def _validate_damping():
"""Sanity-checks DAMPING control options
Raises
------
ValidationError
If any of |scf__damping_percentage|, |scf__damping_convergence|
don't play well together.
Returns
-------
bool
Whether DAMPING is enabled during scf.
"""
# Q: I changed the enabled criterion get_option <-- has_option_changed
enabled = (core.get_option('SCF', 'DAMPING_PERCENTAGE') > 0.0)
if enabled:
parameter = core.get_option('SCF', "DAMPING_PERCENTAGE")
if parameter < 0.0 or parameter > 100.0:
raise ValidationError('SCF DAMPING_PERCENTAGE ({}) must be between 0 and 100'.format(parameter))
stop = core.get_option('SCF', 'DAMPING_CONVERGENCE')
if stop < 0.0:
raise ValidationError('SCF DAMPING_CONVERGENCE ({}) must be > 0'.format(stop))
return enabled
def _validate_diis():
"""Sanity-checks DIIS control options
Raises
------
ValidationError
If any of |scf__diis|, |scf__diis_start|,
|scf__diis_min_vecs|, |scf__diis_max_vecs| don't play well together.
Returns
-------
bool
Whether DIIS is enabled during scf.
"""
enabled = bool(core.get_option('SCF', 'DIIS'))
if enabled:
start = core.get_option('SCF', 'DIIS_START')
if start < 1:
raise ValidationError('SCF DIIS_START ({}) must be at least 1'.format(start))
minvecs = core.get_option('SCF', 'DIIS_MIN_VECS')
if minvecs < 1:
raise ValidationError('SCF DIIS_MIN_VECS ({}) must be at least 1'.format(minvecs))
maxvecs = core.get_option('SCF', 'DIIS_MAX_VECS')
if maxvecs < minvecs:
raise ValidationError(
'SCF DIIS_MAX_VECS ({}) must be at least DIIS_MIN_VECS ({})'.format(maxvecs, minvecs))
return enabled
def get_qm_atoms_opts(mol):
"""Provides list of coordinates of quantum mechanical atoms from
psi4.core.Molecule `mol` to pylibefp.core.efp() `efpobj`. Also
converts from `read_options("EFP"` to pylibefp opts dictionary.
"""
efpobj = mol.EFP
ptc = []
coords = []
for iat in range(mol.natom()):
ptc.append(mol.charge(iat))
coords.append(mol.x(iat))
coords.append(mol.y(iat))
coords.append(mol.z(iat))
# set options
# * 'chtr', 'qm_exch', 'qm_disp', 'qm_chtr' may be enabled in a future libefp release
opts = {}
for opt in ['elst', 'exch', 'ind', 'disp',
'elst_damping', 'ind_damping', 'disp_damping']:
psiopt = 'EFP_' + opt.upper()
if core.has_option_changed('EFP', psiopt):
opts[opt] = core.get_option('EFP', psiopt)
for opt in ['elst', 'ind']:
psiopt = 'EFP_QM_' + opt.upper()
if core.has_option_changed('EFP', psiopt):
opts['qm_' + opt] = core.get_option('EFP', psiopt)
return ptc, coords, opts
def _converged(e_delta, d_rms, e_conv=None, d_conv=None):
if e_conv is None:
e_conv = core.get_option("SCF", "E_CONVERGENCE")
if d_conv is None:
d_conv = core.get_option("SCF", "D_CONVERGENCE")
return (abs(e_delta) < e_conv and d_rms < d_conv)
def _validate_soscf():
"""Sanity-checks SOSCF control options
Raises
------
ValidationError
If any of |scf__soscf|, |scf__soscf_start_convergence|,
|scf__soscf_min_iter|, |scf__soscf_max_iter| don't play well together.
Returns
-------
bool
Whether SOSCF is enabled during scf.
"""
enabled = core.get_option('SCF', 'SOSCF')
if enabled:
start = core.get_option('SCF', 'SOSCF_START_CONVERGENCE')
if start < 0.0:
raise ValidationError('SCF SOSCF_START_CONVERGENCE ({}) must be positive'.format(start))
miniter = core.get_option('SCF', 'SOSCF_MIN_ITER')
if miniter < 1:
raise ValidationError('SCF SOSCF_MIN_ITER ({}) must be at least 1'.format(miniter))
maxiter = core.get_option('SCF', 'SOSCF_MAX_ITER')
if maxiter < miniter:
raise ValidationError(
'SCF SOSCF_MAX_ITER ({}) must be at least SOSCF_MIN_ITER ({})'.format(maxiter, miniter))
conv = core.get_option('SCF', 'SOSCF_CONV')
if conv < 1.e-10:
raise ValidationError('SCF SOSCF_CONV ({}) must be achievable'.format(conv))
return enabled
def fisapt_fdrop(self):
"""Drop output files from FSAPT calculation. FISAPT::fdrop"""
core.print_out(" ==> F-SAPT Output <==\n\n")
filepath = core.get_option("FISAPT", "FISAPT_FSAPT_FILEPATH")
os.makedirs(filepath, exist_ok=True)
core.print_out(" F-SAPT Data Filepath = {}\n\n".format(filepath))
geomfile = filepath + os.sep + 'geom.xyz'
xyz = self.molecule().to_string(dtype='xyz', units='Angstrom')
with open(geomfile, 'w') as fh:
fh.write(xyz)
vectors = self.vectors()
matrices = self.matrices()
matrices["Qocc0A"].name = "QA"
matrices["Qocc0B"].name = "QB"
matrices["Elst_AB"].name = "Elst"
matrices["Exch_AB"].name = "Exch"
matrices["IndAB_AB"].name = "IndAB"
matrices["IndBA_AB"].name = "IndBA"
matrices["Disp_AB"].name = "Disp"
_drop(vectors["ZA"], filepath)
_drop(vectors["ZB"], filepath)
_drop(matrices["Qocc0A"], filepath)
_drop(matrices["Qocc0B"], filepath)
_drop(matrices["Elst_AB"], filepath)
_drop(matrices["Exch_AB"], filepath)
_drop(matrices["IndAB_AB"], filepath)
_drop(matrices["IndBA_AB"], filepath)
_drop(matrices["Disp_AB"], filepath)
if core.get_option("FISAPT", "SSAPT0_SCALE"):
ssapt_filepath = core.get_option("FISAPT", "FISAPT_FSSAPT_FILEPATH")
os.makedirs(ssapt_filepath, exist_ok=True)
core.print_out(" sF-SAPT Data Filepath = {}\n\n".format(ssapt_filepath))
geomfile = ssapt_filepath + os.sep + 'geom.xyz'
with open(geomfile, 'w') as fh:
fh.write(xyz)
matrices["sIndAB_AB"].name = "IndAB"
matrices["sIndBA_AB"].name = "IndBA"
matrices["sDisp_AB"].name = "Disp"
_drop(vectors["ZA"], ssapt_filepath)
_drop(vectors["ZB"], ssapt_filepath)
_drop(matrices["Qocc0A"], ssapt_filepath)
_drop(matrices["Qocc0B"], ssapt_filepath)
_drop(matrices["Elst_AB"], ssapt_filepath)
_drop(matrices["Exch_AB"], ssapt_filepath)
_drop(matrices["sIndAB_AB"], ssapt_filepath)
_drop(matrices["sIndBA_AB"], ssapt_filepath)
_drop(matrices["sDisp_AB"], ssapt_filepath)
def scf_initialize(self):
"""Specialized initialization, compute integrals and does everything to prepare for iterations"""
self.iteration_ = 0
efp_enabled = hasattr(self.molecule(), 'EFP')
if core.get_option('SCF', "PRINT") > 0:
core.print_out(" ==> Pre-Iterations <==\n\n")
self.print_preiterations()
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(field_fn)
if self.attempt_number_ == 1:
mints = core.MintsHelper(self.basisset())
if core.get_global_option('RELATIVISTIC') in ['X2C', 'DKH']:
mints.set_rel_basisset(self.get_basisset('BASIS_RELATIVISTIC'))
mints.one_electron_integrals()
self.integrals()
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.timer_on("HF: Guess")
self.guess()
core.timer_off("HF: Guess")
else:
# We're reading the orbitals from the previous set of iterations.
self.form_D()
self.set_energies("Total Energy", self.compute_initial_E())
def pybuild_JK(orbital_basis, aux=None, jk_type=None):
"""
Constructs a Psi4 JK object from an input basis.
Parameters
----------
orbital_basis : :py:class:`~psi4.core.BasisSet`
Orbital basis to use in the JK object.
aux : :py:class:`~psi4.core.BasisSet`
Optional auxiliary basis set for density-fitted tensors. Defaults
to the DF_BASIS_SCF if set, otherwise the correspond JKFIT basis
to the passed in orbital_basis.
type : str
Type of JK object to build (DF, Direct, PK, etc). Defaults to the
current global SCF_TYPE option.
Returns
-------
:py:class:`~psi4.core.JK`
Uninitialized JK object.
Example
-------
jk = psi4.core.JK.build(bas)
jk.set_memory(int(5e8)) # 4GB of memory
jk.initialize()
...
jk.C_left_add(matirx)
jk.compute()
jk.C_clear()
...
"""
optstash = optproc.OptionsState(["SCF_TYPE"])
if jk_type is not None:
core.set_global_option("SCF_TYPE", jk_type)
if aux is None:
if core.get_option("SCF", "SCF_TYPE") == "DF":
aux = core.BasisSet.build(orbital_basis.molecule(), "DF_BASIS_SCF",
core.get_option("SCF", "DF_BASIS_SCF"),
"JKFIT", core.get_global_option('BASIS'),
orbital_basis.has_puream())
else:
aux = core.BasisSet.zero_ao_basis_set()
jk = core.JK.build_JK(orbital_basis, aux)
optstash.restore()
return jk
def scf_set_reference_local(name):
"""
Figures out the correct SCF reference to set locally
"""
optstash = p4util.OptionsState(
['SCF', 'DFT_FUNCTIONAL'],
['SCF', 'SCF_TYPE'],
['SCF', 'REFERENCE'])
# Alter default algorithm
if not core.has_option_changed('SCF', 'SCF_TYPE'):
core.set_local_option('SCF', 'SCF_TYPE', 'DF')
if name == 'hf':
if core.get_option('SCF','REFERENCE') == 'RKS':
core.set_local_option('SCF','REFERENCE','RHF')
elif core.get_option('SCF','REFERENCE') == 'UKS':
core.set_local_option('SCF','REFERENCE','UHF')
elif name == 'scf':
if core.get_option('SCF','REFERENCE') == 'RKS':
if (len(core.get_option('SCF', 'DFT_FUNCTIONAL')) > 0) or core.get_option('SCF', 'DFT_CUSTOM_FUNCTIONAL') is not None:
pass
else:
core.set_local_option('SCF','REFERENCE','RHF')
elif core.get_option('SCF','REFERENCE') == 'UKS':
if (len(core.get_option('SCF', 'DFT_FUNCTIONAL')) > 0) or core.get_option('SCF', 'DFT_CUSTOM_FUNCTIONAL') is not None:
pass
else:
core.set_local_option('SCF','REFERENCE','UHF')
return optstash
def dft_set_reference_local(name):
"""
Figures out the correct DFT reference to set locally
"""
optstash = p4util.OptionsState(
['SCF', 'DFT_FUNCTIONAL'],
['SCF', 'REFERENCE'],
['SCF', 'SCF_TYPE'],
['DF_BASIS_MP2'],
['DFMP2', 'MP2_OS_SCALE'],
['DFMP2', 'MP2_SS_SCALE'])
# Alter default algorithm
if not core.has_option_changed('SCF', 'SCF_TYPE'):
core.set_local_option('SCF', 'SCF_TYPE', 'DF')
core.set_local_option('SCF', 'DFT_FUNCTIONAL', name)
user_ref = core.get_option('SCF', 'REFERENCE')
if (user_ref == 'RHF'):
core.set_local_option('SCF', 'REFERENCE', 'RKS')
elif (user_ref == 'UHF'):
core.set_local_option('SCF', 'REFERENCE', 'UKS')
elif (user_ref == 'ROHF'):
raise ValidationError('ROHF reference for DFT is not available.')
elif (user_ref == 'CUHF'):
raise ValidationError('CUHF reference for DFT is not available.')
return optstash
def df_mp2_sapt_dispersion(dimer_wfn, wfn_A, wfn_B, primary_basis, aux_basis, cache, do_print=True):
if do_print:
core.print_out("\n ==> E20 Dispersion (MP2) <== \n\n")
optstash = p4util.OptionsState(['SAPT', 'SAPT0_E10'], ['SAPT', 'SAPT0_E20IND'], ['SAPT', 'SAPT0_E20DISP'],
['SAPT', 'SAPT_QUIET'])
core.set_local_option("SAPT", "SAPT0_E10", False)
core.set_local_option("SAPT", "SAPT0_E20IND", False)
core.set_local_option("SAPT", "SAPT0_E20DISP", True)
core.set_local_option("SAPT", "SAPT_QUIET", True)
if core.get_option('SCF', 'REFERENCE') == 'RHF':
core.IO.change_file_namespace(psif.PSIF_SAPT_MONOMERA, 'monomerA', 'dimer')
core.IO.change_file_namespace(psif.PSIF_SAPT_MONOMERB, 'monomerB', 'dimer')
core.IO.set_default_namespace('dimer')
dimer_wfn.set_basisset("DF_BASIS_SAPT", aux_basis)
dimer_wfn.set_basisset("DF_BASIS_ELST", aux_basis)
e_sapt = core.sapt(dimer_wfn, wfn_A, wfn_B)
optstash.restore()
svars = dimer_wfn.variables()
core.print_out("\n")
core.print_out(print_sapt_var("Disp20 (MP2)", svars["E DISP20"], short=True) + "\n")
core.print_out(print_sapt_var("Exch-Disp20,u", svars["E EXCH-DISP20"], short=True) + "\n")
ret = {}
ret["Exch-Disp20,u"] = svars["E EXCH-DISP20"]
ret["Disp20,u"] = svars["E DISP20"]
return ret
def scf_set_reference_local(name, is_dft=False):
"""
Figures out the correct SCF reference to set locally
"""
optstash = p4util.OptionsState(
['SCF_TYPE'],
['SCF', 'REFERENCE'])
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DF')
# Alter reference name if needed
user_ref = core.get_option('SCF', 'REFERENCE')
sup = build_superfunctional_from_dictionary(functionals[name], 1, 1, True)[0]
if sup.needs_xc() or is_dft:
if (user_ref == 'RHF'):
core.set_local_option('SCF', 'REFERENCE', 'RKS')
elif (user_ref == 'UHF'):
core.set_local_option('SCF', 'REFERENCE', 'UKS')
elif (user_ref == 'ROHF'):
raise ValidationError('ROHF reference for DFT is not available.')
elif (user_ref == 'CUHF'):
raise ValidationError('CUHF reference for DFT is not available.')
# else we are doing HF and nothing needs to be overloaded
return optstash
def scf_set_reference_local(name, is_dft=False):
"""
Figures out the correct SCF reference to set locally
"""
optstash = p4util.OptionsState(
['SCF', 'SCF_TYPE'],
['SCF', 'REFERENCE'])
# Alter default algorithm
if not core.has_option_changed('SCF', 'SCF_TYPE'):
core.set_local_option('SCF', 'SCF_TYPE', 'DF')
# Alter reference name if needed
user_ref = core.get_option('SCF', 'REFERENCE')
if (name not in dft_funcs.superfunctional_noxc_names) or (is_dft):
if (user_ref == 'RHF'):
core.set_local_option('SCF', 'REFERENCE', 'RKS')
elif (user_ref == 'UHF'):
core.set_local_option('SCF', 'REFERENCE', 'UKS')
elif (user_ref == 'ROHF'):
raise ValidationError('ROHF reference for DFT is not available.')
elif (user_ref == 'CUHF'):
raise ValidationError('CUHF reference for DFT is not available.')
# else we are doing HF and nothing needs to be overloaded
return optstash
def scf_compute_energy(self):
"""Base class Wavefunction requires this function. Here it is
simply a wrapper around initialize(), iterations(), finalize_energy(). It
returns the SCF energy computed by finalize_energy().
"""
if core.get_option('SCF', 'DF_SCF_GUESS') and (core.get_global_option('SCF_TYPE') == 'DIRECT'):
# speed up DIRECT algorithm (recomputes full (non-DF) integrals
# each iter) by first converging via fast DF iterations, then
# fully converging in fewer slow DIRECT iterations. aka Andy trick 2.0
core.print_out(" Starting with a DF guess...\n\n")
with p4util.OptionsStateCM(['SCF_TYPE']):
core.set_global_option('SCF_TYPE', 'DF')
self.initialize()
try:
self.iterations()
except SCFConvergenceError:
self.finalize()
raise SCFConvergenceError("""SCF DF preiterations""", self.iteration_, self, 0, 0)
core.print_out("\n DF guess converged.\n\n")
# reset the DIIS & JK objects in prep for DIRECT
if self.initialized_diis_manager_:
self.diis_manager().reset_subspace()
self.initialize_jk(self.memory_jk_)
else:
self.initialize()
try:
self.iterations()
except SCFConvergenceError as e:
if core.get_option("SCF", "FAIL_ON_MAXITER"):
core.print_out(" Failed to converge.\n")
# energy = 0.0
# A P::e fn to either throw or protest upon nonconvergence
# die_if_not_converged()
raise e
else:
core.print_out(" Energy did not converge, but proceeding anyway.\n\n")
else:
core.print_out(" Energy converged.\n\n")
scf_energy = self.finalize_energy()
return scf_energy
def check_non_symmetric_jk_density(name):
"""
Ensure non-symmetric density matrices are supported for the selected JK routine.
"""
scf_type = core.get_option('SCF', 'SCF_TYPE')
supp_jk_type = ['DF', 'CD', 'PK', 'DIRECT', 'OUT_OF_CORE']
supp_string = ', '.join(supp_jk_type[:-1]) + ', or ' + supp_jk_type[-1] + '.'
if scf_type not in supp_jk_type:
raise ValidationError("Method %s: Requires support for non-symmetric density matrices.\n"
" Please set SCF_TYPE to %s" % (name, supp_string))
def fisapt_plot(self):
"""Filesystem wrapper for FISAPT::plot."""
filepath = core.get_option("FISAPT", "FISAPT_PLOT_FILEPATH")
os.makedirs(filepath, exist_ok=True)
geomfile = filepath + os.sep + 'geom.xyz'
xyz = self.molecule().to_string(dtype='xyz', units='Angstrom')
with open(geomfile, 'w') as fh:
fh.write(xyz)
self.raw_plot(filepath)
def _validate_MOM():
"""Sanity-checks MOM control options
Raises
------
ValidationError
If any of |scf__mom_start|, |scf__mom_occ| don't play well together.
Returns
-------
bool
Whether excited-state MOM (not just the plain stabilizing MOM) is enabled during scf.
"""
enabled = (core.get_option('SCF', "MOM_START") != 0 and len(core.get_option('SCF', "MOM_OCC")) > 0)
if enabled:
start = core.get_option('SCF', "MOM_START")
if enabled < 0:
raise ValidationError('SCF MOM_START ({}) must be at least 1'.format(start))
return enabled
def scf_print_energies(self):
enuc = self.get_energies('Nuclear')
e1 = self.get_energies('One-Electron')
e2 = self.get_energies('Two-Electron')
exc = self.get_energies('XC')
ed = self.get_energies('-D')
#self.del_variable('-D Energy')
evv10 = self.get_energies('VV10')
eefp = self.get_energies('EFP')
epcm = self.get_energies('PCM Polarization')
hf_energy = enuc + e1 + e2
dft_energy = hf_energy + exc + ed + evv10
total_energy = dft_energy + eefp + epcm
core.print_out(" => Energetics <=\n\n")
core.print_out(" Nuclear Repulsion Energy = {:24.16f}\n".format(enuc))
core.print_out(" One-Electron Energy = {:24.16f}\n".format(e1))
core.print_out(" Two-Electron Energy = {:24.16f}\n".format(e2))
if self.functional().needs_xc():
core.print_out(" DFT Exchange-Correlation Energy = {:24.16f}\n".format(exc))
core.print_out(" Empirical Dispersion Energy = {:24.16f}\n".format(ed))
core.print_out(" VV10 Nonlocal Energy = {:24.16f}\n".format(evv10))
if core.get_option('SCF', 'PCM'):
core.print_out(" PCM Polarization Energy = {:24.16f}\n".format(epcm))
if hasattr(self.molecule(), 'EFP'):
core.print_out(" EFP Energy = {:24.16f}\n".format(eefp))
core.print_out(" Total Energy = {:24.16f}\n".format(total_energy))
self.set_variable('NUCLEAR REPULSION ENERGY', enuc)
self.set_variable('ONE-ELECTRON ENERGY', e1)
self.set_variable('TWO-ELECTRON ENERGY', e2)
if self.functional().needs_xc():
self.set_variable('DFT XC ENERGY', exc)
self.set_variable('DFT VV10 ENERGY', evv10)
self.set_variable('DFT FUNCTIONAL TOTAL ENERGY', hf_energy + exc + evv10)
#self.set_variable(self.functional().name() + ' FUNCTIONAL TOTAL ENERGY', hf_energy + exc + evv10)
self.set_variable('DFT TOTAL ENERGY', dft_energy) # overwritten later for DH
else:
self.set_variable('HF TOTAL ENERGY', hf_energy)
if hasattr(self, "_disp_functor"):
self.set_variable('DISPERSION CORRECTION ENERGY', ed)
#if abs(ed) > 1.0e-14:
# for pv, pvv in self.variables().items():
# if abs(pvv - ed) < 1.0e-14:
# if pv.endswith('DISPERSION CORRECTION ENERGY') and pv.startswith(self.functional().name()):
# fctl_plus_disp_name = pv.split()[0]
# self.set_variable(fctl_plus_disp_name + ' TOTAL ENERGY', dft_energy) # overwritten later for DH
#else:
# self.set_variable(self.functional().name() + ' TOTAL ENERGY', dft_energy) # overwritten later for DH
self.set_variable('SCF ITERATIONS', self.iteration_)
def prepare_options_for_modules(changedOnly=False, commandsInsteadDict=False):
"""Function to return a string of commands to replicate the
current state of user-modified options. Used to capture C++
options information for distributed (sow/reap) input files.
.. caution:: Some features are not yet implemented. Buy a developer a coffee.
- Need some option to get either all or changed
- Need some option to either get dict or set string or psimod command list
- command return doesn't revoke has_changed setting for unchanged with changedOnly=False
"""
options = collections.defaultdict(dict)
commands = ''
for opt in core.get_global_option_list():
if core.has_global_option_changed(opt) 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': core.has_global_option_changed(opt)}
if isinstance(val, basestring):
commands += """core.set_global_option('%s', '%s')\n""" % (opt, val)
else:
commands += """core.set_global_option('%s', %s)\n""" % (opt, val)
#if changedOnly:
# print('Appending module %s option %s value %s has_changed %s.' % \
# ('GLOBALS', opt, core.get_global_option(opt), core.has_global_option_changed(opt)))
for module in _modules:
if core.option_exists_in_module(module, opt):
hoc = core.has_option_changed(module, opt)
if hoc or not changedOnly:
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 changedOnly:
# print('Appending module %s option %s value %s has_changed %s.' % \
# (module, opt, core.get_option(module, opt), hoc))
if commandsInsteadDict:
return commands
else:
return options
def check_iwl_file_from_scf_type(scf_type, wfn):
"""
Ensures that a IWL file has been written based on input SCF type.
"""
if scf_type in ['DF', 'DISK_DF', 'MEM_DF', 'CD', 'PK', 'DIRECT']:
mints = core.MintsHelper(wfn.basisset())
if core.get_global_option("RELATIVISTIC") in ["X2C", "DKH"]:
rel_bas = core.BasisSet.build(wfn.molecule(), "BASIS_RELATIVISTIC",
core.get_option("SCF", "BASIS_RELATIVISTIC"),
"DECON", core.get_global_option('BASIS'),
puream=wfn.basisset().has_puream())
mints.set_rel_basisset(rel_bas)
mints.set_print(1)
mints.integrals()
def set_cholesky_from(mtd_type):
type_val = core.get_global_option(mtd_type)
if type_val == 'CD':
core.set_local_option('GPU_DFCC', 'DF_BASIS_CC', 'CHOLESKY')
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
optstash.add_option(['SCF_TYPE'])
core.set_global_option('SCF_TYPE', 'CD')
core.print_out(""" SCF Algorithm Type (re)set to CD.\n""")
elif type_val in ['DF', 'DISK_DF']:
if core.get_option('GPU_DFCC', 'DF_BASIS_CC') == 'CHOLESKY':
core.set_local_option('GPU_DFCC', 'DF_BASIS_CC', '')
proc_util.check_disk_df(name.upper(), optstash)
else:
raise ValidationError("""Invalid type '%s' for DFCC""" % type_val)
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 scf_print_energies(self):
enuc = self.get_energies('Nuclear')
e1 = self.get_energies('One-Electron')
e2 = self.get_energies('Two-Electron')
exc = self.get_energies('XC')
ed = self.get_energies('-D')
evv10 = self.get_energies('VV10')
eefp = self.get_energies('EFP')
epcm = self.get_energies('PCM Polarization')
hf_energy = enuc + e1 + e2
dft_energy = hf_energy + exc + ed + evv10
total_energy = dft_energy + eefp + epcm
core.print_out(" => Energetics <=\n\n")
core.print_out(" Nuclear Repulsion Energy = {:24.16f}\n".format(enuc))
core.print_out(" One-Electron Energy = {:24.16f}\n".format(e1))
core.print_out(" Two-Electron Energy = {:24.16f}\n".format(e2))
if self.functional().needs_xc():
core.print_out(" DFT Exchange-Correlation Energy = {:24.16f}\n".format(exc))
core.print_out(" Empirical Dispersion Energy = {:24.16f}\n".format(ed))
core.print_out(" VV10 Nonlocal Energy = {:24.16f}\n".format(evv10))
if core.get_option('SCF', 'PCM'):
core.print_out(" PCM Polarization Energy = {:24.16f}\n".format(epcm))
if hasattr(self.molecule(), 'EFP'):
core.print_out(" EFP Energy = {:24.16f}\n".format(eefp))
core.print_out(" Total Energy = {:24.16f}\n".format(total_energy))
self.set_variable('NUCLEAR REPULSION ENERGY', enuc)
self.set_variable('ONE-ELECTRON ENERGY', e1)
self.set_variable('TWO-ELECTRON ENERGY', e2)
if self.functional().needs_xc():
self.set_variable('DFT XC ENERGY', exc)
self.set_variable('DFT VV10 ENERGY', evv10)
self.set_variable('DFT FUNCTIONAL TOTAL ENERGY', hf_energy + exc + evv10)
self.set_variable('DFT TOTAL ENERGY', dft_energy)
else:
self.set_variable('HF TOTAL ENERGY', hf_energy)
if hasattr(self, "_disp_functor"):
self.set_variable('DISPERSION CORRECTION ENERGY', ed)
self.set_variable('SCF ITERATIONS', self.iteration_)
def _validate_frac():
"""Sanity-checks FRAC control options
Raises
------
ValidationError
If any of |scf__frac_start| don't play well together.
Returns
-------
bool
Whether FRAC is enabled during scf.
"""
enabled = (core.get_option('SCF', 'FRAC_START') != 0)
if enabled:
if enabled < 0:
raise ValidationError('SCF FRAC_START ({}) must be at least 1'.format(enabled))
return enabled
def scf_initialize(self):
"""Specialized initialization, compute integrals and does everything to prepare for iterations"""
self.iteration_ = 0
efp_enabled = hasattr(self.molecule(), 'EFP')
if core.get_option('SCF', "PRINT") > 0:
core.print_out(" ==> Pre-Iterations <==\n\n")
self.print_preiterations()
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(field_fn)
if self.attempt_number_ == 1:
mints = core.MintsHelper(self.basisset())
if core.get_global_option('RELATIVISTIC') in ['X2C', 'DKH']:
mints.set_rel_basisset(self.get_basisset('BASIS_RELATIVISTIC'))
mints.one_electron_integrals()
self.integrals()
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.timer_on("HF: Guess")
self.guess()
core.timer_off("HF: Guess")
else:
# We're reading the orbitals from the previous set of iterations.
self.form_D()
self.set_energies("Total Energy", self.compute_initial_E())
def fisapt_fdrop(self):
"""Drop output files from FSAPT calculation. FISAPT::fdrop"""
core.print_out(" ==> F-SAPT Output <==\n\n")
filepath = core.get_option("FISAPT", "FISAPT_FSAPT_FILEPATH")
os.makedirs(filepath, exist_ok=True)
core.print_out(" F-SAPT Data Filepath = {}\n\n".format(filepath))
geomfile = filepath + os.sep + 'geom.xyz'
xyz = self.molecule().to_string(dtype='xyz', units='Angstrom')
with open(geomfile, 'w') as fh:
fh.write(xyz)
vectors = self.vectors()
matrices = self.matrices()
matrices["Qocc0A"].name = "QA"
matrices["Qocc0B"].name = "QB"
matrices["Elst_AB"].name = "Elst"
matrices["Exch_AB"].name = "Exch"
matrices["IndAB_AB"].name = "IndAB"
matrices["IndBA_AB"].name = "IndBA"
_drop(vectors["ZA"], filepath)
_drop(vectors["ZB"], filepath)
_drop(matrices["Qocc0A"], filepath)
_drop(matrices["Qocc0B"], filepath)
_drop(matrices["Elst_AB"], filepath)
_drop(matrices["Exch_AB"], filepath)
_drop(matrices["IndAB_AB"], filepath)
_drop(matrices["IndBA_AB"], filepath)
if core.get_option("FISAPT", "FISAPT_DO_FSAPT_DISP"):
matrices["Disp_AB"].name = "Disp"
_drop(matrices["Disp_AB"], filepath)
if core.get_option("FISAPT", "SSAPT0_SCALE"):
ssapt_filepath = core.get_option("FISAPT", "FISAPT_FSSAPT_FILEPATH")
os.makedirs(ssapt_filepath, exist_ok=True)
core.print_out(
" sF-SAPT Data Filepath = {}\n\n".format(ssapt_filepath))
geomfile = ssapt_filepath + os.sep + 'geom.xyz'
with open(geomfile, 'w') as fh:
fh.write(xyz)
matrices["sIndAB_AB"].name = "IndAB"
matrices["sIndBA_AB"].name = "IndBA"
_drop(vectors["ZA"], ssapt_filepath)
_drop(vectors["ZB"], ssapt_filepath)
_drop(matrices["Qocc0A"], ssapt_filepath)
_drop(matrices["Qocc0B"], ssapt_filepath)
_drop(matrices["Elst_AB"], ssapt_filepath)
_drop(matrices["Exch_AB"], ssapt_filepath)
_drop(matrices["sIndAB_AB"], ssapt_filepath)
_drop(matrices["sIndBA_AB"], ssapt_filepath)
if core.get_option("FISAPT", "FISAPT_DO_FSAPT_DISP"):
matrices["sDisp_AB"].name = "Disp"
_drop(matrices["sDisp_AB"], ssapt_filepath)
def build_superfunctional(name, restricted):
npoints = core.get_option("SCF", "DFT_BLOCK_MAX_POINTS")
deriv = 1 # Default depth for now
# We are a XC generating function
if hasattr(name, '__call__'):
custom_error = "SCF: Custom functional type must either be a SuperFunctional or a tuple of (SuperFunctional, (base_name, dashparam))."
sfunc = name("name", npoints, deriv, restricted)
# Without Dispersion
if isinstance(sfunc, core.SuperFunctional):
sup = (sfunc, False)
# With Dispersion
elif isinstance(sup[0], core.SuperFunctional):
sup = sfunc
# Can we validate dispersion?
else:
raise ValidationError(custom_error)
# Double check that the SuperFunctional is correctly sized (why dont we always do this?)
sup[0].set_max_points(npoints)
sup[0].set_deriv(deriv)
sup[0].allocate()
# Check for supplied dict_func functionals
elif isinstance(name, dict):
sup = dict_builder.build_superfunctional_from_dictionary(
name, npoints, deriv, restricted)
# Check for pre-defined dict-based functionals
elif name.lower() in dict_builder.functionals:
sup = dict_builder.build_superfunctional_from_dictionary(
dict_builder.functionals[name.lower()], npoints, deriv, restricted)
else:
raise ValidationError("SCF: Functional (%s) not found!" % name)
if (core.get_global_option('INTEGRAL_PACKAGE')
== 'ERD') and (sup[0].is_x_lrc() or sup[0].is_c_lrc()):
raise ValidationError(
"INTEGRAL_PACKAGE ERD does not play nicely with omega ERI's, so stopping."
)
# Lock and unlock the functional
sup[0].set_lock(False)
# Set options
if core.has_option_changed("SCF", "DFT_OMEGA") and sup[0].is_x_lrc():
omega = core.get_option("SCF", "DFT_OMEGA")
sup[0].set_x_omega(omega)
# We also need to loop through all of the exchange functionals
if sup[0].is_libxc_func():
# Full libxc funcs are dropped in c_functionals (smooth move!)
sup[0].c_functionals()[0].set_omega(omega)
else:
for x_func in sup[0].x_functionals():
x_func.set_omega(omega)
if core.has_option_changed("SCF", "DFT_OMEGA_C") and sup[0].is_c_lrc():
sup[0].set_c_omega(core.get_option("SCF", "DFT_OMEGA_C"))
if core.has_option_changed("SCF", "DFT_ALPHA"):
sup[0].set_x_alpha(core.get_option("SCF", "DFT_ALPHA"))
if core.has_option_changed("SCF", "DFT_ALPHA_C"):
sup[0].set_c_alpha(core.get_option("SCF", "DFT_ALPHA_C"))
# add VV10 correlation to any functional or modify existing
# custom procedures using name 'scf' without any quadrature grid like HF will fail and are not detected
if (core.has_option_changed("SCF", "NL_DISPERSION_PARAMETERS")
and sup[0].vv10_b() > 0.0):
if (name.lower() == 'hf'):
raise ValidationError("SCF: HF with -NL not implemented")
nl_tuple = core.get_option("SCF", "NL_DISPERSION_PARAMETERS")
sup[0].set_vv10_b(nl_tuple[0])
if len(nl_tuple) > 1:
sup[0].set_vv10_c(nl_tuple[1])
if len(nl_tuple) > 2:
raise ValidationError(
"too many entries in NL_DISPERSION_PARAMETERS for DFT-NL")
elif core.has_option_changed("SCF", "DFT_VV10_B"):
if (name.lower() == 'hf'):
raise ValidationError("SCF: HF with -NL not implemented")
vv10_b = core.get_option("SCF", "DFT_VV10_B")
sup[0].set_vv10_b(vv10_b)
if core.has_option_changed("SCF", "DFT_VV10_C"):
vv10_c = core.get_option("SCF", "DFT_VV10_C")
sup[0].set_vv10_c(vv10_c)
if (abs(sup[0].vv10_c() - 0.0) <= 1e-8):
core.print_out(
"SCF: VV10_C not specified. Using default (C=0.0093)!")
sup[0].set_vv10_c(0.0093)
if (core.has_option_changed("SCF", "NL_DISPERSION_PARAMETERS")
and core.has_option_changed("SCF", "DFT_VV10_B")):
raise ValidationError(
"SCF: Decide between NL_DISPERSION_PARAMETERS and DFT_VV10_B !!")
# Check SCF_TYPE
if sup[0].is_x_lrc() and (core.get_global_option("SCF_TYPE")
not in ["DIRECT", "DF", "OUT_OF_CORE", "PK"]):
raise ValidationError(
"SCF: SCF_TYPE (%s) not supported for range-separated functionals, plese use SCF_TYPE = 'DF' to automatically select the correct JK build."
% core.get_global_option("SCF_TYPE"))
if (core.get_global_option('INTEGRAL_PACKAGE')
== 'ERD') and (sup[0].is_x_lrc()):
raise ValidationError(
'INTEGRAL_PACKAGE ERD does not play nicely with LRC DFT functionals, so stopping.'
)
sup[0].set_lock(True)
return sup
def run_gcp(self,
func=None,
dertype=None,
verbose=False): # dashlvl=None, dashparam=None
"""Function to call Grimme's dftd3 program (http://toc.uni-muenster.de/DFTD3/)
to compute the -D correction of level *dashlvl* using parameters for
the functional *func*. The dictionary *dashparam* can be used to supply
a full set of dispersion parameters in the absense of *func* or to supply
individual overrides in the presence of *func*. Returns energy if *dertype* is 0,
gradient if *dertype* is 1, else tuple of energy and gradient if *dertype*
unspecified. The dftd3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
*self* may be either a qcdb.Molecule (sensibly) or a psi4.Molecule
(works b/c psi4.Molecule has been extended by this method py-side and
only public interface fns used) or a string that can be instantiated
into a qcdb.Molecule.
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(self, Molecule):
# called on a qcdb.Molecule
pass
elif isinstance(self, core.Molecule):
# called on a python export of a psi4.core.Molecule (py-side through Psi4's driver)
self.create_psi4_string_from_molecule()
elif isinstance(self, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
self = Molecule(self)
else:
raise ValidationError(
"""Argument mol must be psi4string or qcdb.Molecule""")
self.update_geometry()
# # Validate arguments
# dashlvl = dashlvl.lower()
# dashlvl = dash_alias['-' + dashlvl][1:] if ('-' + dashlvl) in dash_alias.keys() else dashlvl
# if dashlvl not in dashcoeff.keys():
# raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
if dertype is None:
dertype = -1
elif util.der0th.match(str(dertype)):
dertype = 0
elif util.der1st.match(str(dertype)):
dertype = 1
# elif der2nd.match(str(dertype)):
# raise ValidationError('Requested derivative level \'dertype\' %s not valid for run_dftd3.' % (dertype))
else:
raise ValidationError(
'Requested derivative level \'dertype\' %s not valid for run_dftd3.'
% (dertype))
# if func is None:
# if dashparam is None:
# # defunct case
# raise ValidationError("""Parameters for -D correction missing. Provide a func or a dashparam kwarg.""")
# else:
# # case where all param read from dashparam dict (which must have all correct keys)
# func = 'custom'
# dashcoeff[dashlvl][func] = {}
# dashparam = dict((k.lower(), v) for k, v in dashparam.iteritems())
# for key in dashcoeff[dashlvl]['b3lyp'].keys():
# if key in dashparam.keys():
# dashcoeff[dashlvl][func][key] = dashparam[key]
# else:
# raise ValidationError("""Parameter %s is missing from dashparam dict %s.""" % (key, dashparam))
# else:
# func = func.lower()
# if func not in dashcoeff[dashlvl].keys():
# raise ValidationError("""Functional %s is not available for -D level %s.""" % (func, dashlvl))
# if dashparam is None:
# # (normal) case where all param taken from dashcoeff above
# pass
# else:
# # case where items in dashparam dict can override param taken from dashcoeff above
# dashparam = dict((k.lower(), v) for k, v in dashparam.iteritems())
# for key in dashcoeff[dashlvl]['b3lyp'].keys():
# if key in dashparam.keys():
# dashcoeff[dashlvl][func][key] = dashparam[key]
# TODO temp until figure out paramfile
allowed_funcs = [
'HF/MINIS',
'DFT/MINIS',
'HF/MINIX',
'DFT/MINIX',
'HF/SV',
'DFT/SV',
'HF/def2-SV(P)',
'DFT/def2-SV(P)',
'HF/def2-SVP',
'DFT/def2-SVP',
'HF/DZP',
'DFT/DZP',
'HF/def-TZVP',
'DFT/def-TZVP',
'HF/def2-TZVP',
'DFT/def2-TZVP',
'HF/631Gd',
'DFT/631Gd',
'HF/def2-TZVP',
'DFT/def2-TZVP',
'HF/cc-pVDZ',
'DFT/cc-pVDZ',
'HF/aug-cc-pVDZ',
'DFT/aug-cc-pVDZ',
'DFT/SV(P/h,c)',
'DFT/LANL',
'DFT/pobTZVP',
'TPSS/def2-SVP',
'PW6B95/def2-SVP',
# specials
'hf3c',
'pbeh3c'
]
allowed_funcs = [f.lower() for f in allowed_funcs]
if func.lower() not in allowed_funcs:
raise Dftd3Error("""bad gCP func: %s. need one of: %r""" %
(func, allowed_funcs))
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser(
'~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
isP4regime = False
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
gcp_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.gcp.' + str(uuid.uuid4())[:8]
else:
gcp_tmpdir = os.environ['HOME'] + os.sep + 'gcp_' + str(
uuid.uuid4())[:8]
if os.path.exists(gcp_tmpdir) is False:
os.mkdir(gcp_tmpdir)
os.chdir(gcp_tmpdir)
# Write gcp_parameters file that governs cp correction
# paramcontents = gcp_server(func, dashlvl, 'dftd3')
# paramfile1 = 'dftd3_parameters' # older patched name
# with open(paramfile1, 'w') as handle:
# handle.write(paramcontents)
# paramfile2 = '.gcppar'
# with open(paramfile2, 'w') as handle:
# handle.write(paramcontents)
###Two kinds of parameter files can be read in: A short and an extended version. Both are read from
###$HOME/.gcppar.$HOSTNAME by default. If the option -local is specified the file is read in from
###the current working directory: .gcppar
###The short version reads in: basis-keywo
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = self.natom()
geom = self.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './gcp_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call gcp program
command = ['gcp', geomfile]
command.extend(['-level', func])
if dertype != 0:
command.append('-grad')
try:
#print('command', command)
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program gcp not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Egcp:', line):
sline = line.split()
dashd = float(sline[1])
if re.match(' normal termination of gCP', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful gCP run.""")
# Parse grad output
if dertype != 0:
derivfile = './gcp_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([
float(x.replace('D', 'E'))
for x in dfile.readline().split()
])
dfile.close()
if len(dashdderiv) != self.natom():
raise ValidationError('Program gcp gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), self.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('GCP CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> GCP Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# # Clean up files and remove scratch directory
# os.unlink(paramfile1)
# os.unlink(paramfile2)
# os.unlink(geomfile)
# if dertype != 0:
# os.unlink(derivfile)
# if defmoved is True:
# os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
# try:
# shutil.rmtree(dftd3_tmpdir)
# except OSError as e:
# ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def print_sapt_dft_summary(data, name, short=False):
ret = " %s Results\n" % name
ret += " " + "-" * 105 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst1,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
# Induction
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
if "Delta HF Correction" in list(data):
ind += data["Delta HF Correction"]
ret += print_sapt_var("Induction", ind) + "\n"
ret += print_sapt_var(" Ind2,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind2,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba) + "\n"
if "Delta HF Correction" in list(data):
ret += print_sapt_var(" delta HF,r (2)",
data["Delta HF Correction"]) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
# Exchange-dispersion scaling
exch_disp_scheme = core.get_option("SAPT",
"SAPT_DFT_EXCH_DISP_SCALE_SCHEME")
if exch_disp_scheme == "NONE":
data["Exch-Disp20,r"] = data["Exch-Disp20,u"]
elif exch_disp_scheme == "FIXED":
exch_disp_scale = core.get_option("SAPT",
"SAPT_DFT_EXCH_DISP_FIXED_SCALE")
data["Exch-Disp20,r"] = exch_disp_scale * data["Exch-Disp20,u"]
elif exch_disp_scheme == "DISP":
exch_disp_scale = data["Disp20"] / data["Disp20,u"]
data["Exch-Disp20,r"] = exch_disp_scale * data["Exch-Disp20,u"]
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,r"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp2,r", data["Disp20"]) + "\n"
ret += print_sapt_var(" Disp2,u", data["Disp20,u"]) + "\n"
if exch_disp_scheme != "NONE":
ret += print_sapt_var(" Est. Exch-Disp2,r",
data["Exch-Disp20,r"]) + "\n"
ret += print_sapt_var(" Exch-Disp2,u", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-17s" % name, total,
start_spacer=" ") + "\n"
core.set_variable("SAPT(DFT) TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 105 + "\n"
return ret
def print_ci_results(ciwfn, rname, scf_e, ci_e, print_opdm_no=False):
"""
Printing for all CI Wavefunctions
"""
# Print out energetics
core.print_out("\n ==> Energetics <==\n\n")
core.print_out(" SCF energy = %20.15f\n" % scf_e)
if "CI" in rname:
core.print_out(" Total CI energy = %20.15f\n" % ci_e)
elif "MP" in rname:
core.print_out(" Total MP energy = %20.15f\n" % ci_e)
elif "ZAPT" in rname:
core.print_out(" Total ZAPT energy = %20.15f\n" % ci_e)
else:
core.print_out(" Total MCSCF energy = %20.15f\n" % ci_e)
# Nothing to be done for ZAPT or MP
if ("MP" in rname) or ("ZAPT" in rname):
core.print_out("\n")
return
# Initial info
ci_nroots = core.get_option("DETCI", "NUM_ROOTS")
irrep_labels = ciwfn.molecule().irrep_labels()
# Grab the D-vector
dvec = ciwfn.D_vector()
dvec.init_io_files(True)
for root in range(ci_nroots):
core.print_out("\n ==> %s root %d information <==\n\n" % (rname, root))
# Print total energy
root_e = core.get_variable("CI ROOT %d TOTAL ENERGY" % (root))
core.print_out(" %s Root %d energy = %20.15f\n" % (rname, root, root_e))
# Print natural occupations
if print_opdm_no:
core.print_out("\n Active Space Natural occupation numbers:\n\n")
occs_list = []
r_opdm = ciwfn.get_opdm(root, root, "SUM", False)
for h in range(len(r_opdm.nph)):
if 0 in r_opdm.nph[h].shape:
continue
nocc, rot = np.linalg.eigh(r_opdm.nph[h])
for e in nocc:
occs_list.append((e, irrep_labels[h]))
occs_list.sort(key=lambda x: -x[0])
cnt = 0
for value, label in occs_list:
value, label = occs_list[cnt]
core.print_out(" %4s % 8.6f" % (label, value))
cnt += 1
if (cnt % 3) == 0:
core.print_out("\n")
if (cnt % 3):
core.print_out("\n")
# Print CIVector information
ciwfn.print_vector(dvec, root)
# True to keep the file
dvec.close_io_files(True)
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_sf_sapt(name, **kwargs):
optstash = p4util.OptionsState(['SCF_TYPE'], ['SCF', 'REFERENCE'],
['SCF', 'DFT_GRAC_SHIFT'],
['SCF', 'SAVE_JK'])
core.tstart()
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DF')
core.prepare_options_for_module("SAPT")
# Get the molecule of interest
ref_wfn = kwargs.get('ref_wfn', None)
if ref_wfn is None:
sapt_dimer = kwargs.pop('molecule', core.get_active_molecule())
else:
core.print_out(
'Warning! SAPT argument "ref_wfn" is only able to use molecule information.'
)
sapt_dimer = ref_wfn.molecule()
sapt_dimer, monomerA, monomerB = proc_util.prepare_sapt_molecule(
sapt_dimer, "dimer")
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " + "Spin-Flip SAPT Procedure".center(58) + "\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Konrad Patkowski".center(58) +
"\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" JK Algorithm %12s\n" %
core.get_option("SCF", "SCF_TYPE"))
core.print_out("\n")
core.print_out(" Required computations:\n")
core.print_out(" HF (Monomer A)\n")
core.print_out(" HF (Monomer B)\n")
core.print_out("\n")
if (core.get_option('SCF', 'REFERENCE') != 'ROHF'):
raise ValidationError(
'Spin-Flip SAPT currently only supports restricted open-shell references.'
)
# Run the two monomer computations
core.IO.set_default_namespace('dimer')
data = {}
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.set_global_option('DF_INTS_IO', 'SAVE')
# Compute dimer wavefunction
wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SF-SAPT: HF Monomer A",
**kwargs)
core.set_global_option("SAVE_JK", True)
wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SF-SAPT: HF Monomer B",
**kwargs)
sapt_jk = wfn_B.jk()
core.set_global_option("SAVE_JK", False)
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " +
"Spin-Flip SAPT Exchange and Electrostatics".center(58) +
"\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Konrad Patkowski".center(58) +
"\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
sf_data = sapt_sf_terms.compute_sapt_sf(sapt_dimer, sapt_jk, wfn_A, wfn_B)
# Print the results
core.print_out(" Spin-Flip SAPT Results\n")
core.print_out(" " + "-" * 103 + "\n")
for key, value in sf_data.items():
value = sf_data[key]
print_vals = (key, value * 1000, value * constants.hartree2kcalmol,
value * constants.hartree2kJmol)
string = " %-26s % 15.8f [mEh] % 15.8f [kcal/mol] % 15.8f [kJ/mol]\n" % print_vals
core.print_out(string)
core.print_out(" " + "-" * 103 + "\n\n")
dimer_wfn = core.Wavefunction.build(sapt_dimer, wfn_A.basisset())
# Set variables
psivar_tanslator = {
"Elst10": "SAPT ELST ENERGY",
"Exch10(S^2) [diagonal]": "SAPT EXCH10(S^2),DIAGONAL ENERGY",
"Exch10(S^2) [off-diagonal]": "SAPT EXCH10(S^2),OFF-DIAGONAL ENERGY",
"Exch10(S^2) [highspin]": "SAPT EXCH10(S^2),HIGHSPIN ENERGY",
}
for k, v in sf_data.items():
psi_k = psivar_tanslator[k]
dimer_wfn.set_variable(psi_k, v)
core.set_variable(psi_k, v)
# Copy over highspin
core.set_variable("SAPT EXCH ENERGY", sf_data["Exch10(S^2) [highspin]"])
core.tstop()
return dimer_wfn
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)
# Print out initial docc/socc/etc data
if self.get_print():
core.print_out(" ==> Pre-Iterations <==\n\n")
self.print_preiterations()
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(field_fn)
# Initilize all integratals and perform the first guess
if self.attempt_number_ == 1:
mints = core.MintsHelper(self.basisset())
if core.get_global_option('RELATIVISTIC') in ['X2C', 'DKH']:
mints.set_rel_basisset(self.get_basisset('BASIS_RELATIVISTIC'))
mints.one_electron_integrals()
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.timer_on("HF: Guess")
self.guess()
core.timer_off("HF: Guess")
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)
def sapt_dft(dimer_wfn,
wfn_A,
wfn_B,
sapt_jk=None,
sapt_jk_B=None,
data=None,
print_header=True,
cleanup_jk=True):
"""
The primary SAPT(DFT) algorithm to compute the interaction energy once the wavefunctions have been built.
Example
-------
dimer = psi4.geometry('''
Ne
--
Ar 1 6.5
units bohr
''')
psi4.set_options({"BASIS": "aug-cc-pVDZ"})
# Prepare the fragments
sapt_dimer, monomerA, monomerB = psi4.proc_util.prepare_sapt_molecule(sapt_dimer, "dimer")
# Run the first monomer
set DFT_GRAC_SHIFT 0.203293
wfnA, energyA = psi4.energy("PBE0", monomer=monomerA, return_wfn=True)
# Run the second monomer
set DFT_GRAC_SHIFT 0.138264
wfnB, energyB = psi4.energy("PBE0", monomer=monomerB, return_wfn=True)
# Build the dimer wavefunction
wfnD = psi4.core.Wavefunction.build(sapt_dimer)
# Compute SAPT(DFT) from the provided wavefunctions
data = psi4.procrouting.sapt.sapt_dft(wfnD, wfnA, wfnB)
"""
# Handle the input options
core.timer_on("SAPT(DFT):SAPT(DFT):JK")
if print_header:
sapt_dft_header()
if sapt_jk is None:
core.print_out("\n => Building SAPT JK object <= \n\n")
sapt_jk = core.JK.build(dimer_wfn.basisset())
sapt_jk.set_do_J(True)
sapt_jk.set_do_K(True)
if wfn_A.functional().is_x_lrc():
sapt_jk.set_do_wK(True)
sapt_jk.set_omega(wfn_A.functional().x_omega())
sapt_jk.initialize()
sapt_jk.print_header()
if wfn_B.functional().is_x_lrc() and (wfn_A.functional().x_omega() !=
wfn_B.functional().x_omega()):
core.print_out(" => Monomer B: Building SAPT JK object <= \n\n")
core.print_out(
" Reason: MonomerA Omega != MonomerB Omega\n\n")
sapt_jk_B = core.JK.build(dimer_wfn.basisset())
sapt_jk_B.set_do_J(True)
sapt_jk_B.set_do_K(True)
sapt_jk_B.set_do_wK(True)
sapt_jk_B.set_omega(wfn_B.functional().x_omega())
sapt_jk_B.initialize()
sapt_jk_B.print_header()
else:
sapt_jk.set_do_K(True)
if data is None:
data = {}
# Build SAPT cache
cache = sapt_jk_terms.build_sapt_jk_cache(wfn_A, wfn_B, sapt_jk, True)
core.timer_off("SAPT(DFT):SAPT(DFT):JK")
# Electrostatics
core.timer_on("SAPT(DFT):SAPT(DFT):elst")
elst = sapt_jk_terms.electrostatics(cache, True)
data.update(elst)
core.timer_off("SAPT(DFT):SAPT(DFT):elst")
# Exchange
core.timer_on("SAPT(DFT):SAPT(DFT):exch")
exch = sapt_jk_terms.exchange(cache, sapt_jk, True)
data.update(exch)
core.timer_off("SAPT(DFT):SAPT(DFT):exch")
# Induction
core.timer_on("SAPT(DFT):SAPT(DFT):ind")
ind = sapt_jk_terms.induction(cache,
sapt_jk,
True,
sapt_jk_B=sapt_jk_B,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT",
"D_CONVERGENCE"),
Sinf=core.get_option("SAPT",
"DO_IND_EXCH_SINF"))
data.update(ind)
core.timer_off("SAPT(DFT):SAPT(DFT):ind")
# Blow away JK object before doing MP2 for memory considerations
if cleanup_jk:
sapt_jk.finalize()
# Dispersion
core.timer_on("SAPT(DFT):SAPT(DFT):disp")
primary_basis = wfn_A.basisset()
core.print_out("\n")
aux_basis = core.BasisSet.build(dimer_wfn.molecule(), "DF_BASIS_MP2",
core.get_option("DFMP2", "DF_BASIS_MP2"),
"RIFIT", core.get_global_option('BASIS'))
fdds_disp = sapt_mp2_terms.df_fdds_dispersion(primary_basis, aux_basis,
cache)
data.update(fdds_disp)
if core.get_option("SAPT", "SAPT_DFT_MP2_DISP_ALG") == "FISAPT":
mp2_disp = sapt_mp2_terms.df_mp2_fisapt_dispersion(wfn_A,
primary_basis,
aux_basis,
cache,
do_print=True)
else:
mp2_disp = sapt_mp2_terms.df_mp2_sapt_dispersion(dimer_wfn,
wfn_A,
wfn_B,
primary_basis,
aux_basis,
cache,
do_print=True)
data.update(mp2_disp)
core.timer_off("SAPT(DFT):SAPT(DFT):disp")
# Print out final data
core.print_out("\n")
core.print_out(print_sapt_dft_summary(data, "SAPT(DFT)"))
return data
def fcidump(wfn, fname='INTDUMP', oe_ints=None):
"""Save integrals to file in FCIDUMP format as defined in Comp. Phys. Commun. 54 75 (1989)
Additional one-electron integrals, including orbital energies, can also be saved.
This latter format can be used with the HANDE QMC code but is not standard.
:returns: None
:raises: ValidationError when SCF wavefunction is not RHF
:type wfn: :py:class:`~psi4.core.Wavefunction`
:param wfn: set of molecule, basis, orbitals from which to generate cube files
:param fname: name of the integrals file, defaults to INTDUMP
:param oe_ints: list of additional one-electron integrals to save to file.
So far only EIGENVALUES is a valid option.
:examples:
>>> # [1] Save one- and two-electron integrals to standard FCIDUMP format
>>> E, wfn = energy('scf', return_wfn=True)
>>> fcidump(wfn)
>>> # [2] Save orbital energies, one- and two-electron integrals.
>>> E, wfn = energy('scf', return_wfn=True)
>>> fcidump(wfn, oe_ints=['EIGENVALUES'])
"""
# Get some options
reference = core.get_option('SCF', 'REFERENCE')
ints_tolerance = core.get_global_option('INTS_TOLERANCE')
# Some sanity checks
if reference not in ['RHF', 'UHF']:
raise ValidationError('FCIDUMP not implemented for {} references\n'.format(reference))
if oe_ints is None:
oe_ints = []
molecule = wfn.molecule()
docc = wfn.doccpi()
frzcpi = wfn.frzcpi()
frzvpi = wfn.frzvpi()
active_docc = docc - frzcpi
active_socc = wfn.soccpi()
active_mopi = wfn.nmopi() - frzcpi - frzvpi
nbf = active_mopi.sum() if wfn.same_a_b_orbs() else 2 * active_mopi.sum()
nirrep = wfn.nirrep()
nelectron = 2 * active_docc.sum() + active_socc.sum()
irrep_map = _irrep_map(wfn)
wfn_irrep = 0
for h, n_socc in enumerate(active_socc):
if n_socc % 2 == 1:
wfn_irrep ^= h
core.print_out('Writing integrals in FCIDUMP format to ' + fname + '\n')
# Generate FCIDUMP header
header = '&FCI\n'
header += 'NORB={:d},\n'.format(nbf)
header += 'NELEC={:d},\n'.format(nelectron)
header += 'MS2={:d},\n'.format(wfn.nalpha() - wfn.nbeta())
header += 'UHF=.{}.,\n'.format(not wfn.same_a_b_orbs()).upper()
orbsym = ''
for h in range(active_mopi.n()):
for n in range(frzcpi[h], frzcpi[h] + active_mopi[h]):
orbsym += '{:d},'.format(irrep_map[h])
if not wfn.same_a_b_orbs():
orbsym += '{:d},'.format(irrep_map[h])
header += 'ORBSYM={}\n'.format(orbsym)
header += 'ISYM={:d},\n'.format(irrep_map[wfn_irrep])
header += '&END\n'
with open(fname, 'w') as intdump:
intdump.write(header)
# Get an IntegralTransform object
check_iwl_file_from_scf_type(core.get_global_option('SCF_TYPE'), wfn)
spaces = [core.MOSpace.all()]
trans_type = core.IntegralTransform.TransformationType.Restricted
if not wfn.same_a_b_orbs():
trans_type = core.IntegralTransform.TransformationType.Unrestricted
ints = core.IntegralTransform(wfn, spaces, trans_type)
ints.transform_tei(core.MOSpace.all(), core.MOSpace.all(), core.MOSpace.all(), core.MOSpace.all())
core.print_out('Integral transformation complete!\n')
DPD_info = {'instance_id': ints.get_dpd_id(), 'alpha_MO': ints.DPD_ID('[A>=A]+'), 'beta_MO': 0}
if not wfn.same_a_b_orbs():
DPD_info['beta_MO'] = ints.DPD_ID("[a>=a]+")
# Write TEI to fname in FCIDUMP format
core.fcidump_tei_helper(nirrep, wfn.same_a_b_orbs(), DPD_info, ints_tolerance, fname)
# Read-in OEI and write them to fname in FCIDUMP format
# Indexing functions to translate from zero-based (C and Python) to
# one-based (Fortran)
mo_idx = lambda x: x + 1
alpha_mo_idx = lambda x: 2 * x + 1
beta_mo_idx = lambda x: 2 * (x + 1)
with open(fname, 'a') as intdump:
core.print_out('Writing frozen core operator in FCIDUMP format to ' + fname + '\n')
if reference == 'RHF':
PSIF_MO_FZC = 'MO-basis Frozen-Core Operator'
moH = core.Matrix(PSIF_MO_FZC, wfn.nmopi(), wfn.nmopi())
moH.load(core.IO.shared_object(), psif.PSIF_OEI)
mo_slice = core.Slice(frzcpi, active_mopi)
MO_FZC = moH.get_block(mo_slice, mo_slice)
offset = 0
for h, block in enumerate(MO_FZC.nph):
il = np.tril_indices(block.shape[0])
for index, x in np.ndenumerate(block[il]):
row = mo_idx(il[0][index] + offset)
col = mo_idx(il[1][index] + offset)
if (abs(x) > ints_tolerance):
intdump.write('{:29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(x, row, col, 0, 0))
offset += block.shape[0]
# Additional one-electron integrals as requested in oe_ints
# Orbital energies
core.print_out('Writing orbital energies in FCIDUMP format to ' + fname + '\n')
if 'EIGENVALUES' in oe_ints:
eigs_dump = write_eigenvalues(wfn.epsilon_a().get_block(mo_slice).to_array(), mo_idx)
intdump.write(eigs_dump)
else:
PSIF_MO_A_FZC = 'MO-basis Alpha Frozen-Core Oper'
moH_A = core.Matrix(PSIF_MO_A_FZC, wfn.nmopi(), wfn.nmopi())
moH_A.load(core.IO.shared_object(), psif.PSIF_OEI)
mo_slice = core.Slice(frzcpi, active_mopi)
MO_FZC_A = moH_A.get_block(mo_slice, mo_slice)
offset = 0
for h, block in enumerate(MO_FZC_A.nph):
il = np.tril_indices(block.shape[0])
for index, x in np.ndenumerate(block[il]):
row = alpha_mo_idx(il[0][index] + offset)
col = alpha_mo_idx(il[1][index] + offset)
if (abs(x) > ints_tolerance):
intdump.write('{:29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(x, row, col, 0, 0))
offset += block.shape[0]
PSIF_MO_B_FZC = 'MO-basis Beta Frozen-Core Oper'
moH_B = core.Matrix(PSIF_MO_B_FZC, wfn.nmopi(), wfn.nmopi())
moH_B.load(core.IO.shared_object(), psif.PSIF_OEI)
mo_slice = core.Slice(frzcpi, active_mopi)
MO_FZC_B = moH_B.get_block(mo_slice, mo_slice)
offset = 0
for h, block in enumerate(MO_FZC_B.nph):
il = np.tril_indices(block.shape[0])
for index, x in np.ndenumerate(block[il]):
row = beta_mo_idx(il[0][index] + offset)
col = beta_mo_idx(il[1][index] + offset)
if (abs(x) > ints_tolerance):
intdump.write('{:29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(x, row, col, 0, 0))
offset += block.shape[0]
# Additional one-electron integrals as requested in oe_ints
# Orbital energies
core.print_out('Writing orbital energies in FCIDUMP format to ' + fname + '\n')
if 'EIGENVALUES' in oe_ints:
alpha_eigs_dump = write_eigenvalues(wfn.epsilon_a().get_block(mo_slice).to_array(), alpha_mo_idx)
beta_eigs_dump = write_eigenvalues(wfn.epsilon_b().get_block(mo_slice).to_array(), beta_mo_idx)
intdump.write(alpha_eigs_dump + beta_eigs_dump)
# Dipole integrals
#core.print_out('Writing dipole moment OEI in FCIDUMP format to ' + fname + '\n')
# Traceless quadrupole integrals
#core.print_out('Writing traceless quadrupole moment OEI in FCIDUMP format to ' + fname + '\n')
# Frozen core + nuclear repulsion energy
core.print_out('Writing frozen core + nuclear repulsion energy in FCIDUMP format to ' + fname + '\n')
e_fzc = ints.get_frozen_core_energy()
e_nuc = molecule.nuclear_repulsion_energy(wfn.get_dipole_field_strength())
intdump.write('{: 29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(e_fzc + e_nuc, 0, 0, 0, 0))
core.print_out('Done generating {} with integrals in FCIDUMP format.\n'.format(fname))
def df_fdds_dispersion(primary,
auxiliary,
cache,
leg_points=10,
leg_lambda=0.3,
do_print=True):
rho_thresh = core.get_option("SAPT", "SAPT_FDDS_V2_RHO_CUTOFF")
if do_print:
core.print_out("\n ==> E20 Dispersion (CHF FDDS) <== \n\n")
core.print_out(" Legendre Points: % 10d\n" % leg_points)
core.print_out(" Lambda Shift: % 10.3f\n" % leg_lambda)
core.print_out(" Fxc Kernal: % 10s\n" % "ALDA")
core.print_out(" (P|Fxc|Q) Thresh: % 8.3e\n" % rho_thresh)
# Build object
df_matrix_keys = ["Cocc_A", "Cvir_A", "Cocc_B", "Cvir_B"]
fdds_matrix_cache = {key: cache[key] for key in df_matrix_keys}
df_vector_keys = ["eps_occ_A", "eps_vir_A", "eps_occ_B", "eps_vir_B"]
fdds_vector_cache = {key: cache[key] for key in df_vector_keys}
fdds_obj = core.FDDS_Dispersion(primary, auxiliary, fdds_matrix_cache,
fdds_vector_cache)
# Aux Densities
D = fdds_obj.project_densities([cache["D_A"], cache["D_B"]])
# Temps
half_Saux = fdds_obj.aux_overlap().clone()
half_Saux.power(-0.5, 1.e-12)
halfp_Saux = fdds_obj.aux_overlap().clone()
halfp_Saux.power(0.5, 1.e-12)
# Builds potentials
W_A = fdds_obj.metric().clone()
W_A.axpy(1.0,
_compute_fxc(D[0], half_Saux, halfp_Saux, rho_thresh=rho_thresh))
W_B = fdds_obj.metric().clone()
W_B.axpy(1.0,
_compute_fxc(D[1], half_Saux, halfp_Saux, rho_thresh=rho_thresh))
# Nuke the densities
del D
metric = fdds_obj.metric()
metric_inv = fdds_obj.metric_inv()
# Integrate
core.print_out("\n => Time Integration <= \n\n")
val_pack = ("Omega", "Weight", "Disp20,u", "Disp20", "time [s]")
core.print_out("% 12s % 12s % 14s % 14s % 10s\n" % val_pack)
# print("% 12s % 12s % 14s % 14s % 10s" % val_pack)
start_time = time.time()
total_uc = 0
total_c = 0
for point, weight in zip(*np.polynomial.legendre.leggauss(leg_points)):
omega = leg_lambda * (1.0 - point) / (1.0 + point)
lambda_scale = ((2.0 * leg_lambda) / (point + 1.0)**2)
# Monomer A
X_A = fdds_obj.form_unc_amplitude("A", omega)
# Coupled A
X_A_coupled = X_A.clone()
XSW_A = core.triplet(X_A, metric_inv, W_A, False, False, False)
amplitude_inv = metric.clone()
amplitude_inv.axpy(1.0, XSW_A)
nremoved = 0
amplitude = amplitude_inv.pseudoinverse(1.e-13, nremoved)
amplitude.transpose_this() # Why is this coming out transposed?
X_A_coupled.axpy(
-1.0, core.triplet(XSW_A, amplitude, X_A, False, False, False))
del XSW_A, amplitude
X_B = fdds_obj.form_unc_amplitude("B", omega)
# print(np.linalg.norm(X_B))
# Coupled B
X_B_coupled = X_B.clone()
XSW_B = core.triplet(X_B, metric_inv, W_B, False, False, False)
amplitude_inv = metric.clone()
amplitude_inv.axpy(1.0, XSW_B)
amplitude = amplitude_inv.pseudoinverse(1.e-13, nremoved)
amplitude.transpose_this() # Why is this coming out transposed?
X_B_coupled.axpy(
-1.0, core.triplet(XSW_B, amplitude, X_B, False, False, False))
del XSW_B, amplitude
# Make sure the results are symmetrized
for tensor in [X_A, X_B, X_A_coupled, X_B_coupled]:
tensor.add(tensor.transpose())
tensor.scale(0.5)
# Combine
tmp_uc = core.triplet(metric_inv, X_A, metric_inv, False, False, False)
value_uc = tmp_uc.vector_dot(X_B)
del tmp_uc
tmp_c = core.triplet(metric_inv, X_A_coupled, metric_inv, False, False,
False)
value_c = tmp_c.vector_dot(X_B_coupled)
del tmp_c
# Tally
total_uc += value_uc * weight * lambda_scale
total_c += value_c * weight * lambda_scale
if do_print:
tmp_disp_unc = value_uc * weight * lambda_scale
tmp_disp = value_c * weight * lambda_scale
fdds_time = time.time() - start_time
val_pack = (omega, weight, tmp_disp_unc, tmp_disp, fdds_time)
core.print_out("% 12.3e % 12.3e % 14.3e % 14.3e %10d\n" % val_pack)
# print("% 12.3e % 12.3e % 14.3e % 14.3e %10d" % val_pack)
Disp20_uc = -1.0 / (2.0 * np.pi) * total_uc
Disp20_c = -1.0 / (2.0 * np.pi) * total_c
core.print_out("\n")
core.print_out(print_sapt_var("Disp20,u", Disp20_uc, short=True) + "\n")
core.print_out(print_sapt_var("Disp20", Disp20_c, short=True) + "\n")
return {"Disp20,FDDS (unc)": Disp20_uc, "Disp20": Disp20_c}
def ip_fitting(name, omega_l=0.05, omega_r=2.5, omega_convergence=1.0e-3, maxiter=20, **kwargs):
"""Optimize DFT omega parameter for molecular system.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
omega_l : float, optional
Minimum omega to be considered during fitting.
omega_r : float, optional
Maximum omega to be considered during fitting.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
omega_convergence : float, optional
Threshold below which to consider omega converged. (formerly omega_tolerance)
maxiter : int, optional
Maximum number of iterations towards omega convergence.
Returns
-------
float
Optimal omega parameter.
"""
optstash = p4util.OptionsState(
['SCF', 'REFERENCE'],
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
['SCF', 'DFT_OMEGA'],
['DOCC'],
['SOCC'])
kwargs = p4util.kwargs_lower(kwargs)
# By default, do not read previous 180 orbitals file
read = False
read180 = ''
if 'read' in kwargs:
read = True
read180 = kwargs['read']
if core.get_option('SCF', 'REFERENCE') != 'UKS':
core.print_out(""" Requested procedure `ip_fitting` runs further calculations with UKS reference.\n""")
core.set_local_option('SCF', 'REFERENCE', 'UKS')
# 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("""IP Fitting requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(""" Requested procedure `ip_fitting` 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()
# How many electrons are there?
N = 0
for A in range(molecule.natom()):
N += molecule.Z(A)
N -= charge0
N = int(N)
Nb = int((N - mult0 + 1) / 2)
Na = int(N - Nb)
# Work in the ot namespace for this procedure
core.IO.set_default_namespace("ot")
# Burn in to determine orbital eigenvalues
if read:
core.set_local_option("SCF", "GUESS", "READ")
copy_file_to_scratch(read180, 'psi', 'ot', 180)
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Burn-in', **kwargs)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
if not wfn.functional().is_x_lrc():
raise ValidationError("""Not sensible to optimize omega for non-long-range-correction functional.""")
# Determine H**O, to determine mult1
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
Na1 = Na
Nb1 = Nb
if H**O > 0:
Na1 -= 1
else:
Nb1 -= 1
charge1 = charge0 + 1
mult1 = Na1 - Nb1 + 1
omegas = []
E0s = []
E1s = []
kIPs = []
IPs = []
types = []
# Right endpoint
core.set_local_option('SCF', 'DFT_OMEGA', omega_r)
# Neutral
if read:
core.set_local_option("SCF", "GUESS", "READ")
p4util.copy_file_to_scratch(read180, 'psi', 'ot', 180)
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
E0r, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Neutral, Right Endpoint', **kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
E_HOMOr = E_HOMO
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
if read:
core.set_local_option("SCF", "GUESS", "READ")
p4util.copy_file_to_scratch(read180, 'psi', 'ot', 180)
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
E1r = driver.energy('scf', dft_functional=name, molecule=molecule,
banner='IP Fitting SCF: Cation, Right Endpoint', **kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IPr = E1r - E0r
kIPr = -E_HOMOr
delta_r = IPr - kIPr
if IPr > kIPr:
raise ValidationError("""\n***IP Fitting Error: Right Omega limit should have kIP > IP: {} !> {}""".format(kIPr, IPr))
omegas.append(omega_r)
types.append('Right Limit')
E0s.append(E0r)
E1s.append(E1r)
IPs.append(IPr)
kIPs.append(kIPr)
# Use previous orbitals from here out
core.set_local_option("SCF", "GUESS", "READ")
# Left endpoint
core.set_local_option('SCF', 'DFT_OMEGA', omega_l)
# Neutral
core.IO.change_file_namespace(180, "neutral", "ot")
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
core.set_global_option("DOCC", [Nb])
core.set_global_option("SOCC", [Na - Nb])
E0l, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Neutral, Left Endpoint', **kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
E_HOMOl = E_HOMO
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
core.IO.change_file_namespace(180, "cation", "ot")
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
core.set_global_option("DOCC", [Nb1])
core.set_global_option("SOCC", [Na1 - Nb1])
E1l = driver.energy('scf', dft_functional=name, molecule=molecule,
banner='IP Fitting SCF: Cation, Left Endpoint', **kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IPl = E1l - E0l
kIPl = -E_HOMOl
delta_l = IPl - kIPl
if IPl < kIPl:
raise ValidationError("""\n***IP Fitting Error: Left Omega limit should have kIP < IP: {} !< {}""".format(kIPl, IPl))
omegas.append(omega_l)
types.append('Left Limit')
E0s.append(E0l)
E1s.append(E1l)
IPs.append(IPl)
kIPs.append(kIPl)
converged = False
repeat_l = 0
repeat_r = 0
for step in range(maxiter):
# Regula Falsi (modified)
if repeat_l > 1:
delta_l /= 2.0
if repeat_r > 1:
delta_r /= 2.0
omega = - (omega_r - omega_l) / (delta_r - delta_l) * delta_l + omega_l
core.set_local_option('SCF', 'DFT_OMEGA', omega)
# Neutral
core.IO.change_file_namespace(180, "neutral", "ot")
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
core.set_global_option("DOCC", [Nb])
core.set_global_option("SOCC", [Na - Nb])
E0, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Neutral, Omega = {:11.3E}'.format(omega), **kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
core.IO.change_file_namespace(180, "cation", "ot")
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
core.set_global_option("DOCC", [Nb1])
core.set_global_option("SOCC", [Na1 - Nb1])
E1 = driver.energy('scf', dft_functional=name, molecule=molecule,
banner='IP Fitting SCF: Cation, Omega = {:11.3E}'.format(omega), **kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IP = E1 - E0
kIP = -E_HOMO
delta = IP - kIP
if kIP > IP:
omega_r = omega
E0r = E0
E1r = E1
IPr = IP
kIPr = kIP
delta_r = delta
repeat_r = 0
repeat_l += 1
else:
omega_l = omega
E0l = E0
E1l = E1
IPl = IP
kIPl = kIP
delta_l = delta
repeat_l = 0
repeat_r += 1
omegas.append(omega)
types.append('Regula-Falsi')
E0s.append(E0)
E1s.append(E1)
IPs.append(IP)
kIPs.append(kIP)
# Termination
if abs(omega_l - omega_r) < omega_convergence:
converged = True
break
core.IO.set_default_namespace("")
core.print_out("""\n ==> IP Fitting Results <==\n\n""")
core.print_out(""" => Occupation Determination <= \n\n""")
core.print_out(""" %6s %6s %6s %6s %6s %6s\n""" % ('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
core.print_out(""" Neutral: %6d %6d %6d %6d %6d %6d\n""" % (N, Na, Nb, charge0, mult0, H**O))
core.print_out(""" Cation: %6d %6d %6d %6d %6d\n\n""" % (N - 1, Na1, Nb1, charge1, mult1))
core.print_out(""" => Regula Falsi Iterations <=\n\n""")
core.print_out(""" %3s %11s %14s %14s %14s %s\n""" % ('N','Omega','IP','kIP','Delta','Type'))
for k in range(len(omegas)):
core.print_out(""" %3d %11.3E %14.6E %14.6E %14.6E %s\n""" %
(k + 1, omegas[k], IPs[k], kIPs[k], IPs[k] - kIPs[k], types[k]))
optstash.restore()
if converged:
core.print_out("""\n IP Fitting Converged\n""")
core.print_out(""" Final omega = %14.6E\n""" % ((omega_l + omega_r) / 2))
core.print_out("""\n "M,I. does the dying. Fleet just does the flying."\n""")
core.print_out(""" -Starship Troopers\n""")
else:
raise ConvergenceError("""IP Fitting """, step + 1)
return ((omega_l + omega_r) / 2)
def _initialize_findif(mol,
freq_irrep_only,
mode,
initialize_string,
verbose=0):
"""Perform initialization tasks needed by all primary functions.
Parameters
----------
mol : qcdb.molecule or :py:class:`~psi4.core.Molecule`
The molecule to displace
freq_irrep_only : int
The Cotton ordered irrep to get frequencies for. Choose -1 for all
irreps.
mode : {"1_0", "2_0", "2_1"}
The first number specifies the derivative level determined from
displacements, and the second number is the level determined at.
initialize_string : function
A function that returns the string to print to show the caller was entered.
The string is both caller-specific and dependent on values determined
in this function.
verbose : int
Set to 0 to silence extra print information, regardless of the print level.
Used so the information is printed only during geometry generation, and not
during the derivative computation as well.
Returns
-------
data : dict
Miscellaneous information required by callers.
"""
core.print_out(
"\n ----------------------------------------------------------\n"
)
core.print_out(" FINDIF\n")
core.print_out(" R. A. King and Jonathon Misiewicz\n")
core.print_out(
" ---------------------------------------------------------\n\n"
)
print_lvl = core.get_option("FINDIF", "PRINT")
num_pts = core.get_option("FINDIF", "POINTS")
disp_size = core.get_option("FINDIF", "DISP_SIZE")
data = {"print_lvl": print_lvl, "num_pts": num_pts, "disp_size": disp_size}
if print_lvl:
core.print_out(initialize_string(data))
# Get settings for CdSalcList, then get the CdSalcList.
method_allowed_irreps = 0x1 if mode == "1_0" else 0xFF
t_project = not core.get_global_option("EXTERN") and (
not core.get_global_option("PERTURB_H"))
# core.get_option returns an int, but CdSalcList expect a bool, so re-cast
r_project = t_project and bool(core.get_option("FINDIF", "FD_PROJECT"))
salc_list = core.CdSalcList(mol, method_allowed_irreps, t_project,
r_project)
n_atom = mol.natom()
n_irrep = salc_list.nirrep()
n_salc = salc_list.ncd()
if print_lvl and verbose:
core.print_out(" Number of atoms is {:d}.\n".format(n_atom))
if method_allowed_irreps != 0x1:
core.print_out(" Number of irreps is {:d}.\n".format(n_irrep))
core.print_out(" Number of {!s}SALCs is {:d}.\n".format(
"" if method_allowed_irreps != 0x1 else "symmetric ", n_salc))
core.print_out(
" Translations projected? {:d}. Rotations projected? {:d}.\n".
format(t_project, r_project))
# TODO: Replace with a generator from a stencil to a set of points.
# Diagonal displacements differ between the totally symmetric irrep, compared to all others.
# Off-diagonal displacements are the same for both.
pts_dict = {
3: {
"sym_irr": ((-1, ), (1, )),
"asym_irr": ((-1, ), ),
"off": ((1, 1), (-1, -1))
},
5: {
"sym_irr": ((-2, ), (-1, ), (1, ), (2, )),
"asym_irr": ((-2, ), (-1, )),
"off": ((-1, -2), (-2, -1), (-1, -1), (1, -1), (-1, 1), (1, 1),
(2, 1), (1, 2))
}
}
if num_pts not in pts_dict:
raise ValidationError("FINDIF: Invalid number of points!")
# Convention: x_pi means x_per_irrep. The ith element is x for irrep i, with Cotton ordering.
salc_indices_pi = [[] for h in range(n_irrep)]
# Validate that we have an irrep matching the user-specified irrep, if any.
try:
salc_indices_pi[freq_irrep_only]
except (TypeError, IndexError):
if freq_irrep_only != -1:
raise ValidationError("FINDIF: Irrep value not in valid range.")
# Populate salc_indices_pi for all irreps.
for i, salc in enumerate(salc_list):
salc_indices_pi[salc.irrep_index()].append(i)
# If the method allows more than one irrep, print how the irreps partition the SALCS.
if print_lvl and method_allowed_irreps != 0x1 and verbose:
core.print_out(" Index of SALCs per irrep:\n")
for h in range(n_irrep):
if print_lvl > 1 or freq_irrep_only in {h, -1}:
tmp = (" {:d} " *
len(salc_indices_pi[h])).format(*salc_indices_pi[h])
core.print_out(" {:d} : ".format(h + 1) + tmp + "\n")
core.print_out(" Number of SALCs per irrep:\n")
for h in range(n_irrep):
if print_lvl > 1 or freq_irrep_only in {h, -1}:
core.print_out(" Irrep {:d}: {:d}\n".format(
h + 1, len(salc_indices_pi[h])))
# Now that we've printed the SALCs, clear any that are not of user-specified symmetry.
if freq_irrep_only != -1:
for h in range(n_irrep):
if h != freq_irrep_only:
salc_indices_pi[h].clear()
n_disp_pi = []
disps = pts_dict[num_pts] # We previously validated num_pts in pts_dict.
for irrep, indices in enumerate(salc_indices_pi):
n_disp = len(indices) * len(
disps["asym_irr" if irrep != 0 else "sym_irr"])
if mode == "2_0":
# Either len(indices) or len(indices)-1 is even, so dividing by two is safe.
n_disp += len(indices) * (len(indices) - 1) // 2 * len(
disps["off"])
n_disp_pi.append(n_disp)
# Let's print out the number of geometries, the displacement multiplicity, and the CdSALCs!
if print_lvl and verbose:
core.print_out(
" Number of geometries (including reference) is {:d}.\n".format(
sum(n_disp_pi) + 1))
if method_allowed_irreps != 0x1:
core.print_out(" Number of displacements per irrep:\n")
for i, ndisp in enumerate(n_disp_pi, start=1):
core.print_out(" Irrep {:d}: {:d}\n".format(i, ndisp))
if print_lvl > 1 and verbose:
for salc in salc_list:
salc.print_out()
data.update({
"n_disp_pi": n_disp_pi,
"n_irrep": n_irrep,
"n_salc": n_salc,
"n_atom": n_atom,
"salc_list": salc_list,
"salc_indices_pi": salc_indices_pi,
"disps": disps,
"project_translations": t_project,
"project_rotations": r_project
})
return data
def run_gaussian_2(name, **kwargs):
# throw an exception for open-shells
if (core.get_option('SCF', 'REFERENCE') != 'RHF'):
raise ValidationError("""g2 computations require "reference rhf".""")
# stash user options:
optstash = p4util.OptionsState(['FNOCC', 'COMPUTE_TRIPLES'],
['FNOCC', 'COMPUTE_MP4_TRIPLES'],
['FREEZE_CORE'], ['MP2_TYPE'],
['SCF', 'SCF_TYPE'])
# override default scf_type
core.set_local_option('SCF', 'SCF_TYPE', 'PK')
# optimize geometry at scf level
core.clean()
core.set_global_option('BASIS', "6-31G(D)")
driver.optimize('scf')
core.clean()
# scf frequencies for zpe
# NOTE This line should not be needed, but without it there's a seg fault
scf_e, ref = driver.frequency('scf', return_wfn=True)
# thermodynamic properties
du = core.get_variable('INTERNAL ENERGY CORRECTION')
dh = core.get_variable('ENTHALPY CORRECTION')
dg = core.get_variable('GIBBS FREE ENERGY CORRECTION')
freqs = ref.frequencies()
nfreq = freqs.dim(0)
freqsum = 0.0
for i in range(0, nfreq):
freqsum += freqs.get(i)
zpe = freqsum / p4const.psi_hartree2wavenumbers * 0.8929 * 0.5
core.clean()
# optimize geometry at mp2 (no frozen core) level
# note: freeze_core isn't an option in MP2
core.set_global_option('FREEZE_CORE', "FALSE")
core.set_global_option('MP2_TYPE', 'CONV')
driver.optimize('mp2')
core.clean()
# qcisd(t)
core.set_local_option('FNOCC', 'COMPUTE_MP4_TRIPLES', "TRUE")
core.set_global_option('FREEZE_CORE', "TRUE")
core.set_global_option('BASIS', "6-311G(D_P)")
ref = driver.proc.run_fnocc('qcisd(t)', return_wfn=True, **kwargs)
# HLC: high-level correction based on number of valence electrons
nirrep = ref.nirrep()
frzcpi = ref.frzcpi()
nfzc = 0
for i in range(0, nirrep):
nfzc += frzcpi[i]
nalpha = ref.nalpha() - nfzc
nbeta = ref.nbeta() - nfzc
# hlc of gaussian-2
hlc = -0.00481 * nalpha - 0.00019 * nbeta
# hlc of gaussian-1
hlc1 = -0.00614 * nalpha
eqci_6311gdp = core.get_variable("QCISD(T) TOTAL ENERGY")
emp4_6311gd = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311gd = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# correction for diffuse functions
core.set_global_option('BASIS', "6-311+G(D_P)")
driver.energy('mp4')
emp4_6311pg_dp = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311pg_dp = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# correction for polarization functions
core.set_global_option('BASIS', "6-311G(2DF_P)")
driver.energy('mp4')
emp4_6311g2dfp = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311g2dfp = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# big basis mp2
core.set_global_option('BASIS', "6-311+G(3DF_2P)")
#run_fnocc('_mp2',**kwargs)
driver.energy('mp2')
emp2_big = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
eqci = eqci_6311gdp
e_delta_g2 = emp2_big + emp2_6311gd - emp2_6311g2dfp - emp2_6311pg_dp
e_plus = emp4_6311pg_dp - emp4_6311gd
e_2df = emp4_6311g2dfp - emp4_6311gd
eg2 = eqci + e_delta_g2 + e_plus + e_2df
eg2_mp2_0k = eqci + (emp2_big - emp2_6311gd) + hlc + zpe
core.print_out('\n')
core.print_out(' ==> G1/G2 Energy Components <==\n')
core.print_out('\n')
core.print_out(' QCISD(T): %20.12lf\n' % eqci)
core.print_out(' E(Delta): %20.12lf\n' % e_delta_g2)
core.print_out(' E(2DF): %20.12lf\n' % e_2df)
core.print_out(' E(+): %20.12lf\n' % e_plus)
core.print_out(' E(G1 HLC): %20.12lf\n' % hlc1)
core.print_out(' E(G2 HLC): %20.12lf\n' % hlc)
core.print_out(' E(ZPE): %20.12lf\n' % zpe)
core.print_out('\n')
core.print_out(' ==> 0 Kelvin Results <==\n')
core.print_out('\n')
eg2_0k = eg2 + zpe + hlc
core.print_out(' G1: %20.12lf\n' %
(eqci + e_plus + e_2df + hlc1 + zpe))
core.print_out(' G2(MP2): %20.12lf\n' % eg2_mp2_0k)
core.print_out(' G2: %20.12lf\n' % eg2_0k)
core.set_variable("G1 TOTAL ENERGY", eqci + e_plus + e_2df + hlc1 + zpe)
core.set_variable("G2 TOTAL ENERGY", eg2_0k)
core.set_variable("G2(MP2) TOTAL ENERGY", eg2_mp2_0k)
core.print_out('\n')
T = core.get_global_option('T')
core.print_out(' ==> %3.0lf Kelvin Results <==\n' % T)
core.print_out('\n')
internal_energy = eg2_mp2_0k + du - zpe / 0.8929
enthalpy = eg2_mp2_0k + dh - zpe / 0.8929
gibbs = eg2_mp2_0k + dg - zpe / 0.8929
core.print_out(' G2(MP2) energy: %20.12lf\n' % internal_energy)
core.print_out(' G2(MP2) enthalpy: %20.12lf\n' % enthalpy)
core.print_out(' G2(MP2) free energy: %20.12lf\n' % gibbs)
core.print_out('\n')
core.set_variable("G2(MP2) INTERNAL ENERGY", internal_energy)
core.set_variable("G2(MP2) ENTHALPY", enthalpy)
core.set_variable("G2(MP2) FREE ENERGY", gibbs)
internal_energy = eg2_0k + du - zpe / 0.8929
enthalpy = eg2_0k + dh - zpe / 0.8929
gibbs = eg2_0k + dg - zpe / 0.8929
core.print_out(' G2 energy: %20.12lf\n' % internal_energy)
core.print_out(' G2 enthalpy: %20.12lf\n' % enthalpy)
core.print_out(' G2 free energy: %20.12lf\n' % gibbs)
core.set_variable("CURRENT ENERGY", eg2_0k)
core.set_variable("G2 INTERNAL ENERGY", internal_energy)
core.set_variable("G2 ENTHALPY", enthalpy)
core.set_variable("G2 FREE ENERGY", gibbs)
core.clean()
optstash.restore()
# return 0K g2 results
return eg2_0k
def scf_iterate(self, e_conv=None, d_conv=None):
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
verbose = core.get_option('SCF', "PRINT")
reference = core.get_option('SCF', "REFERENCE")
# self.member_data_ signals are non-local, used internally by c-side fns
self.diis_enabled_ = _validate_diis()
self.MOM_excited_ = _validate_MOM()
self.diis_start_ = core.get_option('SCF', 'DIIS_START')
damping_enabled = _validate_damping()
soscf_enabled = _validate_soscf()
frac_enabled = _validate_frac()
efp_enabled = hasattr(self.molecule(), 'EFP')
diis_rms = core.get_option('SCF', 'DIIS_RMS_ERROR')
if self.iteration_ < 2:
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"))
# SCF iterations!
SCFE_old = 0.0
Dnorm = 0.0
while True:
self.iteration_ += 1
diis_performed = False
soscf_performed = False
self.frac_performed_ = False
#self.MOM_performed_ = False # redundant from common_init()
self.save_density_and_energy()
if efp_enabled:
# EFP: Add efp contribution to Fock matrix
self.H().copy(self.Horig)
global mints_psi4_yo
mints_psi4_yo = core.MintsHelper(self.basisset())
Vefp = modify_Fock_induced(self.molecule().EFP,
mints_psi4_yo,
verbose=verbose - 1)
Vefp = core.Matrix.from_array(Vefp)
self.H().add(Vefp)
SCFE = 0.0
self.clear_external_potentials()
core.timer_on("HF: Form G")
self.form_G()
core.timer_off("HF: Form G")
upcm = 0.0
if core.get_option('SCF', 'PCM'):
calc_type = core.PCM.CalcType.Total
if core.get_option("PCM", "PCM_SCF_TYPE") == "SEPARATE":
calc_type = core.PCM.CalcType.NucAndEle
Dt = self.Da().clone()
Dt.add(self.Db())
upcm, Vpcm = self.get_PCM().compute_PCM_terms(Dt, calc_type)
SCFE += upcm
self.push_back_external_potential(Vpcm)
self.set_variable("PCM POLARIZATION ENERGY", upcm)
self.set_energies("PCM Polarization", upcm)
core.timer_on("HF: Form F")
# SAD: since we don't have orbitals yet, we might not be able
# to form the real Fock matrix. Instead, build an initial one
if (self.iteration_ == 0) and self.sad_:
self.form_initial_F()
else:
self.form_F()
core.timer_off("HF: Form F")
if verbose > 3:
self.Fa().print_out()
self.Fb().print_out()
SCFE += self.compute_E()
if efp_enabled:
global efp_Dt_psi4_yo
# EFP: Add efp contribution to energy
efp_Dt_psi4_yo = self.Da().clone()
efp_Dt_psi4_yo.add(self.Db())
SCFE += self.molecule().EFP.get_wavefunction_dependent_energy()
self.set_energies("Total Energy", SCFE)
core.set_variable("SCF ITERATION ENERGY", SCFE)
Ediff = SCFE - SCFE_old
SCFE_old = SCFE
status = []
# Check if we are doing SOSCF
if (soscf_enabled and (self.iteration_ >= 3) and
(Dnorm < core.get_option('SCF', 'SOSCF_START_CONVERGENCE'))):
Dnorm = self.compute_orbital_gradient(
False, core.get_option('SCF', 'DIIS_MAX_VECS'))
diis_performed = False
if self.functional().needs_xc():
base_name = "SOKS, nmicro="
else:
base_name = "SOSCF, nmicro="
if not _converged(Ediff, Dnorm, e_conv=e_conv, d_conv=d_conv):
nmicro = self.soscf_update(
core.get_option('SCF', 'SOSCF_CONV'),
core.get_option('SCF', 'SOSCF_MIN_ITER'),
core.get_option('SCF', 'SOSCF_MAX_ITER'),
core.get_option('SCF', 'SOSCF_PRINT'))
# if zero, the soscf call bounced for some reason
soscf_performed = (nmicro > 0)
if soscf_performed:
self.find_occupation()
status.append(base_name + str(nmicro))
else:
if verbose > 0:
core.print_out(
"Did not take a SOSCF step, using normal convergence methods\n"
)
else:
# need to ensure orthogonal orbitals and set epsilon
status.append(base_name + "conv")
core.timer_on("HF: Form C")
self.form_C()
core.timer_off("HF: Form C")
soscf_performed = True # Stops DIIS
if not soscf_performed:
# Normal convergence procedures if we do not do SOSCF
# SAD: form initial orbitals from the initial Fock matrix, and
# reset the occupations. From here on, the density matrices
# are correct.
if (self.iteration_ == 0) and self.sad_:
self.form_initial_C()
self.reset_occupation()
self.find_occupation()
else:
# Run DIIS
core.timer_on("HF: DIIS")
diis_performed = False
add_to_diis_subspace = self.diis_enabled_ and self.iteration_ >= self.diis_start_
Dnorm = self.compute_orbital_gradient(
add_to_diis_subspace,
core.get_option('SCF', 'DIIS_MAX_VECS'))
if (add_to_diis_subspace
and core.get_option('SCF', 'DIIS_MIN_VECS') - 1):
diis_performed = self.diis()
if diis_performed:
status.append("DIIS")
core.timer_off("HF: DIIS")
if verbose > 4 and diis_performed:
core.print_out(" After DIIS:\n")
self.Fa().print_out()
self.Fb().print_out()
# frac, MOM invoked here from Wfn::HF::find_occupation
core.timer_on("HF: Form C")
self.form_C()
core.timer_off("HF: Form C")
if self.MOM_performed_:
status.append("MOM")
if self.frac_performed_:
status.append("FRAC")
# Reset occupations if necessary
if (self.iteration_ == 0) and self.reset_occ_:
self.reset_occupation()
self.find_occupation()
# Form new density matrix
core.timer_on("HF: Form D")
self.form_D()
core.timer_off("HF: Form D")
self.set_variable("SCF ITERATION ENERGY", SCFE)
# After we've built the new D, damp the update
if (damping_enabled and self.iteration_ > 1
and Dnorm > core.get_option('SCF', 'DAMPING_CONVERGENCE')):
damping_percentage = core.get_option('SCF', "DAMPING_PERCENTAGE")
self.damping_update(damping_percentage * 0.01)
status.append("DAMP={}%".format(round(damping_percentage)))
if verbose > 3:
self.Ca().print_out()
self.Cb().print_out()
self.Da().print_out()
self.Db().print_out()
# Print out the iteration
core.print_out(
" @%s%s iter %3s: %20.14f %12.5e %-11.5e %s\n" %
("DF-" if is_dfjk else "", reference, "SAD" if
((self.iteration_ == 0) and self.sad_) else self.iteration_, SCFE,
Ediff, Dnorm, '/'.join(status)))
# if a an excited MOM is requested but not started, don't stop yet
if self.MOM_excited_ and not self.MOM_performed_:
continue
# if a fractional occupation is requested but not started, don't stop yet
if frac_enabled and not self.frac_performed_:
continue
# Call any postiteration callbacks
if not ((self.iteration_ == 0) and self.sad_) and _converged(
Ediff, Dnorm, e_conv=e_conv, d_conv=d_conv):
break
if self.iteration_ >= core.get_option('SCF', 'MAXITER'):
raise SCFConvergenceError("""SCF iterations""", self.iteration_,
self, Ediff, Dnorm)
def _set_convergence_criterion(ptype,
method_name,
scf_Ec,
pscf_Ec,
scf_Dc,
pscf_Dc,
gen_Ec,
verbose=1):
r"""
This function will set local SCF and global energy convergence criterion
to the defaults listed at:
http://www.psicode.org/psi4manual/master/scf.html#convergence-and-
algorithm-defaults. SCF will be converged more tightly if a post-SCF
method is select (pscf_Ec, and pscf_Dc) else the looser (scf_Ec, and
scf_Dc convergence criterion will be used).
ptype - Procedure type (energy, gradient, etc). Nearly always test on
procedures['energy'] since that's guaranteed to exist for a method.
method_name - Name of the method
scf_Ec - E convergence criterion for scf target method
pscf_Ec - E convergence criterion for scf of post-scf target method
scf_Dc - D convergence criterion for scf target method
pscf_Dc - D convergence criterion for scf of post-scf target method
gen_Ec - E convergence criterion for post-scf target method
"""
optstash = p4util.OptionsState(['SCF', 'E_CONVERGENCE'],
['SCF', 'D_CONVERGENCE'], ['E_CONVERGENCE'])
# Kind of want to move this out of here
_method_exists(ptype, method_name)
if verbose >= 2:
print(' Setting convergence', end=' ')
# Set method-dependent scf convergence criteria, check against energy routines
if not core.has_option_changed('SCF', 'E_CONVERGENCE'):
if procedures['energy'][method_name] in [proc.run_scf, proc.run_dft]:
core.set_local_option('SCF', 'E_CONVERGENCE', scf_Ec)
if verbose >= 2:
print(scf_Ec, end=' ')
else:
core.set_local_option('SCF', 'E_CONVERGENCE', pscf_Ec)
if verbose >= 2:
print(pscf_Ec, end=' ')
else:
if verbose >= 2:
print('CUSTOM', core.get_option('SCF', 'E_CONVERGENCE'), end=' ')
if not core.has_option_changed('SCF', 'D_CONVERGENCE'):
if procedures['energy'][method_name] in [proc.run_scf, proc.run_dft]:
core.set_local_option('SCF', 'D_CONVERGENCE', scf_Dc)
if verbose >= 2:
print(scf_Dc, end=' ')
else:
core.set_local_option('SCF', 'D_CONVERGENCE', pscf_Dc)
if verbose >= 2:
print(pscf_Dc, end=' ')
else:
if verbose >= 2:
print('CUSTOM', core.get_option('SCF', 'D_CONVERGENCE'), end=' ')
# Set post-scf convergence criteria (global will cover all correlated modules)
if not core.has_global_option_changed('E_CONVERGENCE'):
if procedures['energy'][method_name] not in [
proc.run_scf, proc.run_dft
]:
core.set_global_option('E_CONVERGENCE', gen_Ec)
if verbose >= 2:
print(gen_Ec, end=' ')
else:
if procedures['energy'][method_name] not in [
proc.run_scf, proc.run_dft
]:
if verbose >= 2:
print('CUSTOM',
core.get_global_option('E_CONVERGENCE'),
end=' ')
if verbose >= 2:
print('')
return optstash
def scf_finalize_energy(self):
"""Performs stability analysis and calls back SCF with new guess
if needed, Returns the SCF energy. This function should be called
once orbitals are ready for energy/property computations, usually
after iterations() is called.
"""
# post-scf vv10 correlation
if core.get_option(
'SCF', "DFT_VV10_POSTSCF") and self.functional().vv10_b() > 0.0:
self.functional().set_lock(False)
self.functional().set_do_vv10(True)
self.functional().set_lock(True)
core.print_out(
" ==> Computing Non-Self-Consistent VV10 Energy Correction <==\n\n"
)
SCFE = 0.0
self.form_V()
SCFE += self.compute_E()
self.set_energies("Total Energy", SCFE)
# Perform wavefunction stability analysis before doing
# anything on a wavefunction that may not be truly converged.
if core.get_option('SCF', 'STABILITY_ANALYSIS') != "NONE":
# Don't bother computing needed integrals if we can't do anything with them.
if self.functional().needs_xc():
raise ValidationError(
"Stability analysis not yet supported for XC functionals.")
# We need the integral file, make sure it is written and
# compute it if needed
if core.get_option('SCF', 'REFERENCE') != "UHF":
psio = core.IO.shared_object()
#psio.open(constants.PSIF_SO_TEI, 1) # PSIO_OPEN_OLD
#try:
# psio.tocscan(constants.PSIF_SO_TEI, "IWL Buffers")
#except TypeError:
# # "IWL Buffers" actually found but psio_tocentry can't be returned to Py
# psio.close(constants.PSIF_SO_TEI, 1)
#else:
# # tocscan returned None
# psio.close(constants.PSIF_SO_TEI, 1)
# logic above foiled by psio_tocentry not returning None<--nullptr in pb11 2.2.1
# so forcibly recomputing for now until stability revamp
core.print_out(" SO Integrals not on disk. Computing...")
mints = core.MintsHelper(self.basisset())
#next 2 lines fix a bug that prohibits relativistic stability analysis
if core.get_global_option('RELATIVISTIC') in ['X2C', 'DKH']:
mints.set_rel_basisset(self.get_basisset('BASIS_RELATIVISTIC'))
mints.integrals()
core.print_out("done.\n")
# Q: Not worth exporting all the layers of psio, right?
follow = self.stability_analysis()
while follow and self.attempt_number_ <= core.get_option(
'SCF', 'MAX_ATTEMPTS'):
self.attempt_number_ += 1
core.print_out(
" Running SCF again with the rotated orbitals.\n")
if self.initialized_diis_manager_:
self.diis_manager().reset_subspace()
# reading the rotated orbitals in before starting iterations
self.form_D()
self.set_energies("Total Energy", self.compute_initial_E())
self.iterations()
follow = self.stability_analysis()
if follow and self.attempt_number_ > core.get_option(
'SCF', 'MAX_ATTEMPTS'):
core.print_out(
" There's still a negative eigenvalue. Try modifying FOLLOW_STEP_SCALE\n"
)
core.print_out(
" or increasing MAX_ATTEMPTS (not available for PK integrals).\n"
)
# At this point, we are not doing any more SCF cycles
# and we can compute and print final quantities.
if hasattr(self.molecule(), 'EFP'):
efpobj = self.molecule().EFP
efpobj.compute() # do_gradient=do_gradient)
efpene = efpobj.get_energy(label='psi')
efp_wfn_independent_energy = efpene['total'] - efpene['ind']
self.set_energies("EFP", efpene['total'])
SCFE = self.get_energies("Total Energy")
SCFE += efp_wfn_independent_energy
self.set_energies("Total Energy", SCFE)
core.print_out(efpobj.energy_summary(scfefp=SCFE, label='psi'))
self.set_variable(
'EFP ELST ENERGY',
efpene['electrostatic'] + efpene['charge_penetration'] +
efpene['electrostatic_point_charges'])
self.set_variable('EFP IND ENERGY', efpene['polarization'])
self.set_variable('EFP DISP ENERGY', efpene['dispersion'])
self.set_variable('EFP EXCH ENERGY', efpene['exchange_repulsion'])
self.set_variable('EFP TOTAL ENERGY', efpene['total'])
self.set_variable('CURRENT ENERGY', efpene['total'])
core.print_out("\n ==> Post-Iterations <==\n\n")
self.check_phases()
self.compute_spin_contamination()
self.frac_renormalize()
reference = core.get_option("SCF", "REFERENCE")
energy = self.get_energies("Total Energy")
# fail_on_maxiter = core.get_option("SCF", "FAIL_ON_MAXITER")
# if converged or not fail_on_maxiter:
#
# if print_lvl > 0:
# self.print_orbitals()
#
# if converged:
# core.print_out(" Energy converged.\n\n")
# else:
# core.print_out(" Energy did not converge, but proceeding anyway.\n\n")
if core.get_option('SCF', 'PRINT') > 0:
self.print_orbitals()
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
core.print_out(" @%s%s Final Energy: %20.14f" %
('DF-' if is_dfjk else '', reference, energy))
# if (perturb_h_) {
# core.print_out(" with %f %f %f perturbation" %
# (dipole_field_strength_[0], dipole_field_strength_[1], dipole_field_strength_[2]))
# }
core.print_out("\n\n")
self.print_energies()
self.clear_external_potentials()
if core.get_option('SCF', 'PCM'):
calc_type = core.PCM.CalcType.Total
if core.get_option("PCM", "PCM_SCF_TYPE") == "SEPARATE":
calc_type = core.PCM.CalcType.NucAndEle
Dt = self.Da().clone()
Dt.add(self.Db())
_, Vpcm = self.get_PCM().compute_PCM_terms(Dt, calc_type)
self.push_back_external_potential(Vpcm)
# Properties
# Comments so that autodoc utility will find these PSI variables
# Process::environment.globals["SCF DIPOLE X"] =
# Process::environment.globals["SCF DIPOLE Y"] =
# Process::environment.globals["SCF DIPOLE Z"] =
# Process::environment.globals["SCF QUADRUPOLE XX"] =
# Process::environment.globals["SCF QUADRUPOLE XY"] =
# Process::environment.globals["SCF QUADRUPOLE XZ"] =
# Process::environment.globals["SCF QUADRUPOLE YY"] =
# Process::environment.globals["SCF QUADRUPOLE YZ"] =
# Process::environment.globals["SCF QUADRUPOLE ZZ"] =
# Orbitals are always saved, in case an MO guess is requested later
# save_orbitals()
# Shove variables into global space
for k, v in self.variables().items():
core.set_variable(k, v)
# TODO re-enable
self.finalize()
if self.V_potential():
self.V_potential().clear_collocation_cache()
core.print_out("\nComputation Completed\n")
return energy
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 run_dftd3(mol, func=None, dashlvl=None, dashparam=None, dertype=None, verbose=False):
"""Compute dispersion correction using Grimme's DFTD3 executable.
Function to call Grimme's dftd3 program to compute the -D correction
of level `dashlvl` using parameters for the functional `func`.
`dashparam` can supply a full set of dispersion parameters in the
absence of `func` or individual overrides in the presence of `func`.
The DFTD3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
Parameters
----------
mol : qcdb.Molecule or psi4.core.Molecule or str
Molecule on which to run dispersion calculation. Both qcdb and
psi4.core Molecule classes have been extended by this method, so
either allowed. Alternately, a string that can be instantiated
into a qcdb.Molecule.
func : str or None
Density functional (Psi4, not Turbomole, names) for which to
load parameters from dashcoeff[dashlvl][func]. This is not
passed to DFTD3 and thus may be a dummy or `None`. Any or all
parameters initialized can be overwritten via `dashparam`.
dashlvl : {'d2p4', 'd2gr', 'd3zero', 'd3bj', 'd3mzero', d3mbj', 'd', 'd2', 'd3', 'd3m'}
Flavor of a posteriori dispersion correction for which to load
parameters and call procedure in DFTD3. Must be a keys in
dashcoeff dict (or a key in dashalias that resolves to one).
dashparam : dict, optional
Dictionary of the same keys as dashcoeff[dashlvl] used to
override any or all values initialized by
dashcoeff[dashlvl][func].
dertype : {None, 0, 'none', 'energy', 1, 'first', 'gradient'}, optional
Maximum derivative level at which to run DFTD3. For large
`mol`, energy-only calculations can be significantly more
efficient. Also controls return values, see below.
verbose : bool, optional
When `True`, additionally include DFTD3 output in output.
Returns
-------
energy : float, optional
When `dertype` is 0, energy [Eh].
gradient : list of lists of floats or psi4.core.Matrix, optional
When `dertype` is 1, (nat, 3) gradient [Eh/a0].
(energy, gradient) : float and list of lists of floats or psi4.core.Matrix, optional
When `dertype` is unspecified, both energy [Eh] and (nat, 3) gradient [Eh/a0].
Notes
-----
research site: https://www.chemie.uni-bonn.de/pctc/mulliken-center/software/dft-d3
Psi4 mode: When `psi4` the python module is importable at `import qcdb`
time, Psi4 mode is activated, with the following alterations:
* output goes to output file
* gradient returned as psi4.core.Matrix, not list o'lists
* scratch is written to randomly named subdirectory of psi scratch
* psivar "DISPERSION CORRECTION ENERGY" is set
* `verbose` triggered when PRINT keywork of SCF module >=3
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(mol, (Molecule, core.Molecule)):
# 1st: called on a qcdb.Molecule
# 2nd: called on a python export of a psi4.Molecule (py-side through Psi4's driver)
pass
elif isinstance(mol, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
mol = Molecule(mol)
else:
raise ValidationError("""Argument mol must be psi4string or qcdb.Molecule""")
mol.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError("""Requested derivative level 'dertype' %s not valid for run_dftd3.""" % (dertype))
else:
raise ValidationError("""Requested derivative level 'dertype' %s not valid for run_dftd3.""" % (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = get_dispersion_aliases()[dashlvl] if dashlvl in get_dispersion_aliases() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser('~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(uuid.uuid4())[:8]
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(uuid.uuid4())[:8]
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = mol.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = mol.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful run. Possibly -D variant not available in dftd3 version.""")
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([float(x.replace('D', 'E')) for x in dfile.readline().split()])
dfile.close()
if len(dashdderiv) != mol.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), mol.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def fisapt_compute_energy(self):
"""Computes the FSAPT energy. FISAPT::compute_energy"""
# => Header <=
self.print_header()
# => Zero-th Order Wavefunction <=
core.timer_on("FISAPT: Setup")
self.localize()
self.partition()
self.overlap()
self.kinetic()
self.nuclear()
self.coulomb()
core.timer_off("FISAPT: Setup")
core.timer_on("FISAPT: Monomer SCF")
self.scf()
core.timer_off("FISAPT: Monomer SCF")
self.freeze_core()
self.unify()
core.timer_on("FISAPT: Subsys E")
self.dHF()
core.timer_off("FISAPT: Subsys E")
# => SAPT0 <=
core.timer_on("FISAPT:SAPT:elst")
self.elst()
core.timer_off("FISAPT:SAPT:elst")
core.timer_on("FISAPT:SAPT:exch")
self.exch()
core.timer_off("FISAPT:SAPT:exch")
core.timer_on("FISAPT:SAPT:ind")
self.ind()
core.timer_off("FISAPT:SAPT:ind")
if not core.get_option("FISAPT", "FISAPT_DO_FSAPT"):
core.timer_on("FISAPT:SAPT:disp")
self.disp(matrices_, vectors_, true) # Expensive, only do if needed
core.timer_off("FISAPT:SAPT:disp")
# => F-SAPT0 <=
if core.get_option("FISAPT", "FISAPT_DO_FSAPT"):
core.timer_on("FISAPT:FSAPT:loc")
self.flocalize()
core.timer_off("FISAPT:FSAPT:loc")
core.timer_on("FISAPT:FSAPT:elst")
self.felst()
core.timer_off("FISAPT:FSAPT:elst")
core.timer_on("FISAPT:FSAPT:exch")
self.fexch()
core.timer_off("FISAPT:FSAPT:exch")
core.timer_on("FISAPT:FSAPT:ind")
self.find()
core.timer_off("FISAPT:FSAPT:ind")
core.timer_on("FISAPT:FSAPT:disp")
self.fdisp()
core.timer_off("FISAPT:FSAPT:disp")
self.fdrop()
# => Scalar-Field Analysis <=
if core.get_option("FISAPT", "FISAPT_DO_PLOT"):
core.timer_on("FISAPT:FSAPT:cubeplot")
self.plot()
core.timer_off("FISAPT:FSAPT:cubeplot")
# => Summary <=
self.print_trailer()
def run_dftd3(mol, func=None, dashlvl=None, dashparam=None, dertype=None, verbose=False):
"""Compute dispersion correction using Grimme's DFTD3 executable.
Function to call Grimme's dftd3 program to compute the -D correction
of level `dashlvl` using parameters for the functional `func`.
`dashparam` can supply a full set of dispersion parameters in the
absence of `func` or individual overrides in the presence of `func`.
The DFTD3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
Parameters
----------
mol : qcdb.Molecule or psi4.core.Molecule or str
Molecule on which to run dispersion calculation. Both qcdb and
psi4.core Molecule classes have been extended by this method, so
either allowed. Alternately, a string that can be instantiated
into a qcdb.Molecule.
func : str or None
Density functional (Psi4, not Turbomole, names) for which to
load parameters from dashcoeff[dashlvl][func]. This is not
passed to DFTD3 and thus may be a dummy or `None`. Any or all
parameters initialized can be overwritten via `dashparam`.
dashlvl : {'d2p4', 'd2gr', 'd3zero', 'd3bj', 'd3mzero', d3mbj', 'd', 'd2', 'd3', 'd3m'}
Flavor of a posteriori dispersion correction for which to load
parameters and call procedure in DFTD3. Must be a keys in
dashcoeff dict (or a key in dashalias that resolves to one).
dashparam : dict, optional
Dictionary of the same keys as dashcoeff[dashlvl] used to
override any or all values initialized by
dashcoeff[dashlvl][func].
dertype : {None, 0, 'none', 'energy', 1, 'first', 'gradient'}, optional
Maximum derivative level at which to run DFTD3. For large
`mol`, energy-only calculations can be significantly more
efficient. Also controls return values, see below.
verbose : bool, optional
When `True`, additionally include DFTD3 output in output.
Returns
-------
energy : float, optional
When `dertype` is 0, energy [Eh].
gradient : list of lists of floats or psi4.core.Matrix, optional
When `dertype` is 1, (nat, 3) gradient [Eh/a0].
(energy, gradient) : float and list of lists of floats or psi4.core.Matrix, optional
When `dertype` is unspecified, both energy [Eh] and (nat, 3) gradient [Eh/a0].
Notes
-----
research site: https://www.chemie.uni-bonn.de/pctc/mulliken-center/software/dft-d3
Psi4 mode: When `psi4` the python module is importable at `import qcdb`
time, Psi4 mode is activated, with the following alterations:
* output goes to output file
* gradient returned as psi4.core.Matrix, not list o'lists
* scratch is written to randomly named subdirectory of psi scratch
* psivar "DISPERSION CORRECTION ENERGY" is set
* `verbose` triggered when PRINT keywork of SCF module >=3
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(mol, (Molecule, core.Molecule)):
# 1st: called on a qcdb.Molecule
# 2nd: called on a python export of a psi4.Molecule (py-side through Psi4's driver)
pass
elif isinstance(mol, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
mol = Molecule(mol)
else:
raise ValidationError("""Argument mol must be psi4string or qcdb.Molecule""")
mol.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError("""Requested derivative level 'dertype' %s not valid for run_dftd3.""" % (dertype))
else:
raise ValidationError("""Requested derivative level 'dertype' %s not valid for run_dftd3.""" % (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = get_dispersion_aliases()[dashlvl] if dashlvl in get_dispersion_aliases() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser('~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(uuid.uuid4())[:8]
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(uuid.uuid4())[:8]
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = mol.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
os.chdir(current_directory)
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = mol.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful run. Possibly -D variant not available in dftd3 version.""")
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([float(x.replace('D', 'E')) for x in dfile.readline().split()])
dfile.close()
if len(dashdderiv) != mol.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), mol.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def run_dftd3(self,
func=None,
dashlvl=None,
dashparam=None,
dertype=None,
verbose=False):
"""Function to call Grimme's dftd3 program (http://toc.uni-muenster.de/DFTD3/)
to compute the -D correction of level *dashlvl* using parameters for
the functional *func*. The dictionary *dashparam* can be used to supply
a full set of dispersion parameters in the absense of *func* or to supply
individual overrides in the presence of *func*. Returns energy if *dertype* is 0,
gradient if *dertype* is 1, else tuple of energy and gradient if *dertype*
unspecified. The dftd3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
*self* may be either a qcdb.Molecule (sensibly) or a psi4.Molecule
(works b/c psi4.Molecule has been extended by this method py-side and
only public interface fns used) or a string that can be instantiated
into a qcdb.Molecule.
func - functional alias or None
dashlvl - functional type d2gr/d3zero/d3bj/d3mzero/d3mbj
dashparam - dictionary
dertype = derivative level
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(self, Molecule):
# called on a qcdb.Molecule
pass
elif isinstance(self, core.Molecule):
# called on a python export of a psi4.Molecule (py-side through Psi4's driver)
self.create_psi4_string_from_molecule()
elif isinstance(self, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
self = Molecule(self)
else:
raise ValidationError(
"""Argument mol must be psi4string or qcdb.Molecule""")
self.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError(
'Requested derivative level \'dertype\' %s not valid for run_dftd3.'
% (dertype))
else:
raise ValidationError(
'Requested derivative level \'dertype\' %s not valid for run_dftd3.'
% (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = dash_alias['-' + dashlvl][1:] if (
'-' + dashlvl) in dash_alias.keys() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError(
"""-D correction level %s is not available. Choose among %s."""
% (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser(
'~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(random.randint(0, 99999))
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(
random.randint(0, 99999))
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = self.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = self.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error(
"""Unsuccessful run. Possibly -D variant not available in dftd3 version."""
)
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([
float(x.replace('D', 'E'))
for x in dfile.readline().split()
])
dfile.close()
if len(dashdderiv) != self.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), self.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix(self.natom(), 3)
psi_dashdderiv.set(dashdderiv)
# Print program output to file if verbose
if isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def _basis_projection(self, oldcalc, newcalc):
# There's a bug in Psi4 upcasting between custom basis sets
# https://github.com/psi4/psi4/issues/719, so we do it ourselves.
start_time = time.time()
assert (oldcalc.B, oldcalc.Z) != (newcalc.B, newcalc.Z)
read_filename = self._fmt_mo_fn(oldcalc)
data = np.load(read_filename)
Ca_occ = core.Matrix.np_read(data, "Ca_occ")
Cb_occ = core.Matrix.np_read(data, "Cb_occ")
puream = int(data["BasisSet PUREAM"])
old_molecule = self.molecule(oldcalc)
with psiopts('BASIS %s' % self.basis_sets[oldcalc.Z]):
old_basis = core.BasisSet.build(old_molecule,
"ORBITAL",
self.basis_sets[oldcalc.Z],
puream=puream)
if isinstance(old_basis, tuple) and len(old_basis) == 2:
# newer versions of psi return a second ECP basis
old_basis = old_basis[0]
new_molecule = self.molecule(newcalc)
with psiopts('BASIS %s' % self.basis_sets[newcalc.Z]):
new_basis = core.BasisSet.build(new_molecule,
'ORBITAL',
self.basis_sets[newcalc.Z],
puream=puream)
if isinstance(new_basis, tuple) and len(new_basis) == 2:
# newer versions of psi return a second ECP basis
base_wfn = core.Wavefunction(new_molecule, *new_basis)
new_basis = new_basis[0]
else:
base_wfn = core.Wavefunction(new_molecule, new_basis)
nalphapi = core.Dimension.from_list(data["nalphapi"])
nbetapi = core.Dimension.from_list(data["nbetapi"])
pCa = base_wfn.basis_projection(Ca_occ, nalphapi, old_basis, new_basis)
pCb = base_wfn.basis_projection(Cb_occ, nbetapi, old_basis, new_basis)
new_data = {}
new_data.update(pCa.np_write(None, prefix="Ca_occ"))
new_data.update(pCb.np_write(None, prefix="Cb_occ"))
new_data["reference"] = core.get_option('SCF', 'REFERENCE')
new_data["symmetry"] = new_molecule.schoenflies_symbol()
new_data["BasisSet"] = new_basis.name()
new_data["BasisSet PUREAM"] = puream
core.print_out(
'\n Computing basis set projection from {calc1} to {calc2} (elapsed={time:.2f})\n'
.format(
calc1=self._display_name(oldcalc).lower(),
calc2=self._display_name(newcalc).lower(),
time=time.time() - start_time,
))
# Workaround for https://github.com/psi4/psi4/pull/750
for key, value in new_data.items():
if isinstance(value,
np.ndarray) and value.flags['OWNDATA'] == False:
new_data[key] = np.copy(value)
return new_data
def fisapt_compute_energy(self):
"""Computes the FSAPT energy. FISAPT::compute_energy"""
# => Header <=
self.print_header()
# => Zero-th Order Wavefunction <=
core.timer_on("FISAPT: Setup")
self.localize()
self.partition()
self.overlap()
self.kinetic()
self.nuclear()
self.coulomb()
core.timer_off("FISAPT: Setup")
core.timer_on("FISAPT: Monomer SCF")
self.scf()
core.timer_off("FISAPT: Monomer SCF")
self.freeze_core()
self.unify()
core.timer_on("FISAPT: Subsys E")
self.dHF()
core.timer_off("FISAPT: Subsys E")
# => SAPT0 <=
core.timer_on("FISAPT:SAPT:elst")
self.elst()
core.timer_off("FISAPT:SAPT:elst")
core.timer_on("FISAPT:SAPT:exch")
self.exch()
core.timer_off("FISAPT:SAPT:exch")
core.timer_on("FISAPT:SAPT:ind")
self.ind()
core.timer_off("FISAPT:SAPT:ind")
if not core.get_option("FISAPT", "FISAPT_DO_FSAPT"):
core.timer_on("FISAPT:SAPT:disp")
self.disp(
self.matrices(), self.vectors(), True
) # Expensive, only do if needed # unteseted translation of below
# self.disp(matrices_, vectors_, true) # Expensive, only do if needed
core.timer_off("FISAPT:SAPT:disp")
# => F-SAPT0 <=
if core.get_option("FISAPT", "FISAPT_DO_FSAPT"):
core.timer_on("FISAPT:FSAPT:loc")
self.flocalize()
core.timer_off("FISAPT:FSAPT:loc")
core.timer_on("FISAPT:FSAPT:elst")
self.felst()
core.timer_off("FISAPT:FSAPT:elst")
core.timer_on("FISAPT:FSAPT:exch")
self.fexch()
core.timer_off("FISAPT:FSAPT:exch")
core.timer_on("FISAPT:FSAPT:ind")
self.find()
core.timer_off("FISAPT:FSAPT:ind")
if core.get_option("FISAPT", "FISAPT_DO_FSAPT_DISP"):
core.timer_on("FISAPT:FSAPT:disp")
self.fdisp()
core.timer_off("FISAPT:FSAPT:disp")
#else:
# # Build Empirical Dispersion
# dashD = empirical_dispersion.EmpiricalDispersion(name_hint='SAPT0-D3M')
# dashD.print_out()
# # Compute -D
# Edisp = dashD.compute_energy(core.get_active_molecule())
# core.set_variable('{} DISPERSION CORRECTION ENERGY'.format(dashD.fctldash), Edisp) # Printing
# text = []
# text.append(" => {}: Empirical Dispersion <=".format(dashD.fctldash.upper()))
# text.append(" ")
# text.append(dashD.description)
# text.append(dashD.dashlevel_citation.rstrip())
# text.append("\n Empirical Dispersion Energy [Eh] = {:24.16f}\n".format(Edisp))
# text.append('\n')
# core.print_out('\n'.join(text))
self.fdrop()
# => Scalar-Field Analysis <=
if core.get_option("FISAPT", "FISAPT_DO_PLOT"):
core.timer_on("FISAPT:FSAPT:cubeplot")
self.plot()
core.timer_off("FISAPT:FSAPT:cubeplot")
# => Summary <=
self.print_trailer()
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 _set_convergence_criterion(ptype, method_name, scf_Ec, pscf_Ec, scf_Dc, pscf_Dc, gen_Ec, verbose=1):
r"""
This function will set local SCF and global energy convergence criterion
to the defaults listed at:
http://www.psicode.org/psi4manual/master/scf.html#convergence-and-
algorithm-defaults. SCF will be converged more tightly if a post-SCF
method is select (pscf_Ec, and pscf_Dc) else the looser (scf_Ec, and
scf_Dc convergence criterion will be used).
ptype - Procedure type (energy, gradient, etc). Nearly always test on
procedures['energy'] since that's guaranteed to exist for a method.
method_name - Name of the method
scf_Ec - E convergence criterion for scf target method
pscf_Ec - E convergence criterion for scf of post-scf target method
scf_Dc - D convergence criterion for scf target method
pscf_Dc - D convergence criterion for scf of post-scf target method
gen_Ec - E convergence criterion for post-scf target method
"""
optstash = p4util.OptionsState(
['SCF', 'E_CONVERGENCE'],
['SCF', 'D_CONVERGENCE'],
['E_CONVERGENCE'])
# Kind of want to move this out of here
_method_exists(ptype, method_name)
if verbose >= 2:
print(' Setting convergence', end=' ')
# Set method-dependent scf convergence criteria, check against energy routines
if not core.has_option_changed('SCF', 'E_CONVERGENCE'):
if procedures['energy'][method_name] == proc.run_scf:
core.set_local_option('SCF', 'E_CONVERGENCE', scf_Ec)
if verbose >= 2:
print(scf_Ec, end=' ')
else:
core.set_local_option('SCF', 'E_CONVERGENCE', pscf_Ec)
if verbose >= 2:
print(pscf_Ec, end=' ')
else:
if verbose >= 2:
print('CUSTOM', core.get_option('SCF', 'E_CONVERGENCE'), end=' ')
if not core.has_option_changed('SCF', 'D_CONVERGENCE'):
if procedures['energy'][method_name] == proc.run_scf:
core.set_local_option('SCF', 'D_CONVERGENCE', scf_Dc)
if verbose >= 2:
print(scf_Dc, end=' ')
else:
core.set_local_option('SCF', 'D_CONVERGENCE', pscf_Dc)
if verbose >= 2:
print(pscf_Dc, end=' ')
else:
if verbose >= 2:
print('CUSTOM', core.get_option('SCF', 'D_CONVERGENCE'), end=' ')
# Set post-scf convergence criteria (global will cover all correlated modules)
if not core.has_global_option_changed('E_CONVERGENCE'):
if procedures['energy'][method_name] != proc.run_scf:
core.set_global_option('E_CONVERGENCE', gen_Ec)
if verbose >= 2:
print(gen_Ec, end=' ')
else:
if procedures['energy'][method_name] != proc.run_scf:
if verbose >= 2:
print('CUSTOM', core.get_global_option('E_CONVERGENCE'), end=' ')
if verbose >= 2:
print('')
return optstash
def scf_print_energies(self):
enuc = self.get_energies('Nuclear')
e1 = self.get_energies('One-Electron')
e2 = self.get_energies('Two-Electron')
exc = self.get_energies('XC')
ed = self.get_energies('-D')
self.del_variable('-D Energy')
evv10 = self.get_energies('VV10')
eefp = self.get_energies('EFP')
epcm = self.get_energies('PCM Polarization')
hf_energy = enuc + e1 + e2
dft_energy = hf_energy + exc + ed + evv10
total_energy = dft_energy + eefp + epcm
core.print_out(" => Energetics <=\n\n")
core.print_out(
" Nuclear Repulsion Energy = {:24.16f}\n".format(enuc))
core.print_out(
" One-Electron Energy = {:24.16f}\n".format(e1))
core.print_out(
" Two-Electron Energy = {:24.16f}\n".format(e2))
if self.functional().needs_xc():
core.print_out(
" DFT Exchange-Correlation Energy = {:24.16f}\n".format(exc))
core.print_out(
" Empirical Dispersion Energy = {:24.16f}\n".format(ed))
core.print_out(
" VV10 Nonlocal Energy = {:24.16f}\n".format(evv10))
if core.get_option('SCF', 'PCM'):
core.print_out(
" PCM Polarization Energy = {:24.16f}\n".format(epcm))
if hasattr(self.molecule(), 'EFP'):
core.print_out(
" EFP Energy = {:24.16f}\n".format(eefp))
core.print_out(" Total Energy = {:24.16f}\n".format(
total_energy))
self.set_variable('NUCLEAR REPULSION ENERGY', enuc)
self.set_variable('ONE-ELECTRON ENERGY', e1)
self.set_variable('TWO-ELECTRON ENERGY', e2)
if self.functional().needs_xc():
self.set_variable('DFT XC ENERGY', exc)
self.set_variable('DFT VV10 ENERGY', evv10)
self.set_variable('DFT FUNCTIONAL TOTAL ENERGY',
hf_energy + exc + evv10)
#self.set_variable(self.functional().name() + ' FUNCTIONAL TOTAL ENERGY', hf_energy + exc + evv10)
self.set_variable('DFT TOTAL ENERGY',
dft_energy) # overwritten later for DH
else:
self.set_variable('HF TOTAL ENERGY', hf_energy)
if hasattr(self, "_disp_functor"):
self.set_variable('DISPERSION CORRECTION ENERGY', ed)
#if abs(ed) > 1.0e-14:
# for pv, pvv in self.variables().items():
# if abs(pvv - ed) < 1.0e-14:
# if pv.endswith('DISPERSION CORRECTION ENERGY') and pv.startswith(self.functional().name()):
# fctl_plus_disp_name = pv.split()[0]
# self.set_variable(fctl_plus_disp_name + ' TOTAL ENERGY', dft_energy) # overwritten later for DH
#else:
# self.set_variable(self.functional().name() + ' TOTAL ENERGY', dft_energy) # overwritten later for DH
self.set_variable('SCF ITERATIONS', self.iteration_)
def fcidump(wfn, fname='INTDUMP', oe_ints=None):
"""Save integrals to file in FCIDUMP format as defined in Comp. Phys. Commun. 54 75 (1989)
Additional one-electron integrals, including orbital energies, can also be saved.
This latter format can be used with the HANDE QMC code but is not standard.
:returns: None
:raises: ValidationError when SCF wavefunction is not RHF
:type wfn: :py:class:`~psi4.core.Wavefunction`
:param wfn: set of molecule, basis, orbitals from which to generate cube files
:param fname: name of the integrals file, defaults to INTDUMP
:param oe_ints: list of additional one-electron integrals to save to file.
So far only EIGENVALUES is a valid option.
:examples:
>>> # [1] Save one- and two-electron integrals to standard FCIDUMP format
>>> E, wfn = energy('scf', return_wfn=True)
>>> fcidump(wfn)
>>> # [2] Save orbital energies, one- and two-electron integrals.
>>> E, wfn = energy('scf', return_wfn=True)
>>> fcidump(wfn, oe_ints=['EIGENVALUES'])
"""
# Get some options
reference = core.get_option('SCF', 'REFERENCE')
ints_tolerance = core.get_global_option('INTS_TOLERANCE')
# Some sanity checks
if reference not in ['RHF', 'UHF']:
raise ValidationError('FCIDUMP not implemented for {} references\n'.format(reference))
if oe_ints is None:
oe_ints = []
molecule = wfn.molecule()
docc = wfn.doccpi()
frzcpi = wfn.frzcpi()
frzvpi = wfn.frzvpi()
active_docc = docc - frzcpi
active_socc = wfn.soccpi()
active_mopi = wfn.nmopi() - frzcpi - frzvpi
nbf = active_mopi.sum() if wfn.same_a_b_orbs() else 2 * active_mopi.sum()
nirrep = wfn.nirrep()
nelectron = 2 * active_docc.sum() + active_socc.sum()
core.print_out('Writing integrals in FCIDUMP format to ' + fname + '\n')
# Generate FCIDUMP header
header = '&FCI\n'
header += 'NORB={:d},\n'.format(nbf)
header += 'NELEC={:d},\n'.format(nelectron)
header += 'MS2={:d},\n'.format(wfn.nalpha() - wfn.nbeta())
header += 'UHF=.{}.,\n'.format(not wfn.same_a_b_orbs()).upper()
orbsym = ''
for h in range(active_mopi.n()):
for n in range(frzcpi[h], frzcpi[h] + active_mopi[h]):
orbsym += '{:d},'.format(h + 1)
if not wfn.same_a_b_orbs():
orbsym += '{:d},'.format(h + 1)
header += 'ORBSYM={}\n'.format(orbsym)
header += '&END\n'
with open(fname, 'w') as intdump:
intdump.write(header)
# Get an IntegralTransform object
check_iwl_file_from_scf_type(core.get_global_option('SCF_TYPE'), wfn)
spaces = [core.MOSpace.all()]
trans_type = core.IntegralTransform.TransformationType.Restricted
if not wfn.same_a_b_orbs():
trans_type = core.IntegralTransform.TransformationType.Unrestricted
ints = core.IntegralTransform(wfn, spaces, trans_type)
ints.transform_tei(core.MOSpace.all(), core.MOSpace.all(), core.MOSpace.all(), core.MOSpace.all())
core.print_out('Integral transformation complete!\n')
DPD_info = {'instance_id': ints.get_dpd_id(), 'alpha_MO': ints.DPD_ID('[A>=A]+'), 'beta_MO': 0}
if not wfn.same_a_b_orbs():
DPD_info['beta_MO'] = ints.DPD_ID("[a>=a]+")
# Write TEI to fname in FCIDUMP format
core.fcidump_tei_helper(nirrep, wfn.same_a_b_orbs(), DPD_info, ints_tolerance, fname)
# Read-in OEI and write them to fname in FCIDUMP format
# Indexing functions to translate from zero-based (C and Python) to
# one-based (Fortran)
mo_idx = lambda x: x + 1
alpha_mo_idx = lambda x: 2 * x + 1
beta_mo_idx = lambda x: 2 * (x + 1)
with open(fname, 'a') as intdump:
core.print_out('Writing frozen core operator in FCIDUMP format to ' + fname + '\n')
if reference == 'RHF':
PSIF_MO_FZC = 'MO-basis Frozen-Core Operator'
moH = core.Matrix(PSIF_MO_FZC, wfn.nmopi(), wfn.nmopi())
moH.load(core.IO.shared_object(), psif.PSIF_OEI)
mo_slice = core.Slice(frzcpi, active_mopi)
MO_FZC = moH.get_block(mo_slice, mo_slice)
offset = 0
for h, block in enumerate(MO_FZC.nph):
il = np.tril_indices(block.shape[0])
for index, x in np.ndenumerate(block[il]):
row = mo_idx(il[0][index] + offset)
col = mo_idx(il[1][index] + offset)
if (abs(x) > ints_tolerance):
intdump.write('{:29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(x, row, col, 0, 0))
offset += block.shape[0]
# Additional one-electron integrals as requested in oe_ints
# Orbital energies
core.print_out('Writing orbital energies in FCIDUMP format to ' + fname + '\n')
if 'EIGENVALUES' in oe_ints:
eigs_dump = write_eigenvalues(wfn.epsilon_a().get_block(mo_slice).to_array(), mo_idx)
intdump.write(eigs_dump)
else:
PSIF_MO_A_FZC = 'MO-basis Alpha Frozen-Core Oper'
moH_A = core.Matrix(PSIF_MO_A_FZC, wfn.nmopi(), wfn.nmopi())
moH_A.load(core.IO.shared_object(), psif.PSIF_OEI)
mo_slice = core.Slice(frzcpi, active_mopi)
MO_FZC_A = moH_A.get_block(mo_slice, mo_slice)
offset = 0
for h, block in enumerate(MO_FZC_A.nph):
il = np.tril_indices(block.shape[0])
for index, x in np.ndenumerate(block[il]):
row = alpha_mo_idx(il[0][index] + offset)
col = alpha_mo_idx(il[1][index] + offset)
if (abs(x) > ints_tolerance):
intdump.write('{:29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(x, row, col, 0, 0))
offset += block.shape[0]
PSIF_MO_B_FZC = 'MO-basis Beta Frozen-Core Oper'
moH_B = core.Matrix(PSIF_MO_B_FZC, wfn.nmopi(), wfn.nmopi())
moH_B.load(core.IO.shared_object(), psif.PSIF_OEI)
mo_slice = core.Slice(frzcpi, active_mopi)
MO_FZC_B = moH_B.get_block(mo_slice, mo_slice)
offset = 0
for h, block in enumerate(MO_FZC_B.nph):
il = np.tril_indices(block.shape[0])
for index, x in np.ndenumerate(block[il]):
row = beta_mo_idx(il[0][index] + offset)
col = beta_mo_idx(il[1][index] + offset)
if (abs(x) > ints_tolerance):
intdump.write('{:29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(x, row, col, 0, 0))
offset += block.shape[0]
# Additional one-electron integrals as requested in oe_ints
# Orbital energies
core.print_out('Writing orbital energies in FCIDUMP format to ' + fname + '\n')
if 'EIGENVALUES' in oe_ints:
alpha_eigs_dump = write_eigenvalues(wfn.epsilon_a().get_block(mo_slice).to_array(), alpha_mo_idx)
beta_eigs_dump = write_eigenvalues(wfn.epsilon_b().get_block(mo_slice).to_array(), beta_mo_idx)
intdump.write(alpha_eigs_dump + beta_eigs_dump)
# Dipole integrals
#core.print_out('Writing dipole moment OEI in FCIDUMP format to ' + fname + '\n')
# Traceless quadrupole integrals
#core.print_out('Writing traceless quadrupole moment OEI in FCIDUMP format to ' + fname + '\n')
# Frozen core + nuclear repulsion energy
core.print_out('Writing frozen core + nuclear repulsion energy in FCIDUMP format to ' + fname + '\n')
e_fzc = ints.get_frozen_core_energy()
e_nuc = molecule.nuclear_repulsion_energy(wfn.get_dipole_field_strength())
intdump.write('{: 29.20E} {:4d} {:4d} {:4d} {:4d}\n'.format(e_fzc + e_nuc, 0, 0, 0, 0))
core.print_out('Done generating {} with integrals in FCIDUMP format.\n'.format(fname))
def mcscf_solver(ref_wfn):
# Build CIWavefunction
core.prepare_options_for_module("DETCI")
ciwfn = core.CIWavefunction(ref_wfn)
ciwfn.set_module("detci")
# 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 = ciwfn.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 = ciwfn.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.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.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.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
ciwfn.set_variable("CI ROOT %d TOTAL ENERGY" % 1, current_energy)
ciwfn.set_variable("CURRENT ENERGY", current_energy)
ciwfn.set_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
ciwfn.set_variable("CURRENT ENERGY", ciwfn.variable("MCSCF TOTAL ENERGY"))
ciwfn.set_energy(ciwfn.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 df_fdds_dispersion(primary, auxiliary, cache, leg_points=10, leg_lambda=0.3, do_print=True):
rho_thresh = core.get_option("SAPT", "SAPT_FDDS_V2_RHO_CUTOFF")
if do_print:
core.print_out("\n ==> E20 Dispersion (CHF FDDS) <== \n\n")
core.print_out(" Legendre Points: % 10d\n" % leg_points)
core.print_out(" Lambda Shift: % 10.3f\n" % leg_lambda)
core.print_out(" Fxc Kernal: % 10s\n" % "ALDA")
core.print_out(" (P|Fxc|Q) Thresh: % 8.3e\n" % rho_thresh)
# Build object
df_matrix_keys = ["Cocc_A", "Cvir_A", "Cocc_B", "Cvir_B"]
fdds_matrix_cache = {key: cache[key] for key in df_matrix_keys}
df_vector_keys = ["eps_occ_A", "eps_vir_A", "eps_occ_B", "eps_vir_B"]
fdds_vector_cache = {key: cache[key] for key in df_vector_keys}
fdds_obj = core.FDDS_Dispersion(primary, auxiliary, fdds_matrix_cache, fdds_vector_cache)
# Aux Densities
D = fdds_obj.project_densities([cache["D_A"], cache["D_B"]])
# Temps
half_Saux = fdds_obj.aux_overlap().clone()
half_Saux.power(-0.5, 1.e-12)
halfp_Saux = fdds_obj.aux_overlap().clone()
halfp_Saux.power(0.5, 1.e-12)
# Builds potentials
W_A = fdds_obj.metric().clone()
W_A.axpy(1.0, _compute_fxc(D[0], half_Saux, halfp_Saux, rho_thresh=rho_thresh))
W_B = fdds_obj.metric().clone()
W_B.axpy(1.0, _compute_fxc(D[1], half_Saux, halfp_Saux, rho_thresh=rho_thresh))
# Nuke the densities
del D
metric = fdds_obj.metric()
metric_inv = fdds_obj.metric_inv()
# Integrate
core.print_out("\n => Time Integration <= \n\n")
val_pack = ("Omega", "Weight", "Disp20,u", "Disp20", "time [s]")
core.print_out("% 12s % 12s % 14s % 14s % 10s\n" % val_pack)
# print("% 12s % 12s % 14s % 14s % 10s" % val_pack)
start_time = time.time()
total_uc = 0
total_c = 0
for point, weight in zip(*np.polynomial.legendre.leggauss(leg_points)):
omega = leg_lambda * (1.0 - point) / (1.0 + point)
lambda_scale = ((2.0 * leg_lambda) / (point + 1.0)**2)
# Monomer A
X_A = fdds_obj.form_unc_amplitude("A", omega)
# Coupled A
X_A_coupled = X_A.clone()
XSW_A = core.triplet(X_A, metric_inv, W_A, False, False, False)
amplitude_inv = metric.clone()
amplitude_inv.axpy(1.0, XSW_A)
nremoved = 0
amplitude = amplitude_inv.pseudoinverse(1.e-13, nremoved)
amplitude.transpose_this() # Why is this coming out transposed?
X_A_coupled.axpy(-1.0, core.triplet(XSW_A, amplitude, X_A, False, False, False))
del XSW_A, amplitude
X_B = fdds_obj.form_unc_amplitude("B", omega)
# print(np.linalg.norm(X_B))
# Coupled B
X_B_coupled = X_B.clone()
XSW_B = core.triplet(X_B, metric_inv, W_B, False, False, False)
amplitude_inv = metric.clone()
amplitude_inv.axpy(1.0, XSW_B)
amplitude = amplitude_inv.pseudoinverse(1.e-13, nremoved)
amplitude.transpose_this() # Why is this coming out transposed?
X_B_coupled.axpy(-1.0, core.triplet(XSW_B, amplitude, X_B, False, False, False))
del XSW_B, amplitude
# Make sure the results are symmetrized
for tensor in [X_A, X_B, X_A_coupled, X_B_coupled]:
tensor.add(tensor.transpose())
tensor.scale(0.5)
# Combine
tmp_uc = core.triplet(metric_inv, X_A, metric_inv, False, False, False)
value_uc = tmp_uc.vector_dot(X_B)
del tmp_uc
tmp_c = core.triplet(metric_inv, X_A_coupled, metric_inv, False, False, False)
value_c = tmp_c.vector_dot(X_B_coupled)
del tmp_c
# Tally
total_uc += value_uc * weight * lambda_scale
total_c += value_c * weight * lambda_scale
if do_print:
tmp_disp_unc = value_uc * weight * lambda_scale
tmp_disp = value_c * weight * lambda_scale
fdds_time = time.time() - start_time
val_pack = (omega, weight, tmp_disp_unc, tmp_disp, fdds_time)
core.print_out("% 12.3e % 12.3e % 14.3e % 14.3e %10d\n" % val_pack)
# print("% 12.3e % 12.3e % 14.3e % 14.3e %10d" % val_pack)
Disp20_uc = -1.0 / (2.0 * np.pi) * total_uc
Disp20_c = -1.0 / (2.0 * np.pi) * total_c
core.print_out("\n")
core.print_out(print_sapt_var("Disp20,u", Disp20_uc, short=True) + "\n")
core.print_out(print_sapt_var("Disp20", Disp20_c, short=True) + "\n")
return {"Disp20,FDDS (unc)": Disp20_uc, "Disp20": Disp20_c}
def _core_jk_build(orbital_basis: core.BasisSet,
aux: core.BasisSet = None,
jk_type: str = None,
do_wK: bool = None,
memory: int = None) -> core.JK:
"""
Constructs a Psi4 JK object from an input basis.
Parameters
----------
orbital_basis
Orbital basis to use in the JK object.
aux
Optional auxiliary basis set for density-fitted tensors. Defaults
to the DF_BASIS_SCF if set, otherwise the correspond JKFIT basis
to the passed in `orbital_basis`.
jk_type
Type of JK object to build (DF, Direct, PK, etc). Defaults to the
current global SCF_TYPE option.
Returns
-------
JK
Uninitialized JK object.
Example
-------
jk = psi4.core.JK.build(bas)
jk.set_memory(int(5e8)) # 4GB of memory
jk.initialize()
...
jk.C_left_add(matirx)
jk.compute()
jk.C_clear()
...
"""
optstash = optproc.OptionsState(["SCF_TYPE"])
if jk_type is not None:
core.set_global_option("SCF_TYPE", jk_type)
if aux is None:
if core.get_global_option("SCF_TYPE") == "DF":
aux = core.BasisSet.build(orbital_basis.molecule(), "DF_BASIS_SCF",
core.get_option("SCF", "DF_BASIS_SCF"),
"JKFIT", orbital_basis.name(),
orbital_basis.has_puream())
else:
aux = core.BasisSet.zero_ao_basis_set()
if (do_wK is None) or (memory is None):
jk = core.JK.build_JK(orbital_basis, aux)
else:
jk = core.JK.build_JK(orbital_basis, aux, bool(do_wK), int(memory))
optstash.restore()
return jk
def print_ci_results(ciwfn, rname, scf_e, ci_e, print_opdm_no=False):
"""
Printing for all CI Wavefunctions
"""
# Print out energetics
core.print_out("\n ==> Energetics <==\n\n")
core.print_out(" SCF energy = %20.15f\n" % scf_e)
if "CI" in rname:
core.print_out(" Total CI energy = %20.15f\n" % ci_e)
elif "MP" in rname:
core.print_out(" Total MP energy = %20.15f\n" % ci_e)
elif "ZAPT" in rname:
core.print_out(" Total ZAPT energy = %20.15f\n" % ci_e)
else:
core.print_out(" Total MCSCF energy = %20.15f\n" % ci_e)
# Nothing to be done for ZAPT or MP
if ("MP" in rname) or ("ZAPT" in rname):
core.print_out("\n")
return
# Initial info
ci_nroots = core.get_option("DETCI", "NUM_ROOTS")
irrep_labels = ciwfn.molecule().irrep_labels()
# Grab the D-vector
dvec = ciwfn.D_vector()
dvec.init_io_files(True)
for root in range(ci_nroots):
core.print_out("\n ==> %s root %d information <==\n\n" % (rname, root))
# Print total energy
root_e = core.variable("CI ROOT %d TOTAL ENERGY" % (root))
core.print_out(" %s Root %d energy = %20.15f\n" % (rname, root, root_e))
# Print natural occupations
if print_opdm_no:
core.print_out("\n Active Space Natural occupation numbers:\n\n")
occs_list = []
r_opdm = ciwfn.get_opdm(root, root, "SUM", False)
for h in range(len(r_opdm.nph)):
if 0 in r_opdm.nph[h].shape:
continue
nocc, rot = np.linalg.eigh(r_opdm.nph[h])
for e in nocc:
occs_list.append((e, irrep_labels[h]))
occs_list.sort(key=lambda x: -x[0])
cnt = 0
for value, label in occs_list:
value, label = occs_list[cnt]
core.print_out(" %4s % 8.6f" % (label, value))
cnt += 1
if (cnt % 3) == 0:
core.print_out("\n")
if (cnt % 3):
core.print_out("\n")
# Print CIVector information
ciwfn.print_vector(dvec, root)
# True to keep the file
dvec.close_io_files(True)
def run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF', 'SCF_TYPE'], ['SCF', 'REFERENCE'],
['SCF', 'DFT_FUNCTIONAL'],
['SCF', 'DFT_GRAC_SHIFT'],
['SCF', 'SAVE_JK'])
core.tstart()
# Alter default algorithm
if not core.has_option_changed('SCF', 'SCF_TYPE'):
core.set_local_option('SCF', 'SCF_TYPE', 'DF')
core.prepare_options_for_module("SAPT")
# Get the molecule of interest
ref_wfn = kwargs.get('ref_wfn', None)
if ref_wfn is None:
sapt_dimer = kwargs.pop('molecule', core.get_active_molecule())
else:
core.print_out(
'Warning! SAPT argument "ref_wfn" is only able to use molecule information.'
)
sapt_dimer = ref_wfn.molecule()
# Shifting to C1 so we need to copy the active molecule
if sapt_dimer.schoenflies_symbol() != 'c1':
core.print_out(
' SAPT does not make use of molecular symmetry, further calculations in C1 point group.\n'
)
# Make sure the geometry doesnt shift or rotate
sapt_dimer = sapt_dimer.clone()
sapt_dimer.reset_point_group('c1')
sapt_dimer.fix_orientation(True)
sapt_dimer.fix_com(True)
sapt_dimer.update_geometry()
# Grab overall settings
mon_a_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_A")
mon_b_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_B")
do_delta_hf = core.get_option("SAPT", "SAPT_DFT_DO_DHF")
sapt_dft_functional = core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL")
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " + "SAPT(DFT) Procedure".center(58) + "\n")
core.print_out("\n")
core.print_out(" " + "by Daniel G. A. Smith".center(58) + "\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" %
str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" %
("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" %
core.get_option("SCF", "SCF_TYPE"))
core.print_out("\n")
core.print_out(" Required computations:\n")
if (do_delta_hf):
core.print_out(" HF (Dimer)\n")
core.print_out(" HF (Monomer A)\n")
core.print_out(" HF (Monomer B)\n")
core.print_out(" DFT (Monomer A)\n")
core.print_out(" DFT (Monomer B)\n")
core.print_out("\n")
if (mon_a_shift == 0.0) or (mon_b_shift == 0.0):
raise ValidationError(
'SAPT(DFT): must set both "SAPT_DFT_GRAC_SHIFT_A" and "B".')
if (core.get_option('SCF', 'REFERENCE') != 'RHF'):
raise ValidationError(
'SAPT(DFT) currently only supports restricted references.')
nfrag = sapt_dimer.nfragments()
if nfrag != 2:
raise ValidationError(
'SAPT requires active molecule to have 2 fragments, not %s.' %
(nfrag))
monomerA = sapt_dimer.extract_subsets(1, 2)
monomerA.set_name('monomerA')
monomerB = sapt_dimer.extract_subsets(2, 1)
monomerB.set_name('monomerB')
core.IO.set_default_namespace('dimer')
data = {}
core.set_global_option("SAVE_JK", True)
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.set_global_option('DF_INTS_IO', 'SAVE')
# # Compute dimer wavefunction
hf_cache = {}
hf_wfn_dimer = None
if do_delta_hf:
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.set_global_option('DF_INTS_IO', 'SAVE')
hf_data = {}
hf_wfn_dimer = scf_helper("SCF",
molecule=sapt_dimer,
banner="SAPT(DFT): delta HF Dimer",
**kwargs)
hf_data["HF DIMER"] = core.get_variable("CURRENT ENERGY")
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
hf_wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SAPT(DFT): delta HF Monomer A",
**kwargs)
hf_data["HF MONOMER A"] = core.get_variable("CURRENT ENERGY")
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
hf_wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SAPT(DFT): delta HF Monomer B",
**kwargs)
hf_data["HF MONOMER B"] = core.get_variable("CURRENT ENERGY")
# Move it back to monomer A
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerB', 'dimer')
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out(" " +
"SAPT(DFT): delta HF Segement".center(58) + "\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Rob Parrish".center(58) +
"\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out("\n")
# Build cache and JK
sapt_jk = hf_wfn_B.jk()
hf_cache = sapt_jk_terms.build_sapt_jk_cache(hf_wfn_A, hf_wfn_B,
sapt_jk, True)
# Electostatics
elst = sapt_jk_terms.electrostatics(hf_cache, True)
hf_data.update(elst)
# Exchange
exch = sapt_jk_terms.exchange(hf_cache, sapt_jk, True)
hf_data.update(exch)
# Induction
ind = sapt_jk_terms.induction(
hf_cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT", "D_CONVERGENCE"))
hf_data.update(ind)
dhf_value = hf_data["HF DIMER"] - hf_data["HF MONOMER A"] - hf_data[
"HF MONOMER B"]
core.print_out("\n")
core.print_out(
print_sapt_hf_summary(hf_data, "SAPT(HF)", delta_hf=dhf_value))
data["Delta HF Correction"] = core.get_variable("SAPT(DFT) Delta HF")
if hf_wfn_dimer is None:
dimer_wfn = core.Wavefunction.build(sapt_dimer,
core.get_global_option("BASIS"))
else:
dimer_wfn = hf_wfn_dimer
# Set the primary functional
core.set_global_option("DFT_FUNCTIONAL",
core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL"))
core.set_local_option('SCF', 'REFERENCE', 'RKS')
# Compute Monomer A wavefunction
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
if mon_a_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_a_shift)
# Save the JK object
core.IO.set_default_namespace('monomerA')
wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SAPT(DFT): DFT Monomer A",
**kwargs)
data["DFT MONOMERA"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Compute Monomer B wavefunction
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
if mon_b_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_b_shift)
core.IO.set_default_namespace('monomerB')
wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SAPT(DFT): DFT Monomer B",
**kwargs)
data["DFT MONOMERB"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " +
"SAPT(DFT): Intermolecular Interaction Segment".center(58) +
"\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Rob Parrish".center(58) + "\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" %
str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" %
("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" %
core.get_option("SCF", "SCF_TYPE"))
# Build cache and JK
sapt_jk = wfn_B.jk()
cache = sapt_jk_terms.build_sapt_jk_cache(wfn_A, wfn_B, sapt_jk, True)
# Electostatics
elst = sapt_jk_terms.electrostatics(cache, True)
data.update(elst)
# Exchange
exch = sapt_jk_terms.exchange(cache, sapt_jk, True)
data.update(exch)
# Induction
ind = sapt_jk_terms.induction(cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT",
"D_CONVERGENCE"))
data.update(ind)
# Dispersion
primary_basis = wfn_A.basisset()
core.print_out("\n")
aux_basis = core.BasisSet.build(sapt_dimer, "DF_BASIS_MP2",
core.get_option("DFMP2", "DF_BASIS_MP2"),
"RIFIT", core.get_global_option('BASIS'))
fdds_disp = sapt_mp2_terms.df_fdds_dispersion(primary_basis, aux_basis,
cache)
data.update(fdds_disp)
if core.get_option("SAPT", "SAPT_DFT_MP2_DISP_ALG") == "FISAPT":
mp2_disp = sapt_mp2_terms.df_mp2_fisapt_dispersion(wfn_A,
primary_basis,
aux_basis,
cache,
do_print=True)
else:
mp2_disp = sapt_mp2_terms.df_mp2_sapt_dispersion(dimer_wfn,
wfn_A,
wfn_B,
primary_basis,
aux_basis,
cache,
do_print=True)
data.update(mp2_disp)
# Print out final data
core.print_out("\n")
core.print_out(print_sapt_dft_summary(data, "SAPT(DFT)"))
core.tstop()
return dimer_wfn
def run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF_TYPE'], ['SCF', 'REFERENCE'],
['SCF', 'DFT_GRAC_SHIFT'],
['SCF', 'SAVE_JK'])
core.tstart()
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DF')
core.prepare_options_for_module("SAPT")
# Get the molecule of interest
ref_wfn = kwargs.get('ref_wfn', None)
if ref_wfn is None:
sapt_dimer = kwargs.pop('molecule', core.get_active_molecule())
else:
core.print_out(
'Warning! SAPT argument "ref_wfn" is only able to use molecule information.'
)
sapt_dimer = ref_wfn.molecule()
sapt_dimer, monomerA, monomerB = proc_util.prepare_sapt_molecule(
sapt_dimer, "dimer")
# Grab overall settings
mon_a_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_A")
mon_b_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_B")
do_delta_hf = core.get_option("SAPT", "SAPT_DFT_DO_DHF")
sapt_dft_functional = core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL")
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " + "SAPT(DFT) Procedure".center(58) + "\n")
core.print_out("\n")
core.print_out(" " + "by Daniel G. A. Smith".center(58) + "\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(
" !!! WARNING: SAPT(DFT) capability is in beta. Please use with caution. !!!\n\n"
)
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" %
str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" %
("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" %
core.get_global_option("SCF_TYPE"))
core.print_out("\n")
core.print_out(" Required computations:\n")
if (do_delta_hf):
core.print_out(" HF (Dimer)\n")
core.print_out(" HF (Monomer A)\n")
core.print_out(" HF (Monomer B)\n")
core.print_out(" DFT (Monomer A)\n")
core.print_out(" DFT (Monomer B)\n")
core.print_out("\n")
if (sapt_dft_functional != "HF") and ((mon_a_shift == 0.0) or
(mon_b_shift == 0.0)):
raise ValidationError(
'SAPT(DFT): must set both "SAPT_DFT_GRAC_SHIFT_A" and "B".')
if (core.get_option('SCF', 'REFERENCE') != 'RHF'):
raise ValidationError(
'SAPT(DFT) currently only supports restricted references.')
core.IO.set_default_namespace('dimer')
data = {}
if (core.get_global_option('SCF_TYPE') == 'DF'):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.set_global_option('DF_INTS_IO', 'SAVE')
# # Compute dimer wavefunction
hf_wfn_dimer = None
if do_delta_hf:
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.set_global_option('DF_INTS_IO', 'SAVE')
core.timer_on("SAPT(DFT): Dimer SCF")
hf_data = {}
hf_wfn_dimer = scf_helper("SCF",
molecule=sapt_dimer,
banner="SAPT(DFT): delta HF Dimer",
**kwargs)
hf_data["HF DIMER"] = core.get_variable("CURRENT ENERGY")
core.timer_off("SAPT(DFT): Dimer SCF")
core.timer_on("SAPT(DFT): Monomer A SCF")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
hf_wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SAPT(DFT): delta HF Monomer A",
**kwargs)
hf_data["HF MONOMER A"] = core.get_variable("CURRENT ENERGY")
core.timer_off("SAPT(DFT): Monomer A SCF")
core.timer_on("SAPT(DFT): Monomer B SCF")
core.set_global_option("SAVE_JK", True)
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
hf_wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SAPT(DFT): delta HF Monomer B",
**kwargs)
hf_data["HF MONOMER B"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("SAVE_JK", False)
core.timer_off("SAPT(DFT): Monomer B SCF")
# Grab JK object and set to A (so we do not save many JK objects)
sapt_jk = hf_wfn_B.jk()
hf_wfn_A.set_jk(sapt_jk)
core.set_global_option("SAVE_JK", False)
# Move it back to monomer A
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerB', 'dimer')
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out(" " + "SAPT(DFT): delta HF Segment".center(58) +
"\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Rob Parrish".center(58) +
"\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out("\n")
# Build cache
hf_cache = sapt_jk_terms.build_sapt_jk_cache(hf_wfn_A, hf_wfn_B,
sapt_jk, True)
# Electrostatics
core.timer_on("SAPT(DFT):SAPT:elst")
elst = sapt_jk_terms.electrostatics(hf_cache, True)
hf_data.update(elst)
core.timer_off("SAPT(DFT):SAPT:elst")
# Exchange
core.timer_on("SAPT(DFT):SAPT:exch")
exch = sapt_jk_terms.exchange(hf_cache, sapt_jk, True)
hf_data.update(exch)
core.timer_off("SAPT(DFT):SAPT:exch")
# Induction
core.timer_on("SAPT(DFT):SAPT:ind")
ind = sapt_jk_terms.induction(
hf_cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT", "D_CONVERGENCE"),
Sinf=core.get_option("SAPT", "DO_IND_EXCH_SINF"))
hf_data.update(ind)
core.timer_off("SAPT(DFT):SAPT:ind")
dhf_value = hf_data["HF DIMER"] - hf_data["HF MONOMER A"] - hf_data[
"HF MONOMER B"]
core.print_out("\n")
core.print_out(
print_sapt_hf_summary(hf_data, "SAPT(HF)", delta_hf=dhf_value))
data["Delta HF Correction"] = core.get_variable("SAPT(DFT) Delta HF")
sapt_jk.finalize()
del hf_wfn_A, hf_wfn_B, sapt_jk
if hf_wfn_dimer is None:
dimer_wfn = core.Wavefunction.build(sapt_dimer,
core.get_global_option("BASIS"))
else:
dimer_wfn = hf_wfn_dimer
# Set the primary functional
core.set_local_option('SCF', 'REFERENCE', 'RKS')
# Compute Monomer A wavefunction
core.timer_on("SAPT(DFT): Monomer A DFT")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
if mon_a_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_a_shift)
core.IO.set_default_namespace('monomerA')
wfn_A = scf_helper(sapt_dft_functional,
post_scf=False,
molecule=monomerA,
banner="SAPT(DFT): DFT Monomer A",
**kwargs)
data["DFT MONOMERA"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
core.timer_off("SAPT(DFT): Monomer A DFT")
# Compute Monomer B wavefunction
core.timer_on("SAPT(DFT): Monomer B DFT")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
if mon_b_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_b_shift)
core.set_global_option("SAVE_JK", True)
core.IO.set_default_namespace('monomerB')
wfn_B = scf_helper(sapt_dft_functional,
post_scf=False,
molecule=monomerB,
banner="SAPT(DFT): DFT Monomer B",
**kwargs)
data["DFT MONOMERB"] = core.get_variable("CURRENT ENERGY")
# Save JK object
sapt_jk = wfn_B.jk()
wfn_A.set_jk(sapt_jk)
core.set_global_option("SAVE_JK", False)
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
core.timer_off("SAPT(DFT): Monomer B DFT")
# Write out header
scf_alg = core.get_global_option("SCF_TYPE")
sapt_dft_header(sapt_dft_functional, mon_a_shift, mon_b_shift,
bool(do_delta_hf), scf_alg)
# Call SAPT(DFT)
sapt_jk = wfn_B.jk()
sapt_dft(dimer_wfn,
wfn_A,
wfn_B,
sapt_jk=sapt_jk,
data=data,
print_header=False)
# Copy data back into globals
for k, v in data.items():
core.set_variable(k, v)
core.tstop()
return dimer_wfn
def build_superfunctional(alias, restricted):
name = alias.lower()
npoints = core.get_option("SCF", "DFT_BLOCK_MAX_POINTS")
deriv = 1 # Default depth for now
# Grab out superfunctional
if name in ["gen", ""]:
sup = (core.get_option("DFT_CUSTOM_FUNCTIONAL"), False)
if not isinstance(sup[0], core.SuperFunctional):
raise KeyError(
"SCF: Custom Functional requested, but nothing provided in DFT_CUSTOM_FUNCTIONAL"
)
elif name in superfunctionals.keys():
sup = superfunctionals[name](name, npoints, deriv, restricted)
elif name.upper() in superfunctionals.keys():
sup = superfunctionals[name.upper()](name, npoints, deriv, restricted)
elif any(name.endswith(al) for al in dftd3.full_dash_keys):
# Odd hack for b97-d
if 'b97-d' in name:
name = name.replace('b97', 'b97-d')
dashparam = [x for x in dftd3.full_dash_keys if name.endswith(x)]
if len(dashparam) > 1:
raise Exception("Dashparam %s is ambiguous.")
else:
dashparam = dashparam[0]
base_name = name.replace('-' + dashparam, '')
if dashparam in ['d2', 'd']:
dashparam = 'd2p4'
if dashparam == 'd3':
dashparam = 'd3zero'
if dashparam == 'd3m':
dashparam = 'd3mzero'
if base_name not in superfunctionals.keys():
raise KeyError("SCF: Functional (%s) with base (%s) not found!" %
(alias, base_name))
func = superfunctionals[base_name](base_name, npoints, deriv,
restricted)[0]
base_name = base_name.replace('wpbe', 'lcwpbe')
sup = (func, (base_name, dashparam))
else:
raise KeyError("SCF: Functional (%s) not found!" % alias)
if (core.get_global_option('INTEGRAL_PACKAGE')
== 'ERD') and (sup[0].is_x_lrc() or sup[0].is_c_lrc()):
raise ValidationError(
"INTEGRAL_PACKAGE ERD does not play nicely with omega ERI's, so stopping."
)
# Set options
if core.has_option_changed("SCF", "DFT_OMEGA") and sup[0].is_x_lrc():
sup[0].set_x_omega(core.get_option("SCF", "DFT_OMEGA"))
if core.has_option_changed("SCF", "DFT_OMEGA_C") and sup[0].is_c_lrc():
sup[0].set_c_omega(core.get_option("SCF", "DFT_OMEGA_C"))
if core.has_option_changed("SCF", "DFT_ALPHA"):
sup[0].set_x_alpha(core.get_option("SCF", "DFT_ALPHA"))
if core.has_option_changed("SCF", "DFT_ALPHA_C"):
sup[0].set_c_alpha(core.get_option("SCF", "DFT_ALPHA_C"))
# Check SCF_TYPE
if sup[0].is_x_lrc() and (core.get_option("SCF", "SCF_TYPE")
not in ["DIRECT", "DF", "OUT_OF_CORE", "PK"]):
raise KeyError(
"SCF: SCF_TYPE (%s) not supported for range-seperated functionals."
% core.get_option("SCF", "SCF_TYPE"))
if (core.get_global_option('INTEGRAL_PACKAGE')
== 'ERD') and (sup[0].is_x_lrc()):
raise ValidationError(
'INTEGRAL_PACKAGE ERD does not play nicely with LRC DFT functionals, so stopping.'
)
return sup
