_dimension('OA'),
ciwfn.get_dimension('AV'))
start_orbs = ciwfn.get_orbitals("ROT").clone()
ciwfn.set_orbitals("ROT", start_orbs)
# Grab da options
mcscf_orb_grad_conv = core.get_option("DETCI", "MCSCF_R_CONVERGENCE")
mcscf_e_conv = core.get_option("DETCI", "MCSCF_E_CONVERGENCE")
mcscf_max_macroiteration = core.get_option("DETCI", "MCSCF_MAXITER")
mcscf_type = core.get_option("DETCI", "MCSCF_TYPE")
mcscf_d_file = core.get_option("DETCI", "CI_FILE_START") + 3
mcscf_nroots = core.get_option("DETCI", "NUM_ROOTS")
mcscf_wavefunction_type = core.get_option("DETCI", "WFN")
mcscf_ndet = ciwfn.ndet()
mcscf_nuclear_energy = ciwfn.molecule().nuclear_repulsion_energy()
mcscf_steplimit = core.get_option("DETCI", "MCSCF_MAX_ROT")
mcscf_rotate = core.get_option("DETCI", "MCSCF_ROTATE")
# DIIS info
mcscf_diis_start = core.get_option("DETCI", "MCSCF_DIIS_START")
mcscf_diis_freq = core.get_option("DETCI", "MCSCF_DIIS_FREQ")
mcscf_diis_error_type = core.get_option("DETCI", "MCSCF_DIIS_ERROR_TYPE")
mcscf_diis_max_vecs = core.get_option("DETCI", "MCSCF_DIIS_MAX_VECS")
# One-step info
mcscf_target_conv_type = core.get_option("DETCI", "MCSCF_ALGORITHM")
mcscf_so_start_grad = core.get_option("DETCI", "MCSCF_SO_START_GRAD")
mcscf_so_start_e = core.get_option("DETCI", "MCSCF_SO_START_E")
mcscf_current_step_type = 'Initial CI'
# Start with SCF energy and other params
scf_energy = ciwfn.variable("HF TOTAL ENERGY")
eold = scf_energy
norb_iter = 1
converged = False
ah_step = False
qc_step = False
approx_integrals_only = True
# Fake info to start with the initial diagonalization
ediff = 1.e-4
orb_grad_rms = 1.e-3
# Grab needed objects
diis_obj = solvers.DIIS(mcscf_diis_max_vecs)
mcscf_obj = ciwfn.mcscf_object()
# Execute the rotate command
for rot in mcscf_rotate:
if len(rot) != 4:
raise p4util.PsiException(
"Each element of the MCSCF rotate command requires 4 arguements (irrep, orb1, orb2, theta)."
)
irrep, orb1, orb2, theta = rot
if irrep > ciwfn.Ca().nirrep():
raise p4util.PsiException(
"MCSCF_ROTATE: Expression %s irrep number is larger than the number of irreps"
% (str(rot)))
if max(orb1, orb2) > ciwfn.Ca().coldim()[irrep]:
raise p4util.PsiException(
"MCSCF_ROTATE: Expression %s orbital number exceeds number of orbitals in irrep"
% (str(rot)))
theta = np.deg2rad(theta)
x = ciwfn.Ca().nph[irrep][:, orb1].copy()
y = ciwfn.Ca().nph[irrep][:, orb2].copy()
xp = np.cos(theta) * x - np.sin(theta) * y
yp = np.sin(theta) * x + np.cos(theta) * y
ciwfn.Ca().nph[irrep][:, orb1] = xp
ciwfn.Ca().nph[irrep][:, orb2] = yp
# Limited RAS functionality
if core.get_local_option(
"DETCI", "WFN") == "RASSCF" and mcscf_target_conv_type != "TS":
core.print_out(
"\n Warning! Only the TS algorithm for RASSCF wavefunction is currently supported.\n"
)
core.print_out(" Switching to the TS algorithm.\n\n")
mcscf_target_conv_type = "TS"
# Print out headers
if mcscf_type == "CONV":
mtype = " @MCSCF"
core.print_out("\n ==> Starting MCSCF iterations <==\n\n")
core.print_out(
" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n"
)
elif mcscf_type == "DF":
mtype = " @DF-MCSCF"
core.print_out("\n ==> Starting DF-MCSCF iterations <==\n\n")
core.print_out(
" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n"
)
else:
mtype = " @AO-MCSCF"
core.print_out("\n ==> Starting AO-MCSCF iterations <==\n\n")
core.print_out(
" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n"
)
# Iterate !
for mcscf_iter in range(1, mcscf_max_macroiteration + 1):
# Transform integrals, diagonalize H
ciwfn.transform_mcscf_integrals(approx_integrals_only)
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
# After the first diag we need to switch to READ
ciwfn.set_ci_guess("DFILE")
ciwfn.form_opdm()
ciwfn.form_tpdm()
ci_grad_rms = core.variable("DETCI AVG DVEC NORM")
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
current_energy = core.variable("MCSCF TOTAL ENERGY")
orb_grad_rms = mcscf_obj.gradient_rms()
ediff = current_energy - eold
# Print iterations
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms,
ci_grad_rms, nci_iter, norb_iter,
mcscf_current_step_type)
eold = current_energy
if mcscf_current_step_type == 'Initial CI':
mcscf_current_step_type = 'TS'
# Check convergence
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) < abs(mcscf_e_conv)) and\
(mcscf_iter > 3) and not qc_step:
core.print_out("\n %s has converged!\n\n" % mtype)
converged = True
break
# Which orbital convergence are we doing?
if ah_step:
converged, norb_iter, step = ah_iteration(mcscf_obj,
print_micro=False)
norb_iter += 1
if converged:
mcscf_current_step_type = 'AH'
else:
core.print_out(
" !Warning. Augmented Hessian did not converge. Taking an approx step.\n"
)
step = mcscf_obj.approx_solve()
mcscf_current_step_type = 'TS, AH failure'
else:
step = mcscf_obj.approx_solve()
step_type = 'TS'
maxstep = step.absmax()
if maxstep > mcscf_steplimit:
core.print_out(
' Warning! Maxstep = %4.2f, scaling to %4.2f\n' %
(maxstep, mcscf_steplimit))
step.scale(mcscf_steplimit / maxstep)
xstep = total_step.clone()
total_step.add(step)
# Do or add DIIS
if (mcscf_iter >= mcscf_diis_start) and ("TS"
in mcscf_current_step_type):
# Figure out DIIS error vector
if mcscf_diis_error_type == "GRAD":
error = core.Matrix.triplet(ciwfn.get_orbitals("OA"),
mcscf_obj.gradient(),
ciwfn.get_orbitals("AV"), False,
False, True)
else:
error = step
diis_obj.add(total_step, error)
if not (mcscf_iter % mcscf_diis_freq):
total_step = diis_obj.extrapolate()
mcscf_current_step_type = 'TS, DIIS'
# Build the rotation by continuous updates
if mcscf_iter == 1:
totalU = mcscf_obj.form_rotation_matrix(total_step)
else:
xstep.axpy(-1.0, total_step)
xstep.scale(-1.0)
Ustep = mcscf_obj.form_rotation_matrix(xstep)
totalU = core.Matrix.doublet(totalU, Ustep, False, False)
# Build the rotation directly (not recommended)
# orbs_mat = mcscf_obj.Ck(start_orbs, total_step)
# Finally rotate and set orbitals
orbs_mat = core.Matrix.doublet(start_orbs, totalU, False, False)
ciwfn.set_orbitals("ROT", orbs_mat)
# Figure out what the next step should be
if (orb_grad_rms < mcscf_so_start_grad) and (abs(ediff) < abs(mcscf_so_start_e)) and\
(mcscf_iter >= 2):
if mcscf_target_conv_type == 'AH':
approx_integrals_only = False
ah_step = True
elif mcscf_target_conv_type == 'OS':
approx_integrals_only = False
mcscf_current_step_type = 'OS, Prep'
break
else:
continue
#raise p4util.PsiException("")
# If we converged do not do onestep
if converged or (mcscf_target_conv_type != 'OS'):
one_step_iters = []
# If we are not converged load in Dvec and build iters array
else:
one_step_iters = range(mcscf_iter + 1, mcscf_max_macroiteration + 1)
dvec = ciwfn.D_vector()
dvec.init_io_files(True)
dvec.read(0, 0)
dvec.symnormalize(1.0, 0)
ci_grad = ciwfn.new_civector(1, mcscf_d_file + 1, True, True)
ci_grad.set_nvec(1)
ci_grad.init_io_files(True)
# Loop for onestep
for mcscf_iter in one_step_iters:
# Transform integrals and update the MCSCF object
ciwfn.transform_mcscf_integrals(ciwfn.H(), False)
ciwfn.form_opdm()
ciwfn.form_tpdm()
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
orb_grad_rms = mcscf_obj.gradient_rms()
# Warning! Does not work for SA-MCSCF
current_energy = mcscf_obj.current_total_energy()
current_energy += mcscf_nuclear_energy
core.set_variable("CI ROOT %d TOTAL ENERGY" % 1, current_energy)
core.set_variable("CURRENT ENERGY", current_energy)
docc_energy = mcscf_obj.current_docc_energy()
ci_energy = mcscf_obj.current_ci_energy()
# Compute CI gradient
ciwfn.sigma(dvec, ci_grad, 0, 0)
ci_grad.scale(2.0, 0)
ci_grad.axpy(-2.0 * ci_energy, dvec, 0, 0)
ci_grad_rms = ci_grad.norm(0)
orb_grad_rms = mcscf_obj.gradient().rms()
ediff = current_energy - eold
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms,
ci_grad_rms, nci_iter, norb_iter,
mcscf_current_step_type)
mcscf_current_step_type = 'OS'
eold = current_energy
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) <
abs(mcscf_e_conv)):
core.print_out("\n %s has converged!\n\n" % mtype)
converged = True
break
# Take a step
converged, norb_iter, nci_iter, step = qc_iteration(
dvec, ci_grad, ciwfn, mcscf_obj)
# Rotate integrals to new frame
total_step.add(step)
orbs_mat = mcscf_obj.Ck(ciwfn.get_orbitals("ROT"), step)
ciwfn.set_orbitals("ROT", orbs_mat)
core.print_out(mtype + " Final Energy: %20.15f\n" % current_energy)
# Die if we did not converge
if (not converged):
if core.get_global_option("DIE_IF_NOT_CONVERGED"):
raise p4util.PsiException("MCSCF: Iterations did not converge!")
else:
core.print_out("\nWarning! MCSCF iterations did not converge!\n\n")
# Print out CI vector information
if mcscf_target_conv_type == 'OS':
dvec.close_io_files()
ci_grad.close_io_files()
# For orbital invariant methods we transform the orbitals to the natural or
# semicanonical basis. Frozen doubly occupied and virtual orbitals are not
# modified.
if core.get_option("DETCI", "WFN") == "CASSCF":
# Do we diagonalize the opdm?
if core.get_option("DETCI", "NAT_ORBS"):
ciwfn.ci_nat_orbs()
else:
ciwfn.semicanonical_orbs()
# Retransform intragrals and update CI coeffs., OPDM, and TPDM
ciwfn.transform_mcscf_integrals(approx_integrals_only)
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
ciwfn.set_ci_guess("DFILE")
ciwfn.form_opdm()
ciwfn.form_tpdm()
proc_util.print_ci_results(ciwfn,
"MCSCF",
scf_energy,
current_energy,
print_opdm_no=True)
# Set final energy
core.set_variable("CURRENT ENERGY", core.variable("MCSCF TOTAL ENERGY"))
# What do we need to cleanup?
if core.get_option("DETCI", "MCSCF_CI_CLEANUP"):
ciwfn.cleanup_ci()
if core.get_option("DETCI", "MCSCF_DPD_CLEANUP"):
ciwfn.cleanup_dpd()
del diis_obj
del mcscf_obj
return ciwfn
def prepare_options_for_modules(
changedOnly: bool = False,
commandsInsteadDict: bool = False,
globalsOnly: bool = False,
stateInsteadMediated: bool = False,
) -> Union[Dict, str]:
"""Capture current state of C++ psi4.core.Options information.
Parameters
----------
changedOnly
Record info only for options that have been set (may still be default).
When False, records values for every option.
commandsInsteadDict
Return string of commands to exec to reset options in current form.
When False, return nested dictionary with globals in 'GLOBALS' subdictionary
and locals in subdictionaries by module.
globalsOnly
Record only global options to save time querying the psi4.core.Options object.
When False, record module-level options, too.
stateInsteadMediated
When ``True``, querying this function for options to be later *reset* into the same
state -- the raw values and has_changed status at the global and local levels.
When ``False``, querying this function for mediated options to be *used* -- the results
of the globals/locals handshake as computed by the Options object itself. Here,
``dict[module][option][value]`` is the value to be used by module.
Returns
-------
dict
When ``commandsInsteadDict=False``.
str
When ``commandsInsteadDict=True``.
.. caution:: Some features are not yet implemented. Buy a developer a coffee.
- command return doesn't revoke has_changed setting for unchanged with changedOnly=False
- not all kwargs are independent
"""
has_changed_snapshot = {module: core.options_to_python(module) for module in _modules}
options = collections.defaultdict(dict)
commands = ''
for opt in core.get_global_option_list():
hoc = core.has_global_option_changed(opt)
if hoc or not changedOnly:
if opt in ['DFT_CUSTOM_FUNCTIONAL', 'EXTERN']: # Feb 2017 hack
continue
val = core.get_global_option(opt)
options['GLOBALS'][opt] = {'value': val, 'has_changed': hoc}
if isinstance(val, str):
commands += """core.set_global_option('%s', '%s')\n""" % (opt, val)
else:
commands += """core.set_global_option('%s', %s)\n""" % (opt, val)
if globalsOnly:
continue
opt_snapshot = {k: v[opt] for k, v in has_changed_snapshot.items() if opt in v}
for module, (lhoc, ohoc) in opt_snapshot.items():
if stateInsteadMediated:
hoc = lhoc
else:
hoc = ohoc
if hoc or not changedOnly:
if stateInsteadMediated:
val = core.get_local_option(module, opt)
else:
val = core.get_option(module, opt)
options[module][opt] = {'value': val, 'has_changed': hoc}
if isinstance(val, str):
commands += """core.set_local_option('%s', '%s', '%s')\n""" % (module, opt, val)
else:
commands += """core.set_local_option('%s', '%s', %s)\n""" % (module, opt, val)
if commandsInsteadDict:
return commands
else:
return options
def run_roa(name, **kwargs):
"""
Main driver for managing Raman Optical activity computations with
CC response theory.
Uses distributed finite differences approach -->
1. Sets up a database to keep track of running/finished/waiting
computations.
2. Generates separate input files for displaced geometries.
3. When all displacements are run, collects the necessary information
from each displaced computation, and computes final result.
"""
# Get list of omega values -> Make sure we only have one wavelength
# Catch this now before any real work gets done
omega = core.get_option('CCRESPONSE', 'OMEGA')
if len(omega) > 2:
raise Exception('ROA scattering can only be performed for one wavelength.')
else:
pass
core.print_out(
'Running ROA computation. Subdirectories for each '
'required displaced geometry have been created.\n\n')
dbno = 0
# Initialize database
db = shelve.open('database', writeback=True)
# Check if final result is in here
# ->if we have already computed roa, back up the dict
# ->copy it setting this flag to false and continue
if ('roa_computed' in db) and ( db['roa_computed'] ):
db2 = shelve.open('.database.bak{}'.format(dbno), writeback=True)
dbno += 1
for key,value in db.items():
db2[key]=value
db2.close()
db['roa_computed'] = False
else:
db['roa_computed'] = False
if 'inputs_generated' not in db:
findif_response_utils.initialize_database(db,name,"roa", ["roa_tensor"])
# Generate input files
if not db['inputs_generated']:
findif_response_utils.generate_inputs(db,name)
# handled by helper db['inputs_generated'] = True
# Check job status
if db['inputs_generated'] and not db['jobs_complete']:
print('Checking status')
findif_response_utils.stat(db)
for job, status in db['job_status'].items():
print("{} --> {}".format(job, status))
# Compute ROA Scattering
if db['jobs_complete']:
mygauge = core.get_option('CCRESPONSE', 'GAUGE')
consider_gauge = {
'LENGTH': ['Length Gauge'],
'VELOCITY': ['Modified Velocity Gauge'],
'BOTH': ['Length Gauge', 'Modified Velocity Gauge']
}
gauge_list = ["{} Results".format(x) for x in consider_gauge[mygauge]]
# Gather data
dip_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, 'Dipole Polarizability', 3)
opt_rot_list = [
x for x in (
findif_response_utils.collect_displaced_matrix_data(
db,
"Optical Rotation Tensor ({})".format(gauge),
3
)
for gauge in consider_gauge[mygauge]
)
]
dip_quad_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, "Electric-Dipole/Quadrupole Polarizability", 9)
# Compute Scattering
# Run new function (src/bin/ccresponse/scatter.cc)
core.print_out('Running scatter function')
step = core.get_local_option('FINDIF', 'DISP_SIZE')
for g_idx, gauge in enumerate(opt_rot_list):
print('\n\n----------------------------------------------------------------------')
print('\t%%%%%%%%%% {} %%%%%%%%%%'.format(gauge_list[g_idx]))
print('----------------------------------------------------------------------\n\n')
core.print_out('\n\n----------------------------------------------------------------------\n')
core.print_out('\t%%%%%%%%%% {} %%%%%%%%%%\n'.format(gauge_list[g_idx]))
core.print_out('----------------------------------------------------------------------\n\n')
print('roa.py:85 I am not being passed a molecule, grabbing from global :(')
core.scatter(core.get_active_molecule(), step, dip_polar_list, gauge, dip_quad_polar_list)
db['roa_computed'] = True
db.close()
def frac_traverse(name, **kwargs):
"""Scan electron occupancy from +1 electron to -1.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
cation_mult : int, optional
Multiplicity of cation, if not neutral multiplicity + 1.
anion_mult : int, optional
Multiplicity of anion, if not neutral multiplicity + 1.
frac_start : int, optional
Iteration at which to start frac procedure when not reading previous
guess. Defaults to 25.
HOMO_occs : list, optional
Occupations to step through for cation, by default `[1 - 0.1 * x for x in range(11)]`.
LUMO_occs : list, optional
Occupations to step through for anion, by default `[1 - 0.1 * x for x in range(11)]`.
H**O : int, optional
Index of H**O.
LUMO : int, optional
Index of LUMO.
frac_diis : bool, optional
Do use DIIS for non-1.0-occupied points?
neutral_guess : bool, optional
Do use neutral orbitals as guess for the anion?
hf_guess: bool, optional
Do use UHF guess before UKS?
continuous_guess : bool, optional
Do carry along guess rather than reguessing at each occupation?
filename : str, optional
Result filename, if not name of molecule.
Returns
-------
dict
Dictionary associating SCF energies with occupations.
"""
optstash = p4util.OptionsState(
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
['SCF', 'REFERENCE'],
["SCF", "FRAC_START"],
["SCF", "FRAC_RENORMALIZE"],
#["SCF", "FRAC_LOAD"],
["SCF", "FRAC_OCC"],
["SCF", "FRAC_VAL"],
["SCF", "FRAC_DIIS"])
kwargs = p4util.kwargs_lower(kwargs)
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError("""frac_traverse requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(""" Requested procedure `frac_traverse` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
chargep = charge0 + 1
chargem = charge0 - 1
multp = kwargs.get('cation_mult', mult0 + 1)
multm = kwargs.get('anion_mult', mult0 + 1)
# By default, we start the frac procedure on the 25th iteration
# when not reading a previous guess
frac_start = kwargs.get('frac_start', 25)
# By default, we occupy by tenths of electrons
HOMO_occs = kwargs.get('HOMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
LUMO_occs = kwargs.get('LUMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
# By default, H**O and LUMO are both in alpha
Z = 0
for A in range(molecule.natom()):
Z += molecule.Z(A)
Z -= charge0
H**O = kwargs.get('H**O', (Z / 2 + 1 if (Z % 2) else Z / 2))
LUMO = kwargs.get('LUMO', H**O + 1)
# By default, DIIS in FRAC (1.0 occupation is always DIIS'd)
frac_diis = kwargs.get('frac_diis', True)
# By default, use the neutral orbitals as a guess for the anion
neutral_guess = kwargs.get('neutral_guess', True)
# By default, burn-in with UHF first, if UKS
hf_guess = False
if core.get_local_option('SCF', 'REFERENCE') == 'UKS':
hf_guess = kwargs.get('hf_guess', True)
# By default, re-guess at each N
continuous_guess = kwargs.get('continuous_guess', False)
# By default, drop the files to the molecule's name
root = kwargs.get('filename', molecule.name())
traverse_filename = root + '.traverse.dat'
# => Traverse <= #
occs = []
energies = []
potentials = []
convs = []
# => Run the neutral for its orbitals, if requested <= #
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
old_guess = core.get_local_option("SCF", "GUESS")
if (neutral_guess):
if (hf_guess):
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the anion first <= #
molecule.set_molecular_charge(chargem)
molecule.set_multiplicity(multm)
# => Burn the anion in with hf, if requested <= #
if hf_guess:
core.set_local_option("SCF", "REFERENCE","UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
for occ in LUMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [LUMO])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule, **kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(LUMO) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(LUMO) - 1))
occs.append(occ)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
#core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the neutral next <= #
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
# Burn the neutral in with hf, if requested <= #
if not continuous_guess:
core.set_local_option("SCF", "GUESS", old_guess)
if hf_guess:
core.set_local_option("SCF", "FRAC_START", 0)
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
for occ in HOMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [H**O])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule, **kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(H**O) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(H**O) - 1))
occs.append(occ - 1.0)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
# NYI core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Print the results out <= #
E = {}
core.print_out("""\n ==> Fractional Occupation Traverse Results <==\n\n""")
core.print_out(""" %-11s %-24s %-24s %11s\n""" % ('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
core.print_out(""" %11.3E %24.16E %24.16E %11d\n""" % (occs[k], energies[k], potentials[k], convs[k]))
E[occs[k]] = energies[k]
core.print_out("""
You trying to be a hero Watkins?
Just trying to kill some bugs sir!
-Starship Troopers""")
# Drop the files out
with open(traverse_filename, 'w') as fh:
fh.write(""" %-11s %-24s %-24s %11s\n""" % ('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
fh.write(""" %11.3E %24.16E %24.16E %11d\n""" % (occs[k], energies[k], potentials[k], convs[k]))
optstash.restore()
return E
