def compute_hessian(self, molecule):
"""
#magic (if magic was easy)
"""
optstash = p4util.OptionsState(['PRINT'])
core.set_global_option('PRINT', 0)
core.print_out("\n\n Analytical Dispersion Hessians are not supported by dftd3 or gcp.\n")
core.print_out(" Computing the Hessian through finite difference of gradients.\n\n")
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_parent_symmetry(molecule.schoenflies_symbol())
gradients = []
for geom in core.fd_geoms_freq_1(molecule, -1):
molclone.set_geometry(geom)
molclone.update_geometry()
gradients.append(self.compute_gradient(molclone))
H = core.fd_freq_1(molecule, gradients, -1)
# H.print_out()
optstash.restore()
return H
def cubeprop(wfn, **kwargs):
"""Evaluate properties on a grid and generate cube files.
.. versionadded:: 0.5
*wfn* parameter passed explicitly
:returns: None
:type wfn: :py:class:`~psi4.core.Wavefunction`
:param wfn: set of molecule, basis, orbitals from which to generate cube files
:examples:
>>> # [1] Cube files for all orbitals
>>> E, wfn = energy('b3lyp', return_wfn=True)
>>> cubeprop(wfn)
>>> # [2] Cube files for density (alpha, beta, total, spin) and four orbitals
>>> # (two alpha, two beta)
>>> set cubeprop_tasks ['orbitals', 'density']
>>> set cubeprop_orbitals [5, 6, -5, -6]
>>> E, wfn = energy('scf', return_wfn=True)
>>> cubeprop(wfn)
"""
# By default compute the orbitals
if not core.has_global_option_changed('CUBEPROP_TASKS'):
core.set_global_option('CUBEPROP_TASKS', ['ORBITALS'])
if ((core.get_global_option('INTEGRAL_PACKAGE') == 'ERD') and ('ESP' in core.get_global_option('CUBEPROP_TASKS'))):
raise ValidationError('INTEGRAL_PACKAGE ERD does not play nicely with electrostatic potential, so stopping.')
cp = core.CubeProperties(wfn)
cp.compute_properties()
def compute_hessian(self, molecule):
"""
#magic (if magic was easy)
"""
optstash = p4util.OptionsState(['PRINT'])
core.set_global_option('PRINT', 0)
core.print_out(
"\n\n Analytical Dispersion Hessians are not supported by dftd3 or gcp.\n"
)
core.print_out(
" Computing the Hessian through finite difference of gradients.\n\n"
)
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_parent_symmetry(molecule.schoenflies_symbol())
gradients = []
for geom in core.fd_geoms_freq_1(molecule, -1):
molclone.set_geometry(geom)
molclone.update_geometry()
gradients.append(self.compute_gradient(molclone))
H = core.fd_freq_1(molecule, gradients, -1)
# H.print_out()
optstash.restore()
return H
def set_options(options_dict):
"""
Sets Psi4 global options from an input dictionary.
"""
for k, v, in options_dict.items():
core.set_global_option(k.upper(), v)
def set_options(options_dict):
"""
Sets Psi4 global options from an input dictionary.
"""
for k, v, in options_dict.items():
core.set_global_option(k.upper(), v)
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_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 restore(self):
core.set_global_option(self.option, self.value_global)
if not self.haschanged_global:
core.revoke_global_option_changed(self.option)
if self.module:
core.set_local_option(self.module, self.option, self.value_local)
if not self.haschanged_local:
core.revoke_local_option_changed(self.module, self.option)
def restore(self):
core.set_global_option(self.option, self.value_global)
if not self.haschanged_global:
core.revoke_global_option_changed(self.option)
if self.module:
core.set_local_option(self.module, self.option, self.value_local)
if not self.haschanged_local:
core.revoke_local_option_changed(self.module, self.option)
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 restore(self):
"""Restore value and has_changed status to saved condition."""
core.set_global_option(self.option, self.value_global)
if not self.haschanged_global:
core.revoke_global_option_changed(self.option)
if self.module:
core.set_local_option(self.module, self.option, self.value_local)
if not self.haschanged_local:
core.revoke_local_option_changed(self.module, self.option)
def _core_jk_build(orbital_basis, aux=None, jk_type=None, do_wK=None, memory=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
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 : str, optional
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_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 check_disk_df(name, optstash):
optstash.add_option(['SCF_TYPE'])
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE') or core.get_global_option('SCF_TYPE') == "DF":
core.set_global_option('SCF_TYPE', 'DISK_DF')
core.print_out(f""" For method '{name}', SCF Algorithm Type (re)set to DISK_DF.\n""")
else:
if core.get_global_option('SCF_TYPE') == "MEM_DF":
raise ValidationError(f" Method '{name}' requires SCF_TYPE = DISK_DF, please use SCF_TYPE = DF to automatically choose the correct DFJK implementation.")
def compute_hessian(
self,
molecule: 'psi4.core.Molecule',
wfn: 'psi4.core.Wavefunction' = None) -> 'psi4.core.Matrix':
"""Compute dispersion Hessian based on engine, dispersion level, and parameters in `self`.
Uses finite difference, as no dispersion engine has analytic second derivatives.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
wfn :
Location to set QCVariables
Returns
-------
psi4.core.Matrix
(3*nat, 3*nat) dispersion Hessian [Eh/a0/a0].
"""
optstash = p4util.OptionsState(['PRINT'], ['PARENT_SYMMETRY'])
core.set_global_option('PRINT', 0)
core.print_out(
"\n\n Analytical Dispersion Hessians are not supported by dftd3 or gcp.\n"
)
core.print_out(
" Computing the Hessian through finite difference of gradients.\n\n"
)
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_global_option("PARENT_SYMMETRY",
molecule.schoenflies_symbol())
findif_meta_dict = driver_findif.hessian_from_gradients_geometries(
molclone, -1)
for displacement in findif_meta_dict["displacements"].values():
geom_array = np.reshape(displacement["geometry"], (-1, 3))
molclone.set_geometry(core.Matrix.from_array(geom_array))
molclone.update_geometry()
displacement["gradient"] = self.compute_gradient(
molclone).np.ravel().tolist()
H = driver_findif.assemble_hessian_from_gradients(findif_meta_dict, -1)
if wfn is not None:
wfn.set_variable('DISPERSION CORRECTION HESSIAN', H)
optstash.restore()
return core.Matrix.from_array(H)
def set_options(options_dict, verbose=1):
"""Sets Psi4 options from an input dictionary.
Parameters
----------
options_dict : dict
Dictionary where keys are "option_name" for global options or
"module_name__option_name" (double underscore separation) for
option local to "module_name". Values are the option value. All
are case insensitive.
verbose : int, optional
Control print volume.
Returns
-------
None
"""
optionre = re.compile(r'\A(?P<module>\w+__)?(?P<option>\w+)\Z',
re.IGNORECASE)
rejected = {}
for k, v, in options_dict.items():
mobj = optionre.match(k.strip())
module = mobj.group('module').upper()[:-2] if mobj.group(
'module') else None
option = mobj.group('option').upper()
if module:
if ((module, option, v) not in [
('SCF', 'GUESS', 'READ')
]) and ((module, option) not in [('PCM', 'INPUT')]):
# TODO guess/read exception is for distributed driver. should be handled differently.
try:
core.set_local_option(module, option, v)
except RuntimeError as err:
rejected[k] = (v, err)
if verbose > 1:
print('Setting: core.set_local_option', module, option, v)
if (module, option) == ("PCM", "INPUT"):
pcm_helper(v)
else:
try:
core.set_global_option(option, v)
except RuntimeError as err:
rejected[k] = (v, err)
if verbose > 1:
print('Setting: core.set_global_option', option, v)
if rejected:
raise ValidationError(f'Error setting options: {rejected}')
def __enter__(self):
self.optstash = p4util.optproc.OptionsState(*self.psikwargs.keys())
for k, v in self.psikwargs.items():
# Integer options need to be passed as an integer type,
# or else it fails to set and doesn't throw an error.
if re.match('^\d+$', v) is not None:
v = int(v)
if len(k) == 1:
core.set_global_option(k[0], v)
else:
core.set_local_option(k[0], k[1], v)
def psi4_set_options(my_options,mod_name,wfn):
for i in my_options.get_keys():
opt=""
if mod_name in pulsar_2_psi4 and i in pulsar_2_psi4[mod_name]:
opt=pulsar_2_psi4[mod_name][i]
elif i in pulsar_2_psi4["GLOBAL"]:
opt=pulsar_2_psi4["GLOBAL"][i]
else: continue
da_opt=my_options.get(i)
if i=="BASIS_SET":
bs_name=wfn.system.as_universe()[0].basis_sets[da_opt].description
psi4.set_global_option(opt,bs_name)
else:psi4.set_global_option(opt,da_opt)
def check_disk_df(name, optstash):
optstash.add_option(['SCF_TYPE'])
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DISK_DF')
core.print_out(""" Method '%s' requires SCF_TYPE = DISK_DF, setting.\n""" % name)
elif core.get_global_option('SCF_TYPE') == "DF":
core.set_global_option('SCF_TYPE', 'DISK_DF')
core.print_out(""" Method '%s' requires SCF_TYPE = DISK_DF, setting.\n""" % name)
else:
if core.get_global_option('SCF_TYPE') != "DISK_DF":
raise ValidationError(" %s requires SCF_TYPE = DISK_DF, please use SCF_TYPE = DF to automatically choose the correct DFJK implementation." % name)
def check_disk_df(name, optstash):
optstash.add_option(['SCF_TYPE'])
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DISK_DF')
core.print_out(""" Method '%s' requires SCF_TYPE = DISK_DF, setting.\n""" % name)
elif core.get_global_option('SCF_TYPE') == "DF":
core.set_global_option('SCF_TYPE', 'DISK_DF')
core.print_out(""" Method '%s' requires SCF_TYPE = DISK_DF, setting.\n""" % name)
else:
if core.get_global_option('SCF_TYPE') != "DISK_DF":
raise ValidationError(" %s requires SCF_TYPE = DISK_DF, please use SCF_TYPE = DF to automatically choose the correct DFJK implementation." % name)
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 and/or wave function did not converge, but proceeding anyway.\n\n"
)
else:
core.print_out(" Energy and wave function converged.\n\n")
scf_energy = self.finalize_energy()
return scf_energy
def _core_set_global_option_python(key, EXTERN):
"""
This is a fairly hacky way to get around EXTERN issues. Effectively we are routing this option Python side through attributes until the general Options overhaul.
"""
if (key != "EXTERN"):
raise ValidationError("Options: set_global_option_python does not recognize keyword %s" % key)
if EXTERN is None:
core.EXTERN = None
core.set_global_option("EXTERN", False)
elif isinstance(EXTERN, core.ExternalPotential):
# Well this is probably the worst hack I have done, thats saying something
core.EXTERN = EXTERN
core.set_global_option("EXTERN", True)
else:
raise ValidationError("Options: set_global_option_python can either be a NULL or External Potential object")
def _core_set_global_option_python(key, EXTERN):
"""
This is a fairly hacky way to get around EXTERN issues. Effectively we are routing this option Python side through attributes until the general Options overhaul.
"""
if (key != "EXTERN"):
raise ValidationError("Options: set_global_option_python does not recognize keyword %s" % key)
if EXTERN is None:
core.EXTERN = None
core.set_global_option("EXTERN", False)
elif isinstance(EXTERN, core.ExternalPotential):
# Well this is probably the worst hack I have done, thats saying something
core.EXTERN = EXTERN
core.set_global_option("EXTERN", True)
else:
raise ValidationError("Options: set_global_option_python can either be a NULL or External Potential object")
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 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 == '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 set_options(options_dict, verbose=1):
"""Sets Psi4 options from an input dictionary.
Parameters
----------
options_dict : dict
Dictionary where keys are "option_name" for global options or
"module_name__option_name" (double underscore separation) for
option local to "module_name". Values are the option value. All
are case insensitive.
verbose : int, optional
Control print volume.
Returns
-------
None
"""
optionre = re.compile(r'\A(?P<module>\w+__)?(?P<option>\w+)\Z', re.IGNORECASE)
rejected = {}
for k, v, in options_dict.items():
mobj = optionre.match(k)
module = mobj.group('module').upper()[:-2] if mobj.group('module') else None
option = mobj.group('option').upper()
if module:
if (module, option, v) not in [('SCF', 'GUESS', 'READ')]:
# TODO guess/read exception is for distributed driver. should be handled differently.
try:
core.set_local_option(module, option, v)
except RuntimeError as err:
rejected[k] = (v, err)
if verbose > 1:
print('Setting: core.set_local_option', module, option, v)
else:
try:
core.set_global_option(option, v)
except RuntimeError as err:
rejected[k] = (v, err)
if verbose > 1:
print('Setting: core.set_global_option', option, v)
if rejected:
raise ValidationError(f'Error setting options: {rejected}')
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 pcm_helper(block):
"""
Passes multiline string *block* to PCMSolver parser.
Parameters
----------
block: multiline string with PCM input in PCMSolver syntax.
"""
suffix = str(os.getpid()) + '.' + str(uuid.uuid4())[:8]
pcmsolver_fname = 'pcmsolver.' + suffix + '.inp'
with open(pcmsolver_fname, 'w') as handle:
handle.write(block)
import pcmsolver
parsed_pcm = pcmsolver.parse_pcm_input(pcmsolver_fname)
os.remove(pcmsolver_fname)
pcmsolver_parsed_fname = '@pcmsolver.' + suffix
with open(pcmsolver_parsed_fname, 'w') as tmp:
tmp.write(parsed_pcm)
core.set_global_option('PCMSOLVER_PARSED_FNAME', '{}'.format(pcmsolver_parsed_fname))
def pcm_helper(block):
"""
Passes multiline string *block* to PCMSolver parser.
Parameters
----------
block: multiline string with PCM input in PCMSolver syntax.
"""
suffix = str(os.getpid()) + '.' + str(uuid.uuid4())[:8]
pcmsolver_fname = 'pcmsolver.' + suffix + '.inp'
with open(pcmsolver_fname, 'w') as handle:
handle.write(block)
import pcmsolver
parsed_pcm = pcmsolver.parse_pcm_input(pcmsolver_fname)
os.remove(pcmsolver_fname)
pcmsolver_parsed_fname = '@pcmsolver.' + suffix
with open(pcmsolver_parsed_fname, 'w') as tmp:
tmp.write(parsed_pcm)
core.set_global_option('PCMSOLVER_PARSED_FNAME', '{}'.format(pcmsolver_parsed_fname))
def process_option(spaces, module, key, value, line):
"""Function to process a line with set or in a set block
into global/local domain and keyword/value.
"""
module = module.upper()
key = key.upper()
isbasis = True if 'BASIS' in key else False
value = quotify(value.strip(), isbasis=isbasis)
if module == "GLOBALS" or module == "GLOBAL" or module == "" or module.isspace():
# If it's really a global, we need slightly different syntax
if runalso:
core.set_global_option(key, dequotify(value))
return "%score.set_global_option(\"%s\", %s)\n" % (spaces, key, value)
else:
# It's a local option, so we need the module name in there too
if runalso:
core.set_local_option(module, key, dequotify(value))
return "%score.set_local_option(\"%s\", \"%s\", %s)\n" % (spaces, module, key, value)
def process_option(spaces, module, key, value, line):
"""Function to process a line with set or in a set block
into global/local domain and keyword/value.
"""
module = module.upper()
key = key.upper()
isbasis = True if 'BASIS' in key else False
value = quotify(value.strip(), isbasis=isbasis)
if module == "GLOBALS" or module == "GLOBAL" or module == "" or module.isspace():
# If it's really a global, we need slightly different syntax
if runalso:
core.set_global_option(key, dequotify(value))
return "%score.set_global_option(\"%s\", %s)\n" % (spaces, key, value)
else:
# It's a local option, so we need the module name in there too
if runalso:
core.set_local_option(module, key, dequotify(value))
return "%score.set_local_option(\"%s\", \"%s\", %s)\n" % (spaces, module, key, value)
def compute_hessian(self, molecule):
"""Compute dispersion Hessian based on engine, dispersion level, and parameters in `self`.
Uses finite difference, as no dispersion engine has analytic second derivatives.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
Returns
-------
psi4.core.Matrix
(3*nat, 3*nat) dispersion Hessian [Eh/a0/a0].
"""
optstash = p4util.OptionsState(['PRINT'])
core.set_global_option('PRINT', 0)
core.print_out("\n\n Analytical Dispersion Hessians are not supported by dftd3 or gcp.\n")
core.print_out(" Computing the Hessian through finite difference of gradients.\n\n")
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_parent_symmetry(molecule.schoenflies_symbol())
findif_meta_dict = driver_findif.hessian_from_gradient_geometries(molclone, -1)
for displacement in findif_meta_dict["displacements"].values():
geom_array = np.reshape(displacement["geometry"], (-1, 3))
molclone.set_geometry(core.Matrix.from_array(geom_array))
molclone.update_geometry()
displacement["gradient"] = self.compute_gradient(molclone).np.ravel().tolist()
H = driver_findif.compute_hessian_from_gradients(findif_meta_dict, -1)
optstash.restore()
return core.Matrix.from_array(H)
def compute_hessian(self, molecule):
"""Compute dispersion Hessian based on engine, dispersion level, and parameters in `self`.
Uses finite difference, as no dispersion engine has analytic second derivatives.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
Returns
-------
psi4.core.Matrix
(3*nat, 3*nat) dispersion Hessian [Eh/a0/a0].
"""
optstash = p4util.OptionsState(['PRINT'])
core.set_global_option('PRINT', 0)
core.print_out("\n\n Analytical Dispersion Hessians are not supported by dftd3 or gcp.\n")
core.print_out(" Computing the Hessian through finite difference of gradients.\n\n")
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_parent_symmetry(molecule.schoenflies_symbol())
gradients = []
for geom in driver_findif.hessian_from_gradient_geometries(molecule, -1):
molclone.set_geometry(geom)
molclone.update_geometry()
gradients.append(self.compute_gradient(molclone))
H = driver_findif.compute_hessian_from_gradient(molecule, gradients, -1)
optstash.restore()
return core.Matrix.from_array(H)
def reset_pe_options(pofm):
"""Acts on Process::environment.options to clear it, the set it to state encoded in `pofm`.
Parameters
----------
pofm : dict
Result of psi4.driver.p4util.prepare_options_for_modules(changedOnly=True, commandsInsteadDict=False)
Returns
-------
None
"""
core.clean_options()
for go, dgo in pofm['GLOBALS'].items():
if dgo['has_changed']:
core.set_global_option(go, dgo['value'])
for module in _modules:
for lo, dlo in pofm[module].items():
if dlo['has_changed']:
core.set_local_option(module, lo, dlo['value'])
def _reset_pe_options(pofm: Dict):
"""Acts on ``Process::environment.options`` to clear it, then set it to state encoded in **pofm**.
Parameters
----------
pofm
Result of :py:func:`psi4.driver.p4util.prepare_options_for_modules(changedOnly=True, commandsInsteadDict=False, stateInsteadMediated=True)`
Returns
-------
None
"""
core.clean_options()
for go, dgo in pofm['GLOBALS'].items():
if dgo['has_changed']:
core.set_global_option(go, dgo['value'])
for module in _modules:
for lo, dlo in pofm[module].items():
if dlo['has_changed']:
core.set_local_option(module, lo, dlo['value'])
def reset_pe_options(pofm):
"""Acts on Process::environment.options to clear it, the set it to state encoded in `pofm`.
Parameters
----------
pofm : dict
Result of psi4.driver.p4util.prepare_options_for_modules(changedOnly=True, commandsInsteadDict=False)
Returns
-------
None
"""
core.clean_options()
for go, dgo in pofm['GLOBALS'].items():
if dgo['has_changed']:
core.set_global_option(go, dgo['value'])
for module in _modules:
for lo, dlo in pofm[module].items():
if dlo['has_changed']:
core.set_local_option(module, lo, dlo['value'])
def _init_df(self, calc):
if self.no_reuse:
return
if calc.B == 'd':
candidates = [
calcid('m1', 'd', calc.Z),
calcid('m2', 'd', calc.Z),
calcid('d', 'd', calc.Z)
]
core.set_global_option("DF_INTS_IO", "SAVE")
for c in filter(lambda c: c in self.wfn_cache, candidates):
oldns = self.fmt_ns(c)
newns = self.fmt_ns(calc)
core.IO.change_file_namespace(psif.PSIF_DFSCF_BJ, oldns, newns)
core.set_global_option("DF_INTS_IO", "LOAD")
else:
core.set_global_option("DF_INTS_IO", "NONE")
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.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.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.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.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.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.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 run_json_qcschema(json_data, clean, json_serialization, keep_wfn=False):
"""
An implementation of the QC JSON Schema (molssi-qc-schema.readthedocs.io/en/latest/index.html#) implementation in Psi4.
Parameters
----------
json_data : JSON
Please see molssi-qc-schema.readthedocs.io/en/latest/spec_components.html for further details.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/MolSSI/QC_JSON_Schema.
Examples
--------
"""
# Clean a few things
_clean_psi_environ(clean)
# This is currently a forced override
if json_data["schema_name"] in ["qc_schema_input", "qcschema_input"]:
json_data["schema_name"] = "qcschema_input"
else:
raise KeyError("Schema name of '{}' not understood".format(
json_data["schema_name"]))
if json_data["schema_version"] != 1:
raise KeyError("Schema version of '{}' not understood".format(
json_data["schema_version"]))
if json_data.get("nthreads", False) is not False:
core.set_num_threads(json_data["nthreads"], quiet=True)
# Build molecule
if "schema_name" in json_data["molecule"]:
molschemus = json_data["molecule"] # dtype >=2
else:
molschemus = json_data # dtype =1
mol = core.Molecule.from_schema(molschemus)
# Update molecule geometry as we orient and fix_com
json_data["molecule"]["geometry"] = mol.geometry().np.ravel().tolist()
# Set options
kwargs = json_data["keywords"].pop("function_kwargs", {})
p4util.set_options(json_data["keywords"])
# Setup the computation
method = json_data["model"]["method"]
core.set_global_option("BASIS", json_data["model"]["basis"])
kwargs.update({"return_wfn": True, "molecule": mol})
# Handle special properties case
if json_data["driver"] == "properties":
if "properties" not in kwargs:
kwargs["properties"] = list(default_properties_)
# Actual driver run
val, wfn = methods_dict_[json_data["driver"]](method, **kwargs)
# Pull out a standard set of SCF properties
if "extras" not in json_data:
json_data["extras"] = {}
json_data["extras"]["qcvars"] = {}
current_qcvars_only = json_data["extras"].get("current_qcvars_only", False)
if json_data["extras"].get("wfn_qcvars_only", False):
psi_props = wfn.variables(
include_deprecated_keys=(not current_qcvars_only))
else:
psi_props = core.variables(
include_deprecated_keys=(not current_qcvars_only))
for k, v in psi_props.items():
if k not in json_data["extras"]["qcvars"]:
json_data["extras"]["qcvars"][k] = _serial_translation(
v, json=json_serialization)
# Still a bit of a mess at the moment add in local vars as well.
for k, v in wfn.variables().items():
if k not in json_data["extras"]["qcvars"]:
# interpreting wfn_qcvars_only as no deprecated qcvars either
if not (json_data["extras"].get("wfn_qcvars_only", False) and
(any([
k.upper().endswith(" DIPOLE " + cart)
for cart in ["X", "Y", "Z"]
]) or any([
k.upper().endswith(" QUADRUPOLE " + cart)
for cart in ["XX", "YY", "ZZ", "XY", "XZ", "YZ"]
]) or k.upper() in [
"SOS-MP2 CORRELATION ENERGY",
"SOS-MP2 TOTAL ENERGY",
"SOS-PI-MP2 CORRELATION ENERGY",
"SOS-PI-MP2 TOTAL ENERGY",
"SCS-MP3 CORRELATION ENERGY",
"SCS-MP3 TOTAL ENERGY",
])):
json_data["extras"]["qcvars"][k] = _serial_translation(
v, json=json_serialization)
# Handle the return result
if json_data["driver"] == "energy":
json_data["return_result"] = val
elif json_data["driver"] in ["gradient", "hessian"]:
json_data["return_result"] = _serial_translation(
val, json=json_serialization)
elif json_data["driver"] == "properties":
ret = {}
mtd = json_data["model"]["method"].upper()
# Dipole/quadrupole still special case
if "dipole" in kwargs["properties"]:
ret["dipole"] = _serial_translation(psi_props[mtd + " DIPOLE"],
json=json_serialization)
if "quadrupole" in kwargs["properties"]:
ret["quadrupole"] = _serial_translation(psi_props[mtd +
" QUADRUPOLE"],
json=json_serialization)
ret.update(
_convert_variables(wfn.variables(),
context="properties",
json=json_serialization))
json_data["return_result"] = ret
else:
raise KeyError("Did not understand Driver key %s." %
json_data["driver"])
props = {
"calcinfo_nbasis": wfn.nso(),
"calcinfo_nmo": wfn.nmo(),
"calcinfo_nalpha": wfn.nalpha(),
"calcinfo_nbeta": wfn.nbeta(),
"calcinfo_natom": mol.geometry().shape[0],
"nuclear_repulsion_energy": mol.nuclear_repulsion_energy(
), # use this b/c psivar is monomer for SAPT
}
props.update(
_convert_variables(psi_props,
context="generics",
json=json_serialization))
if not list(
set(['CBS NUMBER', 'NBODY NUMBER', 'FINDIF NUMBER'])
& set(json_data["extras"]["qcvars"].keys())):
props.update(
_convert_variables(psi_props,
context="scf",
json=json_serialization))
# Write out post-SCF keywords
if "MP2 CORRELATION ENERGY" in psi_props:
props.update(
_convert_variables(psi_props,
context="mp2",
json=json_serialization))
if "CCSD CORRELATION ENERGY" in psi_props:
props.update(
_convert_variables(psi_props,
context="ccsd",
json=json_serialization))
if "CCSD(T) CORRELATION ENERGY" in psi_props:
props.update(
_convert_variables(psi_props,
context="ccsd(t)",
json=json_serialization))
json_data["properties"] = props
json_data["success"] = True
json_data["provenance"]["module"] = wfn.module()
if keep_wfn:
json_data["wavefunction"] = _convert_wavefunction(wfn)
files = {
"psi4.grad":
Path(core.get_writer_file_prefix(wfn.molecule().name()) + ".grad"),
"psi4.hess":
Path(core.get_writer_file_prefix(wfn.molecule().name()) + ".hess"),
# binary "psi4.180.npy": Path(core.get_writer_file_prefix(wfn.molecule().name()) + ".180.npy"),
"timer.dat":
Path(
"timer.dat"
), # ok for `psi4 --qcschema` but no file collected for `qcengine.run_program(..., "psi4")`
}
json_data["native_files"] = {
fl: flpath.read_text()
for fl, flpath in files.items() if flpath.exists()
}
# Reset state
_clean_psi_environ(clean)
json_data["schema_name"] = "qcschema_output"
return json_data
def run_json_qc_schema(json_data, clean):
"""
An implementation of the QC JSON Schema (molssi-qc-schema.readthedocs.io/en/latest/index.html#) implementation in Psi4.
Parameters
----------
json_data : JSON
Please see molssi-qc-schema.readthedocs.io/en/latest/spec_components.html for further details.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/MolSSI/QC_JSON_Schema.
Examples
--------
"""
# Clean a few things
_clean_psi_environ(clean)
# This is currently a forced override
if json_data["schema_name"] != "qc_schema_input":
raise KeyError("Schema name of '{}' not understood".format(json_data["schema_name"]))
if json_data["schema_version"] != 1:
raise KeyError("Schema version of '{}' not understood".format(json_data["schema_version"]))
if json_data.get("nthreads", False) is not False:
psi4.set_num_threads(json_data["nthreads"], quiet=True)
json_data["provenance"] = {"creator": "Psi4", "version": psi4.__version__, "routine": "psi4.json.run_json"}
# Build molecule
mol = core.Molecule.from_schema(json_data)
# Update molecule geometry as we orient and fix_com
json_data["molecule"]["geometry"] = mol.geometry().np.ravel().tolist()
# Set options
for k, v in json_data["keywords"].items():
core.set_global_option(k, v)
# Setup the computation
method = json_data["model"]["method"]
core.set_global_option("BASIS", json_data["model"]["basis"])
kwargs = {"return_wfn": True, "molecule": mol}
# Handle special properties case
if json_data["driver"] == "properties":
if "properties" in json_data["model"]:
kwargs["properties"] = [x.lower() for x in json_data["model"]["properties"]]
extra = set(kwargs["properties"]) - can_do_properties_
if len(extra):
raise KeyError("Did not understand property key %s." % kwargs["properties"])
else:
kwargs["properties"] = list(can_do_properties_)
# Actual driver run
val, wfn = methods_dict_[json_data["driver"]](method, **kwargs)
# Pull out a standard set of SCF properties
psi_props = psi4.core.get_variables()
json_data["psi4:qcvars"] = psi_props
# Handle the return result
if json_data["driver"] == "energy":
json_data["return_result"] = val
elif json_data["driver"] in ["gradient", "hessian"]:
json_data["return_result"] = val.np.ravel().tolist()
elif json_data["driver"] == "properties":
ret = {}
mtd = json_data["model"]["method"].upper()
if "dipole" in kwargs["properties"]:
ret["dipole"] = [psi_props[mtd + " DIPOLE " + x] for x in ["X", "Y", "Z"]]
if "quadrupole" in kwargs["properties"]:
ret["quadrupole"] = [psi_props[mtd + " QUADRUPOLE " + x] for x in ["XX", "XY", "XZ", "YY", "YZ", "ZZ"]]
if "mulliken_charges" in kwargs["properties"]:
ret["mulliken_charges"] = wfn.get_array("MULLIKEN_CHARGES").np.ravel().tolist()
if "lowdin_charges" in kwargs["properties"]:
ret["lowdin_charges"] = wfn.get_array("LOWDIN_CHARGES").np.ravel().tolist()
if "wiberg_lowdin_indices" in kwargs["properties"]:
ret["wiberg_lowdin_indices"] = wfn.get_array("WIBERG_LOWDIN_INDICES").np.ravel().tolist()
if "mayer_indices" in kwargs["properties"]:
ret["mayer_indices"] = wfn.get_array("MAYER_INDICES").np.ravel().tolist()
json_data["return_result"] = ret
else:
raise KeyError("Did not understand Driver key %s." % json_data["driver"])
props = {
"calcinfo_nbasis": wfn.nso(),
"calcinfo_nmo": wfn.nmo(),
"calcinfo_nalpha": wfn.nalpha(),
"calcinfo_nbeta": wfn.nbeta(),
"calcinfo_natom": mol.geometry().shape[0],
"scf_one_electron_energy": psi_props["ONE-ELECTRON ENERGY"],
"scf_two_electron_energy": psi_props["TWO-ELECTRON ENERGY"],
"nuclear_repulsion_energy": psi_props["NUCLEAR REPULSION ENERGY"],
"scf_dipole_moment": [psi_props[x] for x in ["SCF DIPOLE X", "SCF DIPOLE Y", "SCF DIPOLE Z"]],
"scf_iterations": int(psi_props["SCF ITERATIONS"]),
"scf_total_energy": psi_props["SCF TOTAL ENERGY"],
"return_energy": psi_props["CURRENT ENERGY"],
}
# Pull out optional SCF keywords
other_scf = [("DFT VV10 ENERGY", "scf_vv10_energy"), ("DFT XC ENERGY", "scf_xc_energy"),
("DISPERSION CORRECTION ENERGY", "scf_dispersion_correction_energy")]
for pkey, skey in other_scf:
if (pkey in psi_props) and (psi_props[pkey] != 0):
props[skey] = psi_props[pkey]
# Write out MP2 keywords
if "MP2 CORRELATION ENERGY" in psi_props:
props["mp2_same_spin_correlation_energy"] = psi_props["MP2 SAME-SPIN CORRELATION ENERGY"]
props["mp2_opposite_spin_correlation_energy"] = psi_props["MP2 OPPOSITE-SPIN CORRELATION ENERGY"]
props["mp2_singles_energy"] = 0.0
props["mp2_doubles_energy"] = psi_props["MP2 CORRELATION ENERGY"]
props["mp2_total_correlation_energy"] = psi_props["MP2 CORRELATION ENERGY"]
props["mp2_total_energy"] = psi_props["MP2 TOTAL ENERGY"]
json_data["properties"] = props
json_data["success"] = True
# Reset state
_clean_psi_environ(clean)
json_data["schema_name"] = "qc_schema_output"
return json_data
def run_json_original_v1_1(json_data, clean):
"""
Runs and updates the input JSON data.
This was a trial specification introduced in Psi4 v1.1. This will be deprecated in Psi4 v1.3 in favour of the QC JSON format
found here: http://molssi-qc-schema.readthedocs.io/en/latest/index.html#
Parameters
----------
json_data : JSON input
Required input fields:
- molecule : str
A string representation of the molecule. Any valid psi4 synatx is valid.
- driver : str
The driver method to use valid arguments are "energy", "gradient", "property"
- args : str
Input arguments to the driver function
- kwargs : dict
Input dictionary to the driver function
Optional input fields:
- kwargs : dict
Additional kwargs to be passed to the driver.
- options : dict
Global options to set in the Psi4 run.
- scratch_location : str
Overide the default scratch location.
- return_output : bool
Return the full string reprsentation of the output or not.
Output fields:
- return_value : float, psi4.core.Vector, psi4.core.Matrix
The return value of the input function.
- variables : dict
The list of Psi4 variables generated from the run.
- success : bool
Indicates if the run was successful or not.
- error : str
If an error is raised the result is returned here.
- raw_output : str
The full Psi4 output if requested.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/psi4/psi4.
Examples
--------
# CP corrected Helium dimer energy in the STO-3G basis.
>>> json_data = {}
>>> json_data["molecule"] = "He 0 0 0\n--\nHe 0 0 1"
>>> json_data["driver"] = "energy"
>>> json_data["method"] = 'SCF'
>>> json_data["kwargs"] = {"bsse_type": "cp"}
>>> json_data["options"] = {"BASIS": "STO-3G"}
>>> run_json(json_data)
{
"raw_output": "Output storing was not requested.",
"options": {
"BASIS": "STO-3G"
},
"driver": "energy",
"molecule": "He 0 0 0\n--\nHe 0 0 1",
"method": "SCF",
"variables": {
"SCF N ITERS": 2.0,
"SCF DIPOLE Y": 0.0,
"CURRENT DIPOLE Y": 0.0,
"CP-CORRECTED 2-BODY INTERACTION ENERGY": 0.1839360538612116,
"HF TOTAL ENERGY": -5.433191881443323,
"SCF TOTAL ENERGY": -5.433191881443323,
"TWO-ELECTRON ENERGY": 4.124089347186247,
"SCF ITERATION ENERGY": -5.433191881443323,
"CURRENT DIPOLE X": 0.0,
"CURRENT DIPOLE Z": 0.0,
"CURRENT REFERENCE ENERGY": -5.433191881443323,
"CURRENT ENERGY": 0.1839360538612116,
"COUNTERPOISE CORRECTED TOTAL ENERGY": -5.433191881443323,
"SCF DIPOLE Z": 0.0,
"COUNTERPOISE CORRECTED INTERACTION ENERGY": 0.1839360538612116,
"NUCLEAR REPULSION ENERGY": 2.11670883436,
"SCF DIPOLE X": 0.0,
"ONE-ELECTRON ENERGY": -11.67399006298957
},
"return_value": 0.1839360538612116,
"error": "",
"success": true,
"provenance": {
"creator": "Psi4",
"routine": "psi4.run_json",
"version": "1.1a1"
},
"kwargs": {
"bsse_type": "cp"
}
}
"""
# Clean a few things
_clean_psi_environ(clean)
# Set a few variables
json_data["error"] = ""
json_data["success"] = False
json_data["raw_output"] = None
json_data[
"warning"] = "Warning! This format will be deprecated in Psi4 v1.3. Please switch the standard QC JSON format."
# Add the provenance data
prov = {}
prov["version"] = psi4.__version__
prov["routine"] = "psi4.run_json"
prov["creator"] = "Psi4"
json_data["provenance"] = prov
# Check input
for check in ["driver", "method", "molecule"]:
if check not in list(json_data):
json_data["error"] = "Minimum input requires the %s field." % check
return json_data
# Check driver call
if json_data["driver"] not in list(methods_dict_):
json_data["error"] = "Driver parameters '%s' not recognized" % str(json_data["driver"])
return json_data
# Set options
if "options" in json_data.keys() and (json_data["options"] is not None):
for k, v in json_data["options"].items():
core.set_global_option(k, v)
# Rework args
args = json_data["method"]
if not isinstance(args, (list, tuple)):
args = [args]
# Deep copy kwargs
if "kwargs" in list(json_data):
kwargs = copy.deepcopy(json_data["kwargs"])
else:
kwargs = {}
kwargs["molecule"] = molutil.geometry(json_data["molecule"])
# Full driver call
kwargs["return_wfn"] = True
if json_data["driver"] not in methods_dict_:
raise KeyError("Driver type '%s' not recognized." % str(json_data["driver"]))
val, wfn = methods_dict_[json_data["driver"]](*args, **kwargs)
if isinstance(val, (float, int)):
json_data["return_value"] = val
elif isinstance(val, (core.Matrix, core.Vector)):
json_data["return_value"] = val.to_serial()
else:
raise TypeError("Unrecognized return value of type %s\n" % type(val))
json_data["variables"] = core.get_variables()
json_data["success"] = True
# Reset state
_clean_psi_environ(clean)
return json_data
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 run_json_qcschema(json_data, clean):
"""
An implementation of the QC JSON Schema (molssi-qc-schema.readthedocs.io/en/latest/index.html#) implementation in Psi4.
Parameters
----------
json_data : JSON
Please see molssi-qc-schema.readthedocs.io/en/latest/spec_components.html for further details.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/MolSSI/QC_JSON_Schema.
Examples
--------
"""
# Clean a few things
_clean_psi_environ(clean)
# This is currently a forced override
if json_data["schema_name"] in ["qc_schema_input", "qcschema_input"]:
json_data["schema_name"] = "qcschema_input"
else:
raise KeyError("Schema name of '{}' not understood".format(json_data["schema_name"]))
if json_data["schema_version"] != 1:
raise KeyError("Schema version of '{}' not understood".format(json_data["schema_version"]))
if json_data.get("nthreads", False) is not False:
psi4.set_num_threads(json_data["nthreads"], quiet=True)
json_data["provenance"] = {"creator": "Psi4", "version": psi4.__version__, "routine": "psi4.json.run_json"}
# Build molecule
if "schema_name" in json_data["molecule"]:
molschemus = json_data["molecule"] # dtype >=2
else:
molschemus = json_data # dtype =1
mol = core.Molecule.from_schema(molschemus)
# Update molecule geometry as we orient and fix_com
json_data["molecule"]["geometry"] = mol.geometry().np.ravel().tolist()
# Set options
kwargs = json_data["keywords"].pop("function_kwargs", {})
psi4.set_options(json_data["keywords"])
# Setup the computation
method = json_data["model"]["method"]
core.set_global_option("BASIS", json_data["model"]["basis"])
kwargs.update({"return_wfn": True, "molecule": mol})
# Handle special properties case
if json_data["driver"] == "properties":
if "properties" in json_data["model"]:
kwargs["properties"] = [x.lower() for x in json_data["model"]["properties"]]
extra = set(kwargs["properties"]) - can_do_properties_
if len(extra):
raise KeyError("Did not understand property key %s." % kwargs["properties"])
else:
kwargs["properties"] = list(can_do_properties_)
# Actual driver run
val, wfn = methods_dict_[json_data["driver"]](method, **kwargs)
# Pull out a standard set of SCF properties
if "extras" not in json_data:
json_data["extras"] = {}
json_data["extras"]["qcvars"] = {}
if json_data["extras"].get("wfn_qcvars_only", False):
psi_props = wfn.variables()
else:
psi_props = psi4.core.variables()
for k, v in psi_props.items():
if k not in json_data["extras"]["qcvars"]:
json_data["extras"]["qcvars"][k] = _json_translation(v)
# Still a bit of a mess at the moment add in local vars as well.
for k, v in wfn.variables().items():
if k not in json_data["extras"]["qcvars"]:
json_data["extras"]["qcvars"][k] = _json_translation(v)
# Handle the return result
if json_data["driver"] == "energy":
json_data["return_result"] = val
elif json_data["driver"] in ["gradient", "hessian"]:
json_data["return_result"] = val.np.ravel().tolist()
elif json_data["driver"] == "properties":
ret = {}
mtd = json_data["model"]["method"].upper()
# Dipole/quadrupole still special case
if "dipole" in kwargs["properties"]:
ret["dipole"] = [psi_props[mtd + " DIPOLE " + x] for x in ["X", "Y", "Z"]]
if "quadrupole" in kwargs["properties"]:
ret["quadrupole"] = [psi_props[mtd + " QUADRUPOLE " + x] for x in ["XX", "XY", "XZ", "YY", "YZ", "ZZ"]]
ret.update(_convert_variables(wfn.variables(), context="properties"))
json_data["return_result"] = ret
else:
raise KeyError("Did not understand Driver key %s." % json_data["driver"])
props = {
"calcinfo_nbasis": wfn.nso(),
"calcinfo_nmo": wfn.nmo(),
"calcinfo_nalpha": wfn.nalpha(),
"calcinfo_nbeta": wfn.nbeta(),
"calcinfo_natom": mol.geometry().shape[0],
}
props.update(_convert_variables(psi_props, context="generics"))
if not list(set(['CBS NUMBER', 'NBODY NUMBER', 'FINDIF NUMBER']) & set(json_data["extras"]["qcvars"].keys())):
props.update(_convert_variables(psi_props, context="scf"))
# Write out MP2 keywords
if "MP2 CORRELATION ENERGY" in psi_props:
props.update(_convert_variables(psi_props, context="mp2"))
json_data["properties"] = props
json_data["success"] = True
# Reset state
_clean_psi_environ(clean)
json_data["schema_name"] = "qcschema_output"
return json_data
def basis_helper(block, name='', key='BASIS', set_option=True):
"""For PsiAPI mode, forms a basis specification function from *block*
and associates it with keyword *key* under handle *name*. Registers
the basis spec with Psi4 so that it can be applied again to future
molecules. For usage, see mints2, mints9, and cc54 test cases. Unless
*set_option* is False, *name* will be set as current active *key*,
equivalent to `set key name` or `set_option({key: name})`.
"""
key = key.upper()
name = ('anonymous' + str(uuid.uuid4())[:8]) if name == '' else name
cleanbas = basname(name).replace('-', '') # further remove hyphens so can be function name
block = qcel.util.filter_comments(block)
command_lines = re.split('\n', block)
symbol_re = re.compile(r'^\s*assign\s+(?P<symbol>[A-Z]{1,3})\s+(?P<basis>[-+*\(\)\w]+)\s*$', re.IGNORECASE)
label_re = re.compile(
r'^\s*assign\s+(?P<label>(?P<symbol>[A-Z]{1,3})(?:(_\w+)|(\d+))?)\s+(?P<basis>[-+*\(\)\w]+)\s*$',
re.IGNORECASE)
all_re = re.compile(r'^\s*assign\s+(?P<basis>[-+*\(\)\w]+)\s*$', re.IGNORECASE)
basislabel = re.compile(r'\s*\[\s*([-*\(\)\w]+)\s*\]\s*')
def anon(mol, role):
basstrings = {}
# Start by looking for assign lines, and remove them
leftover_lines = []
assignments = False
for line in command_lines:
if symbol_re.match(line):
m = symbol_re.match(line)
mol.set_basis_by_symbol(m.group('symbol'), m.group('basis'), role=role)
assignments = True
elif label_re.match(line):
m = label_re.match(line)
mol.set_basis_by_label(m.group('label'), m.group('basis'), role=role)
assignments = True
elif all_re.match(line):
m = all_re.match(line)
mol.set_basis_all_atoms(m.group('basis'), role=role)
assignments = True
else:
# Ignore blank lines and accumulate remainder
if line and not line.isspace():
leftover_lines.append(line.strip())
# Now look for regular basis set definitions
basblock = list(filter(None, basislabel.split('\n'.join(leftover_lines))))
if len(basblock) == 1:
if not assignments:
# case with no [basname] markers where whole block is contents of gbs file
mol.set_basis_all_atoms(name, role=role)
basstrings[basname(name)] = basblock[0]
else:
message = (
"Conflicting basis set specification: assign lines present but shells have no [basname] label."
"")
raise TestComparisonError(message)
else:
# case with specs separated by [basname] markers
for idx in range(0, len(basblock), 2):
basstrings[basname(basblock[idx])] = basblock[idx + 1]
return basstrings
anon.__name__ = 'basisspec_psi4_yo__' + cleanbas
qcdb.libmintsbasisset.basishorde[name.upper()] = anon
if set_option:
core.set_global_option(key, name)
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 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 _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 basis_helper(block, name='', key='BASIS', set_option=True):
"""For PsiAPI mode, forms a basis specification function from *block*
and associates it with keyword *key* under handle *name*. Registers
the basis spec with Psi4 so that it can be applied again to future
molecules. For usage, see mints2, mints9, and cc54 test cases. Unless
*set_option* is False, *name* will be set as current active *key*,
equivalent to `set key name` or `set_option({key: name})`.
"""
key = key.upper()
name = ('anonymous' + str(uuid.uuid4())[:8]) if name == '' else name
cleanbas = basname(name).replace(
'-', '') # further remove hyphens so can be function name
block = qcel.util.filter_comments(block)
command_lines = re.split('\n', block)
symbol_re = re.compile(
r'^\s*assign\s+(?P<symbol>[A-Z]{1,3})\s+(?P<basis>[-+*\(\)\w]+)\s*$',
re.IGNORECASE)
label_re = re.compile(
r'^\s*assign\s+(?P<label>(?P<symbol>[A-Z]{1,3})(?:(_\w+)|(\d+))?)\s+(?P<basis>[-+*\(\)\w]+)\s*$',
re.IGNORECASE)
all_re = re.compile(r'^\s*assign\s+(?P<basis>[-+*\(\)\w]+)\s*$',
re.IGNORECASE)
basislabel = re.compile(r'\s*\[\s*([-*\(\)\w]+)\s*\]\s*')
def anon(mol, role):
basstrings = {}
# Start by looking for assign lines, and remove them
leftover_lines = []
assignments = False
for line in command_lines:
if symbol_re.match(line):
m = symbol_re.match(line)
mol.set_basis_by_symbol(m.group('symbol'),
m.group('basis'),
role=role)
assignments = True
elif label_re.match(line):
m = label_re.match(line)
mol.set_basis_by_label(m.group('label'),
m.group('basis'),
role=role)
assignments = True
elif all_re.match(line):
m = all_re.match(line)
mol.set_basis_all_atoms(m.group('basis'), role=role)
assignments = True
else:
# Ignore blank lines and accumulate remainder
if line and not line.isspace():
leftover_lines.append(line.strip())
# Now look for regular basis set definitions
basblock = list(
filter(None, basislabel.split('\n'.join(leftover_lines))))
if len(basblock) == 1:
if not assignments:
# case with no [basname] markers where whole block is contents of gbs file
mol.set_basis_all_atoms(name, role=role)
basstrings[basname(name)] = basblock[0]
else:
message = (
"Conflicting basis set specification: assign lines present but shells have no [basname] label."
"")
raise TestComparisonError(message)
else:
# case with specs separated by [basname] markers
for idx in range(0, len(basblock), 2):
basstrings[basname(basblock[idx])] = basblock[idx + 1]
return basstrings
anon.__name__ = 'basisspec_psi4_yo__' + cleanbas
qcdb.libmintsbasisset.basishorde[name.upper()] = anon
if set_option:
core.set_global_option(key, name)
def run_json(json_data):
"""
Runs and updates the input JSON data.
Parameters
----------
json_data : JSON input
Required input fields:
- molecule : str
A string representation of the molecule. Any valid psi4 synatx is valid.
- driver : str
The driver method to use valid arguments are "energy", "gradient", "property"
- args : str
Input arguments to the driver function
- kwargs : dict
Input dictionary to the driver function
Optional input fields:
- kwargs : dict
Additional kwargs to be passed to the driver.
- options : dict
Global options to set in the Psi4 run.
- scratch_location : str
Overide the default scratch location.
- return_output : bool
Return the full string reprsentation of the output or not.
Output fields:
- return_value : float, psi4.core.Vector, psi4.core.Matrix
The return value of the input function.
- variables : dict
The list of Psi4 variables generated from the run.
- success : bool
Indicates if the run was successful or not.
- error : str
If an error is raised the result is returned here.
- raw_output : str
The full Psi4 output if requested.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/psi4/psi4.
Examples
--------
# CP corrected Helium dimer energy in the STO-3G basis.
>>> json_data = {}
>>> json_data["molecule"] = "He 0 0 0\n--\nHe 0 0 1"
>>> json_data["driver"] = "energy"
>>> json_data["method"] = 'SCF'
>>> json_data["kwargs"] = {"bsse_type": "cp"}
>>> json_data["options"] = {"BASIS": "STO-3G"}
>>> run_json(json_data)
{
"raw_output": "Output storing was not requested.",
"options": {
"BASIS": "STO-3G"
},
"driver": "energy",
"molecule": "He 0 0 0\n--\nHe 0 0 1",
"method": "SCF",
"variables": {
"SCF N ITERS": 2.0,
"SCF DIPOLE Y": 0.0,
"CURRENT DIPOLE Y": 0.0,
"CP-CORRECTED 2-BODY INTERACTION ENERGY": 0.1839360538612116,
"HF TOTAL ENERGY": -5.433191881443323,
"SCF TOTAL ENERGY": -5.433191881443323,
"TWO-ELECTRON ENERGY": 4.124089347186247,
"SCF ITERATION ENERGY": -5.433191881443323,
"CURRENT DIPOLE X": 0.0,
"CURRENT DIPOLE Z": 0.0,
"CURRENT REFERENCE ENERGY": -5.433191881443323,
"CURRENT ENERGY": 0.1839360538612116,
"COUNTERPOISE CORRECTED TOTAL ENERGY": -5.433191881443323,
"SCF DIPOLE Z": 0.0,
"COUNTERPOISE CORRECTED INTERACTION ENERGY": 0.1839360538612116,
"NUCLEAR REPULSION ENERGY": 2.11670883436,
"SCF DIPOLE X": 0.0,
"ONE-ELECTRON ENERGY": -11.67399006298957
},
"return_value": 0.1839360538612116,
"error": "",
"success": true,
"provenance": {
"creator": "Psi4",
"routine": "psi4.run_json",
"version": "1.1a1"
},
"kwargs": {
"bsse_type": "cp"
}
}
"""
# Set a few variables
json_data["error"] = ""
json_data["raw_output"] = "Output storing was not requested."
json_data["success"] = False
json_data["raw_output"] = None
# Check input
for check in ["driver", "method", "molecule"]:
if check not in list(json_data):
json_data["error"] = "Minimum input requires the %s field." % check
return json_data
# Check driver call
if json_data["driver"] not in list(methods_dict):
json_data["error"] = "Driver parameters '%s' not recognized" % str(json_data["driver"])
return json_data
# Set scratch
if "scratch_location" in list(json_data):
psi4_io = core.IOManager.shared_object()
psi4_io.set_default_path(json_data["scratch_location"])
# Set memory
if "memory" in list(json_data):
psi4.core.set_memory(int(json_data["memory"]))
# Do we return the output?
return_output = json_data.pop("return_output", False)
if return_output:
outfile = str(uuid.uuid4()) + ".json_out"
core.set_output_file(outfile, False)
json_data["raw_output"] = "Not yet run."
try:
# Set options
if "options" in json_data.keys() and (json_data["options"] is not None):
for k, v in json_data["options"].items():
core.set_global_option(k, v)
# Rework args
args = json_data["method"]
if not isinstance(args, (list, tuple)):
args = [args]
# Deep copy kwargs
if "kwargs" in list(json_data):
kwargs = copy.deepcopy(json_data["kwargs"])
else:
kwargs = {}
kwargs["molecule"] = molutil.geometry(json_data["molecule"])
# Full driver call
kwargs["return_wfn"] = True
val, wfn = methods_dict[json_data["driver"]](*args, **kwargs)
if isinstance(val, (float)):
json_data["return_value"] = val
elif isinstance(val, (core.Matrix, core.Vector)):
json_data["return_value"] = val.to_serial()
else:
json_data["error"] += "Unrecognized return value of type %s\n" % type(val)
json_data["return_value"] = val
json_data["variables"] = core.get_variables()
json_data["success"] = True
prov = {}
prov["version"] = psi4.__version__
prov["routine"] = "psi4.run_json"
prov["creator"] = "Psi4"
json_data["provenance"] = prov
except Exception as error:
json_data["error"] += repr(error)
json_data["success"] = False
if return_output:
with open(outfile, 'r') as f:
json_data["raw_output"] = f.read()
os.unlink(outfile)
return json_data
def run_json_original_v1_1(json_data, clean):
"""
Runs and updates the input JSON data.
This was a trial specification introduced in Psi4 v1.1. This will be deprecated in Psi4 v1.3 in favour of the QC JSON format
found here: http://molssi-qc-schema.readthedocs.io/en/latest/index.html#
Parameters
----------
json_data : JSON input
Required input fields:
- molecule : str
A string representation of the molecule. Any valid psi4 synatx is valid.
- driver : str
The driver method to use valid arguments are "energy", "gradient", "property"
- args : str
Input arguments to the driver function
- kwargs : dict
Input dictionary to the driver function
Optional input fields:
- kwargs : dict
Additional kwargs to be passed to the driver.
- options : dict
Global options to set in the Psi4 run.
- scratch_location : str
Overide the default scratch location.
- return_output : bool
Return the full string reprsentation of the output or not.
Output fields:
- return_value : float, psi4.core.Vector, psi4.core.Matrix
The return value of the input function.
- variables : dict
The list of Psi4 variables generated from the run.
- success : bool
Indicates if the run was successful or not.
- error : str
If an error is raised the result is returned here.
- raw_output : str
The full Psi4 output if requested.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/psi4/psi4.
Examples
--------
# CP corrected Helium dimer energy in the STO-3G basis.
>>> json_data = {}
>>> json_data["molecule"] = "He 0 0 0\n--\nHe 0 0 1"
>>> json_data["driver"] = "energy"
>>> json_data["method"] = 'SCF'
>>> json_data["kwargs"] = {"bsse_type": "cp"}
>>> json_data["options"] = {"BASIS": "STO-3G"}
>>> run_json(json_data)
{
"raw_output": "Output storing was not requested.",
"options": {
"BASIS": "STO-3G"
},
"driver": "energy",
"molecule": "He 0 0 0\n--\nHe 0 0 1",
"method": "SCF",
"variables": {
"SCF N ITERS": 2.0,
"SCF DIPOLE Y": 0.0,
"CURRENT DIPOLE Y": 0.0,
"CP-CORRECTED 2-BODY INTERACTION ENERGY": 0.1839360538612116,
"HF TOTAL ENERGY": -5.433191881443323,
"SCF TOTAL ENERGY": -5.433191881443323,
"TWO-ELECTRON ENERGY": 4.124089347186247,
"SCF ITERATION ENERGY": -5.433191881443323,
"CURRENT DIPOLE X": 0.0,
"CURRENT DIPOLE Z": 0.0,
"CURRENT REFERENCE ENERGY": -5.433191881443323,
"CURRENT ENERGY": 0.1839360538612116,
"COUNTERPOISE CORRECTED TOTAL ENERGY": -5.433191881443323,
"SCF DIPOLE Z": 0.0,
"COUNTERPOISE CORRECTED INTERACTION ENERGY": 0.1839360538612116,
"NUCLEAR REPULSION ENERGY": 2.11670883436,
"SCF DIPOLE X": 0.0,
"ONE-ELECTRON ENERGY": -11.67399006298957
},
"return_value": 0.1839360538612116,
"error": "",
"success": true,
"provenance": {
"creator": "Psi4",
"routine": "psi4.run_json",
"version": "1.1a1"
},
"kwargs": {
"bsse_type": "cp"
}
}
"""
# Clean a few things
_clean_psi_environ(clean)
# Set a few variables
json_data["error"] = ""
json_data["success"] = False
json_data["raw_output"] = None
json_data[
"warning"] = "Warning! This format will be deprecated in Psi4 v1.3. Please switch the standard QC JSON format."
# Add the provenance data
prov = {}
prov["version"] = psi4.__version__
prov["routine"] = "psi4.run_json"
prov["creator"] = "Psi4"
json_data["provenance"] = prov
# Check input
for check in ["driver", "method", "molecule"]:
if check not in list(json_data):
json_data["error"] = "Minimum input requires the %s field." % check
return json_data
# Check driver call
if json_data["driver"] not in list(methods_dict_):
json_data["error"] = "Driver parameters '%s' not recognized" % str(json_data["driver"])
return json_data
# Set options
if "options" in json_data.keys() and (json_data["options"] is not None):
for k, v in json_data["options"].items():
core.set_global_option(k, v)
# Rework args
args = json_data["method"]
if not isinstance(args, (list, tuple)):
args = [args]
# Deep copy kwargs
if "kwargs" in list(json_data):
kwargs = copy.deepcopy(json_data["kwargs"])
else:
kwargs = {}
kwargs["molecule"] = molutil.geometry(json_data["molecule"])
# Full driver call
kwargs["return_wfn"] = True
if json_data["driver"] not in methods_dict_:
raise KeyError("Driver type '%s' not recognized." % str(json_data["driver"]))
val, wfn = methods_dict_[json_data["driver"]](*args, **kwargs)
if isinstance(val, (float, int)):
json_data["return_value"] = val
elif isinstance(val, (core.Matrix, core.Vector)):
json_data["return_value"] = val.to_serial()
else:
raise TypeError("Unrecognized return value of type %s\n" % type(val))
json_data["variables"] = core.get_variables()
json_data["success"] = True
# Reset state
_clean_psi_environ(clean)
return json_data
def run_json_qc_schema(json_data, clean):
"""
An implementation of the QC JSON Schema (molssi-qc-schema.readthedocs.io/en/latest/index.html#) implementation in Psi4.
Parameters
----------
json_data : JSON
Please see molssi-qc-schema.readthedocs.io/en/latest/spec_components.html for further details.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/MolSSI/QC_JSON_Schema.
Examples
--------
"""
# Clean a few things
_clean_psi_environ(clean)
# This is currently a forced override
if json_data["schema_name"] != "qc_schema_input":
raise KeyError("Schema name of '{}' not understood".format(json_data["schema_name"]))
if json_data["schema_version"] != 1:
raise KeyError("Schema version of '{}' not understood".format(json_data["schema_version"]))
if json_data.get("nthreads", False) is not False:
psi4.set_num_threads(json_data["nthreads"], quiet=True)
json_data["provenance"] = {"creator": "Psi4", "version": psi4.__version__, "routine": "psi4.json.run_json"}
# Build molecule
mol = core.Molecule.from_schema(json_data)
# Update molecule geometry as we orient and fix_com
json_data["molecule"]["geometry"] = mol.geometry().np.ravel().tolist()
# Set options
for k, v in json_data["keywords"].items():
core.set_global_option(k, v)
# Setup the computation
method = json_data["model"]["method"]
core.set_global_option("BASIS", json_data["model"]["basis"])
kwargs = {"return_wfn": True, "molecule": mol}
# Handle special properties case
if json_data["driver"] == "properties":
if "properties" in json_data["model"]:
kwargs["properties"] = [x.lower() for x in json_data["model"]["properties"]]
extra = set(kwargs["properties"]) - can_do_properties_
if len(extra):
raise KeyError("Did not understand property key %s." % kwargs["properties"])
else:
kwargs["properties"] = list(can_do_properties_)
# Actual driver run
val, wfn = methods_dict_[json_data["driver"]](method, **kwargs)
# Pull out a standard set of SCF properties
psi_props = psi4.core.get_variables()
json_data["psi4:qcvars"] = psi_props
# Handle the return result
if json_data["driver"] == "energy":
json_data["return_result"] = val
elif json_data["driver"] in ["gradient", "hessian"]:
json_data["return_result"] = val.np.ravel().tolist()
elif json_data["driver"] == "properties":
ret = {}
mtd = json_data["model"]["method"].upper()
if "dipole" in kwargs["properties"]:
ret["dipole"] = [psi_props[mtd + " DIPOLE " + x] for x in ["X", "Y", "Z"]]
if "quadrupole" in kwargs["properties"]:
ret["quadrupole"] = [psi_props[mtd + " QUADRUPOLE " + x] for x in ["XX", "XY", "XZ", "YY", "YZ", "ZZ"]]
if "mulliken_charges" in kwargs["properties"]:
ret["mulliken_charges"] = wfn.get_array("MULLIKEN_CHARGES").np.ravel().tolist()
if "lowdin_charges" in kwargs["properties"]:
ret["lowdin_charges"] = wfn.get_array("LOWDIN_CHARGES").np.ravel().tolist()
if "wiberg_lowdin_indices" in kwargs["properties"]:
ret["wiberg_lowdin_indices"] = wfn.get_array("WIBERG_LOWDIN_INDICES").np.ravel().tolist()
if "mayer_indices" in kwargs["properties"]:
ret["mayer_indices"] = wfn.get_array("MAYER_INDICES").np.ravel().tolist()
json_data["return_result"] = ret
else:
raise KeyError("Did not understand Driver key %s." % json_data["driver"])
props = {
"calcinfo_nbasis": wfn.nso(),
"calcinfo_nmo": wfn.nmo(),
"calcinfo_nalpha": wfn.nalpha(),
"calcinfo_nbeta": wfn.nbeta(),
"calcinfo_natom": mol.geometry().shape[0],
"scf_one_electron_energy": psi_props["ONE-ELECTRON ENERGY"],
"scf_two_electron_energy": psi_props["TWO-ELECTRON ENERGY"],
"nuclear_repulsion_energy": psi_props["NUCLEAR REPULSION ENERGY"],
"scf_dipole_moment": [psi_props[x] for x in ["SCF DIPOLE X", "SCF DIPOLE Y", "SCF DIPOLE Z"]],
"scf_iterations": int(psi_props["SCF ITERATIONS"]),
"scf_total_energy": psi_props["SCF TOTAL ENERGY"],
"return_energy": psi_props["CURRENT ENERGY"],
}
# Pull out optional SCF keywords
other_scf = [("DFT VV10 ENERGY", "scf_vv10_energy"), ("DFT XC ENERGY", "scf_xc_energy"),
("DISPERSION CORRECTION ENERGY", "scf_dispersion_correction_energy")]
for pkey, skey in other_scf:
if (pkey in psi_props) and (psi_props[pkey] != 0):
props[skey] = psi_props[pkey]
# Write out MP2 keywords
if "MP2 CORRELATION ENERGY" in psi_props:
props["mp2_same_spin_correlation_energy"] = psi_props["MP2 SAME-SPIN CORRELATION ENERGY"]
props["mp2_opposite_spin_correlation_energy"] = psi_props["MP2 OPPOSITE-SPIN CORRELATION ENERGY"]
props["mp2_singles_energy"] = 0.0
props["mp2_doubles_energy"] = psi_props["MP2 CORRELATION ENERGY"]
props["mp2_total_correlation_energy"] = psi_props["MP2 CORRELATION ENERGY"]
props["mp2_total_energy"] = psi_props["MP2 TOTAL ENERGY"]
json_data["properties"] = props
json_data["success"] = True
# Reset state
_clean_psi_environ(clean)
json_data["schema_name"] = "qc_schema_output"
return json_data
def run_json_qc_schema(json_data, clean):
"""
An implementation of the QC JSON Schema (molssi-qc-schema.readthedocs.io/en/latest/index.html#) implementation in Psi4.
Parameters
----------
json_data : JSON
Please see molssi-qc-schema.readthedocs.io/en/latest/spec_components.html for further details.
Notes
-----
!Warning! This function is experimental and likely to change in the future.
Please report any suggestions or uses of this function on github.com/MolSSI/QC_JSON_Schema.
Examples
--------
"""
# Clean a few things
_clean_psi_environ(clean)
# This is currently a forced override
if json_data["schema_name"] in ["qc_schema_input", "qcschema_input"]:
json_data["schema_name"] = "qc_schema_input" # humor qcel 0.1.3
else:
raise KeyError("Schema name of '{}' not understood".format(json_data["schema_name"]))
if json_data["schema_version"] != 1:
raise KeyError("Schema version of '{}' not understood".format(json_data["schema_version"]))
if json_data.get("nthreads", False) is not False:
psi4.set_num_threads(json_data["nthreads"], quiet=True)
json_data["provenance"] = {"creator": "Psi4", "version": psi4.__version__, "routine": "psi4.json.run_json"}
# Build molecule
mol = core.Molecule.from_schema(json_data)
# Update molecule geometry as we orient and fix_com
json_data["molecule"]["geometry"] = mol.geometry().np.ravel().tolist()
# Set options
for k, v in json_data["keywords"].items():
core.set_global_option(k, v)
# Setup the computation
method = json_data["model"]["method"]
core.set_global_option("BASIS", json_data["model"]["basis"])
kwargs = {"return_wfn": True, "molecule": mol}
# Handle special properties case
if json_data["driver"] == "properties":
if "properties" in json_data["model"]:
kwargs["properties"] = [x.lower() for x in json_data["model"]["properties"]]
extra = set(kwargs["properties"]) - can_do_properties_
if len(extra):
raise KeyError("Did not understand property key %s." % kwargs["properties"])
else:
kwargs["properties"] = list(can_do_properties_)
# Actual driver run
val, wfn = methods_dict_[json_data["driver"]](method, **kwargs)
# Pull out a standard set of Psi variables
psi_props = psi4.core.scalar_variables()
json_data["psi4:qcvars"] = psi_props
# Still a bit of a mess at the moment add in local vars as well.
for k, v in wfn.variables().items():
if k not in json_data["psi4:qcvars"]:
json_data["psi4:qcvars"][k] = _json_translation(v)
# Handle the return result
if json_data["driver"] == "energy":
json_data["return_result"] = val
elif json_data["driver"] in ["gradient", "hessian"]:
json_data["return_result"] = val.np.ravel().tolist()
elif json_data["driver"] == "properties":
ret = {}
mtd = json_data["model"]["method"].upper()
# Dipole/quadrupole still special case
if "dipole" in kwargs["properties"]:
ret["dipole"] = [psi_props[mtd + " DIPOLE " + x] for x in ["X", "Y", "Z"]]
if "quadrupole" in kwargs["properties"]:
ret["quadrupole"] = [psi_props[mtd + " QUADRUPOLE " + x] for x in ["XX", "XY", "XZ", "YY", "YZ", "ZZ"]]
ret.update(_convert_variables(wfn.variables(), context="properties"))
json_data["return_result"] = ret
else:
raise KeyError("Did not understand Driver key %s." % json_data["driver"])
props = {
"calcinfo_nbasis": wfn.nso(),
"calcinfo_nmo": wfn.nmo(),
"calcinfo_nalpha": wfn.nalpha(),
"calcinfo_nbeta": wfn.nbeta(),
"calcinfo_natom": mol.geometry().shape[0],
}
props.update(_convert_variables(psi_props, context="generics"))
props.update(_convert_variables(psi_props, context="scf"))
# Write out MP2 keywords
if "MP2 CORRELATION ENERGY" in psi_props:
props.update(_convert_variables(psi_props, context="mp2"))
json_data["properties"] = props
json_data["success"] = True
# Reset state
_clean_psi_environ(clean)
json_data["schema_name"] = "qcschema_output"
return json_data
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 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()
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_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)"))
# 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_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')
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_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.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)
# 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 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
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()
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
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)
# 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_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)
# 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 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
