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 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 negotiate_convergence_criterion(dermode: Union[Tuple[str, str],
Tuple[int, int]],
method: str,
return_optstash: bool = False):
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 (pscf_Ec and pscf_Dc)
if a post-SCF method is selected. For a final SCF method, the looser
(scf_Ec and scf_Dc) convergence criterion will be used.
dermode - Tuple of target and means derivative level or ("prop", "prop"). E.g., analytic gradient
is (1, 1); frequency by energies is (2, 0). Nearly always test on
procedures['energy'] since that's guaranteed to exist for a method.
method - 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
"""
scf_Ec, pscf_Ec, scf_Dc, pscf_Dc, gen_Ec = {
(0, 0): [6, 8, 6, 8, 6],
(1, 0): [8, 10, 8, 10, 8],
(2, 0): [10, 11, 10, 11, 10],
(1, 1): [8, 10, 8, 10, 8],
(2, 1): [8, 10, 8, 10, 8],
(2, 2): [8, 10, 8, 10, 8],
("prop", "prop"): [6, 10, 6, 10, 8]
}[dermode]
# Set method-dependent scf convergence criteria, check against energy routines
# Set post-scf convergence criteria (global will cover all correlated modules)
cc = {}
if procedures['energy'][method] in [proc.run_scf, proc.run_tdscf_energy]:
if not core.has_option_changed('SCF', 'E_CONVERGENCE'):
cc['SCF__E_CONVERGENCE'] = math.pow(10, -scf_Ec)
if not core.has_option_changed('SCF', 'D_CONVERGENCE'):
cc['SCF__D_CONVERGENCE'] = math.pow(10, -scf_Dc)
else:
if not core.has_option_changed('SCF', 'E_CONVERGENCE'):
cc['SCF__E_CONVERGENCE'] = math.pow(10, -pscf_Ec)
if not core.has_option_changed('SCF', 'D_CONVERGENCE'):
cc['SCF__D_CONVERGENCE'] = math.pow(10, -pscf_Dc)
if not core.has_global_option_changed('E_CONVERGENCE'):
cc['E_CONVERGENCE'] = math.pow(10, -gen_Ec)
if return_optstash:
optstash = p4util.OptionsState(['SCF', 'E_CONVERGENCE'],
['SCF', 'D_CONVERGENCE'],
['E_CONVERGENCE'])
p4util.set_options(cc)
return optstash
else:
return cc
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 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 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 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 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')
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 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 prep_findif(mol, irrep, mode, gradient=None):
findif_stencil_size = core.get_option("FINDIF", "POINTS")
findif_step_size = core.get_option("FINDIF", "DISP_SIZE")
translations_projection_sound = (not core.get_option('SCF', 'EXTERN') and
not core.get_option('SCF', 'PERTURB_H')
and not hasattr(mol, 'EFP'))
if gradient is not None:
stationary_criterion = 1.e-2 # pulled out of a hat
stationary_point = _rms(gradient) < stationary_criterion
rotations_projection_sound = translations_projection_sound and stationary_point
core.print_out(
'\n Based on options and gradient (rms={:.2E}), recommend {}projecting translations and {}projecting rotations.\n'
.format(_rms(gradient),
'' if translations_projection_sound else 'not ',
'' if rotations_projection_sound else 'not '))
else:
stationary_point = False # unknown, so F to be safe
rotations_projection_sound_grad = translations_projection_sound
rotations_projection_sound_hess = translations_projection_sound and stationary_point
if core.has_option_changed('FINDIF', 'FD_PROJECT'):
r_project_grad = core.get_option('FINDIF', 'FD_PROJECT')
r_project_hess = core.get_option('FINDIF', 'FD_PROJECT')
else:
r_project_grad = rotations_projection_sound_grad
r_project_hess = rotations_projection_sound_hess
if mode == "1_0":
fdargs = {
"stencil_size": findif_stencil_size,
"step_size": findif_step_size,
"t_project": translations_projection_sound,
"r_project": r_project_grad,
}
elif mode == "2_1":
fdargs = {
"freq_irrep_only": irrep,
"stencil_size": findif_stencil_size,
"step_size": findif_step_size,
"t_project": translations_projection_sound,
"r_project": r_project_hess,
}
elif mode == "2_0":
fdargs = {
"freq_irrep_only": irrep,
"stencil_size": findif_stencil_size,
"step_size": findif_step_size,
"t_project": translations_projection_sound,
"r_project": r_project_hess,
}
return fdargs
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, str):
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 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 __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 __init__(self, option: str, module: Optional[str] = None):
self.option = option.upper()
if module:
self.module = module.upper()
else:
self.module = None
self.value_global = core.get_global_option(option)
self.haschanged_global = core.has_global_option_changed(option)
if self.module:
self.value_local = core.get_local_option(self.module, option)
self.haschanged_local = core.has_local_option_changed(self.module, option)
self.value_used = core.get_option(self.module, option)
self.haschanged_used = core.has_option_changed(self.module, option)
else:
self.value_local = None
self.haschanged_local = None
self.value_used = None
self.haschanged_used = None
def 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 dict-based functionals
elif name.upper() in dict_builder.functionals.keys():
sup = dict_builder.build_superfunctional_from_dictionary(
name.upper(), 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"))
# 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 ValidationError(
"SCF: SCF_TYPE (%s) not supported for range-separated 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.'
)
sup[0].set_lock(True)
return sup
def build_superfunctional(name, restricted, npoints=None, deriv=1):
if npoints is None:
npoints = core.get_option("SCF", "DFT_BLOCK_MAX_POINTS")
# 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(sfunc[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 = dft_builder.build_superfunctional_from_dictionary(
name, npoints, deriv, restricted)
# Check for pre-defined dict-based functionals
elif name.lower() in dft_builder.functionals:
sup = dft_builder.build_superfunctional_from_dictionary(
dft_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 not isinstance(name, dict):
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 not isinstance(name, dict):
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 [
"DISK_DF", "MEM_DF", "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 _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 _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_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_ = self.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')
# 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")
incfock_performed = hasattr(
self.jk(), "do_incfock_iter") and self.jk().do_incfock_iter()
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) # P::e PCM
self.set_energies("PCM Polarization", upcm)
upe = 0.0
if core.get_option('SCF', 'PE'):
Dt = self.Da().clone()
Dt.add(self.Db())
upe, Vpe = self.pe_state.get_pe_contribution(Dt, elec_only=False)
SCFE += upe
self.push_back_external_potential(Vpe)
self.set_variable("PE ENERGY", upe) # P::e PE
self.set_energies("PE Energy", upe)
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. The reset is necessary because SAD
# nalpha_ and nbeta_ are not guaranteed physical.
# 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:
for engine_used in self.diis(Dnorm):
status.append(engine_used)
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")
level_shift = core.get_option("SCF", "LEVEL_SHIFT")
if level_shift > 0 and Dnorm > core.get_option(
'SCF', 'LEVEL_SHIFT_CUTOFF'):
status.append("SHIFT")
self.form_C(level_shift)
else:
self.form_C()
core.timer_off("HF: Form C")
if self.MOM_performed_:
status.append("MOM")
if self.frac_performed_:
status.append("FRAC")
if incfock_performed:
status.append("INCFOCK")
# 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)
core.set_variable("SCF D NORM", Dnorm)
# 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 core.has_option_changed("SCF", "ORBITALS_WRITE"):
filename = core.get_option("SCF", "ORBITALS_WRITE")
self.to_file(filename)
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
# Note that MOM_performed_ just checks initialization, and our convergence measures used the pre-MOM orbitals
if self.MOM_excited_ and ((not self.MOM_performed_) or self.iteration_
== core.get_option('SCF', "MOM_START")):
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 run_cfour(name, **kwargs):
"""Function that prepares environment and input files
for a calculation calling Stanton and Gauss's CFOUR code.
Also processes results back into Psi4 format.
This function is not called directly but is instead called by
:py:func:`~psi4.driver.energy` or :py:func:`~psi4.driver.optimize` when a Cfour
method is requested (through *name* argument). In order to function
correctly, the Cfour executable ``xcfour`` must be present in
:envvar:`PATH` or :envvar:`PSIPATH`.
.. hlist::
:columns: 1
* Many :ref:`PSI Variables <apdx:cfour_psivar>` extracted from the Cfour output
* Python dictionary of associated file constants accessible as ``P4C4_INFO['zmat']``, ``P4C4_INFO['output']``, ``P4C4_INFO['grd']``, *etc.*
:type name: str
:param name: ``'c4-scf'`` || ``'c4-ccsd(t)'`` || ``'cfour'`` || etc.
First argument, usually unlabeled. Indicates the computational
method to be applied to the system.
:type keep: :ref:`boolean <op_py_boolean>`
:param keep: ``'on'`` || |dl| ``'off'`` |dr|
Indicates whether to delete the Cfour scratch directory upon
completion of the Cfour job.
:type path: str
:param path:
Indicates path to Cfour scratch directory (with respect to Psi4
scratch directory). Otherwise, the default is a subdirectory
within the Psi4 scratch directory.
If specified, GENBAS and/or ZMAT within will be used.
:type genbas: str
:param genbas:
Indicates that contents should be used for GENBAS file.
GENBAS is a complicated topic. It is quite unnecessary if the
molecule is from a molecule {...} block and basis is set through
|Psifours| BASIS keyword. In that case, a GENBAS is written from
LibMints and all is well. Otherwise, a GENBAS is looked for in
the usual places: PSIPATH, PATH, PSIDATADIR/basis. If path kwarg is
specified, also looks there preferentially for a GENBAS. Can
also specify GENBAS within an input file through a string and
setting the genbas kwarg. Note that due to the input parser's
aggression, blank lines need to be replaced by the text blankline.
"""
lowername = name.lower()
internal_p4c4_info = {}
return_wfn = kwargs.pop('return_wfn', False)
# Make sure the molecule the user provided is the active one
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
optstash = p4util.OptionsState(['CFOUR', 'TRANSLATE_PSI4'])
# Determine calling function and hence dertype
calledby = inspect.stack()[1][3]
dertype = ['energy', 'gradient', 'hessian'].index(calledby)
#print('I am %s called by %s called by %s.\n' %
# (inspect.stack()[0][3], inspect.stack()[1][3], inspect.stack()[2][3]))
# Save submission directory
current_directory = os.getcwd()
# Move into job scratch directory
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
# Construct and move into cfour subdirectory of job scratch directory
cfour_tmpdir = kwargs['path'] if 'path' in kwargs else \
'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.cfour.' + str(uuid.uuid4())[:8]
if not os.path.exists(cfour_tmpdir):
os.mkdir(cfour_tmpdir)
os.chdir(cfour_tmpdir)
# 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') + ':' + core.get_datadir() +
'/basis',
'GENBAS_PATH':
core.get_datadir() + '/basis',
'CFOUR_NUM_CORES':
os.environ.get('CFOUR_NUM_CORES'),
'MKL_NUM_THREADS':
os.environ.get('MKL_NUM_THREADS'),
'OMP_NUM_THREADS':
os.environ.get('OMP_NUM_THREADS'),
'LD_LIBRARY_PATH':
os.environ.get('LD_LIBRARY_PATH')
}
if 'path' in kwargs:
lenv['PATH'] = kwargs['path'] + ':' + lenv['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}
# Load the GENBAS file
genbas_path = qcdb.search_file('GENBAS', lenv['GENBAS_PATH'])
if genbas_path:
try:
shutil.copy2(genbas_path, psioh.get_default_path() + cfour_tmpdir)
except shutil.Error: # should only fail if src and dest equivalent
pass
core.print_out("\n GENBAS loaded from %s\n" % (genbas_path))
core.print_out(" CFOUR to be run from %s\n" %
(psioh.get_default_path() + cfour_tmpdir))
else:
message = """
GENBAS file for CFOUR interface not found. Either:
[1] Supply a GENBAS by placing it in PATH or PSIPATH
[1a] Use cfour {} block with molecule and basis directives.
[1b] Use molecule {} block and CFOUR_BASIS keyword.
[2] Allow Psi4's internal basis sets to convert to GENBAS
[2a] Use molecule {} block and BASIS keyword.
"""
core.print_out(message)
core.print_out(' Search path that was tried:\n')
core.print_out(lenv['PATH'].replace(':', ', '))
# Generate the ZMAT input file in scratch
if 'path' in kwargs and os.path.isfile('ZMAT'):
core.print_out(" ZMAT loaded from %s\n" %
(psioh.get_default_path() + kwargs['path'] + '/ZMAT'))
else:
with open('ZMAT', 'w') as cfour_infile:
cfour_infile.write(write_zmat(lowername, dertype, molecule))
internal_p4c4_info['zmat'] = open('ZMAT', 'r').read()
#core.print_out('\n====== Begin ZMAT input for CFOUR ======\n')
#core.print_out(open('ZMAT', 'r').read())
#core.print_out('======= End ZMAT input for CFOUR =======\n\n')
#print('\n====== Begin ZMAT input for CFOUR ======')
#print(open('ZMAT', 'r').read())
#print('======= End ZMAT input for CFOUR =======\n')
if 'genbas' in kwargs:
with open('GENBAS', 'w') as cfour_basfile:
cfour_basfile.write(kwargs['genbas'].replace(
'\nblankline\n', '\n\n'))
core.print_out(' GENBAS loaded from kwargs string\n')
# Close psi4 output file and reopen with filehandle
print('output in', current_directory + '/' + core.outfile_name())
pathfill = '' if os.path.isabs(
core.outfile_name()) else current_directory + os.path.sep
# Handle threading
# OMP_NUM_THREADS from env is in lenv from above
# threads from psi4 -n (core.get_num_threads()) is ignored
# CFOUR_OMP_NUM_THREADS psi4 option takes precedence, handled below
if core.has_option_changed('CFOUR', 'CFOUR_OMP_NUM_THREADS'):
lenv['OMP_NUM_THREADS'] = str(
core.get_option('CFOUR', 'CFOUR_OMP_NUM_THREADS'))
#print("""\n\n<<<<< RUNNING CFOUR ... >>>>>\n\n""")
# Call executable xcfour, directing cfour output to the psi4 output file
cfour_executable = kwargs['c4exec'] if 'c4exec' in kwargs else 'xcfour'
try:
retcode = subprocess.Popen([cfour_executable],
bufsize=0,
stdout=subprocess.PIPE,
env=lenv)
except OSError as e:
sys.stderr.write(
'Program %s not found in path or execution failed: %s\n' %
(cfour_executable, e.strerror))
message = ('Program %s not found in path or execution failed: %s\n' %
(cfour_executable, e.strerror))
raise ValidationError(message)
c4out = ''
while True:
data = retcode.stdout.readline()
data = data.decode('utf-8')
if not data:
break
core.print_out(data)
c4out += data
internal_p4c4_info['output'] = c4out
c4files = {}
core.print_out('\n')
for item in ['GRD', 'FCMFINAL', 'DIPOL']:
try:
with open(psioh.get_default_path() + cfour_tmpdir + '/' + item,
'r') as handle:
c4files[item] = handle.read()
core.print_out(' CFOUR scratch file %s has been read\n' %
(item))
core.print_out('%s\n' % c4files[item])
internal_p4c4_info[item.lower()] = c4files[item]
except IOError:
pass
core.print_out('\n')
if molecule.name() == 'blank_molecule_psi4_yo':
qcdbmolecule = None
else:
molecule.update_geometry()
qcdbmolecule = qcdb.Molecule(
molecule.create_psi4_string_from_molecule())
qcdbmolecule.update_geometry()
# c4mol, if it exists, is dinky, just a clue to geometry of cfour results
psivar, c4grad, c4mol = qcdb.cfour.harvest(qcdbmolecule, c4out, **c4files)
# Absorb results into psi4 data structures
for key in psivar.keys():
core.set_variable(key.upper(), float(psivar[key]))
if qcdbmolecule is None and c4mol is not None:
molrec = qcel.molparse.from_string(
c4mol.create_psi4_string_from_molecule(),
enable_qm=True,
missing_enabled_return_qm='minimal',
enable_efp=False,
missing_enabled_return_efp='none',
)
molecule = core.Molecule.from_dict(molrec['qm'])
molecule.set_name('blank_molecule_psi4_yo')
core.set_active_molecule(molecule)
molecule.update_geometry()
# This case arises when no Molecule going into calc (cfour {} block) but want
# to know the orientation at which grad, properties, etc. are returned (c4mol).
# c4mol is dinky, w/o chg, mult, dummies and retains name
# blank_molecule_psi4_yo so as to not interfere with future cfour {} blocks
if c4grad is not None:
mat = core.Matrix.from_list(c4grad)
core.set_gradient(mat)
#print ' <<< [3] C4-GRD-GRAD >>>'
#mat.print()
# exit(1)
# # Things needed core.so module to do
# collect c4out string
# read GRD
# read FCMFINAL
# see if theres an active molecule
# # Things delegatable to qcdb
# parsing c4out
# reading GRD and FCMFINAL strings
# reconciling p4 and c4 molecules (orient)
# reconciling c4out and GRD and FCMFINAL results
# transforming frame of results back to p4
# # Things run_cfour needs to have back
# psivar
# qcdb.Molecule of c4?
# coordinates?
# gradient in p4 frame
# # Process the cfour output
# psivar, c4coord, c4grad = qcdb.cfour.cfour_harvest(c4out)
# for key in psivar.keys():
# core.set_variable(key.upper(), float(psivar[key]))
#
# # Awful Hack - Go Away TODO
# if c4grad:
# molecule = core.get_active_molecule()
# molecule.update_geometry()
#
# if molecule.name() == 'blank_molecule_psi4_yo':
# p4grad = c4grad
# p4coord = c4coord
# else:
# qcdbmolecule = qcdb.Molecule(molecule.create_psi4_string_from_molecule())
# #p4grad = qcdbmolecule.deorient_array_from_cfour(c4coord, c4grad)
# #p4coord = qcdbmolecule.deorient_array_from_cfour(c4coord, c4coord)
#
# with open(psioh.get_default_path() + cfour_tmpdir + '/GRD', 'r') as cfour_grdfile:
# c4outgrd = cfour_grdfile.read()
# print('GRD\n',c4outgrd)
# c4coordGRD, c4gradGRD = qcdb.cfour.cfour_harvest_files(qcdbmolecule, grd=c4outgrd)
#
# p4mat = core.Matrix.from_list(p4grad)
# core.set_gradient(p4mat)
# print(' <<< P4 PSIVAR >>>')
# for item in psivar:
# print(' %30s %16.8f' % (item, psivar[item]))
#print(' <<< P4 COORD >>>')
#for item in p4coord:
# print(' %16.8f %16.8f %16.8f' % (item[0], item[1], item[2]))
# print(' <<< P4 GRAD >>>')
# for item in c4grad:
# print(' %16.8f %16.8f %16.8f' % (item[0], item[1], item[2]))
# Clean up cfour scratch directory unless user instructs otherwise
keep = yes.match(str(kwargs['keep'])) if 'keep' in kwargs else False
os.chdir('..')
try:
if keep or ('path' in kwargs):
core.print_out('\n CFOUR scratch files have been kept in %s\n' %
(psioh.get_default_path() + cfour_tmpdir))
else:
shutil.rmtree(cfour_tmpdir)
except OSError as e:
print('Unable to remove CFOUR temporary directory %s' % e,
file=sys.stderr)
exit(1)
# Return to submission directory and reopen output file
os.chdir(current_directory)
core.print_out('\n')
p4util.banner(' Cfour %s %s Results ' %
(name.lower(), calledby.capitalize()))
core.print_variables()
if c4grad is not None:
core.get_gradient().print_out()
core.print_out('\n')
p4util.banner(' Cfour %s %s Results ' %
(name.lower(), calledby.capitalize()))
core.print_variables()
if c4grad is not None:
core.get_gradient().print_out()
# Quit if Cfour threw error
if 'CFOUR ERROR CODE' in core.variables():
raise ValidationError("""Cfour exited abnormally.""")
P4C4_INFO.clear()
P4C4_INFO.update(internal_p4c4_info)
optstash.restore()
# new skeleton wavefunction w/mol, highest-SCF basis (just to choose one), & not energy
# Feb 2017 hack. Could get proper basis in skel wfn even if not through p4 basis kw
if core.get_global_option('BASIS') in ["", "(AUTO)"]:
gobas = "sto-3g"
else:
gobas = core.get_global_option('BASIS')
basis = core.BasisSet.build(molecule, "ORBITAL", gobas)
if basis.has_ECP():
raise ValidationError("""ECPs not hooked up for Cfour""")
wfn = core.Wavefunction(molecule, basis)
for k, v in psivar.items():
wfn.set_variable(k.upper(), float(v))
optstash.restore()
if dertype == 0:
finalquantity = psivar['CURRENT ENERGY']
elif dertype == 1:
finalquantity = core.get_gradient()
wfn.set_gradient(finalquantity)
if finalquantity.rows(0) < 20:
core.print_out('CURRENT GRADIENT')
finalquantity.print_out()
elif dertype == 2:
pass
#finalquantity = finalhessian
#wfn.set_hessian(finalquantity)
#if finalquantity.rows(0) < 20:
# core.print_out('CURRENT HESSIAN')
# finalquantity.print_out()
return wfn
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()
# Normal string based data
elif name.lower() in superfunctionals.keys():
sup = superfunctionals[name.lower()](name, npoints, deriv, restricted)
elif name.upper() in superfunctionals.keys():
sup = superfunctionals[name.upper()](name, npoints, deriv, restricted)
# Check if we are dispersion
elif any(name.lower().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 ValidationError(
"SCF: Functional (%s) with base (%s) not found!" %
(name, 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 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"))
# 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 ValidationError(
"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.'
)
sup[0].set_lock(True)
return sup
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
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):Build 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):Build 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()
# Hybrid xc kernel check
do_hybrid = core.get_option("SAPT", "SAPT_DFT_DO_HYBRID")
is_x_hybrid = wfn_B.functional().is_x_hybrid()
is_x_lrc = wfn_B.functional().is_x_lrc()
hybrid_specified = core.has_option_changed("SAPT", "SAPT_DFT_DO_HYBRID")
if is_x_lrc:
if do_hybrid:
if hybrid_specified:
raise ValidationError(
"SAPT(DFT): Hybrid xc kernel not yet implemented for range-separated funtionals."
)
else:
core.print_out(
"Warning: Hybrid xc kernel not yet implemented for range-separated funtionals; hybrid kernel capability is turned off.\n"
)
is_hybrid = False
else:
if do_hybrid:
is_hybrid = is_x_hybrid
else:
is_hybrid = False
# 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'))
x_alpha = wfn_B.functional().x_alpha()
if not is_hybrid:
x_alpha = 0.0
fdds_disp = sapt_mp2_terms.df_fdds_dispersion(primary_basis, aux_basis,
cache, is_hybrid, x_alpha)
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 run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF', 'SCF_TYPE'],
['SCF', 'REFERENCE'],
['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()
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(" ==> 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 (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 = {}
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_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(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)
# 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(sapt_dft_functional, post_scf=False, 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)"))
# Copy data back into globals
for k, v in data.items():
core.set_variable(k, v)
core.tstop()
return dimer_wfn
def run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF', 'SCF_TYPE'], ['SCF', 'REFERENCE'], ['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()
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_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 (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_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")
core.set_global_option("SAVE_JK", True)
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")
core.set_global_option("SAVE_JK", False)
# 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"),
Sinf=core.get_option("SAPT", "DO_IND_EXCH_SINF"))
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")
sapt_jk.finalize()
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
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(
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)
# 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.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")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Write out header
scf_alg = core.get_option("SCF", "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 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 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 = dft_builder.build_superfunctional_from_dictionary(name, npoints, deriv, restricted)
# Check for pre-defined dict-based functionals
elif name.lower() in dft_builder.functionals:
sup = dft_builder.build_superfunctional_from_dictionary(dft_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 not isinstance(name, dict):
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 not isinstance(name, dict):
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