def expand_psivars(pvdefs):
"""Dictionary *pvdefs* has keys with names of PsiVariables to be
created and values with dictionary of two keys: 'args', the
PsiVariables that contribute to the key and 'func', a function (or
lambda) to combine them. This function builds those PsiVariables if
all the contributors are available. Helpful printing is available when
PRINT > 2.
"""
verbose = core.get_global_option('PRINT')
for pvar, action in pvdefs.items():
if verbose >= 2:
print("""building %s %s""" % (pvar, '.' * (50 - len(pvar))), end='')
psivars = core.scalar_variables()
data_rich_args = []
for pv in action['args']:
if isinstance(pv, str):
if pv in psivars:
data_rich_args.append(psivars[pv])
else:
if verbose >= 2:
print("""EMPTY, missing {}""".format(pv))
break
else:
data_rich_args.append(pv)
else:
result = action['func'](data_rich_args)
core.set_variable(pvar, result)
if verbose >= 2:
print("""SUCCESS""")
def _print_matrix(descriptors, content, title):
length = len(descriptors)
matrix_header = ' ' + ' {:^10}' * length + '\n'
core.print_out(matrix_header.format(*descriptors))
core.print_out(' -----' + ' ----------' * length + '\n')
for i, desc in enumerate(descriptors):
core.print_out(' {:^5}'.format(desc))
for j in range(length):
core.print_out(' {:>10.5f}'.format(content[i, j]))
# Set the name
var_name = title + " " + descriptors[i] + descriptors[j]
core.set_variable(var_name, content[i, j])
core.print_out('\n')
def compute_gradient(self, molecule):
"""Compute dispersion gradient based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
Returns
-------
psi4.core.Matrix
(nat, 3) dispersion gradient [Eh/a0].
"""
if self.engine in ['dftd3', 'mp2d']:
resinp = {
'schema_name': 'qcschema_input',
'schema_version': 1,
'molecule': molecule.to_schema(dtype=2),
'driver': 'gradient',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'verbose': 1,
},
}
jobrec = qcng.compute(resinp, self.engine, raise_error=True)
jobrec = jobrec.dict()
dashd_part = core.Matrix.from_array(np.array(jobrec['extras']['qcvars']['DISPERSION CORRECTION GRADIENT']).reshape(-1, 3))
for k, qca in jobrec['extras']['qcvars'].items():
if not isinstance(qca, (list, np.ndarray)):
core.set_variable(k, qca)
if self.fctldash in ['hf3c', 'pbeh3c']:
gcp_part = gcp.run_gcp(molecule, self.fctldash, verbose=False, dertype=1)
dashd_part.add(gcp_part)
return dashd_part
else:
return self.disp.compute_gradient(molecule)
def compute_energy(self, molecule):
"""Compute dispersion energy based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
Returns
-------
float
Dispersion energy [Eh].
Notes
-----
DISPERSION CORRECTION ENERGY
Disp always set. Overridden in SCF finalization, but that only changes for "-3C" methods.
self.fctldash + DISPERSION CORRECTION ENERGY
Set if `fctldash` nonempty.
"""
if self.engine in ['dftd3', 'mp2d']:
resinp = {
'schema_name': 'qcschema_input',
'schema_version': 1,
'molecule': molecule.to_schema(dtype=2),
'driver': 'energy',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'verbose': 1,
},
}
jobrec = qcng.compute(resinp, self.engine, raise_error=True)
jobrec = jobrec.dict()
dashd_part = float(jobrec['extras']['qcvars']['DISPERSION CORRECTION ENERGY'])
for k, qca in jobrec['extras']['qcvars'].items():
if not isinstance(qca, (list, np.ndarray)):
core.set_variable(k, qca)
if self.fctldash in ['hf3c', 'pbeh3c']:
gcp_part = gcp.run_gcp(molecule, self.fctldash, verbose=False, dertype=0)
dashd_part += gcp_part
return dashd_part
else:
ene = self.disp.compute_energy(molecule)
core.set_variable('DISPERSION CORRECTION ENERGY', ene)
if self.fctldash:
core.set_variable('{} DISPERSION CORRECTION ENERGY'.format(self.fctldash), ene)
return ene
def compute_gradient(self, molecule):
"""Compute dispersion gradient based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
Returns
-------
psi4.core.Matrix
(nat, 3) dispersion gradient [Eh/a0].
"""
if self.engine == 'dftd3':
jobrec = intf_dftd3.run_dftd3_from_arrays(
molrec=molecule.to_dict(np_out=False),
name_hint=self.fctldash,
level_hint=self.dashlevel,
param_tweaks=self.dashparams,
dashcoeff_supplement=self.dashcoeff_supplement,
ptype='gradient',
verbose=1)
dashd_part = core.Matrix.from_array(jobrec['qcvars']['DISPERSION CORRECTION GRADIENT'].data)
for k, qca in jobrec['qcvars'].items():
if not isinstance(qca.data, np.ndarray):
core.set_variable(k, qca.data)
if self.fctldash in ['hf3c', 'pbeh3c']:
gcp_part = gcp.run_gcp(molecule, self.fctldash, verbose=False, dertype=1)
dashd_part.add(gcp_part)
return dashd_part
else:
return self.disp.compute_gradient(molecule)
def compute_energy(self, molecule):
"""Compute dispersion energy based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
Returns
-------
float
Dispersion energy [Eh].
Notes
-----
DISPERSION CORRECTION ENERGY
Disp always set. Overridden in SCF finalization, but that only changes for "-3C" methods.
self.fctldash + DISPERSION CORRECTION ENERGY
Set if `fctldash` nonempty.
"""
if self.engine == 'dftd3':
jobrec = intf_dftd3.run_dftd3_from_arrays(
molrec=molecule.to_dict(np_out=False),
name_hint=self.fctldash,
level_hint=self.dashlevel,
param_tweaks=self.dashparams,
dashcoeff_supplement=self.dashcoeff_supplement,
ptype='energy',
verbose=1)
dashd_part = float(jobrec['qcvars']['DISPERSION CORRECTION ENERGY'].data)
for k, qca in jobrec['qcvars'].items():
if not isinstance(qca.data, np.ndarray):
core.set_variable(k, qca.data)
if self.fctldash in ['hf3c', 'pbeh3c']:
gcp_part = gcp.run_gcp(molecule, self.fctldash, verbose=False, dertype=0)
dashd_part += gcp_part
return dashd_part
else:
ene = self.disp.compute_energy(molecule)
core.set_variable('DISPERSION CORRECTION ENERGY', ene)
if self.fctldash:
core.set_variable('{} DISPERSION CORRECTION ENERGY'.format(self.fctldash), ene)
return ene
def print_sapt_hf_summary(data, name, short=False, delta_hf=False):
ret = " Partial %s Results, to compute Delta HF (dHF)\n" % name
ret += " " + "-" * 105 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst10,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch10", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch10(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
# Induction (no dHF)
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
ret += print_sapt_var("Induction (no dHF)", ind) + "\n"
ret += print_sapt_var(" Ind20,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind20,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B) (no dHF)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B) (no dHF)", ind_ba) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
if delta_hf:
total_sapt = (data["Elst10,r"] + data["Exch10"] + ind)
sapt_hf_delta = delta_hf - total_sapt
core.set_variable("SAPT(DFT) Delta HF", sapt_hf_delta)
ret += print_sapt_var(
"Subtotal SAPT(HF)", total_sapt, start_spacer=" ") + "\n"
ret += print_sapt_var("Total HF", delta_hf, start_spacer=" ") + "\n"
ret += print_sapt_var("Delta HF", sapt_hf_delta,
start_spacer=" ") + "\n"
ret += " " + "-" * 105 + "\n"
# Induction with dHF and scaling
Sdelta = (ind + sapt_hf_delta) / ind
ret += " Partial %s Results, alternate SAPT0-like display\n" % name
ret += " " + "-" * 105 + "\n"
ret += print_sapt_var("Induction", ind + sapt_hf_delta) + "\n"
ret += print_sapt_var(" Ind20,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind20,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" delta HF,r (2)", sapt_hf_delta) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab * Sdelta) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba * Sdelta) + "\n"
ret += " " + "-" * 105 + "\n"
return ret
else:
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,u"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp20", data["Disp20,u"]) + "\n"
ret += print_sapt_var(" Exch-Disp20", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-15s" % name, total,
start_spacer=" ") + "\n"
core.set_variable("SAPT0 TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 105 + "\n"
return ret
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 / constants.hartree2wavenumbers * 0.8929 * 0.5
core.clean()
# optimize geometry at mp2 (no frozen core) level
# note: freeze_core isn't an option in MP2
core.set_global_option('FREEZE_CORE',"FALSE")
core.set_global_option('MP2_TYPE', 'CONV')
driver.optimize('mp2')
core.clean()
# qcisd(t)
core.set_local_option('FNOCC','COMPUTE_MP4_TRIPLES',"TRUE")
core.set_global_option('FREEZE_CORE',"TRUE")
core.set_global_option('BASIS',"6-311G(D_P)")
ref = driver.proc.run_fnocc('qcisd(t)', return_wfn=True, **kwargs)
# HLC: high-level correction based on number of valence electrons
nirrep = ref.nirrep()
frzcpi = ref.frzcpi()
nfzc = 0
for i in range (0,nirrep):
nfzc += frzcpi[i]
nalpha = ref.nalpha() - nfzc
nbeta = ref.nbeta() - nfzc
# hlc of gaussian-2
hlc = -0.00481 * nalpha -0.00019 * nbeta
# hlc of gaussian-1
hlc1 = -0.00614 * nalpha
eqci_6311gdp = core.get_variable("QCISD(T) TOTAL ENERGY")
emp4_6311gd = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311gd = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# correction for diffuse functions
core.set_global_option('BASIS',"6-311+G(D_P)")
driver.energy('mp4')
emp4_6311pg_dp = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311pg_dp = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# correction for polarization functions
core.set_global_option('BASIS',"6-311G(2DF_P)")
driver.energy('mp4')
emp4_6311g2dfp = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311g2dfp = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# big basis mp2
core.set_global_option('BASIS',"6-311+G(3DF_2P)")
#run_fnocc('_mp2',**kwargs)
driver.energy('mp2')
emp2_big = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
eqci = eqci_6311gdp
e_delta_g2 = emp2_big + emp2_6311gd - emp2_6311g2dfp - emp2_6311pg_dp
e_plus = emp4_6311pg_dp - emp4_6311gd
e_2df = emp4_6311g2dfp - emp4_6311gd
eg2 = eqci + e_delta_g2 + e_plus + e_2df
eg2_mp2_0k = eqci + (emp2_big - emp2_6311gd) + hlc + zpe
core.print_out('\n')
core.print_out(' ==> G1/G2 Energy Components <==\n')
core.print_out('\n')
core.print_out(' QCISD(T): %20.12lf\n' % eqci)
core.print_out(' E(Delta): %20.12lf\n' % e_delta_g2)
core.print_out(' E(2DF): %20.12lf\n' % e_2df)
core.print_out(' E(+): %20.12lf\n' % e_plus)
core.print_out(' E(G1 HLC): %20.12lf\n' % hlc1)
core.print_out(' E(G2 HLC): %20.12lf\n' % hlc)
core.print_out(' E(ZPE): %20.12lf\n' % zpe)
core.print_out('\n')
core.print_out(' ==> 0 Kelvin Results <==\n')
core.print_out('\n')
eg2_0k = eg2 + zpe + hlc
core.print_out(' G1: %20.12lf\n' % (eqci + e_plus + e_2df + hlc1 + zpe))
core.print_out(' G2(MP2): %20.12lf\n' % eg2_mp2_0k)
core.print_out(' G2: %20.12lf\n' % eg2_0k)
core.set_variable("G1 TOTAL ENERGY",eqci + e_plus + e_2df + hlc1 + zpe)
core.set_variable("G2 TOTAL ENERGY",eg2_0k)
core.set_variable("G2(MP2) TOTAL ENERGY",eg2_mp2_0k)
core.print_out('\n')
T = core.get_global_option('T')
core.print_out(' ==> %3.0lf Kelvin Results <==\n'% T)
core.print_out('\n')
internal_energy = eg2_mp2_0k + du - zpe / 0.8929
enthalpy = eg2_mp2_0k + dh - zpe / 0.8929
gibbs = eg2_mp2_0k + dg - zpe / 0.8929
core.print_out(' G2(MP2) energy: %20.12lf\n' % internal_energy )
core.print_out(' G2(MP2) enthalpy: %20.12lf\n' % enthalpy)
core.print_out(' G2(MP2) free energy: %20.12lf\n' % gibbs)
core.print_out('\n')
core.set_variable("G2(MP2) INTERNAL ENERGY",internal_energy)
core.set_variable("G2(MP2) ENTHALPY",enthalpy)
core.set_variable("G2(MP2) FREE ENERGY",gibbs)
internal_energy = eg2_0k + du - zpe / 0.8929
enthalpy = eg2_0k + dh - zpe / 0.8929
gibbs = eg2_0k + dg - zpe / 0.8929
core.print_out(' G2 energy: %20.12lf\n' % internal_energy )
core.print_out(' G2 enthalpy: %20.12lf\n' % enthalpy)
core.print_out(' G2 free energy: %20.12lf\n' % gibbs)
core.set_variable("CURRENT ENERGY",eg2_0k)
core.set_variable("G2 INTERNAL ENERGY",internal_energy)
core.set_variable("G2 ENTHALPY",enthalpy)
core.set_variable("G2 FREE ENERGY",gibbs)
core.clean()
optstash.restore()
# return 0K g2 results
return eg2_0k
def nbody_gufunc(func, method_string, **kwargs):
"""
Computes the nbody interaction energy, gradient, or Hessian depending on input.
This is a generalized univeral function for computing interaction quantities.
:returns: *return type of func* |w--w| The interaction data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| interaction data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
:type func: function
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: string
:param method_string: ``'scf'`` || ``'mp2'`` || ``'ci5'`` || etc.
First argument, lowercase and usually unlabeled. Indicates the computational
method to be passed to func.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:type return_wfn: :ref:`boolean <op_py_boolean>`
:param return_wfn: ``'on'`` || |dl| ``'off'`` |dr|
Indicate to additionally return the :py:class:`~psi4.core.Wavefunction`
calculation result as the second element of a tuple.
:type bsse_type: string or list
:param bsse_type: ``'cp'`` || ``['nocp', 'vmfc']`` || |dl| ``None`` |dr| || etc.
Type of BSSE correction to compute: CP, NoCP, or VMFC. The first in this
list is returned by this function. By default, this function is not called.
:type max_nbody: int
:param max_nbody: ``3`` || etc.
Maximum n-body to compute, cannot exceed the number of fragments in the moleucle.
:type ptype: string
:param ptype: ``'energy'`` || ``'gradient'`` || ``'hessian'``
Type of the procedure passed in.
:type return_total_data: :ref:`boolean <op_py_boolean>`
:param return_total_data: ``'on'`` || |dl| ``'off'`` |dr|
If True returns the total data (energy/gradient/etc) of the system,
otherwise returns interaction data.
"""
### ==> Parse some kwargs <==
kwargs = p4util.kwargs_lower(kwargs)
return_wfn = kwargs.pop('return_wfn', False)
ptype = kwargs.pop('ptype', None)
return_total_data = kwargs.pop('return_total_data', False)
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
core.clean_variables()
if ptype not in ['energy', 'gradient', 'hessian']:
raise ValidationError("""N-Body driver: The ptype '%s' is not regonized.""" % ptype)
# Figure out BSSE types
do_cp = False
do_nocp = False
do_vmfc = False
return_method = False
# Must be passed bsse_type
bsse_type_list = kwargs.pop('bsse_type')
if bsse_type_list is None:
raise ValidationError("N-Body GUFunc: Must pass a bsse_type")
if not isinstance(bsse_type_list, list):
bsse_type_list = [bsse_type_list]
for num, btype in enumerate(bsse_type_list):
if btype.lower() == 'cp':
do_cp = True
if (num == 0): return_method = 'cp'
elif btype.lower() == 'nocp':
do_nocp = True
if (num == 0): return_method = 'nocp'
elif btype.lower() == 'vmfc':
do_vmfc = True
if (num == 0): return_method = 'vmfc'
else:
raise ValidationError("N-Body GUFunc: bsse_type '%s' is not recognized" % btype.lower())
max_nbody = kwargs.get('max_nbody', -1)
max_frag = molecule.nfragments()
if max_nbody == -1:
max_nbody = molecule.nfragments()
else:
max_nbody = min(max_nbody, max_frag)
# What levels do we need?
nbody_range = range(1, max_nbody + 1)
fragment_range = range(1, max_frag + 1)
# Flip this off for now, needs more testing
# If we are doing CP lets save them integrals
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# # Set to save RI integrals for repeated full-basis computations
# ri_ints_io = core.get_global_option('DF_INTS_IO')
# # inquire if above at all applies to dfmp2 or just scf
# core.set_global_option('DF_INTS_IO', 'SAVE')
# psioh = core.IOManager.shared_object()
# psioh.set_specific_retention(97, True)
bsse_str = bsse_type_list[0]
if len(bsse_type_list) >1:
bsse_str = str(bsse_type_list)
core.print_out("\n\n")
core.print_out(" ===> N-Body Interaction Abacus <===\n")
core.print_out(" BSSE Treatment: %s\n" % bsse_str)
cp_compute_list = {x:set() for x in nbody_range}
nocp_compute_list = {x:set() for x in nbody_range}
vmfc_compute_list = {x:set() for x in nbody_range}
vmfc_level_list = {x:set() for x in nbody_range} # Need to sum something slightly different
# Build up compute sets
if do_cp:
# Everything is in dimer basis
basis_tuple = tuple(fragment_range)
for nbody in nbody_range:
for x in it.combinations(fragment_range, nbody):
cp_compute_list[nbody].add( (x, basis_tuple) )
if do_nocp:
# Everything in monomer basis
for nbody in nbody_range:
for x in it.combinations(fragment_range, nbody):
nocp_compute_list[nbody].add( (x, x) )
if do_vmfc:
# Like a CP for all combinations of pairs or greater
for nbody in nbody_range:
for cp_combos in it.combinations(fragment_range, nbody):
basis_tuple = tuple(cp_combos)
for interior_nbody in nbody_range:
for x in it.combinations(cp_combos, interior_nbody):
combo_tuple = (x, basis_tuple)
vmfc_compute_list[interior_nbody].add( combo_tuple )
vmfc_level_list[len(basis_tuple)].add( combo_tuple )
# Build a comprehensive compute_range
compute_list = {x:set() for x in nbody_range}
for n in nbody_range:
compute_list[n] |= cp_compute_list[n]
compute_list[n] |= nocp_compute_list[n]
compute_list[n] |= vmfc_compute_list[n]
core.print_out(" Number of %d-body computations: %d\n" % (n, len(compute_list[n])))
# Build size and slices dictionaries
fragment_size_dict = {frag: molecule.extract_subsets(frag).natom() for
frag in range(1, max_frag+1)}
start = 0
fragment_slice_dict = {}
for k, v in fragment_size_dict.items():
fragment_slice_dict[k] = slice(start, start + v)
start += v
molecule_total_atoms = sum(fragment_size_dict.values())
# Now compute the energies
energies_dict = {}
ptype_dict = {}
for n in compute_list.keys():
core.print_out("\n ==> N-Body: Now computing %d-body complexes <==\n\n" % n)
total = len(compute_list[n])
for num, pair in enumerate(compute_list[n]):
core.print_out("\n N-Body: Computing complex (%d/%d) with fragments %s in the basis of fragments %s.\n\n" %
(num + 1, total, str(pair[0]), str(pair[1])))
ghost = list(set(pair[1]) - set(pair[0]))
current_mol = molecule.extract_subsets(list(pair[0]), ghost)
ptype_dict[pair] = func(method_string, molecule=current_mol, **kwargs)
energies_dict[pair] = core.get_variable("CURRENT ENERGY")
core.print_out("\n N-Body: Complex Energy (fragments = %s, basis = %s: %20.14f)\n" %
(str(pair[0]), str(pair[1]), energies_dict[pair]))
# Flip this off for now, needs more testing
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.clean()
# Final dictionaries
cp_energy_by_level = {n: 0.0 for n in nbody_range}
nocp_energy_by_level = {n: 0.0 for n in nbody_range}
cp_energy_body_dict = {n: 0.0 for n in nbody_range}
nocp_energy_body_dict = {n: 0.0 for n in nbody_range}
vmfc_energy_body_dict = {n: 0.0 for n in nbody_range}
# Build out ptype dictionaries if needed
if ptype != 'energy':
if ptype == 'gradient':
arr_shape = (molecule_total_atoms, 3)
elif ptype == 'hessian':
arr_shape = (molecule_total_atoms * 3, molecule_total_atoms * 3)
else:
raise KeyError("N-Body: ptype '%s' not recognized" % ptype)
cp_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
nocp_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
vmfc_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
cp_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
nocp_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
vmfc_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
else:
cp_ptype_by_level, cp_ptype_body_dict = None, None
nocp_ptype_by_level, nocp_ptype_body_dict = None, None
vmfc_ptype_body_dict = None
# Sum up all of the levels
for n in nbody_range:
# Energy
cp_energy_by_level[n] = sum(energies_dict[v] for v in cp_compute_list[n])
nocp_energy_by_level[n] = sum(energies_dict[v] for v in nocp_compute_list[n])
# Special vmfc case
if n > 1:
vmfc_energy_body_dict[n] = vmfc_energy_body_dict[n - 1]
for tup in vmfc_level_list[n]:
vmfc_energy_body_dict[n] += ((-1) ** (n - len(tup[0]))) * energies_dict[tup]
# Do ptype
if ptype != 'energy':
_sum_cluster_ptype_data(ptype, ptype_dict, cp_compute_list[n],
fragment_slice_dict, fragment_size_dict,
cp_ptype_by_level[n])
_sum_cluster_ptype_data(ptype, ptype_dict, nocp_compute_list[n],
fragment_slice_dict, fragment_size_dict,
nocp_ptype_by_level[n])
_sum_cluster_ptype_data(ptype, ptype_dict, vmfc_level_list[n],
fragment_slice_dict, fragment_size_dict,
vmfc_ptype_by_level[n], vmfc=True)
# Compute cp energy and ptype
if do_cp:
for n in nbody_range:
if n == max_frag:
cp_energy_body_dict[n] = cp_energy_by_level[n]
if ptype != 'energy':
cp_ptype_body_dict[n][:] = cp_ptype_by_level[n]
continue
for k in range(1, n + 1):
take_nk = nCr(max_frag - k - 1, n - k)
sign = ((-1) ** (n - k))
value = cp_energy_by_level[k]
cp_energy_body_dict[n] += take_nk * sign * value
if ptype != 'energy':
value = cp_ptype_by_level[k]
cp_ptype_body_dict[n] += take_nk * sign * value
_print_nbody_energy(cp_energy_body_dict, "Counterpoise Corrected (CP)")
cp_interaction_energy = cp_energy_body_dict[max_nbody] - cp_energy_body_dict[1]
core.set_variable('Counterpoise Corrected Total Energy', cp_energy_body_dict[max_nbody])
core.set_variable('Counterpoise Corrected Interaction Energy', cp_interaction_energy)
for n in nbody_range[1:]:
var_key = 'CP-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key, cp_energy_body_dict[n] - cp_energy_body_dict[1])
# Compute nocp energy and ptype
if do_nocp:
for n in nbody_range:
if n == max_frag:
nocp_energy_body_dict[n] = nocp_energy_by_level[n]
if ptype != 'energy':
nocp_ptype_body_dict[n][:] = nocp_ptype_by_level[n]
continue
for k in range(1, n + 1):
take_nk = nCr(max_frag - k - 1, n - k)
sign = ((-1) ** (n - k))
value = nocp_energy_by_level[k]
nocp_energy_body_dict[n] += take_nk * sign * value
if ptype != 'energy':
value = nocp_ptype_by_level[k]
nocp_ptype_body_dict[n] += take_nk * sign * value
_print_nbody_energy(nocp_energy_body_dict, "Non-Counterpoise Corrected (NoCP)")
nocp_interaction_energy = nocp_energy_body_dict[max_nbody] - nocp_energy_body_dict[1]
core.set_variable('Non-Counterpoise Corrected Total Energy', nocp_energy_body_dict[max_nbody])
core.set_variable('Non-Counterpoise Corrected Interaction Energy', nocp_interaction_energy)
for n in nbody_range[1:]:
var_key = 'NOCP-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key, nocp_energy_body_dict[n] - nocp_energy_body_dict[1])
# Compute vmfc energy and ptype
if do_vmfc:
_print_nbody_energy(vmfc_energy_body_dict, "Valiron-Mayer Function Couterpoise (VMFC)")
vmfc_interaction_energy = vmfc_energy_body_dict[max_nbody] - vmfc_energy_body_dict[1]
core.set_variable('Valiron-Mayer Function Couterpoise Total Energy', vmfc_energy_body_dict[max_nbody])
core.set_variable('Valiron-Mayer Function Couterpoise Interaction Energy', vmfc_interaction_energy)
for n in nbody_range[1:]:
var_key = 'VMFC-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key, vmfc_energy_body_dict[n] - vmfc_energy_body_dict[1])
if return_method == 'cp':
ptype_body_dict = cp_ptype_body_dict
energy_body_dict = cp_energy_body_dict
elif return_method == 'nocp':
ptype_body_dict = nocp_ptype_body_dict
energy_body_dict = nocp_energy_body_dict
elif return_method == 'vmfc':
ptype_body_dict = vmfc_ptype_body_dict
energy_body_dict = vmfc_energy_body_dict
else:
raise ValidationError("N-Body Wrapper: Invalid return type. Should never be here, please post this error on github.")
# Figure out and build return types
if return_total_data:
ret_energy = energy_body_dict[max_nbody]
else:
ret_energy = energy_body_dict[max_nbody]
ret_energy -= energy_body_dict[1]
if ptype != 'energy':
if return_total_data:
np_final_ptype = ptype_body_dict[max_nbody].copy()
else:
np_final_ptype = ptype_body_dict[max_nbody].copy()
np_final_ptype -= ptype_body_dict[1]
ret_ptype = core.Matrix.from_array(np_final_ptype)
else:
ret_ptype = ret_energy
# Build and set a wavefunction
wfn = core.Wavefunction.build(molecule, 'sto-3g')
wfn.nbody_energy = energies_dict
wfn.nbody_ptype = ptype_dict
wfn.nbody_body_energy = energy_body_dict
wfn.nbody_body_ptype = ptype_body_dict
if ptype == 'gradient':
wfn.set_gradient(ret_ptype)
elif ptype == 'hessian':
wfn.set_hessian(ret_ptype)
core.set_variable("CURRENT ENERGY", ret_energy)
if return_wfn:
return (ret_ptype, wfn)
else:
return ret_ptype
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()
optstash.restore()
return dimer_wfn
def scf_finalize_energy(self):
"""Performs stability analysis and calls back SCF with new guess
if needed, Returns the SCF energy. This function should be called
once orbitals are ready for energy/property computations, usually
after iterations() is called.
"""
# post-scf vv10 correlation
if core.get_option(
'SCF', "DFT_VV10_POSTSCF") and self.functional().vv10_b() > 0.0:
self.functional().set_lock(False)
self.functional().set_do_vv10(True)
self.functional().set_lock(True)
core.print_out(
" ==> Computing Non-Self-Consistent VV10 Energy Correction <==\n\n"
)
SCFE = 0.0
self.form_V()
SCFE += self.compute_E()
self.set_energies("Total Energy", SCFE)
# Perform wavefunction stability analysis before doing
# anything on a wavefunction that may not be truly converged.
if core.get_option('SCF', 'STABILITY_ANALYSIS') != "NONE":
# Don't bother computing needed integrals if we can't do anything with them.
if self.functional().needs_xc():
raise ValidationError(
"Stability analysis not yet supported for XC functionals.")
# We need the integral file, make sure it is written and
# compute it if needed
if core.get_option('SCF', 'REFERENCE') != "UHF":
#psio = core.IO.shared_object()
#psio.open(constants.PSIF_SO_TEI, 1) # PSIO_OPEN_OLD
#try:
# psio.tocscan(constants.PSIF_SO_TEI, "IWL Buffers")
#except TypeError:
# # "IWL Buffers" actually found but psio_tocentry can't be returned to Py
# psio.close(constants.PSIF_SO_TEI, 1)
#else:
# # tocscan returned None
# psio.close(constants.PSIF_SO_TEI, 1)
# logic above foiled by psio_tocentry not returning None<--nullptr in pb11 2.2.1
# so forcibly recomputing for now until stability revamp
core.print_out(" SO Integrals not on disk. Computing...")
mints = core.MintsHelper(self.basisset())
#next 2 lines fix a bug that prohibits relativistic stability analysis
mints.integrals()
core.print_out("done.\n")
# Q: Not worth exporting all the layers of psio, right?
follow = self.stability_analysis()
while follow and self.attempt_number_ <= core.get_option(
'SCF', 'MAX_ATTEMPTS'):
self.attempt_number_ += 1
core.print_out(
" Running SCF again with the rotated orbitals.\n")
if self.initialized_diis_manager_:
self.diis_manager().reset_subspace()
# reading the rotated orbitals in before starting iterations
self.form_D()
self.set_energies("Total Energy", self.compute_initial_E())
self.iterations()
follow = self.stability_analysis()
if follow and self.attempt_number_ > core.get_option(
'SCF', 'MAX_ATTEMPTS'):
core.print_out(
" There's still a negative eigenvalue. Try modifying FOLLOW_STEP_SCALE\n"
)
core.print_out(
" or increasing MAX_ATTEMPTS (not available for PK integrals).\n"
)
# At this point, we are not doing any more SCF cycles
# and we can compute and print final quantities.
if hasattr(self.molecule(), 'EFP'):
efpobj = self.molecule().EFP
efpobj.compute() # do_gradient=do_gradient)
efpene = efpobj.get_energy(label='psi')
efp_wfn_independent_energy = efpene['total'] - efpene['ind']
self.set_energies("EFP", efpene['total'])
SCFE = self.get_energies("Total Energy")
SCFE += efp_wfn_independent_energy
self.set_energies("Total Energy", SCFE)
core.print_out(efpobj.energy_summary(scfefp=SCFE, label='psi'))
self.set_variable(
'EFP ELST ENERGY',
efpene['electrostatic'] + efpene['charge_penetration'] +
efpene['electrostatic_point_charges'])
self.set_variable('EFP IND ENERGY', efpene['polarization'])
self.set_variable('EFP DISP ENERGY', efpene['dispersion'])
self.set_variable('EFP EXCH ENERGY', efpene['exchange_repulsion'])
self.set_variable('EFP TOTAL ENERGY', efpene['total'])
self.set_variable('CURRENT ENERGY', efpene['total'])
core.print_out("\n ==> Post-Iterations <==\n\n")
if self.V_potential():
quad = self.V_potential().quadrature_values()
rho_a = quad['RHO_A'] / 2 if self.same_a_b_dens() else quad['RHO_A']
rho_b = quad['RHO_B'] / 2 if self.same_a_b_dens() else quad['RHO_B']
rho_ab = (rho_a + rho_b)
self.set_variable("GRID ELECTRONS TOTAL", rho_ab)
self.set_variable("GRID ELECTRONS ALPHA", rho_a)
self.set_variable("GRID ELECTRONS BETA", rho_b)
dev_a = rho_a - self.nalpha()
dev_b = rho_b - self.nbeta()
core.print_out(f" Electrons on quadrature grid:\n")
if self.same_a_b_dens():
core.print_out(
f" Ntotal = {rho_ab:15.10f} ; deviation = {dev_b+dev_a:.3e} \n\n"
)
else:
core.print_out(
f" Nalpha = {rho_a:15.10f} ; deviation = {dev_a:.3e}\n")
core.print_out(
f" Nbeta = {rho_b:15.10f} ; deviation = {dev_b:.3e}\n")
core.print_out(
f" Ntotal = {rho_ab:15.10f} ; deviation = {dev_b+dev_a:.3e} \n\n"
)
if ((dev_b + dev_a) > 0.1):
core.print_out(
" WARNING: large deviation in the electron count on grid detected. Check grid size!"
)
self.check_phases()
self.compute_spin_contamination()
self.frac_renormalize()
reference = core.get_option("SCF", "REFERENCE")
energy = self.get_energies("Total Energy")
# fail_on_maxiter = core.get_option("SCF", "FAIL_ON_MAXITER")
# if converged or not fail_on_maxiter:
#
# if print_lvl > 0:
# self.print_orbitals()
#
# if converged:
# core.print_out(" Energy converged.\n\n")
# else:
# core.print_out(" Energy did not converge, but proceeding anyway.\n\n")
if core.get_option('SCF', 'PRINT') > 0:
self.print_orbitals()
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
core.print_out(" @%s%s Final Energy: %20.14f" %
('DF-' if is_dfjk else '', reference, energy))
# if (perturb_h_) {
# core.print_out(" with %f %f %f perturbation" %
# (dipole_field_strength_[0], dipole_field_strength_[1], dipole_field_strength_[2]))
# }
core.print_out("\n\n")
self.print_energies()
self.clear_external_potentials()
if core.get_option('SCF', 'PCM'):
calc_type = core.PCM.CalcType.Total
if core.get_option("PCM", "PCM_SCF_TYPE") == "SEPARATE":
calc_type = core.PCM.CalcType.NucAndEle
Dt = self.Da().clone()
Dt.add(self.Db())
_, Vpcm = self.get_PCM().compute_PCM_terms(Dt, calc_type)
self.push_back_external_potential(Vpcm)
# Set callback function for CPSCF
self.set_external_cpscf_perturbation(
"PCM", lambda pert_dm: self.get_PCM().compute_V(pert_dm))
if core.get_option('SCF', 'PE'):
Dt = self.Da().clone()
Dt.add(self.Db())
_, Vpe = self.pe_state.get_pe_contribution(Dt, elec_only=False)
self.push_back_external_potential(Vpe)
# Set callback function for CPSCF
self.set_external_cpscf_perturbation(
"PE", lambda pert_dm: self.pe_state.get_pe_contribution(
pert_dm, elec_only=True)[1])
# Properties
# Comments so that autodoc utility will find these PSI variables
# Process::environment.globals["SCF DIPOLE X"] =
# Process::environment.globals["SCF DIPOLE Y"] =
# Process::environment.globals["SCF DIPOLE Z"] =
# Process::environment.globals["SCF QUADRUPOLE XX"] =
# Process::environment.globals["SCF QUADRUPOLE XY"] =
# Process::environment.globals["SCF QUADRUPOLE XZ"] =
# Process::environment.globals["SCF QUADRUPOLE YY"] =
# Process::environment.globals["SCF QUADRUPOLE YZ"] =
# Process::environment.globals["SCF QUADRUPOLE ZZ"] =
# Orbitals are always saved, in case an MO guess is requested later
# save_orbitals()
# Shove variables into global space
for k, v in self.variables().items():
core.set_variable(k, v)
# TODO re-enable
self.finalize()
if self.V_potential():
self.V_potential().clear_collocation_cache()
core.print_out("\nComputation Completed\n")
return energy
def multi_level(func, **kwargs):
"""
Use different levels of theory for different expansion levels
See kwargs description in driver_nbody.nbody_gufunc
:returns: *return type of func* |w--w| The data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
"""
from psi4.driver.driver_nbody import nbody_gufunc
from psi4.driver.driver_nbody import _print_nbody_energy
ptype = kwargs['ptype']
return_wfn = kwargs.get('return_wfn', False)
kwargs['return_wfn'] = True
levels = kwargs.pop('levels')
for i in levels:
if isinstance(i, str): levels[i.lower()] = levels.pop(i)
supersystem = levels.pop('supersystem', False)
molecule = kwargs.get('molecule', core.get_active_molecule())
kwargs['bsse_type'] = [kwargs['bsse_type']] if isinstance(kwargs['bsse_type'], str) else kwargs['bsse_type']
natoms = molecule.natom()
# Initialize with zeros
energy_result, gradient_result, hessian_result = 0, None, None
energy_body_contribution = {b: {} for b in kwargs['bsse_type']}
energy_body_dict = {b: {} for b in kwargs['bsse_type']}
wfns = {}
if ptype in ['gradient', 'hessian']:
gradient_result = np.zeros((natoms, 3))
if ptype == 'hessian':
hessian_result = np.zeros((natoms * 3, natoms * 3))
if kwargs.get('charge_method', False) and not kwargs.get('embedding_charges', False):
kwargs['embedding_charges'] = compute_charges(kwargs['charge_method'],
kwargs.get('charge_type', 'MULLIKEN_CHARGES').upper(), molecule)
for n in sorted(levels)[::-1]:
molecule.set_name('%i' %n)
kwargs_copy = kwargs.copy()
kwargs_copy['max_nbody'] = n
energy_bsse_dict = {b: 0 for b in kwargs['bsse_type']}
if isinstance(levels[n], str):
# If a new level of theory is provided, compute contribution
ret, wfn = nbody_gufunc(func, levels[n], **kwargs_copy)
wfns[n] = wfn
else:
# For the n-body contribution, use available data from the higher order levels[n]-body
wfn = wfns[levels[n]]
for m in range(n - 1, n + 1):
if m == 0: continue
# Subtract the (n-1)-body contribution from the n-body contribution to get the n-body effect
sign = (-1)**(1 - m // n)
for b in kwargs['bsse_type']:
energy_bsse_dict[b] += sign * wfn.variable('%i%s' % (m, b.lower()))
if ptype in ['gradient', 'hessian']:
gradient_result += sign * np.array(wfn.variable('GRADIENT ' + str(m)))
# Keep 1-body contribution to compute interaction data
if n == 1:
gradient1 = np.array(wfn.variable('GRADIENT ' + str(m)))
if ptype == 'hessian':
hessian_result += sign * np.array(wfn.variable('HESSIAN ' + str(m)))
if n == 1:
hessian1 = np.array(wfn.variable('HESSIAN ' + str(m)))
energy_result += energy_bsse_dict[kwargs['bsse_type'][0]]
for b in kwargs['bsse_type']:
energy_body_contribution[b][n] = energy_bsse_dict[b]
if supersystem:
# Super system recovers higher order effects at a lower level
molecule.set_name('supersystem')
kwargs_copy = kwargs.copy()
kwargs_copy.pop('bsse_type')
kwargs_copy.pop('ptype')
ret, wfn_super = func(supersystem, **kwargs_copy)
core.clean()
kwargs_copy = kwargs.copy()
kwargs_copy['bsse_type'] = 'nocp'
kwargs_copy['max_nbody'] = max(levels)
# Subtract lower order effects to avoid double counting
ret, wfn = nbody_gufunc(func, supersystem, **kwargs_copy)
energy_result += wfn_super.energy() - wfn.variable(str(max(levels)))
for b in kwargs['bsse_type']:
energy_body_contribution[b][molecule.nfragments()] = wfn_super.energy() - wfn.variable(
str(max(levels)))
if ptype in ['gradient', 'hessian']:
gradient_result += np.array(wfn_super.gradient()) - np.array(wfn.variable('GRADIENT ' + str(max(levels))))
if ptype == 'hessian':
hessian_result += np.array(wfn_super.hessian()) - np.array(wfn.variable('HESSIAN ' + str(max(levels))))
levels['supersystem'] = supersystem
for b in kwargs['bsse_type']:
for n in energy_body_contribution[b]:
energy_body_dict[b][n] = sum(
[energy_body_contribution[b][i] for i in range(1, n + 1) if i in energy_body_contribution[b]])
is_embedded = kwargs.get('embedding_charges', False) or kwargs.get('charge_method', False)
for b in kwargs['bsse_type']:
_print_nbody_energy(energy_body_dict[b], '%s-corrected multilevel many-body expansion' % b.upper(),
is_embedded)
if not kwargs['return_total_data']:
# Remove monomer cotribution for interaction data
energy_result -= energy_body_dict[kwargs['bsse_type'][0]][1]
if ptype in ['gradient', 'hessian']:
gradient_result -= gradient1
if ptype == 'hessian':
hessian_result -= hessian1
wfn_out = core.Wavefunction.build(molecule, 'def2-svp')
core.set_variable("CURRENT ENERGY", energy_result)
wfn_out.set_variable("CURRENT ENERGY", energy_result)
gradient_result = core.Matrix.from_array(gradient_result) if gradient_result is not None else None
wfn_out.set_gradient(gradient_result)
hessian_result = core.Matrix.from_array(hessian_result) if hessian_result is not None else None
wfn_out.set_hessian(hessian_result)
ptype_result = eval(ptype + '_result')
for b in kwargs['bsse_type']:
for i in energy_body_dict[b]:
wfn_out.set_variable(str(i) + b, energy_body_dict[b][i])
if kwargs['return_wfn']:
return (ptype_result, wfn_out)
else:
return ptype_result
def compute_energy(self,
molecule: core.Molecule,
wfn: core.Wavefunction = None) -> float:
"""Compute dispersion energy based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
float
Dispersion energy [Eh].
Notes
-----
:psivar:`DISPERSION CORRECTION ENERGY`
Disp always set. Overridden in SCF finalization, but that only changes for "-3C" methods.
:psivar:`fctl DISPERSION CORRECTION ENERGY`
Set if :py:attr:`fctldash` nonempty.
"""
if self.engine in ['dftd3', 'mp2d']:
resi = AtomicInput(
**{
'driver': 'energy',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'pair_resolved': self.save_pairwise_disp,
'verbose': 1,
},
'molecule': molecule.to_schema(dtype=2),
'provenance': p4util.provenance_stamp(__name__),
})
jobrec = qcng.compute(
resi,
self.engine,
raise_error=True,
local_options={
"scratch_directory":
core.IOManager.shared_object().get_default_path()
})
dashd_part = float(
jobrec.extras['qcvars']['DISPERSION CORRECTION ENERGY'])
if wfn is not None:
for k, qca in jobrec.extras['qcvars'].items():
wfn.set_variable(
k,
float(qca) if isinstance(qca, str) else qca)
# Pass along the pairwise dispersion decomposition if we need it
if self.save_pairwise_disp is True:
wfn.set_variable(
"PAIRWISE DISPERSION CORRECTION ANALYSIS",
jobrec.extras['qcvars']
["2-BODY PAIRWISE DISPERSION CORRECTION ANALYSIS"])
if self.fctldash in ['hf3c', 'pbeh3c']:
jobrec = qcng.compute(
resi,
"gcp",
raise_error=True,
local_options={
"scratch_directory":
core.IOManager.shared_object().get_default_path()
})
gcp_part = jobrec.return_result
dashd_part += gcp_part
return dashd_part
else:
ene = self.disp.compute_energy(molecule)
core.set_variable('DISPERSION CORRECTION ENERGY', ene)
if self.fctldash:
core.set_variable(
f"{self.fctldash} DISPERSION CORRECTION ENERGY", ene)
return ene
def run_gcp(self,
func=None,
dertype=None,
verbose=False): # dashlvl=None, dashparam=None
"""Function to call Grimme's dftd3 program (http://toc.uni-muenster.de/DFTD3/)
to compute the -D correction of level *dashlvl* using parameters for
the functional *func*. The dictionary *dashparam* can be used to supply
a full set of dispersion parameters in the absense of *func* or to supply
individual overrides in the presence of *func*. Returns energy if *dertype* is 0,
gradient if *dertype* is 1, else tuple of energy and gradient if *dertype*
unspecified. The dftd3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
*self* may be either a qcdb.Molecule (sensibly) or a psi4.Molecule
(works b/c psi4.Molecule has been extended by this method py-side and
only public interface fns used) or a string that can be instantiated
into a qcdb.Molecule.
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(self, Molecule):
# called on a qcdb.Molecule
pass
elif isinstance(self, core.Molecule):
# called on a python export of a psi4.core.Molecule (py-side through Psi4's driver)
self.create_psi4_string_from_molecule()
elif isinstance(self, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
self = Molecule(self)
else:
raise ValidationError(
"""Argument mol must be psi4string or qcdb.Molecule""")
self.update_geometry()
# # Validate arguments
# dashlvl = dashlvl.lower()
# dashlvl = dash_alias['-' + dashlvl][1:] if ('-' + dashlvl) in dash_alias.keys() else dashlvl
# if dashlvl not in dashcoeff.keys():
# raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
# elif der2nd.match(str(dertype)):
# raise ValidationError('Requested derivative level \'dertype\' %s not valid for run_dftd3.' % (dertype))
else:
raise ValidationError(
'Requested derivative level \'dertype\' %s not valid for run_dftd3.'
% (dertype))
# if func is None:
# if dashparam is None:
# # defunct case
# raise ValidationError("""Parameters for -D correction missing. Provide a func or a dashparam kwarg.""")
# else:
# # case where all param read from dashparam dict (which must have all correct keys)
# func = 'custom'
# dashcoeff[dashlvl][func] = {}
# dashparam = dict((k.lower(), v) for k, v in dashparam.iteritems())
# for key in dashcoeff[dashlvl]['b3lyp'].keys():
# if key in dashparam.keys():
# dashcoeff[dashlvl][func][key] = dashparam[key]
# else:
# raise ValidationError("""Parameter %s is missing from dashparam dict %s.""" % (key, dashparam))
# else:
# func = func.lower()
# if func not in dashcoeff[dashlvl].keys():
# raise ValidationError("""Functional %s is not available for -D level %s.""" % (func, dashlvl))
# if dashparam is None:
# # (normal) case where all param taken from dashcoeff above
# pass
# else:
# # case where items in dashparam dict can override param taken from dashcoeff above
# dashparam = dict((k.lower(), v) for k, v in dashparam.iteritems())
# for key in dashcoeff[dashlvl]['b3lyp'].keys():
# if key in dashparam.keys():
# dashcoeff[dashlvl][func][key] = dashparam[key]
# TODO temp until figure out paramfile
allowed_funcs = [
'HF/MINIS',
'DFT/MINIS',
'HF/MINIX',
'DFT/MINIX',
'HF/SV',
'DFT/SV',
'HF/def2-SV(P)',
'DFT/def2-SV(P)',
'HF/def2-SVP',
'DFT/def2-SVP',
'HF/DZP',
'DFT/DZP',
'HF/def-TZVP',
'DFT/def-TZVP',
'HF/def2-TZVP',
'DFT/def2-TZVP',
'HF/631Gd',
'DFT/631Gd',
'HF/def2-TZVP',
'DFT/def2-TZVP',
'HF/cc-pVDZ',
'DFT/cc-pVDZ',
'HF/aug-cc-pVDZ',
'DFT/aug-cc-pVDZ',
'DFT/SV(P/h,c)',
'DFT/LANL',
'DFT/pobTZVP',
'TPSS/def2-SVP',
'PW6B95/def2-SVP',
# specials
'hf3c',
'pbeh3c'
]
allowed_funcs = [f.lower() for f in allowed_funcs]
if func.lower() not in allowed_funcs:
raise Dftd3Error("""bad gCP func: %s. need one of: %r""" %
(func, allowed_funcs))
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser(
'~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
try:
import psi4
except ImportError as err:
isP4regime = False
else:
isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
gcp_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.gcp.' + str(uuid.uuid4())[:8]
else:
gcp_tmpdir = os.environ['HOME'] + os.sep + 'gcp_' + str(
uuid.uuid4())[:8]
if os.path.exists(gcp_tmpdir) is False:
os.mkdir(gcp_tmpdir)
os.chdir(gcp_tmpdir)
# Write gcp_parameters file that governs cp correction
# paramcontents = gcp_server(func, dashlvl, 'dftd3')
# paramfile1 = 'dftd3_parameters' # older patched name
# with open(paramfile1, 'w') as handle:
# handle.write(paramcontents)
# paramfile2 = '.gcppar'
# with open(paramfile2, 'w') as handle:
# handle.write(paramcontents)
###Two kinds of parameter files can be read in: A short and an extended version. Both are read from
###$HOME/.gcppar.$HOSTNAME by default. If the option -local is specified the file is read in from
###the current working directory: .gcppar
###The short version reads in: basis-keywo
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = self.natom()
geom = self.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './gcp_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call gcp program
command = ['gcp', geomfile]
command.extend(['-level', func])
if dertype != 0:
command.append('-grad')
try:
#print('command', command)
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program gcp not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Egcp:', line):
sline = line.split()
dashd = float(sline[1])
if re.match(' normal termination of gCP', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful gCP run.""")
# Parse grad output
if dertype != 0:
derivfile = './gcp_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([
float(x.replace('D', 'E'))
for x in dfile.readline().split()
])
dfile.close()
if len(dashdderiv) != self.natom():
raise ValidationError('Program gcp gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), self.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('GCP CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> GCP Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# # Clean up files and remove scratch directory
# os.unlink(paramfile1)
# os.unlink(paramfile2)
# os.unlink(geomfile)
# if dertype != 0:
# os.unlink(derivfile)
# if defmoved is True:
# os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
# try:
# shutil.rmtree(dftd3_tmpdir)
# except OSError as e:
# ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def nbody_gufunc(func, method_string, **kwargs):
"""
Computes the nbody interaction energy, gradient, or Hessian depending on input.
This is a generalized univeral function for computing interaction quantities.
:returns: *return type of func* |w--w| The interaction data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| interaction data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
:type func: function
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: string
:param method_string: ``'scf'`` || ``'mp2'`` || ``'ci5'`` || etc.
First argument, lowercase and usually unlabeled. Indicates the computational
method to be passed to func.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:type return_wfn: :ref:`boolean <op_py_boolean>`
:param return_wfn: ``'on'`` || |dl| ``'off'`` |dr|
Indicate to additionally return the :py:class:`~psi4.core.Wavefunction`
calculation result as the second element of a tuple.
:type bsse_type: string or list
:param bsse_type: ``'cp'`` || ``['nocp', 'vmfc']`` || |dl| ``None`` |dr| || etc.
Type of BSSE correction to compute: CP, NoCP, or VMFC. The first in this
list is returned by this function. By default, this function is not called.
:type max_nbody: int
:param max_nbody: ``3`` || etc.
Maximum n-body to compute, cannot exceed the number of fragments in the moleucle.
:type ptype: string
:param ptype: ``'energy'`` || ``'gradient'`` || ``'hessian'``
Type of the procedure passed in.
:type return_total_data: :ref:`boolean <op_py_boolean>`
:param return_total_data: ``'on'`` || |dl| ``'off'`` |dr|
If True returns the total data (energy/gradient/etc) of the system,
otherwise returns interaction data.
"""
# Initialize dictionaries for easy data passing
metadata, component_results, nbody_results = {}, {}, {}
# Parse some kwargs
kwargs = p4util.kwargs_lower(kwargs)
metadata['ptype'] = kwargs.pop('ptype', None)
metadata['return_wfn'] = kwargs.pop('return_wfn', False)
metadata['return_total_data'] = kwargs.pop('return_total_data', False)
metadata['molecule'] = kwargs.pop('molecule', core.get_active_molecule())
metadata['molecule'].update_geometry()
metadata['kwargs'] = kwargs
core.clean_variables()
if metadata['ptype'] not in ['energy', 'gradient', 'hessian']:
raise ValidationError("""N-Body driver: The ptype '%s' is not regonized.""" % metadata['ptype'])
# Parse bsse_type, raise exception if not provided or unrecognized
metadata['bsse_type_list'] = kwargs.pop('bsse_type')
if metadata['bsse_type_list'] is None:
raise ValidationError("N-Body GUFunc: Must pass a bsse_type")
if not isinstance(metadata['bsse_type_list'], list):
metadata['bsse_type_list'] = [metadata['bsse_type_list']]
for num, btype in enumerate(metadata['bsse_type_list']):
metadata['bsse_type_list'][num] = btype.lower()
if btype.lower() not in ['cp', 'nocp', 'vmfc']:
raise ValidationError("N-Body GUFunc: bsse_type '%s' is not recognized" % btype.lower())
metadata['max_nbody'] = kwargs.get('max_nbody', -1)
metadata['max_frag'] = metadata['molecule'].nfragments()
if metadata['max_nbody'] == -1:
metadata['max_nbody'] = metadata['molecule'].nfragments()
else:
metadata['max_nbody'] = min(metadata['max_nbody'], metadata['max_frag'])
# Flip this off for now, needs more testing
# If we are doing CP lets save them integrals
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# # Set to save RI integrals for repeated full-basis computations
# ri_ints_io = core.get_global_option('DF_INTS_IO')
# # inquire if above at all applies to dfmp2 or just scf
# core.set_global_option('DF_INTS_IO', 'SAVE')
# psioh = core.IOManager.shared_object()
# psioh.set_specific_retention(97, True)
bsse_str = metadata['bsse_type_list'][0]
if len(metadata['bsse_type_list']) > 1:
bsse_str = str(metadata['bsse_type_list'])
core.print_out("\n\n")
core.print_out(" ===> N-Body Interaction Abacus <===\n")
core.print_out(" BSSE Treatment: %s\n" % bsse_str)
# Get compute list
metadata = build_nbody_compute_list(metadata)
# Compute N-Body components
component_results = compute_nbody_components(func, method_string, metadata)
# Assemble N-Body quantities
nbody_results = assemble_nbody_components(metadata, component_results)
# Figure out returns
if metadata['ptype'] != 'energy':
if metadata['return_total_data']:
np_final_ptype = nbody_results['ptype_body_dict'][metadata['max_nbody']].copy()
else:
np_final_ptype = nbody_results['ptype_body_dict'][metadata['max_nbody']].copy()
np_final_ptype -= ptype_body_dict[1]
nbody_results['ret_ptype'] = core.Matrix.from_array(np_final_ptype)
else:
nbody_results['ret_ptype'] = nbody_results['ret_energy']
# Build wfn and bind variables
wfn = core.Wavefunction.build(metadata['molecule'], 'def2-svp')
dicts = ['energies', 'ptype', 'intermediates', 'energy_body_dict', 'ptype_body_dict', 'nbody']
for r in [component_results, nbody_results]:
for d in r:
if d in dicts:
for var, value in r[d].items():
wfn.set_variable(str(var), value)
core.set_variable(str(var), value)
if metadata['ptype'] == 'gradient':
wfn.set_gradient(ret_ptype)
elif metadata['ptype'] == 'hessian':
wfn.set_hessian(ret_ptype)
core.set_variable("CURRENT ENERGY", nbody_results['ret_energy'])
wfn.set_variable("CURRENT ENERGY", nbody_results['ret_energy'])
if metadata['return_wfn']:
return (nbody_results['ret_ptype'], wfn)
else:
return nbody_results['ret_ptype']
def print_sapt_dft_summary(data, name, short=False):
ret = " %s Results\n" % name
ret += " " + "-" * 97 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst1,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
# Induction
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
if "Delta HF Correction" in list(data):
ind += data["Delta HF Correction"]
ret += print_sapt_var("Induction", ind) + "\n"
ret += print_sapt_var(" Ind2,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind2,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba) + "\n"
if "Delta HF Correction" in list(data):
ret += print_sapt_var(" delta HF,r (2)", data["Delta HF Correction"]) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,u"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp2,r", data["Disp20"]) + "\n"
ret += print_sapt_var(" Disp2,u", data["Disp20,u"]) + "\n"
ret += print_sapt_var(" Exch-Disp2,u", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-15s" % name, total, start_spacer=" ") + "\n"
core.set_variable("SAPT(DFT) TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 97 + "\n"
return ret
def print_sapt_hf_summary(data, name, short=False, delta_hf=False):
ret = " %s Results\n" % name
ret += " " + "-" * 97 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst10,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch10", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch10(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
ret += print_sapt_var("Induction", ind) + "\n"
ret += print_sapt_var(" Ind20,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind20,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
if delta_hf:
total_sapt = (data["Elst10,r"] + data["Exch10"] + ind)
sapt_hf_delta = delta_hf - total_sapt
core.set_variable("SAPT(DFT) Delta HF", sapt_hf_delta)
ret += print_sapt_var("%-21s" % "Total SAPT", total_sapt, start_spacer=" ") + "\n"
ret += print_sapt_var("%-21s" % "Total HF", delta_hf, start_spacer=" ") + "\n"
ret += print_sapt_var("%-21s" % "Delta HF", sapt_hf_delta, start_spacer=" ") + "\n"
ret += " " + "-" * 97 + "\n"
return ret
else:
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,u"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp20", data["Disp20,u"]) + "\n"
ret += print_sapt_var(" Exch-Disp20", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-15s" % name, total, start_spacer=" ") + "\n"
core.set_variable("SAPT0 TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 97 + "\n"
return ret
def compute_energy(self, molecule: 'psi4.core.Molecule', wfn: 'psi4.core.Wavefunction' = None) -> float:
"""Compute dispersion energy based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule : psi4.core.Molecule
System for which to compute empirical dispersion correction.
wfn :
Location to set QCVariables
Returns
-------
float
Dispersion energy [Eh].
Notes
-----
DISPERSION CORRECTION ENERGY
Disp always set. Overridden in SCF finalization, but that only changes for "-3C" methods.
self.fctldash + DISPERSION CORRECTION ENERGY
Set if `fctldash` nonempty.
"""
if self.engine in ['dftd3', 'mp2d']:
resi = AtomicInput(
**{
'driver': 'energy',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'verbose': 1,
},
'molecule': molecule.to_schema(dtype=2),
'provenance': p4util.provenance_stamp(__name__),
})
jobrec = qcng.compute(resi, self.engine, raise_error=True,
local_options={"scratch_directory": core.IOManager.shared_object().get_default_path()})
dashd_part = float(jobrec.extras['qcvars']['DISPERSION CORRECTION ENERGY'])
if wfn is not None:
for k, qca in jobrec.extras['qcvars'].items():
if 'CURRENT' not in k:
wfn.set_variable(k, p4util.plump_qcvar(qca, k))
if self.fctldash in ['hf3c', 'pbeh3c']:
gcp_part = gcp.run_gcp(molecule, self.fctldash, verbose=False, dertype=0)
dashd_part += gcp_part
return dashd_part
else:
ene = self.disp.compute_energy(molecule)
core.set_variable('DISPERSION CORRECTION ENERGY', ene)
if self.fctldash:
core.set_variable('{} DISPERSION CORRECTION ENERGY'.format(self.fctldash), ene)
return ene
def run_sns_mp2(name, molecule, **kwargs):
"""Run the SNS-MP2 calculation
"""
if len(kwargs) > 0:
core.print_out('Unrecognized options: %s' % str(kwargs))
if parse_version(psi4.__version__) < parse_version('1.3rc2'):
raise ImportError(
'Psi4 {:s} is not compatible (v1.3+ necessary)'.format(
psi4.__version__))
# Force to c1
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.fix_orientation(True)
molecule.fix_com(True)
molecule.update_geometry()
nfrag = molecule.nfragments()
if nfrag != 2:
raise ValueError(
'NN-MP2 requires active molecule to have 2 fragments, not %s.' %
(nfrag))
LOW = 'DESAVTZ'
HIGH = 'DESAVQZ'
inject_desres_basis()
with WavefunctionCache(molecule, low=LOW, high=HIGH) as c:
m1mlow = c.compute('m1', 'm', 'low', mp2=False)
m2mlow = c.compute('m2', 'm', 'low', mp2=False)
m1mhigh = c.compute('m1', 'm', 'high', mp2=True, mp2_dm=True)
m2mhigh = c.compute('m2', 'm', 'high', mp2=True, mp2_dm=True)
m1dlow = c.compute('m1', 'd', 'low', mp2=True)
m2dlow = c.compute('m2', 'd', 'low', mp2=True)
m1dhigh = c.compute('m1', 'd', 'high', mp2=True)
m2dhigh = c.compute('m2', 'd', 'high', mp2=True)
ddlow = c.compute('d', 'd', 'low', mp2=True)
ddhigh = c.compute('d', 'd', 'high', mp2=True)
@psiopts(
'SCF_TYPE DF',
'BASIS %s' % HIGH,
'DF_BASIS_SCF %s-jkfit' % HIGH,
'DF_BASIS_MP2 %s-ri' % HIGH,
'DF_INTS_IO LOAD',
)
def run_espx():
core.tstart()
p4util.banner('ESPX')
dimer_basis = ddhigh.basisset()
aux_basis = core.BasisSet.build(molecule, "DF_BASIS_SCF",
HIGH + '-JKFIT', "JKFIT", HIGH)
decomp = ESHLOVLPDecomposition(m1_basis=m1mhigh.basisset(),
m2_basis=m2mhigh.basisset(),
dimer_basis=dimer_basis,
dimer_aux_basis=aux_basis)
esovlpfunc = decomp.esovlp()
eshf, ovlhf = esovlpfunc(m1mhigh.reference_wavefunction(),
m2mhigh.reference_wavefunction())
esmp, ovlmp = esovlpfunc(m1mhigh, m2mhigh)
del esovlpfunc
hlfunc = decomp.hl()
hl = hlfunc(m1mhigh.reference_wavefunction(),
m2mhigh.reference_wavefunction())
core.tstop()
return {
'eshf': eshf,
'ovlhf': ovlhf,
'esmp': esmp,
'ovlmp': ovlmp,
'hl': hl
}, dimer_basis
###################################################################
@psiopts(
'SAPT SAPT_LEVEL SAPT0',
'SAPT E_CONVERGENCE 10e-10',
'SAPT D_CONVERGENCE 10e-10',
'SAPT NAT_ORBS_T2 TRUE',
'SCF_TYPE DF',
'BASIS %s' % LOW,
'DF_BASIS_SAPT %s-RI' % LOW,
)
def run_sapt():
if ddlow.name() == 'SCF':
dimer_wfn = ddlow
else:
dimer_wfn = ddlow.reference_wavefunction()
aux_basis = core.BasisSet.build(
dimer_wfn.molecule(), "DF_BASIS_SAPT",
core.get_global_option("DF_BASIS_SAPT"), "RIFIT",
core.get_global_option("BASIS"))
dimer_wfn.set_basisset("DF_BASIS_SAPT", aux_basis)
dimer_wfn.set_basisset("DF_BASIS_ELST", aux_basis)
core.sapt(dimer_wfn, m1mlow, m2mlow)
return {
k: core.variable(k)
for k in (
'SAPT ELST10,R ENERGY',
'SAPT EXCH10 ENERGY',
'SAPT EXCH10(S^2) ENERGY',
'SAPT IND20,R ENERGY',
'SAPT EXCH-IND20,R ENERGY',
'SAPT EXCH-DISP20 ENERGY',
'SAPT DISP20 ENERGY',
'SAPT SAME-SPIN EXCH-DISP20 ENERGY',
'SAPT SAME-SPIN DISP20 ENERGY',
'SAPT HF TOTAL ENERGY',
)
}
###################################################################
# Run the three previously defined functions
espx_data, dimer_high_basis = run_espx()
sapt_data = run_sapt()
data = format_espx_human(HIGH, espx_data)
data.update(sapt_data)
data.update(format_intene_human(c))
core.tstart()
e, lines = sns_mp2_model(data)
core.set_variable('SNS-MP2 TOTAL ENERGY', e * KCAL2MEH * 0.001)
core.set_variable('CURRENT ENERGY', e * KCAL2MEH * 0.001)
if os.environ.get('TEST_SNSMP2', False):
for k, v in data.items():
core.set_variable(k, v)
core.print_out(lines)
core.tstop()
return e
def database(name, db_name, **kwargs):
r"""Function to access the molecule objects and reference energies of
popular chemical databases.
:aliases: db()
:returns: (*float*) Mean absolute deviation of the database in kcal/mol
:PSI variables:
.. hlist::
:columns: 1
* :psivar:`db_name DATABASE MEAN SIGNED DEVIATION`
* :psivar:`db_name DATABASE MEAN ABSOLUTE DEVIATION`
* :psivar:`db_name DATABASE ROOT-MEAN-SQUARE DEVIATION`
* Python dictionaries of results accessible as ``DB_RGT`` and ``DB_RXN``.
.. note:: It is very easy to make a database from a collection of xyz files
using the script :source:`psi4/share/psi4/scripts/ixyz2database.py`.
See :ref:`sec:createDatabase` for details.
.. caution:: Some features are not yet implemented. Buy a developer some coffee.
- In sow/reap mode, use only global options (e.g., the local option set by ``set scf scf_type df`` will not be respected).
.. note:: To access a database that is not embedded in a |PSIfour|
distribution, add the path to the directory containing the database
to the environment variable :envvar:`PYTHONPATH`.
:type name: str
:param name: ``'scf'`` || ``'sapt0'`` || ``'ccsd(t)'`` || etc.
First argument, usually unlabeled. Indicates the computational method
to be applied to the database. May be any valid argument to
:py:func:`psi4.driver.energy`.
:type db_name: str
:param db_name: ``'BASIC'`` || ``'S22'`` || ``'HTBH'`` || etc.
Second argument, usually unlabeled. Indicates the requested database
name, matching (case insensitive) the name of a python file in
``psi4/share/databases`` or :envvar:`PYTHONPATH`. Consult that
directory for available databases and literature citations.
:type func: :ref:`function <op_py_function>`
:param func: |dl| ``energy`` |dr| || ``optimize`` || ``cbs``
Indicates the type of calculation to be performed on each database
member. The default performs a single-point ``energy('name')``, while
``optimize`` perfoms a geometry optimization on each reagent, and
``cbs`` performs a compound single-point energy. If a nested series
of python functions is intended (see :ref:`sec:intercalls`), use
keyword ``db_func`` instead of ``func``.
:type mode: str
:param mode: |dl| ``'continuous'`` |dr| || ``'sow'`` || ``'reap'``
Indicates whether the calculations required to complete the
database are to be run in one file (``'continuous'``) or are to be
farmed out in an embarrassingly parallel fashion
(``'sow'``/``'reap'``). For the latter, run an initial job with
``'sow'`` and follow instructions in its output file.
:type cp: :ref:`boolean <op_py_boolean>`
:param cp: ``'on'`` || |dl| ``'off'`` |dr|
Indicates whether counterpoise correction is employed in computing
interaction energies. Use this option and NOT the ``bsse_type="cp"``
function for BSSE correction in database(). Option available
(See :ref:`sec:availableDatabases`) only for databases of bimolecular complexes.
:type rlxd: :ref:`boolean <op_py_boolean>`
:param rlxd: ``'on'`` || |dl| ``'off'`` |dr|
Indicates whether correction for deformation energy is
employed in computing interaction energies. Option available
(See :ref:`sec:availableDatabases`) only for databases of bimolecular complexes
with non-frozen monomers, e.g., HBC6.
:type symm: :ref:`boolean <op_py_boolean>`
:param symm: |dl| ``'on'`` |dr| || ``'off'``
Indicates whether the native symmetry of the database reagents is
employed (``'on'``) or whether it is forced to :math:`C_1` symmetry
(``'off'``). Some computational methods (e.g., SAPT) require no
symmetry, and this will be set by database().
:type zpe: :ref:`boolean <op_py_boolean>`
:param zpe: ``'on'`` || |dl| ``'off'`` |dr|
Indicates whether zero-point-energy corrections are appended to
single-point energy values. Option valid only for certain
thermochemical databases. Disabled until Hessians ready.
:type benchmark: str
:param benchmark: |dl| ``'default'`` |dr| || ``'S22A'`` || etc.
Indicates whether a non-default set of reference energies, if
available (See :ref:`sec:availableDatabases`), are employed for the
calculation of error statistics.
:type tabulate: List[str]
:param tabulate: |dl| ``[]`` |dr| || ``['scf total energy', 'natom']`` || etc.
Indicates whether to form tables of variables other than the
primary requested energy. Available for any PSI variable.
:type subset: Union[str, List[str]]
:param subset:
Indicates a subset of the full database to run. This is a very
flexible option and can be used in three distinct ways, outlined
below. Note that two take a string and the last takes an array.
See :ref:`sec:availableDatabases` for available values.
* ``'small'`` || ``'large'`` || ``'equilibrium'``
Calls predefined subsets of the requested database, either
``'small'``, a few of the smallest database members,
``'large'``, the largest of the database members, or
``'equilibrium'``, the equilibrium geometries for a database
composed of dissociation curves.
* ``'BzBz_S'`` || ``'FaOOFaON'`` || ``'ArNe'`` || ``'HB'`` || etc.
For databases composed of dissociation curves, or otherwise
divided into subsets, individual curves and subsets can be
called by name. Consult the database python files for available
molecular systems (case insensitive).
* ``[1,2,5]`` || ``['1','2','5']`` || ``['BzMe-3.5', 'MeMe-5.0']`` || etc.
Specify a list of database members to run. Consult the
database python files for available molecular systems. This
is the only portion of database input that is case sensitive;
choices for this keyword must match the database python file.
:examples:
>>> # [1] Two-stage SCF calculation on short, equilibrium, and long helium dimer
>>> db('scf','RGC10',cast_up='sto-3g',subset=['HeHe-0.85','HeHe-1.0','HeHe-1.5'], tabulate=['scf total energy','natom'])
>>> # [2] Counterpoise-corrected interaction energies for three complexes in S22
>>> # Error statistics computed wrt an old benchmark, S22A
>>> database('mp2','S22',cp=1,subset=[16,17,8],benchmark='S22A')
>>> # [3] SAPT0 on the neon dimer dissociation curve
>>> db('sapt0',subset='NeNe',cp=0,symm=0,db_name='RGC10')
>>> # [4] Optimize system 1 in database S22, producing tables of scf and mp2 energy
>>> db('mp2','S22',db_func=optimize,subset=[1], tabulate=['mp2 total energy','current energy'])
>>> # [5] CCSD on the smallest systems of HTBH, a hydrogen-transfer database
>>> database('ccsd','HTBH',subset='small', tabulate=['ccsd total energy', 'mp2 total energy'])
"""
lowername = name #TODO
kwargs = p4util.kwargs_lower(kwargs)
# Wrap any positional arguments into kwargs (for intercalls among wrappers)
if not ('name' in kwargs) and name:
kwargs['name'] = name #.lower()
if not ('db_name' in kwargs) and db_name:
kwargs['db_name'] = db_name
# Establish function to call
func = kwargs.pop('db_func', kwargs.pop('func', energy))
kwargs['db_func'] = func
# Bounce to CP if bsse kwarg (someday)
if kwargs.get('bsse_type', None) is not None:
raise ValidationError(
"""Database: Cannot specify bsse_type for database. Use the cp keyword withing database instead."""
)
allowoptexceeded = kwargs.get('allowoptexceeded', False)
optstash = p4util.OptionsState(['WRITER_FILE_LABEL'], ['SCF', 'REFERENCE'])
# Wrapper wholly defines molecule. discard any passed-in
kwargs.pop('molecule', None)
# Paths to search for database files: here + PSIPATH + library + PYTHONPATH
db_paths = []
db_paths.append(os.getcwd())
db_paths.extend(os.environ.get('PSIPATH', '').split(os.path.pathsep))
db_paths.append(os.path.join(core.get_datadir(), 'databases'))
db_paths.append(os.path.dirname(__file__))
db_paths = list(map(os.path.abspath, db_paths))
sys.path[1:1] = db_paths
# TODO this should be modernized a la interface_cfour
# Define path and load module for requested database
database = p4util.import_ignorecase(db_name)
if database is None:
core.print_out('\nPython module for database %s failed to load\n\n' %
(db_name))
core.print_out('\nSearch path that was tried:\n')
core.print_out(", ".join(map(str, sys.path)))
raise ValidationError("Python module loading problem for database " +
str(db_name))
else:
dbse = database.dbse
HRXN = database.HRXN
ACTV = database.ACTV
RXNM = database.RXNM
BIND = database.BIND
TAGL = database.TAGL
GEOS = database.GEOS
try:
DATA = database.DATA
except AttributeError:
DATA = {}
user_writer_file_label = core.get_global_option('WRITER_FILE_LABEL')
user_reference = core.get_global_option('REFERENCE')
# Configuration based upon e_name & db_name options
# Force non-supramolecular if needed
if not hasattr(lowername, '__call__') and re.match(r'^.*sapt', lowername):
try:
database.ACTV_SA
except AttributeError:
raise ValidationError(
'Database %s not suitable for non-supramolecular calculation.'
% (db_name))
else:
ACTV = database.ACTV_SA
# Force open-shell if needed
openshell_override = 0
if user_reference in ['RHF', 'RKS']:
try:
database.isOS
except AttributeError:
pass
else:
if p4util.yes.match(str(database.isOS)):
openshell_override = 1
core.print_out(
'\nSome reagents in database %s require an open-shell reference; will be reset to UHF/UKS as needed.\n'
% (db_name))
# Configuration based upon database keyword options
# Option symmetry- whether symmetry treated normally or turned off (currently req'd for dfmp2 & dft)
db_symm = kwargs.get('symm', True)
symmetry_override = 0
if db_symm is False:
symmetry_override = 1
elif db_symm is True:
pass
else:
raise ValidationError("""Symmetry mode '%s' not valid.""" % (db_symm))
# Option mode of operation- whether db run in one job or files farmed out
db_mode = kwargs.pop('db_mode', kwargs.pop('mode', 'continuous')).lower()
kwargs['db_mode'] = db_mode
if db_mode == 'continuous':
pass
elif db_mode == 'sow':
pass
elif db_mode == 'reap':
db_linkage = kwargs.get('linkage', None)
if db_linkage is None:
raise ValidationError(
"""Database execution mode 'reap' requires a linkage option."""
)
else:
raise ValidationError("""Database execution mode '%s' not valid.""" %
(db_mode))
# Option counterpoise- whether for interaction energy databases run in bsse-corrected or not
db_cp = kwargs.get('cp', False)
if db_cp is True:
try:
database.ACTV_CP
except AttributeError:
raise ValidationError(
"""Counterpoise correction mode 'yes' invalid for database %s."""
% (db_name))
else:
ACTV = database.ACTV_CP
elif db_cp is False:
pass
else:
raise ValidationError(
"""Counterpoise correction mode '%s' not valid.""" % (db_cp))
# Option relaxed- whether for non-frozen-monomer interaction energy databases include deformation correction or not?
db_rlxd = kwargs.get('rlxd', False)
if db_rlxd is True:
if db_cp is True:
try:
database.ACTV_CPRLX
database.RXNM_CPRLX
except AttributeError:
raise ValidationError(
'Deformation and counterpoise correction mode \'yes\' invalid for database %s.'
% (db_name))
else:
ACTV = database.ACTV_CPRLX
RXNM = database.RXNM_CPRLX
elif db_cp is False:
try:
database.ACTV_RLX
except AttributeError:
raise ValidationError(
'Deformation correction mode \'yes\' invalid for database %s.'
% (db_name))
else:
ACTV = database.ACTV_RLX
elif db_rlxd is False:
#elif no.match(str(db_rlxd)):
pass
else:
raise ValidationError('Deformation correction mode \'%s\' not valid.' %
(db_rlxd))
# Option zero-point-correction- whether for thermochem databases jobs are corrected by zpe
db_zpe = kwargs.get('zpe', False)
if db_zpe is True:
raise ValidationError(
'Zero-point-correction mode \'yes\' not yet implemented.')
elif db_zpe is False:
pass
else:
raise ValidationError('Zero-point-correction \'mode\' %s not valid.' %
(db_zpe))
# Option benchmark- whether error statistics computed wrt alternate reference energies
db_benchmark = 'default'
if 'benchmark' in kwargs:
db_benchmark = kwargs['benchmark']
if db_benchmark.lower() == 'default':
pass
else:
BIND = p4util.getattr_ignorecase(database, 'BIND_' + db_benchmark)
if BIND is None:
raise ValidationError(
'Special benchmark \'%s\' not available for database %s.' %
(db_benchmark, db_name))
# Option tabulate- whether tables of variables other than primary energy method are formed
# TODO db(func=cbs,tabulate=[non-current-energy]) # broken
db_tabulate = []
if 'tabulate' in kwargs:
db_tabulate = kwargs['tabulate']
# Option subset- whether all of the database or just a portion is run
db_subset = HRXN
if 'subset' in kwargs:
db_subset = kwargs['subset']
if isinstance(db_subset, (str, bytes)):
if db_subset.lower() == 'small':
try:
database.HRXN_SM
except AttributeError:
raise ValidationError(
"""Special subset 'small' not available for database %s."""
% (db_name))
else:
HRXN = database.HRXN_SM
elif db_subset.lower() == 'large':
try:
database.HRXN_LG
except AttributeError:
raise ValidationError(
"""Special subset 'large' not available for database %s."""
% (db_name))
else:
HRXN = database.HRXN_LG
elif db_subset.lower() == 'equilibrium':
try:
database.HRXN_EQ
except AttributeError:
raise ValidationError(
"""Special subset 'equilibrium' not available for database %s."""
% (db_name))
else:
HRXN = database.HRXN_EQ
else:
HRXN = p4util.getattr_ignorecase(database, db_subset)
if HRXN is None:
HRXN = p4util.getattr_ignorecase(database, 'HRXN_' + db_subset)
if HRXN is None:
raise ValidationError(
"""Special subset '%s' not available for database %s."""
% (db_subset, db_name))
else:
temp = []
for rxn in db_subset:
if rxn in HRXN:
temp.append(rxn)
else:
raise ValidationError(
"""Subset element '%s' not a member of database %s.""" %
(str(rxn), db_name))
HRXN = temp
temp = []
for rxn in HRXN:
temp.append(ACTV['%s-%s' % (dbse, rxn)])
HSYS = p4util.drop_duplicates(sum(temp, []))
# Sow all the necessary reagent computations
core.print_out("\n\n")
p4util.banner(("Database %s Computation" % (db_name)))
core.print_out("\n")
# write index of calcs to output file
instructions = """\n The database single-job procedure has been selected through mode='continuous'.\n"""
instructions += """ Calculations for the reagents will proceed in the order below and will be followed\n"""
instructions += """ by summary results for the database.\n\n"""
for rgt in HSYS:
instructions += """ %-s\n""" % (rgt)
core.print_out(instructions)
# Loop through chemical systems
ERGT = {}
ERXN = {}
VRGT = {}
VRXN = {}
for rgt in HSYS:
VRGT[rgt] = {}
core.print_out('\n')
p4util.banner(' Database {} Computation: Reagent {} \n {}'.format(
db_name, rgt, TAGL[rgt]))
core.print_out('\n')
molecule = core.Molecule.from_dict(GEOS[rgt].to_dict())
molecule.set_name(rgt)
molecule.update_geometry()
if symmetry_override:
molecule.reset_point_group('c1')
molecule.fix_orientation(True)
molecule.fix_com(True)
molecule.update_geometry()
if (openshell_override) and (molecule.multiplicity() != 1):
if user_reference == 'RHF':
core.set_global_option('REFERENCE', 'UHF')
elif user_reference == 'RKS':
core.set_global_option('REFERENCE', 'UKS')
core.set_global_option(
'WRITER_FILE_LABEL', user_writer_file_label +
('' if user_writer_file_label == '' else '-') + rgt)
if allowoptexceeded:
try:
ERGT[rgt] = func(molecule=molecule, **kwargs)
except ConvergenceError:
core.print_out(f"Optimization exceeded cycles for {rgt}")
ERGT[rgt] = 0.0
else:
ERGT[rgt] = func(molecule=molecule, **kwargs)
core.print_variables()
core.print_out(" Database Contributions Map:\n {}\n".format('-' *
75))
for rxn in HRXN:
db_rxn = dbse + '-' + str(rxn)
if rgt in ACTV[db_rxn]:
core.print_out(
' reagent {} contributes by {:.4f} to reaction {}\n'.
format(rgt, RXNM[db_rxn][rgt], db_rxn))
core.print_out('\n')
for envv in db_tabulate:
VRGT[rgt][envv.upper()] = core.variable(envv)
core.set_global_option("REFERENCE", user_reference)
core.clean()
#core.opt_clean()
core.clean_variables()
# Reap all the necessary reaction computations
core.print_out("\n")
p4util.banner(("Database %s Results" % (db_name)))
core.print_out("\n")
maxactv = []
for rxn in HRXN:
maxactv.append(len(ACTV[dbse + '-' + str(rxn)]))
maxrgt = max(maxactv)
table_delimit = '-' * (62 + 20 * maxrgt)
tables = ''
# find any reactions that are incomplete
FAIL = collections.defaultdict(int)
for rxn in HRXN:
db_rxn = dbse + '-' + str(rxn)
for i in range(len(ACTV[db_rxn])):
if abs(ERGT[ACTV[db_rxn][i]]) < 1.0e-12:
if not allowoptexceeded:
FAIL[rxn] = 1
# tabulate requested process::environment variables
tables += """ For each VARIABLE requested by tabulate, a 'Reaction Value' will be formed from\n"""
tables += """ 'Reagent' values according to weightings 'Wt', as for the REQUESTED ENERGY below.\n"""
tables += """ Depending on the nature of the variable, this may or may not make any physical sense.\n"""
for rxn in HRXN:
db_rxn = dbse + '-' + str(rxn)
VRXN[db_rxn] = {}
for envv in db_tabulate:
envv = envv.upper()
tables += """\n ==> %s <==\n\n""" % (envv.title())
tables += _tblhead(maxrgt, table_delimit, 2)
for rxn in HRXN:
db_rxn = dbse + '-' + str(rxn)
if FAIL[rxn]:
tables += """\n%23s %8s %8s %8s %8s""" % (db_rxn, '', '****',
'', '')
for i in range(len(ACTV[db_rxn])):
tables += """ %16.8f %2.0f""" % (VRGT[
ACTV[db_rxn][i]][envv], RXNM[db_rxn][ACTV[db_rxn][i]])
else:
VRXN[db_rxn][envv] = 0.0
for i in range(len(ACTV[db_rxn])):
VRXN[db_rxn][envv] += VRGT[
ACTV[db_rxn][i]][envv] * RXNM[db_rxn][ACTV[db_rxn][i]]
tables += """\n%23s %16.8f """ % (
db_rxn, VRXN[db_rxn][envv])
for i in range(len(ACTV[db_rxn])):
tables += """ %16.8f %2.0f""" % (VRGT[
ACTV[db_rxn][i]][envv], RXNM[db_rxn][ACTV[db_rxn][i]])
tables += """\n %s\n""" % (table_delimit)
# tabulate primary requested energy variable with statistics
count_rxn = 0
minDerror = 100000.0
maxDerror = 0.0
MSDerror = 0.0
MADerror = 0.0
RMSDerror = 0.0
tables += """\n ==> %s <==\n\n""" % ('Requested Energy')
tables += _tblhead(maxrgt, table_delimit, 1)
for rxn in HRXN:
db_rxn = dbse + '-' + str(rxn)
if FAIL[rxn]:
tables += """\n%23s %8.4f %8s %10s %10s""" % (
db_rxn, BIND[db_rxn], '****', '****', '****')
for i in range(len(ACTV[db_rxn])):
tables += """ %16.8f %2.0f""" % (ERGT[ACTV[db_rxn][i]],
RXNM[db_rxn][ACTV[db_rxn][i]])
else:
ERXN[db_rxn] = 0.0
for i in range(len(ACTV[db_rxn])):
ERXN[db_rxn] += ERGT[ACTV[db_rxn][i]] * RXNM[db_rxn][
ACTV[db_rxn][i]]
error = constants.hartree2kcalmol * ERXN[db_rxn] - BIND[db_rxn]
tables += """\n%23s %8.4f %8.4f %10.4f %10.4f""" % (
db_rxn, BIND[db_rxn], constants.hartree2kcalmol * ERXN[db_rxn],
error, error * constants.cal2J)
for i in range(len(ACTV[db_rxn])):
tables += """ %16.8f %2.0f""" % (ERGT[ACTV[db_rxn][i]],
RXNM[db_rxn][ACTV[db_rxn][i]])
if abs(error) < abs(minDerror):
minDerror = error
if abs(error) > abs(maxDerror):
maxDerror = error
MSDerror += error
MADerror += abs(error)
RMSDerror += error * error
count_rxn += 1
tables += """\n %s\n""" % (table_delimit)
if count_rxn:
MSDerror /= float(count_rxn)
MADerror /= float(count_rxn)
RMSDerror = math.sqrt(RMSDerror / float(count_rxn))
tables += """%23s %19s %10.4f %10.4f\n""" % (
'Minimal Dev', '', minDerror, minDerror * constants.cal2J)
tables += """%23s %19s %10.4f %10.4f\n""" % (
'Maximal Dev', '', maxDerror, maxDerror * constants.cal2J)
tables += """%23s %19s %10.4f %10.4f\n""" % (
'Mean Signed Dev', '', MSDerror, MSDerror * constants.cal2J)
tables += """%23s %19s %10.4f %10.4f\n""" % (
'Mean Absolute Dev', '', MADerror, MADerror * constants.cal2J)
tables += """%23s %19s %10.4f %10.4f\n""" % (
'RMS Dev', '', RMSDerror, RMSDerror * constants.cal2J)
tables += """ %s\n""" % (table_delimit)
core.set_variable('%s DATABASE MEAN SIGNED DEVIATION' % (db_name),
MSDerror)
core.set_variable('%s DATABASE MEAN ABSOLUTE DEVIATION' % (db_name),
MADerror)
core.set_variable('%s DATABASE ROOT-MEAN-SQUARE DEVIATION' % (db_name),
RMSDerror)
core.print_out(tables)
finalenergy = MADerror
else:
finalenergy = 0.0
optstash.restore()
DB_RGT.clear()
DB_RGT.update(VRGT)
DB_RXN.clear()
DB_RXN.update(VRXN)
return finalenergy
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 exchange(cache, jk, do_print=True):
"""
Computes the E10 exchange (S^2 and S^inf) from a build_sapt_jk_cache datacache.
"""
if do_print:
core.print_out("\n ==> E10 Exchange <== \n\n")
# Build potenitals
h_A = cache["V_A"].clone()
h_A.axpy(2.0, cache["J_A"])
h_A.axpy(-1.0, cache["K_A"])
h_B = cache["V_B"].clone()
h_B.axpy(2.0, cache["J_B"])
h_B.axpy(-1.0, cache["K_B"])
w_A = cache["V_A"].clone()
w_A.axpy(2.0, cache["J_A"])
w_B = cache["V_B"].clone()
w_B.axpy(2.0, cache["J_B"])
# Build inverse exchange metric
nocc_A = cache["Cocc_A"].shape[1]
nocc_B = cache["Cocc_B"].shape[1]
SAB = core.triplet(
cache["Cocc_A"], cache["S"], cache["Cocc_B"], True, False, False)
num_occ = nocc_A + nocc_B
Sab = core.Matrix(num_occ, num_occ)
Sab.np[:nocc_A, nocc_A:] = SAB.np
Sab.np[nocc_A:, :nocc_A] = SAB.np.T
Sab.np[np.diag_indices_from(Sab.np)] += 1
Sab.power(-1.0, 1.e-14)
Sab.np[np.diag_indices_from(Sab.np)] -= 1.0
Tmo_AA = core.Matrix.from_array(Sab.np[:nocc_A, :nocc_A])
Tmo_BB = core.Matrix.from_array(Sab.np[nocc_A:, nocc_A:])
Tmo_AB = core.Matrix.from_array(Sab.np[:nocc_A, nocc_A:])
T_A = np.dot(cache["Cocc_A"], Tmo_AA).dot(cache["Cocc_A"].np.T)
T_B = np.dot(cache["Cocc_B"], Tmo_BB).dot(cache["Cocc_B"].np.T)
T_AB = np.dot(cache["Cocc_A"], Tmo_AB).dot(cache["Cocc_B"].np.T)
S = cache["S"]
D_A = cache["D_A"]
P_A = cache["P_A"]
D_B = cache["D_B"]
P_B = cache["P_B"]
# Compute the J and K matrices
jk.C_clear()
jk.C_left_add(cache["Cocc_A"])
jk.C_right_add(core.doublet(cache["Cocc_A"], Tmo_AA, False, False))
jk.C_left_add(cache["Cocc_B"])
jk.C_right_add(core.doublet(cache["Cocc_A"], Tmo_AB, False, False))
jk.C_left_add(cache["Cocc_A"])
jk.C_right_add(core.Matrix.chain_dot(P_B, S, cache["Cocc_A"]))
jk.compute()
JT_A, JT_AB, Jij = jk.J()
KT_A, KT_AB, Kij = jk.K()
# Start S^2
Exch_s2 = 0.0
tmp = core.Matrix.chain_dot(D_A, S, D_B, S, P_A)
Exch_s2 -= 2.0 * w_B.vector_dot(tmp)
tmp = core.Matrix.chain_dot(D_B, S, D_A, S, P_B)
Exch_s2 -= 2.0 * w_A.vector_dot(tmp)
tmp = core.Matrix.chain_dot(P_A, S, D_B)
Exch_s2 -= 2.0 * Kij.vector_dot(tmp)
if do_print:
core.print_out(print_sapt_var("Exch10(S^2) ", Exch_s2, short=True))
core.print_out("\n")
# Start Sinf
Exch10 = 0.0
Exch10 -= 2.0 * np.vdot(cache["D_A"], cache["K_B"])
Exch10 += 2.0 * np.vdot(T_A, h_B.np)
Exch10 += 2.0 * np.vdot(T_B, h_A.np)
Exch10 += 2.0 * np.vdot(T_AB, h_A.np + h_B.np)
Exch10 += 4.0 * np.vdot(T_B, JT_AB.np - 0.5 * KT_AB.np)
Exch10 += 4.0 * np.vdot(T_A, JT_AB.np - 0.5 * KT_AB.np)
Exch10 += 4.0 * np.vdot(T_B, JT_A.np - 0.5 * KT_A.np)
Exch10 += 4.0 * np.vdot(T_AB, JT_AB.np - 0.5 * KT_AB.np.T)
if do_print:
core.set_variable("Exch10", Exch10)
core.print_out(print_sapt_var("Exch10", Exch10, short=True))
core.print_out("\n")
return {"Exch10(S^2)": Exch_s2, "Exch10": Exch10}
def run_dftd3(self, func=None, dashlvl=None, dashparam=None, dertype=None, verbose=False):
"""Function to call Grimme's dftd3 program (http://toc.uni-muenster.de/DFTD3/)
to compute the -D correction of level *dashlvl* using parameters for
the functional *func*. The dictionary *dashparam* can be used to supply
a full set of dispersion parameters in the absense of *func* or to supply
individual overrides in the presence of *func*. Returns energy if *dertype* is 0,
gradient if *dertype* is 1, else tuple of energy and gradient if *dertype*
unspecified. The dftd3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
*self* may be either a qcdb.Molecule (sensibly) or a psi4.Molecule
(works b/c psi4.Molecule has been extended by this method py-side and
only public interface fns used) or a string that can be instantiated
into a qcdb.Molecule.
func - functional alias or None
dashlvl - functional type d2gr/d3zero/d3bj/d3mzero/d3mbj
dashparam - dictionary
dertype = derivative level
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(self, Molecule):
# called on a qcdb.Molecule
pass
elif isinstance(self, core.Molecule):
# called on a python export of a psi4.Molecule (py-side through Psi4's driver)
self.create_psi4_string_from_molecule()
elif isinstance(self, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
self = Molecule(self)
else:
raise ValidationError("""Argument mol must be psi4string or qcdb.Molecule""")
self.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError('Requested derivative level \'dertype\' %s not valid for run_dftd3.' % (dertype))
else:
raise ValidationError('Requested derivative level \'dertype\' %s not valid for run_dftd3.' % (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = dash_alias['-' + dashlvl][1:] if ('-' + dashlvl) in dash_alias.keys() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser('~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(uuid.uuid4())[:8]
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(uuid.uuid4())[:8]
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = self.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = self.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful run. Possibly -D variant not available in dftd3 version.""")
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([float(x.replace('D', 'E')) for x in dfile.readline().split()])
dfile.close()
if len(dashdderiv) != self.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), self.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix(self.natom(), 3)
psi_dashdderiv.set(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def run_cfour(name, **kwargs):
"""Function that prepares environment and input files
for a calculation calling Stanton and Gauss's CFOUR code.
Also processes results back into Psi4 format.
This function is not called directly but is instead called by
:py:func:`~psi4.driver.energy` or :py:func:`~psi4.driver.optimize` when a Cfour
method is requested (through *name* argument). In order to function
correctly, the Cfour executable ``xcfour`` must be present in
:envvar:`PATH` or :envvar:`PSIPATH`.
.. hlist::
:columns: 1
* Many :ref:`PSI Variables <apdx:cfour_psivar>` extracted from the Cfour output
* Python dictionary of associated file constants accessible as ``P4C4_INFO['zmat']``, ``P4C4_INFO['output']``, ``P4C4_INFO['grd']``, *etc.*
:type name: str
:param name: ``'c4-scf'`` || ``'c4-ccsd(t)'`` || ``'cfour'`` || etc.
First argument, usually unlabeled. Indicates the computational
method to be applied to the system.
:type keep: :ref:`boolean <op_py_boolean>`
:param keep: ``'on'`` || |dl| ``'off'`` |dr|
Indicates whether to delete the Cfour scratch directory upon
completion of the Cfour job.
:type path: str
:param path:
Indicates path to Cfour scratch directory (with respect to Psi4
scratch directory). Otherwise, the default is a subdirectory
within the Psi4 scratch directory.
If specified, GENBAS and/or ZMAT within will be used.
:type genbas: str
:param genbas:
Indicates that contents should be used for GENBAS file.
GENBAS is a complicated topic. It is quite unnecessary if the
molecule is from a molecule {...} block and basis is set through
|Psifours| BASIS keyword. In that case, a GENBAS is written from
LibMints and all is well. Otherwise, a GENBAS is looked for in
the usual places: PSIPATH, PATH, PSIDATADIR/basis. If path kwarg is
specified, also looks there preferentially for a GENBAS. Can
also specify GENBAS within an input file through a string and
setting the genbas kwarg. Note that due to the input parser's
aggression, blank lines need to be replaced by the text blankline.
"""
lowername = name.lower()
internal_p4c4_info = {}
return_wfn = kwargs.pop('return_wfn', False)
# Make sure the molecule the user provided is the active one
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
optstash = p4util.OptionsState(['CFOUR', 'TRANSLATE_PSI4'])
# Determine calling function and hence dertype
calledby = inspect.stack()[1][3]
dertype = ['energy', 'gradient', 'hessian'].index(calledby)
#print('I am %s called by %s called by %s.\n' %
# (inspect.stack()[0][3], inspect.stack()[1][3], inspect.stack()[2][3]))
# Save submission directory
current_directory = os.getcwd()
# Move into job scratch directory
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
# Construct and move into cfour subdirectory of job scratch directory
cfour_tmpdir = kwargs['path'] if 'path' in kwargs else \
'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.cfour.' + str(uuid.uuid4())[:8]
if not os.path.exists(cfour_tmpdir):
os.mkdir(cfour_tmpdir)
os.chdir(cfour_tmpdir)
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH':
':'.join([
os.path.abspath(x)
for x in os.environ.get('PSIPATH', '').split(':') if x != ''
]) + ':' + os.environ.get('PATH') + ':' + core.get_datadir() +
'/basis',
'GENBAS_PATH':
core.get_datadir() + '/basis',
'CFOUR_NUM_CORES':
os.environ.get('CFOUR_NUM_CORES'),
'MKL_NUM_THREADS':
os.environ.get('MKL_NUM_THREADS'),
'OMP_NUM_THREADS':
os.environ.get('OMP_NUM_THREADS'),
'LD_LIBRARY_PATH':
os.environ.get('LD_LIBRARY_PATH')
}
if 'path' in kwargs:
lenv['PATH'] = kwargs['path'] + ':' + lenv['PATH']
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Load the GENBAS file
genbas_path = qcdb.search_file('GENBAS', lenv['GENBAS_PATH'])
if genbas_path:
try:
shutil.copy2(genbas_path, psioh.get_default_path() + cfour_tmpdir)
except shutil.Error: # should only fail if src and dest equivalent
pass
core.print_out("\n GENBAS loaded from %s\n" % (genbas_path))
core.print_out(" CFOUR to be run from %s\n" %
(psioh.get_default_path() + cfour_tmpdir))
else:
message = """
GENBAS file for CFOUR interface not found. Either:
[1] Supply a GENBAS by placing it in PATH or PSIPATH
[1a] Use cfour {} block with molecule and basis directives.
[1b] Use molecule {} block and CFOUR_BASIS keyword.
[2] Allow Psi4's internal basis sets to convert to GENBAS
[2a] Use molecule {} block and BASIS keyword.
"""
core.print_out(message)
core.print_out(' Search path that was tried:\n')
core.print_out(lenv['PATH'].replace(':', ', '))
# Generate the ZMAT input file in scratch
if 'path' in kwargs and os.path.isfile('ZMAT'):
core.print_out(" ZMAT loaded from %s\n" %
(psioh.get_default_path() + kwargs['path'] + '/ZMAT'))
else:
with open('ZMAT', 'w') as cfour_infile:
cfour_infile.write(write_zmat(lowername, dertype, molecule))
internal_p4c4_info['zmat'] = open('ZMAT', 'r').read()
#core.print_out('\n====== Begin ZMAT input for CFOUR ======\n')
#core.print_out(open('ZMAT', 'r').read())
#core.print_out('======= End ZMAT input for CFOUR =======\n\n')
#print('\n====== Begin ZMAT input for CFOUR ======')
#print(open('ZMAT', 'r').read())
#print('======= End ZMAT input for CFOUR =======\n')
if 'genbas' in kwargs:
with open('GENBAS', 'w') as cfour_basfile:
cfour_basfile.write(kwargs['genbas'].replace(
'\nblankline\n', '\n\n'))
core.print_out(' GENBAS loaded from kwargs string\n')
# Close psi4 output file and reopen with filehandle
print('output in', current_directory + '/' + core.outfile_name())
pathfill = '' if os.path.isabs(
core.outfile_name()) else current_directory + os.path.sep
# Handle threading
# OMP_NUM_THREADS from env is in lenv from above
# threads from psi4 -n (core.get_num_threads()) is ignored
# CFOUR_OMP_NUM_THREADS psi4 option takes precedence, handled below
if core.has_option_changed('CFOUR', 'CFOUR_OMP_NUM_THREADS'):
lenv['OMP_NUM_THREADS'] = str(
core.get_option('CFOUR', 'CFOUR_OMP_NUM_THREADS'))
#print("""\n\n<<<<< RUNNING CFOUR ... >>>>>\n\n""")
# Call executable xcfour, directing cfour output to the psi4 output file
cfour_executable = kwargs['c4exec'] if 'c4exec' in kwargs else 'xcfour'
try:
retcode = subprocess.Popen([cfour_executable],
bufsize=0,
stdout=subprocess.PIPE,
env=lenv)
except OSError as e:
sys.stderr.write(
'Program %s not found in path or execution failed: %s\n' %
(cfour_executable, e.strerror))
message = ('Program %s not found in path or execution failed: %s\n' %
(cfour_executable, e.strerror))
raise ValidationError(message)
c4out = ''
while True:
data = retcode.stdout.readline()
data = data.decode('utf-8')
if not data:
break
core.print_out(data)
c4out += data
internal_p4c4_info['output'] = c4out
c4files = {}
core.print_out('\n')
for item in ['GRD', 'FCMFINAL', 'DIPOL']:
try:
with open(psioh.get_default_path() + cfour_tmpdir + '/' + item,
'r') as handle:
c4files[item] = handle.read()
core.print_out(' CFOUR scratch file %s has been read\n' %
(item))
core.print_out('%s\n' % c4files[item])
internal_p4c4_info[item.lower()] = c4files[item]
except IOError:
pass
core.print_out('\n')
if molecule.name() == 'blank_molecule_psi4_yo':
qcdbmolecule = None
else:
molecule.update_geometry()
qcdbmolecule = qcdb.Molecule(
molecule.create_psi4_string_from_molecule())
qcdbmolecule.update_geometry()
# c4mol, if it exists, is dinky, just a clue to geometry of cfour results
psivar, c4grad, c4mol = qcdb.cfour.harvest(qcdbmolecule, c4out, **c4files)
# Absorb results into psi4 data structures
for key in psivar.keys():
core.set_variable(key.upper(), float(psivar[key]))
if qcdbmolecule is None and c4mol is not None:
molrec = qcel.molparse.from_string(
c4mol.create_psi4_string_from_molecule(),
enable_qm=True,
missing_enabled_return_qm='minimal',
enable_efp=False,
missing_enabled_return_efp='none',
)
molecule = core.Molecule.from_dict(molrec['qm'])
molecule.set_name('blank_molecule_psi4_yo')
core.set_active_molecule(molecule)
molecule.update_geometry()
# This case arises when no Molecule going into calc (cfour {} block) but want
# to know the orientation at which grad, properties, etc. are returned (c4mol).
# c4mol is dinky, w/o chg, mult, dummies and retains name
# blank_molecule_psi4_yo so as to not interfere with future cfour {} blocks
if c4grad is not None:
mat = core.Matrix.from_list(c4grad)
core.set_gradient(mat)
#print ' <<< [3] C4-GRD-GRAD >>>'
#mat.print()
# exit(1)
# # Things needed core.so module to do
# collect c4out string
# read GRD
# read FCMFINAL
# see if theres an active molecule
# # Things delegatable to qcdb
# parsing c4out
# reading GRD and FCMFINAL strings
# reconciling p4 and c4 molecules (orient)
# reconciling c4out and GRD and FCMFINAL results
# transforming frame of results back to p4
# # Things run_cfour needs to have back
# psivar
# qcdb.Molecule of c4?
# coordinates?
# gradient in p4 frame
# # Process the cfour output
# psivar, c4coord, c4grad = qcdb.cfour.cfour_harvest(c4out)
# for key in psivar.keys():
# core.set_variable(key.upper(), float(psivar[key]))
#
# # Awful Hack - Go Away TODO
# if c4grad:
# molecule = core.get_active_molecule()
# molecule.update_geometry()
#
# if molecule.name() == 'blank_molecule_psi4_yo':
# p4grad = c4grad
# p4coord = c4coord
# else:
# qcdbmolecule = qcdb.Molecule(molecule.create_psi4_string_from_molecule())
# #p4grad = qcdbmolecule.deorient_array_from_cfour(c4coord, c4grad)
# #p4coord = qcdbmolecule.deorient_array_from_cfour(c4coord, c4coord)
#
# with open(psioh.get_default_path() + cfour_tmpdir + '/GRD', 'r') as cfour_grdfile:
# c4outgrd = cfour_grdfile.read()
# print('GRD\n',c4outgrd)
# c4coordGRD, c4gradGRD = qcdb.cfour.cfour_harvest_files(qcdbmolecule, grd=c4outgrd)
#
# p4mat = core.Matrix.from_list(p4grad)
# core.set_gradient(p4mat)
# print(' <<< P4 PSIVAR >>>')
# for item in psivar:
# print(' %30s %16.8f' % (item, psivar[item]))
#print(' <<< P4 COORD >>>')
#for item in p4coord:
# print(' %16.8f %16.8f %16.8f' % (item[0], item[1], item[2]))
# print(' <<< P4 GRAD >>>')
# for item in c4grad:
# print(' %16.8f %16.8f %16.8f' % (item[0], item[1], item[2]))
# Clean up cfour scratch directory unless user instructs otherwise
keep = yes.match(str(kwargs['keep'])) if 'keep' in kwargs else False
os.chdir('..')
try:
if keep or ('path' in kwargs):
core.print_out('\n CFOUR scratch files have been kept in %s\n' %
(psioh.get_default_path() + cfour_tmpdir))
else:
shutil.rmtree(cfour_tmpdir)
except OSError as e:
print('Unable to remove CFOUR temporary directory %s' % e,
file=sys.stderr)
exit(1)
# Return to submission directory and reopen output file
os.chdir(current_directory)
core.print_out('\n')
p4util.banner(' Cfour %s %s Results ' %
(name.lower(), calledby.capitalize()))
core.print_variables()
if c4grad is not None:
core.get_gradient().print_out()
core.print_out('\n')
p4util.banner(' Cfour %s %s Results ' %
(name.lower(), calledby.capitalize()))
core.print_variables()
if c4grad is not None:
core.get_gradient().print_out()
# Quit if Cfour threw error
if 'CFOUR ERROR CODE' in core.variables():
raise ValidationError("""Cfour exited abnormally.""")
P4C4_INFO.clear()
P4C4_INFO.update(internal_p4c4_info)
optstash.restore()
# new skeleton wavefunction w/mol, highest-SCF basis (just to choose one), & not energy
# Feb 2017 hack. Could get proper basis in skel wfn even if not through p4 basis kw
if core.get_global_option('BASIS') in ["", "(AUTO)"]:
gobas = "sto-3g"
else:
gobas = core.get_global_option('BASIS')
basis = core.BasisSet.build(molecule, "ORBITAL", gobas)
if basis.has_ECP():
raise ValidationError("""ECPs not hooked up for Cfour""")
wfn = core.Wavefunction(molecule, basis)
for k, v in psivar.items():
wfn.set_variable(k.upper(), float(v))
optstash.restore()
if dertype == 0:
finalquantity = psivar['CURRENT ENERGY']
elif dertype == 1:
finalquantity = core.get_gradient()
wfn.set_gradient(finalquantity)
if finalquantity.rows(0) < 20:
core.print_out('CURRENT GRADIENT')
finalquantity.print_out()
elif dertype == 2:
pass
#finalquantity = finalhessian
#wfn.set_hessian(finalquantity)
#if finalquantity.rows(0) < 20:
# core.print_out('CURRENT HESSIAN')
# finalquantity.print_out()
return wfn
def 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 sapt_empirical_dispersion(name, dimer_wfn, **kwargs):
sapt_dimer = dimer_wfn.molecule()
sapt_dimer, monomerA, monomerB = prepare_sapt_molecule(sapt_dimer, "dimer")
disp_name = name.split("-")[1]
# Get the names right between SAPT0 and FISAPT0
saptd_name = name.split('-')[0].upper()
if saptd_name == "SAPT0":
sapt0_name = "SAPT0"
else:
sapt0_name = "SAPT"
save_pair = (saptd_name == "FISAPT0")
from .proc import build_disp_functor
_, _disp_functor = build_disp_functor('hf-' + disp_name, restricted=True, save_pairwise_disp=save_pair, **kwargs)
## Dimer dispersion
dimer_disp_energy = _disp_functor.compute_energy(dimer_wfn.molecule(), dimer_wfn)
## Monomer dispersion
mon_disp_energy = _disp_functor.compute_energy(monomerA)
mon_disp_energy += _disp_functor.compute_energy(monomerB)
disp_interaction_energy = dimer_disp_energy - mon_disp_energy
core.set_variable(saptd_name + "-D DISP ENERGY", disp_interaction_energy)
core.set_variable("SAPT DISP ENERGY", disp_interaction_energy)
core.set_variable("DISPERSION CORRECTION ENERGY", disp_interaction_energy)
core.set_variable(saptd_name + "DISPERSION CORRECTION ENERGY", disp_interaction_energy)
## Set SAPT0-D3 variables
total = disp_interaction_energy
saptd_en = {}
saptd_en['DISP'] = disp_interaction_energy
for term in ['ELST', 'EXCH', 'IND']:
en = core.variable(' '.join([sapt0_name, term, 'ENERGY']))
saptd_en[term] = en
core.set_variable(' '.join([saptd_name + '-D', term, 'ENERGY']), en)
core.set_variable(' '.join(['SAPT', term, 'ENERGY']), en)
total += en
core.set_variable(saptd_name + '-D TOTAL ENERGY', total)
core.set_variable('SAPT TOTAL ENERGY', total)
core.set_variable('CURRENT ENERGY', total)
## Print Energy Summary
units = (1000.0, constants.hartree2kcalmol, constants.hartree2kJmol)
core.print_out(f" => {saptd_name +'-D'} Energy Summary <=\n")
core.print_out(" " + "-" * 104 + "\n")
core.print_out(
" %-25s % 16.8f [mEh] % 16.8f [kcal/mol] % 16.8f [kJ/mol]\n" %
("Electrostatics", saptd_en['ELST'] * units[0], saptd_en['ELST'] * units[1], saptd_en['ELST'] * units[2]))
core.print_out(" %-25s % 16.8f [mEh] % 16.8f [kcal/mol] % 16.8f [kJ/mol]\n" %
("Exchange", saptd_en['EXCH'] * units[0], saptd_en['EXCH'] * units[1], saptd_en['EXCH'] * units[2]))
core.print_out(" %-25s % 16.8f [mEh] % 16.8f [kcal/mol] % 16.8f [kJ/mol]\n" %
("Induction", saptd_en['IND'] * units[0], saptd_en['IND'] * units[1], saptd_en['IND'] * units[2]))
core.print_out(
" %-25s % 16.8f [mEh] % 16.8f [kcal/mol] % 16.8f [kJ/mol]\n" %
("Dispersion", saptd_en['DISP'] * units[0], saptd_en['DISP'] * units[1], saptd_en['DISP'] * units[2]))
core.print_out(" %-27s % 16.8f [mEh] % 16.8f [kcal/mol] % 16.8f [kJ/mol]\n" %
("Total " + saptd_name + "-D", total * units[0], total * units[1], total * units[2]))
core.print_out(" " + "-" * 104 + "\n")
if saptd_name == "FISAPT0":
pw_disp = dimer_wfn.variable("PAIRWISE DISPERSION CORRECTION ANALYSIS")
pw_disp.name = 'Empirical_Disp'
filepath = core.get_option("FISAPT", "FISAPT_FSAPT_FILEPATH")
fisapt_proc._drop(pw_disp, filepath)
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 mcscf_solver(ref_wfn):
# Build CIWavefunction
core.prepare_options_for_module("DETCI")
ciwfn = core.CIWavefunction(ref_wfn)
# Hush a lot of CI output
ciwfn.set_print(0)
# Begin with a normal two-step
step_type = 'Initial CI'
total_step = core.Matrix("Total step", ciwfn.get_dimension('OA'),
ciwfn.get_dimension('AV'))
start_orbs = ciwfn.get_orbitals("ROT").clone()
ciwfn.set_orbitals("ROT", start_orbs)
# Grab da options
mcscf_orb_grad_conv = core.get_option("DETCI", "MCSCF_R_CONVERGENCE")
mcscf_e_conv = core.get_option("DETCI", "MCSCF_E_CONVERGENCE")
mcscf_max_macroiteration = core.get_option("DETCI", "MCSCF_MAXITER")
mcscf_type = core.get_option("DETCI", "MCSCF_TYPE")
mcscf_d_file = core.get_option("DETCI", "CI_FILE_START") + 3
mcscf_nroots = core.get_option("DETCI", "NUM_ROOTS")
mcscf_wavefunction_type = core.get_option("DETCI", "WFN")
mcscf_ndet = ciwfn.ndet()
mcscf_nuclear_energy = ciwfn.molecule().nuclear_repulsion_energy()
mcscf_steplimit = core.get_option("DETCI", "MCSCF_MAX_ROT")
mcscf_rotate = core.get_option("DETCI", "MCSCF_ROTATE")
# DIIS info
mcscf_diis_start = core.get_option("DETCI", "MCSCF_DIIS_START")
mcscf_diis_freq = core.get_option("DETCI", "MCSCF_DIIS_FREQ")
mcscf_diis_error_type = core.get_option("DETCI", "MCSCF_DIIS_ERROR_TYPE")
mcscf_diis_max_vecs = core.get_option("DETCI", "MCSCF_DIIS_MAX_VECS")
# One-step info
mcscf_target_conv_type = core.get_option("DETCI", "MCSCF_ALGORITHM")
mcscf_so_start_grad = core.get_option("DETCI", "MCSCF_SO_START_GRAD")
mcscf_so_start_e = core.get_option("DETCI", "MCSCF_SO_START_E")
mcscf_current_step_type = 'Initial CI'
# Start with SCF energy and other params
scf_energy = core.get_variable("HF TOTAL ENERGY")
eold = scf_energy
norb_iter = 1
converged = False
ah_step = False
qc_step = False
# Fake info to start with the inital diagonalization
ediff = 1.e-4
orb_grad_rms = 1.e-3
# Grab needed objects
diis_obj = diis_helper.DIIS_helper(mcscf_diis_max_vecs)
mcscf_obj = ciwfn.mcscf_object()
# Execute the rotate command
for rot in mcscf_rotate:
if len(rot) != 4:
raise p4util.PsiException(
"Each element of the MCSCF rotate command requires 4 arguements (irrep, orb1, orb2, theta)."
)
irrep, orb1, orb2, theta = rot
if irrep > ciwfn.Ca().nirrep():
raise p4util.PsiException(
"MCSCF_ROTATE: Expression %s irrep number is larger than the number of irreps"
% (str(rot)))
if max(orb1, orb2) > ciwfn.Ca().coldim()[irrep]:
raise p4util.PsiException(
"MCSCF_ROTATE: Expression %s orbital number exceeds number of orbitals in irrep"
% (str(rot)))
theta = np.deg2rad(theta)
x = ciwfn.Ca().nph[irrep][:, orb1].copy()
y = ciwfn.Ca().nph[irrep][:, orb2].copy()
xp = np.cos(theta) * x - np.sin(theta) * y
yp = np.sin(theta) * x + np.cos(theta) * y
ciwfn.Ca().nph[irrep][:, orb1] = xp
ciwfn.Ca().nph[irrep][:, orb2] = yp
# Limited RAS functionality
if core.get_local_option(
"DETCI", "WFN") == "RASSCF" and mcscf_target_conv_type != "TS":
core.print_out(
"\n Warning! Only the TS algorithm for RASSCF wavefunction is currently supported.\n"
)
core.print_out(" Switching to the TS algorithm.\n\n")
mcscf_target_conv_type = "TS"
# Print out headers
if mcscf_type == "CONV":
mtype = " @MCSCF"
core.print_out("\n ==> Starting MCSCF iterations <==\n\n")
core.print_out(
" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n"
)
else:
mtype = " @DF-MCSCF"
core.print_out("\n ==> Starting DF-MCSCF iterations <==\n\n")
core.print_out(
" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n"
)
# Iterate !
for mcscf_iter in range(1, mcscf_max_macroiteration + 1):
# Transform integrals, diagonalize H
ciwfn.transform_mcscf_integrals(mcscf_current_step_type == 'TS')
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
ciwfn.form_opdm()
ciwfn.form_tpdm()
ci_grad_rms = core.get_variable("DETCI AVG DVEC NORM")
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
current_energy = core.get_variable('CURRENT ENERGY')
orb_grad_rms = mcscf_obj.gradient_rms()
ediff = current_energy - eold
# Print iterations
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms,
ci_grad_rms, nci_iter, norb_iter,
mcscf_current_step_type)
eold = current_energy
if mcscf_current_step_type == 'Initial CI':
mcscf_current_step_type = 'TS'
# Check convergence
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) < abs(mcscf_e_conv)) and\
(mcscf_iter > 3) and not qc_step:
core.print_out("\n %s has converged!\n\n" % mtype)
converged = True
break
# Which orbital convergence are we doing?
if ah_step:
converged, norb_iter, step = ah_iteration(mcscf_obj,
print_micro=False)
norb_iter += 1
if converged:
mcscf_current_step_type = 'AH'
else:
core.print_out(
" !Warning. Augmented Hessian did not converge. Taking an approx step.\n"
)
step = mcscf_obj.approx_solve()
mcscf_current_step_type = 'TS, AH failure'
else:
step = mcscf_obj.approx_solve()
step_type = 'TS'
maxstep = step.absmax()
if maxstep > mcscf_steplimit:
core.print_out(
' Warning! Maxstep = %4.2f, scaling to %4.2f\n' %
(maxstep, mcscf_steplimit))
step.scale(mcscf_steplimit / maxstep)
total_step.add(step)
# Do or add DIIS
if (mcscf_iter >= mcscf_diis_start) and ("TS"
in mcscf_current_step_type):
# Figure out DIIS error vector
if mcscf_diis_error_type == "GRAD":
error = core.Matrix.triplet(ciwfn.get_orbitals("OA"),
mcscf_obj.gradient(),
ciwfn.get_orbitals("AV"), False,
False, True)
else:
error = step
diis_obj.add(total_step, error)
if not (mcscf_iter % mcscf_diis_freq):
total_step = diis_obj.extrapolate()
mcscf_current_step_type = 'TS, DIIS'
# Finally rotate and set orbitals
orbs_mat = mcscf_obj.Ck(start_orbs, total_step)
ciwfn.set_orbitals("ROT", orbs_mat)
# Figure out what the next step should be
if (orb_grad_rms < mcscf_so_start_grad) and (abs(ediff) < abs(mcscf_so_start_e)) and\
(mcscf_iter >= 2):
if mcscf_target_conv_type == 'AH':
ah_step = True
elif mcscf_target_conv_type == 'OS':
mcscf_current_step_type = 'OS, Prep'
break
else:
continue
#raise p4util.PsiException("")
# If we converged do not do onestep
if converged or (mcscf_target_conv_type != 'OS'):
one_step_iters = []
# If we are not converged load in Dvec and build iters array
else:
one_step_iters = range(mcscf_iter + 1, mcscf_max_macroiteration + 1)
dvec = ciwfn.new_civector(1, mcscf_d_file, True, True)
dvec.set_nvec(1)
dvec.init_io_files(True)
dvec.read(0, 0)
dvec.symnormalize(1.0, 0)
ci_grad = ciwfn.new_civector(1, mcscf_d_file + 1, True, True)
ci_grad.set_nvec(1)
ci_grad.init_io_files(True)
# Loop for onestep
for mcscf_iter in one_step_iters:
# Transform integrals and update the MCSCF object
ciwfn.transform_mcscf_integrals(False)
ciwfn.form_opdm()
ciwfn.form_tpdm()
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
orb_grad_rms = mcscf_obj.gradient_rms()
# Warning! Does not work for SA-MCSCF
current_energy = mcscf_obj.current_total_energy()
current_energy += mcscf_nuclear_energy
core.set_variable("CI ROOT %d TOTAL ENERGY" % 1, current_energy)
core.set_variable("CURRENT ENERGY", current_energy)
docc_energy = mcscf_obj.current_docc_energy()
ci_energy = mcscf_obj.current_ci_energy()
# Compute CI gradient
ciwfn.sigma(dvec, ci_grad, 0, 0)
ci_grad.scale(2.0, 0)
ci_grad.axpy(-2.0 * ci_energy, dvec, 0, 0)
ci_grad_rms = ci_grad.norm(0)
orb_grad_rms = mcscf_obj.gradient().rms()
ediff = current_energy - eold
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms,
ci_grad_rms, nci_iter, norb_iter,
mcscf_current_step_type)
mcscf_current_step_type = 'OS'
eold = current_energy
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) <
abs(mcscf_e_conv)):
core.print_out("\n %s has converged!\n\n" % mtype)
converged = True
break
# Take a step
converged, norb_iter, nci_iter, step = qc_iteration(
dvec, ci_grad, ciwfn, mcscf_obj)
# Rotate integrals to new frame
total_step.add(step)
orbs_mat = mcscf_obj.Ck(ciwfn.get_orbitals("ROT"), step)
ciwfn.set_orbitals("ROT", orbs_mat)
core.print_out(mtype + " Final Energy: %20.15f\n" % current_energy)
# Die if we did not converge
if (not converged):
if core.get_global_option("DIE_IF_NOT_CONVERGED"):
raise p4util.PsiException("MCSCF: Iterations did not converge!")
else:
core.print_out("\nWarning! MCSCF iterations did not converge!\n\n")
# Print out energetics
core.print_out("\n ==> Energetics <==\n\n")
core.print_out(" SCF energy = %20.15f\n" % scf_energy)
core.print_out(" Total CI energy = %20.15f\n\n" % current_energy)
# Print out CI vector information
if mcscf_target_conv_type != 'SO':
dvec = ciwfn.new_civector(mcscf_nroots, mcscf_d_file, True, True)
dvec.init_io_files(True)
irrep_labels = ciwfn.molecule().irrep_labels()
for root in range(mcscf_nroots):
core.print_out("\n ==> CI root %d information <==\n\n" % (root + 1))
# Print total energy
root_e = core.get_variable("CI ROOT %d TOTAL ENERGY" % (root + 1))
core.print_out(" CI Root %2d energy = %20.15f\n" %
(root + 1, root_e))
# Print natural occupations
core.print_out("\n Natural occupation numbers:\n\n")
occs_list = []
r_opdm = ciwfn.get_opdm(root, root, "SUM", False)
for h in range(len(r_opdm.nph)):
if 0 in r_opdm.nph[h].shape:
continue
nocc, rot = np.linalg.eigh(r_opdm.nph[h])
for e in nocc:
occs_list.append((e, irrep_labels[h]))
occs_list.sort(key=lambda x: -x[0])
cnt = 0
for value, label in occs_list:
value, label = occs_list[cnt]
core.print_out(" %4s % 8.6f" % (label, value))
cnt += 1
if (cnt % 3) == 0:
core.print_out("\n")
# Print CIVector information
ciwfn.print_vector(dvec, root)
# What do we need to cleanup?
if core.get_option("DETCI", "MCSCF_CI_CLEANUP"):
ciwfn.cleanup_ci()
if core.get_option("DETCI", "MCSCF_DPD_CLEANUP"):
# print('Cleaning up DPD data!')
ciwfn.cleanup_dpd()
del diis_obj
del mcscf_obj
return ciwfn
def scf_iterate(self, e_conv=None, d_conv=None):
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
verbose = core.get_option('SCF', "PRINT")
reference = core.get_option('SCF', "REFERENCE")
# self.member_data_ signals are non-local, used internally by c-side fns
self.diis_enabled_ = _validate_diis()
self.MOM_excited_ = _validate_MOM()
self.diis_start_ = core.get_option('SCF', 'DIIS_START')
damping_enabled = _validate_damping()
soscf_enabled = _validate_soscf()
frac_enabled = _validate_frac()
efp_enabled = hasattr(self.molecule(), 'EFP')
diis_rms = core.get_option('SCF', 'DIIS_RMS_ERROR')
if self.iteration_ < 2:
core.print_out(" ==> Iterations <==\n\n")
core.print_out(
"%s Total Energy Delta E %s |[F,P]|\n\n"
% (" " if is_dfjk else "", "RMS" if diis_rms else "MAX"))
# SCF iterations!
SCFE_old = 0.0
Dnorm = 0.0
while True:
self.iteration_ += 1
diis_performed = False
soscf_performed = False
self.frac_performed_ = False
#self.MOM_performed_ = False # redundant from common_init()
self.save_density_and_energy()
if efp_enabled:
# EFP: Add efp contribution to Fock matrix
self.H().copy(self.Horig)
global mints_psi4_yo
mints_psi4_yo = core.MintsHelper(self.basisset())
Vefp = modify_Fock_induced(self.molecule().EFP,
mints_psi4_yo,
verbose=verbose - 1)
Vefp = core.Matrix.from_array(Vefp)
self.H().add(Vefp)
SCFE = 0.0
self.clear_external_potentials()
core.timer_on("HF: Form G")
self.form_G()
core.timer_off("HF: Form G")
upcm = 0.0
if core.get_option('SCF', 'PCM'):
calc_type = core.PCM.CalcType.Total
if core.get_option("PCM", "PCM_SCF_TYPE") == "SEPARATE":
calc_type = core.PCM.CalcType.NucAndEle
Dt = self.Da().clone()
Dt.add(self.Db())
upcm, Vpcm = self.get_PCM().compute_PCM_terms(Dt, calc_type)
SCFE += upcm
self.push_back_external_potential(Vpcm)
self.set_variable("PCM POLARIZATION ENERGY", upcm)
self.set_energies("PCM Polarization", upcm)
upe = 0.0
if core.get_option('SCF', 'PE'):
Dt = self.Da().clone()
Dt.add(self.Db())
upe, Vpe = self.pe_state.get_pe_contribution(Dt, elec_only=False)
SCFE += upe
self.push_back_external_potential(Vpe)
self.set_variable("PE ENERGY", upe)
self.set_energies("PE Energy", upe)
core.timer_on("HF: Form F")
# SAD: since we don't have orbitals yet, we might not be able
# to form the real Fock matrix. Instead, build an initial one
if (self.iteration_ == 0) and self.sad_:
self.form_initial_F()
else:
self.form_F()
core.timer_off("HF: Form F")
if verbose > 3:
self.Fa().print_out()
self.Fb().print_out()
SCFE += self.compute_E()
if efp_enabled:
global efp_Dt_psi4_yo
# EFP: Add efp contribution to energy
efp_Dt_psi4_yo = self.Da().clone()
efp_Dt_psi4_yo.add(self.Db())
SCFE += self.molecule().EFP.get_wavefunction_dependent_energy()
self.set_energies("Total Energy", SCFE)
core.set_variable("SCF ITERATION ENERGY", SCFE)
Ediff = SCFE - SCFE_old
SCFE_old = SCFE
status = []
# Check if we are doing SOSCF
if (soscf_enabled and (self.iteration_ >= 3) and
(Dnorm < core.get_option('SCF', 'SOSCF_START_CONVERGENCE'))):
Dnorm = self.compute_orbital_gradient(
False, core.get_option('SCF', 'DIIS_MAX_VECS'))
diis_performed = False
if self.functional().needs_xc():
base_name = "SOKS, nmicro="
else:
base_name = "SOSCF, nmicro="
if not _converged(Ediff, Dnorm, e_conv=e_conv, d_conv=d_conv):
nmicro = self.soscf_update(
core.get_option('SCF', 'SOSCF_CONV'),
core.get_option('SCF', 'SOSCF_MIN_ITER'),
core.get_option('SCF', 'SOSCF_MAX_ITER'),
core.get_option('SCF', 'SOSCF_PRINT'))
# if zero, the soscf call bounced for some reason
soscf_performed = (nmicro > 0)
if soscf_performed:
self.find_occupation()
status.append(base_name + str(nmicro))
else:
if verbose > 0:
core.print_out(
"Did not take a SOSCF step, using normal convergence methods\n"
)
else:
# need to ensure orthogonal orbitals and set epsilon
status.append(base_name + "conv")
core.timer_on("HF: Form C")
self.form_C()
core.timer_off("HF: Form C")
soscf_performed = True # Stops DIIS
if not soscf_performed:
# Normal convergence procedures if we do not do SOSCF
# SAD: form initial orbitals from the initial Fock matrix, and
# reset the occupations. From here on, the density matrices
# are correct.
if (self.iteration_ == 0) and self.sad_:
self.form_initial_C()
self.reset_occupation()
self.find_occupation()
else:
# Run DIIS
core.timer_on("HF: DIIS")
diis_performed = False
add_to_diis_subspace = self.diis_enabled_ and self.iteration_ >= self.diis_start_
Dnorm = self.compute_orbital_gradient(
add_to_diis_subspace,
core.get_option('SCF', 'DIIS_MAX_VECS'))
if (add_to_diis_subspace
and core.get_option('SCF', 'DIIS_MIN_VECS') - 1):
diis_performed = self.diis()
if diis_performed:
status.append("DIIS")
core.timer_off("HF: DIIS")
if verbose > 4 and diis_performed:
core.print_out(" After DIIS:\n")
self.Fa().print_out()
self.Fb().print_out()
# frac, MOM invoked here from Wfn::HF::find_occupation
core.timer_on("HF: Form C")
self.form_C()
core.timer_off("HF: Form C")
if self.MOM_performed_:
status.append("MOM")
if self.frac_performed_:
status.append("FRAC")
# Reset occupations if necessary
if (self.iteration_ == 0) and self.reset_occ_:
self.reset_occupation()
self.find_occupation()
# Form new density matrix
core.timer_on("HF: Form D")
self.form_D()
core.timer_off("HF: Form D")
self.set_variable("SCF ITERATION ENERGY", SCFE)
# After we've built the new D, damp the update
if (damping_enabled and self.iteration_ > 1
and Dnorm > core.get_option('SCF', 'DAMPING_CONVERGENCE')):
damping_percentage = core.get_option('SCF', "DAMPING_PERCENTAGE")
self.damping_update(damping_percentage * 0.01)
status.append("DAMP={}%".format(round(damping_percentage)))
if verbose > 3:
self.Ca().print_out()
self.Cb().print_out()
self.Da().print_out()
self.Db().print_out()
# Print out the iteration
core.print_out(
" @%s%s iter %3s: %20.14f %12.5e %-11.5e %s\n" %
("DF-" if is_dfjk else "", reference, "SAD" if
((self.iteration_ == 0) and self.sad_) else self.iteration_, SCFE,
Ediff, Dnorm, '/'.join(status)))
# if a an excited MOM is requested but not started, don't stop yet
if self.MOM_excited_ and not self.MOM_performed_:
continue
# if a fractional occupation is requested but not started, don't stop yet
if frac_enabled and not self.frac_performed_:
continue
# Call any postiteration callbacks
if not ((self.iteration_ == 0) and self.sad_) and _converged(
Ediff, Dnorm, e_conv=e_conv, d_conv=d_conv):
break
if self.iteration_ >= core.get_option('SCF', 'MAXITER'):
raise SCFConvergenceError("""SCF iterations""", self.iteration_,
self, Ediff, Dnorm)
def orig_run_dftd3(mol,
func=None,
dashlvl=None,
dashparam=None,
dertype=None,
verbose=False):
"""Compute dispersion correction using Grimme's DFTD3 executable.
Function to call Grimme's dftd3 program to compute the -D correction
of level `dashlvl` using parameters for the functional `func`.
`dashparam` can supply a full set of dispersion parameters in the
absence of `func` or individual overrides in the presence of `func`.
The DFTD3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
Parameters
----------
mol : qcdb.Molecule or psi4.core.Molecule or str
Molecule on which to run dispersion calculation. Both qcdb and
psi4.core Molecule classes have been extended by this method, so
either allowed. Alternately, a string that can be instantiated
into a qcdb.Molecule.
func : str or None
Density functional (Psi4, not Turbomole, names) for which to
load parameters from dashcoeff[dashlvl][func]. This is not
passed to DFTD3 and thus may be a dummy or `None`. Any or all
parameters initialized can be overwritten via `dashparam`.
dashlvl : {'d2p4', 'd2gr', 'd3zero', 'd3bj', 'd3mzero', d3mbj', 'd', 'd2', 'd3', 'd3m'}
Flavor of a posteriori dispersion correction for which to load
parameters and call procedure in DFTD3. Must be a keys in
dashcoeff dict (or a key in dashalias that resolves to one).
dashparam : dict, optional
Dictionary of the same keys as dashcoeff[dashlvl] used to
override any or all values initialized by
dashcoeff[dashlvl][func].
dertype : {None, 0, 'none', 'energy', 1, 'first', 'gradient'}, optional
Maximum derivative level at which to run DFTD3. For large
`mol`, energy-only calculations can be significantly more
efficient. Also controls return values, see below.
verbose : bool, optional
When `True`, additionally include DFTD3 output in output.
Returns
-------
energy : float, optional
When `dertype` is 0, energy [Eh].
gradient : list of lists of floats or psi4.core.Matrix, optional
When `dertype` is 1, (nat, 3) gradient [Eh/a0].
(energy, gradient) : float and list of lists of floats or psi4.core.Matrix, optional
When `dertype` is unspecified, both energy [Eh] and (nat, 3) gradient [Eh/a0].
Notes
-----
research site: https://www.chemie.uni-bonn.de/pctc/mulliken-center/software/dft-d3
Psi4 mode: When `psi4` the python module is importable at `import qcdb`
time, Psi4 mode is activated, with the following alterations:
* output goes to output file
* gradient returned as psi4.core.Matrix, not list o'lists
* scratch is written to randomly named subdirectory of psi scratch
* psivar "DISPERSION CORRECTION ENERGY" is set
* `verbose` triggered when PRINT keywork of SCF module >=3
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(mol, (Molecule, core.Molecule)):
# 1st: called on a qcdb.Molecule
# 2nd: called on a python export of a psi4.Molecule (py-side through Psi4's driver)
pass
elif isinstance(mol, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
mol = Molecule(mol)
else:
raise ValidationError(
"""Argument mol must be psi4string or qcdb.Molecule""")
mol.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError(
"""Requested derivative level 'dertype' %s not valid for run_dftd3."""
% (dertype))
else:
raise ValidationError(
"""Requested derivative level 'dertype' %s not valid for run_dftd3."""
% (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = dash_alias['-' + dashlvl][1:] if (
'-' + dashlvl) in dash_alias.keys() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError(
"""-D correction level %s is not available. Choose among %s."""
% (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser(
'~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(uuid.uuid4())[:8]
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(
uuid.uuid4())[:8]
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = mol.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = mol.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error(
"""Unsuccessful run. Possibly -D variant not available in dftd3 version."""
)
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([
float(x.replace('D', 'E'))
for x in dfile.readline().split()
])
dfile.close()
if len(dashdderiv) != mol.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), mol.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def nbody_gufunc(func, method_string, **kwargs):
"""
Computes the nbody interaction energy, gradient, or Hessian depending on input.
This is a generalized univeral function for computing interaction quantities.
:returns: *return type of func* |w--w| The interaction data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| interaction data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
:type func: function
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: string
:param method_string: ``'scf'`` || ``'mp2'`` || ``'ci5'`` || etc.
First argument, lowercase and usually unlabeled. Indicates the computational
method to be passed to func.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:type return_wfn: :ref:`boolean <op_py_boolean>`
:param return_wfn: ``'on'`` || |dl| ``'off'`` |dr|
Indicate to additionally return the :py:class:`~psi4.core.Wavefunction`
calculation result as the second element of a tuple.
:type bsse_type: string or list
:param bsse_type: ``'cp'`` || ``['nocp', 'vmfc']`` || |dl| ``None`` |dr| || etc.
Type of BSSE correction to compute: CP, NoCP, or VMFC. The first in this
list is returned by this function. By default, this function is not called.
:type max_nbody: int
:param max_nbody: ``3`` || etc.
Maximum n-body to compute, cannot exceed the number of fragments in the moleucle.
:type ptype: string
:param ptype: ``'energy'`` || ``'gradient'`` || ``'hessian'``
Type of the procedure passed in.
:type return_total_data: :ref:`boolean <op_py_boolean>`
:param return_total_data: ``'on'`` || |dl| ``'off'`` |dr|
If True returns the total data (energy/gradient/etc) of the system,
otherwise returns interaction data.
"""
### ==> Parse some kwargs <==
kwargs = p4util.kwargs_lower(kwargs)
return_wfn = kwargs.pop('return_wfn', False)
ptype = kwargs.pop('ptype', None)
return_total_data = kwargs.pop('return_total_data', False)
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
core.clean_variables()
if ptype not in ['energy', 'gradient', 'hessian']:
raise ValidationError(
"""N-Body driver: The ptype '%s' is not regonized.""" % ptype)
# Figure out BSSE types
do_cp = False
do_nocp = False
do_vmfc = False
return_method = False
# Must be passed bsse_type
bsse_type_list = kwargs.pop('bsse_type')
if bsse_type_list is None:
raise ValidationError("N-Body GUFunc: Must pass a bsse_type")
if not isinstance(bsse_type_list, list):
bsse_type_list = [bsse_type_list]
for num, btype in enumerate(bsse_type_list):
if btype.lower() == 'cp':
do_cp = True
if (num == 0): return_method = 'cp'
elif btype.lower() == 'nocp':
do_nocp = True
if (num == 0): return_method = 'nocp'
elif btype.lower() == 'vmfc':
do_vmfc = True
if (num == 0): return_method = 'vmfc'
else:
raise ValidationError(
"N-Body GUFunc: bsse_type '%s' is not recognized" %
btype.lower())
max_nbody = kwargs.get('max_nbody', -1)
max_frag = molecule.nfragments()
if max_nbody == -1:
max_nbody = molecule.nfragments()
else:
max_nbody = min(max_nbody, max_frag)
# What levels do we need?
nbody_range = range(1, max_nbody + 1)
fragment_range = range(1, max_frag + 1)
# Flip this off for now, needs more testing
# If we are doing CP lets save them integrals
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# # Set to save RI integrals for repeated full-basis computations
# ri_ints_io = core.get_global_option('DF_INTS_IO')
# # inquire if above at all applies to dfmp2 or just scf
# core.set_global_option('DF_INTS_IO', 'SAVE')
# psioh = core.IOManager.shared_object()
# psioh.set_specific_retention(97, True)
bsse_str = bsse_type_list[0]
if len(bsse_type_list) > 1:
bsse_str = str(bsse_type_list)
core.print_out("\n\n")
core.print_out(" ===> N-Body Interaction Abacus <===\n")
core.print_out(" BSSE Treatment: %s\n" %
bsse_str)
cp_compute_list = {x: set() for x in nbody_range}
nocp_compute_list = {x: set() for x in nbody_range}
vmfc_compute_list = {x: set() for x in nbody_range}
vmfc_level_list = {x: set()
for x in nbody_range
} # Need to sum something slightly different
# Build up compute sets
if do_cp:
# Everything is in dimer basis
basis_tuple = tuple(fragment_range)
for nbody in nbody_range:
for x in it.combinations(fragment_range, nbody):
cp_compute_list[nbody].add((x, basis_tuple))
if do_nocp:
# Everything in monomer basis
for nbody in nbody_range:
for x in it.combinations(fragment_range, nbody):
nocp_compute_list[nbody].add((x, x))
if do_vmfc:
# Like a CP for all combinations of pairs or greater
for nbody in nbody_range:
for cp_combos in it.combinations(fragment_range, nbody):
basis_tuple = tuple(cp_combos)
for interior_nbody in nbody_range:
for x in it.combinations(cp_combos, interior_nbody):
combo_tuple = (x, basis_tuple)
vmfc_compute_list[interior_nbody].add(combo_tuple)
vmfc_level_list[len(basis_tuple)].add(combo_tuple)
# Build a comprehensive compute_range
compute_list = {x: set() for x in nbody_range}
for n in nbody_range:
compute_list[n] |= cp_compute_list[n]
compute_list[n] |= nocp_compute_list[n]
compute_list[n] |= vmfc_compute_list[n]
core.print_out(" Number of %d-body computations: %d\n" %
(n, len(compute_list[n])))
# Build size and slices dictionaries
fragment_size_dict = {
frag: molecule.extract_subsets(frag).natom()
for frag in range(1, max_frag + 1)
}
start = 0
fragment_slice_dict = {}
for k, v in fragment_size_dict.items():
fragment_slice_dict[k] = slice(start, start + v)
start += v
molecule_total_atoms = sum(fragment_size_dict.values())
# Now compute the energies
energies_dict = {}
ptype_dict = {}
for n in compute_list.keys():
core.print_out(
"\n ==> N-Body: Now computing %d-body complexes <==\n\n" % n)
total = len(compute_list[n])
for num, pair in enumerate(compute_list[n]):
core.print_out(
"\n N-Body: Computing complex (%d/%d) with fragments %s in the basis of fragments %s.\n\n"
% (num + 1, total, str(pair[0]), str(pair[1])))
ghost = list(set(pair[1]) - set(pair[0]))
current_mol = molecule.extract_subsets(list(pair[0]), ghost)
ptype_dict[pair] = func(method_string,
molecule=current_mol,
**kwargs)
energies_dict[pair] = core.get_variable("CURRENT ENERGY")
core.print_out(
"\n N-Body: Complex Energy (fragments = %s, basis = %s: %20.14f)\n"
% (str(pair[0]), str(pair[1]), energies_dict[pair]))
# Flip this off for now, needs more testing
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.clean()
# Final dictionaries
cp_energy_by_level = {n: 0.0 for n in nbody_range}
nocp_energy_by_level = {n: 0.0 for n in nbody_range}
cp_energy_body_dict = {n: 0.0 for n in nbody_range}
nocp_energy_body_dict = {n: 0.0 for n in nbody_range}
vmfc_energy_body_dict = {n: 0.0 for n in nbody_range}
# Build out ptype dictionaries if needed
if ptype != 'energy':
if ptype == 'gradient':
arr_shape = (molecule_total_atoms, 3)
elif ptype == 'hessian':
arr_shape = (molecule_total_atoms * 3, molecule_total_atoms * 3)
else:
raise KeyError("N-Body: ptype '%s' not recognized" % ptype)
cp_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
nocp_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
vmfc_ptype_by_level = {n: np.zeros(arr_shape) for n in nbody_range}
cp_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
nocp_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
vmfc_ptype_body_dict = {n: np.zeros(arr_shape) for n in nbody_range}
else:
cp_ptype_by_level, cp_ptype_body_dict = None, None
nocp_ptype_by_level, nocp_ptype_body_dict = None, None
vmfc_ptype_body_dict = None
# Sum up all of the levels
for n in nbody_range:
# Energy
cp_energy_by_level[n] = sum(energies_dict[v]
for v in cp_compute_list[n])
nocp_energy_by_level[n] = sum(energies_dict[v]
for v in nocp_compute_list[n])
# Special vmfc case
if n > 1:
vmfc_energy_body_dict[n] = vmfc_energy_body_dict[n - 1]
for tup in vmfc_level_list[n]:
vmfc_energy_body_dict[n] += (
(-1)**(n - len(tup[0]))) * energies_dict[tup]
# Do ptype
if ptype != 'energy':
_sum_cluster_ptype_data(ptype, ptype_dict, cp_compute_list[n],
fragment_slice_dict, fragment_size_dict,
cp_ptype_by_level[n])
_sum_cluster_ptype_data(ptype, ptype_dict, nocp_compute_list[n],
fragment_slice_dict, fragment_size_dict,
nocp_ptype_by_level[n])
_sum_cluster_ptype_data(ptype,
ptype_dict,
vmfc_level_list[n],
fragment_slice_dict,
fragment_size_dict,
vmfc_ptype_by_level[n],
vmfc=True)
# Compute cp energy and ptype
if do_cp:
for n in nbody_range:
if n == max_frag:
cp_energy_body_dict[n] = cp_energy_by_level[n]
if ptype != 'energy':
cp_ptype_body_dict[n][:] = cp_ptype_by_level[n]
continue
for k in range(1, n + 1):
take_nk = nCr(max_frag - k - 1, n - k)
sign = ((-1)**(n - k))
value = cp_energy_by_level[k]
cp_energy_body_dict[n] += take_nk * sign * value
if ptype != 'energy':
value = cp_ptype_by_level[k]
cp_ptype_body_dict[n] += take_nk * sign * value
_print_nbody_energy(cp_energy_body_dict, "Counterpoise Corrected (CP)")
cp_interaction_energy = cp_energy_body_dict[
max_nbody] - cp_energy_body_dict[1]
core.set_variable('Counterpoise Corrected Total Energy',
cp_energy_body_dict[max_nbody])
core.set_variable('Counterpoise Corrected Interaction Energy',
cp_interaction_energy)
for n in nbody_range[1:]:
var_key = 'CP-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(var_key,
cp_energy_body_dict[n] - cp_energy_body_dict[1])
# Compute nocp energy and ptype
if do_nocp:
for n in nbody_range:
if n == max_frag:
nocp_energy_body_dict[n] = nocp_energy_by_level[n]
if ptype != 'energy':
nocp_ptype_body_dict[n][:] = nocp_ptype_by_level[n]
continue
for k in range(1, n + 1):
take_nk = nCr(max_frag - k - 1, n - k)
sign = ((-1)**(n - k))
value = nocp_energy_by_level[k]
nocp_energy_body_dict[n] += take_nk * sign * value
if ptype != 'energy':
value = nocp_ptype_by_level[k]
nocp_ptype_body_dict[n] += take_nk * sign * value
_print_nbody_energy(nocp_energy_body_dict,
"Non-Counterpoise Corrected (NoCP)")
nocp_interaction_energy = nocp_energy_body_dict[
max_nbody] - nocp_energy_body_dict[1]
core.set_variable('Non-Counterpoise Corrected Total Energy',
nocp_energy_body_dict[max_nbody])
core.set_variable('Non-Counterpoise Corrected Interaction Energy',
nocp_interaction_energy)
for n in nbody_range[1:]:
var_key = 'NOCP-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(
var_key, nocp_energy_body_dict[n] - nocp_energy_body_dict[1])
# Compute vmfc energy and ptype
if do_vmfc:
_print_nbody_energy(vmfc_energy_body_dict,
"Valiron-Mayer Function Couterpoise (VMFC)")
vmfc_interaction_energy = vmfc_energy_body_dict[
max_nbody] - vmfc_energy_body_dict[1]
core.set_variable('Valiron-Mayer Function Couterpoise Total Energy',
vmfc_energy_body_dict[max_nbody])
core.set_variable(
'Valiron-Mayer Function Couterpoise Interaction Energy',
vmfc_interaction_energy)
for n in nbody_range[1:]:
var_key = 'VMFC-CORRECTED %d-BODY INTERACTION ENERGY' % n
core.set_variable(
var_key, vmfc_energy_body_dict[n] - vmfc_energy_body_dict[1])
if return_method == 'cp':
ptype_body_dict = cp_ptype_body_dict
energy_body_dict = cp_energy_body_dict
elif return_method == 'nocp':
ptype_body_dict = nocp_ptype_body_dict
energy_body_dict = nocp_energy_body_dict
elif return_method == 'vmfc':
ptype_body_dict = vmfc_ptype_body_dict
energy_body_dict = vmfc_energy_body_dict
else:
raise ValidationError(
"N-Body Wrapper: Invalid return type. Should never be here, please post this error on github."
)
# Figure out and build return types
if return_total_data:
ret_energy = energy_body_dict[max_nbody]
else:
ret_energy = energy_body_dict[max_nbody]
ret_energy -= energy_body_dict[1]
if ptype != 'energy':
if return_total_data:
np_final_ptype = ptype_body_dict[max_nbody].copy()
else:
np_final_ptype = ptype_body_dict[max_nbody].copy()
np_final_ptype -= ptype_body_dict[1]
ret_ptype = core.Matrix.from_array(np_final_ptype)
else:
ret_ptype = ret_energy
# Build and set a wavefunction
wfn = core.Wavefunction.build(molecule, 'sto-3g')
wfn.nbody_energy = energies_dict
wfn.nbody_ptype = ptype_dict
wfn.nbody_body_energy = energy_body_dict
wfn.nbody_body_ptype = ptype_body_dict
if ptype == 'gradient':
wfn.set_gradient(ret_ptype)
elif ptype == 'hessian':
wfn.set_hessian(ret_ptype)
core.set_variable("CURRENT ENERGY", ret_energy)
if return_wfn:
return (ret_ptype, wfn)
else:
return ret_ptype
def scf_iterate(self, e_conv=None, d_conv=None):
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
verbose = core.get_option('SCF', "PRINT")
reference = core.get_option('SCF', "REFERENCE")
# self.member_data_ signals are non-local, used internally by c-side fns
self.diis_enabled_ = _validate_diis()
self.MOM_excited_ = _validate_MOM()
self.diis_start_ = core.get_option('SCF', 'DIIS_START')
damping_enabled = _validate_damping()
soscf_enabled = _validate_soscf()
frac_enabled = _validate_frac()
efp_enabled = hasattr(self.molecule(), 'EFP')
if self.iteration_ < 2:
core.print_out(" ==> Iterations <==\n\n")
core.print_out("%s Total Energy Delta E RMS |[F,P]|\n\n" % (" "
if is_dfjk else ""))
# SCF iterations!
SCFE_old = 0.0
SCFE = 0.0
Drms = 0.0
while True:
self.iteration_ += 1
diis_performed = False
soscf_performed = False
self.frac_performed_ = False
#self.MOM_performed_ = False # redundant from common_init()
self.save_density_and_energy()
if efp_enabled:
# EFP: Add efp contribution to Fock matrix
self.H().copy(self.Horig)
global mints
mints = core.MintsHelper(self.basisset())
Vefp = modify_Fock_induced(self.molecule().EFP, mints, verbose=verbose-1)
Vefp = core.Matrix.from_array(Vefp)
self.H().add(Vefp)
SCFE = 0.0
self.clear_external_potentials()
core.timer_on("HF: Form G")
self.form_G()
core.timer_off("HF: Form G")
# reset fractional SAD occupation
if (self.iteration_ == 0) and self.reset_occ_:
self.reset_occupation()
upcm = 0.0
if core.get_option('SCF', 'PCM'):
calc_type = core.PCM.CalcType.Total
if core.get_option("PCM", "PCM_SCF_TYPE") == "SEPARATE":
calc_type = core.PCM.CalcType.NucAndEle
Dt = self.Da().clone()
Dt.add(self.Db())
upcm, Vpcm = self.get_PCM().compute_PCM_terms(Dt, calc_type)
SCFE += upcm
self.push_back_external_potential(Vpcm)
self.set_variable("PCM POLARIZATION ENERGY", upcm)
self.set_energies("PCM Polarization", upcm)
core.timer_on("HF: Form F")
self.form_F()
core.timer_off("HF: Form F")
if verbose > 3:
self.Fa().print_out()
self.Fb().print_out()
SCFE += self.compute_E()
if efp_enabled:
global efp_Dt
# EFP: Add efp contribution to energy
efp_Dt = self.Da().clone()
efp_Dt.add(self.Db())
SCFE += self.molecule().EFP.get_wavefunction_dependent_energy()
self.set_energies("Total Energy", SCFE)
Ediff = SCFE - SCFE_old
SCFE_old = SCFE
status = []
# We either do SOSCF or DIIS
if (soscf_enabled and (self.iteration_ > 3) and (Drms < core.get_option('SCF', 'SOSCF_START_CONVERGENCE'))):
Drms = self.compute_orbital_gradient(False, core.get_option('SCF', 'DIIS_MAX_VECS'))
diis_performed = False
if self.functional().needs_xc():
base_name = "SOKS, nmicro="
else:
base_name = "SOSCF, nmicro="
if not _converged(Ediff, Drms, e_conv=e_conv, d_conv=d_conv):
nmicro = self.soscf_update(
core.get_option('SCF', 'SOSCF_CONV'),
core.get_option('SCF', 'SOSCF_MIN_ITER'),
core.get_option('SCF', 'SOSCF_MAX_ITER'), core.get_option('SCF', 'SOSCF_PRINT'))
if nmicro > 0:
# if zero, the soscf call bounced for some reason
self.find_occupation()
status.append(base_name + str(nmicro))
soscf_performed = True # Stops DIIS
else:
if verbose > 0:
core.print_out("Did not take a SOSCF step, using normal convergence methods\n")
soscf_performed = False # Back to DIIS
else:
# need to ensure orthogonal orbitals and set epsilon
status.append(base_name + "conv")
core.timer_on("HF: Form C")
self.form_C()
core.timer_off("HF: Form C")
soscf_performed = True # Stops DIIS
if not soscf_performed:
# Normal convergence procedures if we do not do SOSCF
core.timer_on("HF: DIIS")
diis_performed = False
add_to_diis_subspace = False
if self.diis_enabled_ and self.iteration_ >= self.diis_start_:
add_to_diis_subspace = True
Drms = self.compute_orbital_gradient(add_to_diis_subspace, core.get_option('SCF', 'DIIS_MAX_VECS'))
if (self.diis_enabled_
and self.iteration_ >= self.diis_start_ + core.get_option('SCF', 'DIIS_MIN_VECS') - 1):
diis_performed = self.diis()
if diis_performed:
status.append("DIIS")
core.timer_off("HF: DIIS")
if verbose > 4 and diis_performed:
core.print_out(" After DIIS:\n")
self.Fa().print_out()
self.Fb().print_out()
# frac, MOM invoked here from Wfn::HF::find_occupation
core.timer_on("HF: Form C")
self.form_C()
core.timer_off("HF: Form C")
if self.MOM_performed_:
status.append("MOM")
if self.frac_performed_:
status.append("FRAC")
core.timer_on("HF: Form D")
self.form_D()
core.timer_off("HF: Form D")
core.set_variable("SCF ITERATION ENERGY", SCFE)
# After we've built the new D, damp the update
if (damping_enabled and self.iteration_ > 1 and Drms > core.get_option('SCF', 'DAMPING_CONVERGENCE')):
damping_percentage = core.get_option('SCF', "DAMPING_PERCENTAGE")
self.damping_update(damping_percentage * 0.01)
status.append("DAMP={}%".format(round(damping_percentage)))
if verbose > 3:
self.Ca().print_out()
self.Cb().print_out()
self.Da().print_out()
self.Db().print_out()
# Print out the iteration
core.print_out(" @%s%s iter %3d: %20.14f %12.5e %-11.5e %s\n" %
("DF-" if is_dfjk else "", reference, self.iteration_, SCFE, Ediff, Drms, '/'.join(status)))
# if a an excited MOM is requested but not started, don't stop yet
if self.MOM_excited_ and not self.MOM_performed_:
continue
# if a fractional occupation is requested but not started, don't stop yet
if frac_enabled and not self.frac_performed_:
continue
# Call any postiteration callbacks
if _converged(Ediff, Drms, e_conv=e_conv, d_conv=d_conv):
break
if self.iteration_ >= core.get_option('SCF', 'MAXITER'):
raise ConvergenceError("""SCF iterations""", self.iteration_)
def multi_level(func, **kwargs):
"""
Use different levels of theory for different expansion levels
See kwargs description in driver_nbody.nbody_gufunc
:returns: *return type of func* |w--w| The data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
"""
from psi4.driver.driver_nbody import _print_nbody_energy, nbody_gufunc
ptype = kwargs['ptype']
return_wfn = kwargs.get('return_wfn', False)
kwargs['return_wfn'] = True
levels = {}
for k, v in kwargs.pop('levels').items():
if isinstance(k, str):
levels[k.lower()] = v
else:
levels[k] = v
supersystem = levels.pop('supersystem', False)
molecule = kwargs.get('molecule', core.get_active_molecule())
kwargs['bsse_type'] = [kwargs['bsse_type']] if isinstance(
kwargs['bsse_type'], str) else kwargs['bsse_type']
natoms = molecule.natom()
# Initialize with zeros
energy_result, gradient_result, hessian_result = 0, None, None
energy_body_contribution = {b: {} for b in kwargs['bsse_type']}
energy_body_dict = {b: {} for b in kwargs['bsse_type']}
wfns = {}
if ptype in ['gradient', 'hessian']:
gradient_result = np.zeros((natoms, 3))
if ptype == 'hessian':
hessian_result = np.zeros((natoms * 3, natoms * 3))
if kwargs.get('charge_method',
False) and not kwargs.get('embedding_charges', False):
kwargs['embedding_charges'] = compute_charges(
kwargs['charge_method'],
kwargs.get('charge_type', 'MULLIKEN_CHARGES').upper(), molecule)
for n in sorted(levels)[::-1]:
molecule.set_name('%i' % n)
kwargs_copy = kwargs.copy()
kwargs_copy['max_nbody'] = n
energy_bsse_dict = {b: 0 for b in kwargs['bsse_type']}
if isinstance(levels[n], str):
# If a new level of theory is provided, compute contribution
ret, wfn = nbody_gufunc(func, levels[n], **kwargs_copy)
wfns[n] = wfn
else:
# For the n-body contribution, use available data from the higher order levels[n]-body
wfn = wfns[levels[n]]
for m in range(n - 1, n + 1):
if m == 0: continue
# Subtract the (n-1)-body contribution from the n-body contribution to get the n-body effect
sign = (-1)**(1 - m // n)
for b in kwargs['bsse_type']:
energy_bsse_dict[b] += sign * wfn.variable('%i%s' %
(m, b.lower()))
if ptype in ['gradient', 'hessian']:
gradient_result += sign * np.array(
wfn.variable('GRADIENT ' + str(m)))
# Keep 1-body contribution to compute interaction data
if n == 1:
gradient1 = np.array(wfn.variable('GRADIENT ' + str(m)))
if ptype == 'hessian':
hessian_result += sign * np.array(
wfn.variable('HESSIAN ' + str(m)))
if n == 1:
hessian1 = np.array(wfn.variable('HESSIAN ' + str(m)))
energy_result += energy_bsse_dict[kwargs['bsse_type'][0]]
for b in kwargs['bsse_type']:
energy_body_contribution[b][n] = energy_bsse_dict[b]
if supersystem:
# Super system recovers higher order effects at a lower level
molecule.set_name('supersystem')
kwargs_copy = kwargs.copy()
kwargs_copy.pop('bsse_type')
kwargs_copy.pop('ptype')
ret, wfn_super = func(supersystem, **kwargs_copy)
core.clean()
kwargs_copy = kwargs.copy()
kwargs_copy['bsse_type'] = 'nocp'
kwargs_copy['max_nbody'] = max(levels)
# Subtract lower order effects to avoid double counting
ret, wfn = nbody_gufunc(func, supersystem, **kwargs_copy)
energy_result += wfn_super.energy() - wfn.variable(str(max(levels)))
for b in kwargs['bsse_type']:
energy_body_contribution[b][molecule.nfragments(
)] = wfn_super.energy() - wfn.variable(str(max(levels)))
if ptype in ['gradient', 'hessian']:
gradient_result += np.array(wfn_super.gradient()) - np.array(
wfn.variable('GRADIENT ' + str(max(levels))))
if ptype == 'hessian':
hessian_result += np.array(wfn_super.hessian()) - np.array(
wfn.variable('HESSIAN ' + str(max(levels))))
levels['supersystem'] = supersystem
for b in kwargs['bsse_type']:
for n in energy_body_contribution[b]:
energy_body_dict[b][n] = sum([
energy_body_contribution[b][i] for i in range(1, n + 1)
if i in energy_body_contribution[b]
])
is_embedded = kwargs.get('embedding_charges', False) or kwargs.get(
'charge_method', False)
for b in kwargs['bsse_type']:
_print_nbody_energy(
energy_body_dict[b],
'%s-corrected multilevel many-body expansion' % b.upper(),
is_embedded)
if not kwargs['return_total_data']:
# Remove monomer contribution for interaction data
energy_result -= energy_body_dict[kwargs['bsse_type'][0]][1]
if ptype in ['gradient', 'hessian']:
gradient_result -= gradient1
if ptype == 'hessian':
hessian_result -= hessian1
wfn_out = core.Wavefunction.build(molecule, 'def2-svp')
core.set_variable("CURRENT ENERGY", energy_result)
wfn_out.set_variable("CURRENT ENERGY", energy_result)
if gradient_result is not None:
wfn_out.set_gradient(core.Matrix.from_array(gradient_result))
if hessian_result is not None:
wfn_out.set_hessian(core.Matrix.from_array(hessian_result))
ptype_result = eval(ptype + '_result')
for b in kwargs['bsse_type']:
for i in energy_body_dict[b]:
wfn_out.set_variable(str(i) + b, energy_body_dict[b][i])
if kwargs['return_wfn']:
return (ptype_result, wfn_out)
else:
return ptype_result
def scf_finalize_energy(self):
"""Performs stability analysis and calls back SCF with new guess
if needed, Returns the SCF energy. This function should be called
once orbitals are ready for energy/property computations, usually
after iterations() is called.
"""
# post-scf vv10 correlation
if core.get_option('SCF', "DFT_VV10_POSTSCF"):
self.functional().set_lock(False)
self.functional().set_do_vv10(True)
self.functional().set_lock(True)
core.print_out(" ==> Computing Non-Self-Consistent VV10 Energy Correction <==\n\n")
SCFE = 0.0
self.form_V()
SCFE += self.compute_E()
self.set_energies("Total Energy", SCFE)
# Perform wavefunction stability analysis before doing
# anything on a wavefunction that may not be truly converged.
if core.get_option('SCF', 'STABILITY_ANALYSIS') != "NONE":
# We need the integral file, make sure it is written and
# compute it if needed
if core.get_option('SCF', 'REFERENCE') != "UHF":
psio = core.IO.shared_object()
#psio.open(constants.PSIF_SO_TEI, 1) # PSIO_OPEN_OLD
#try:
# psio.tocscan(constants.PSIF_SO_TEI, "IWL Buffers")
#except TypeError:
# # "IWL Buffers" actually found but psio_tocentry can't be returned to Py
# psio.close(constants.PSIF_SO_TEI, 1)
#else:
# # tocscan returned None
# psio.close(constants.PSIF_SO_TEI, 1)
# logic above foiled by psio_tocentry not returning None<--nullptr in pb11 2.2.1
# so forcibly recomputing for now until stability revamp
core.print_out(" SO Integrals not on disk. Computing...")
mints = core.MintsHelper(self.basisset())
mints.integrals()
core.print_out("done.\n")
# Q: Not worth exporting all the layers of psio, right?
follow = self.stability_analysis()
while follow and self.attempt_number_ <= core.get_option('SCF', 'MAX_ATTEMPTS'):
self.attempt_number_ += 1
core.print_out(" Running SCF again with the rotated orbitals.\n")
if self.initialized_diis_manager_:
self.diis_manager().reset_subspace()
# reading the rotated orbitals in before starting iterations
self.form_D()
self.set_energies("Total Energy", self.compute_initial_E())
self.iterations()
follow = self.stability_analysis()
if follow and self.attempt_number_ > core.get_option('SCF', 'MAX_ATTEMPTS'):
core.print_out(" There's still a negative eigenvalue. Try modifying FOLLOW_STEP_SCALE\n")
core.print_out(" or increasing MAX_ATTEMPTS (not available for PK integrals).\n")
# At this point, we are not doing any more SCF cycles
# and we can compute and print final quantities.
if hasattr(self.molecule(), 'EFP'):
efpobj = self.molecule().EFP
efpobj.compute() # do_gradient=do_gradient)
efpene = efpobj.get_energy(label='psi')
efp_wfn_independent_energy = efpene['total'] - efpene['ind']
self.set_energies("EFP", efpene['total'])
SCFE = self.get_energies("Total Energy")
SCFE += efp_wfn_independent_energy
self.set_energies("Total Energy", SCFE)
core.print_out(efpobj.energy_summary(scfefp=SCFE, label='psi'))
core.set_variable('EFP ELST ENERGY', efpene['electrostatic'] + efpene['charge_penetration'] + efpene['electrostatic_point_charges'])
core.set_variable('EFP IND ENERGY', efpene['polarization'])
core.set_variable('EFP DISP ENERGY', efpene['dispersion'])
core.set_variable('EFP EXCH ENERGY', efpene['exchange_repulsion'])
core.set_variable('EFP TOTAL ENERGY', efpene['total'])
core.set_variable('CURRENT ENERGY', efpene['total'])
core.print_out("\n ==> Post-Iterations <==\n\n")
self.check_phases()
self.compute_spin_contamination()
self.frac_renormalize()
reference = core.get_option("SCF", "REFERENCE")
energy = self.get_energies("Total Energy")
# fail_on_maxiter = core.get_option("SCF", "FAIL_ON_MAXITER")
# if converged or not fail_on_maxiter:
#
# if print_lvl > 0:
# self.print_orbitals()
#
# if converged:
# core.print_out(" Energy converged.\n\n")
# else:
# core.print_out(" Energy did not converge, but proceeding anyway.\n\n")
if core.get_option('SCF', 'PRINT') > 0:
self.print_orbitals()
is_dfjk = core.get_global_option('SCF_TYPE').endswith('DF')
core.print_out(" @%s%s Final Energy: %20.14f" % ('DF-' if is_dfjk else '', reference, energy))
# if (perturb_h_) {
# core.print_out(" with %f %f %f perturbation" %
# (dipole_field_strength_[0], dipole_field_strength_[1], dipole_field_strength_[2]))
# }
core.print_out("\n\n")
self.print_energies()
self.clear_external_potentials()
if core.get_option('SCF', 'PCM'):
calc_type = core.PCM.CalcType.Total
if core.get_option("PCM", "PCM_SCF_TYPE") == "SEPARATE":
calc_type = core.PCM.CalcType.NucAndEle
Dt = self.Da().clone()
Dt.add(self.Db())
_, Vpcm = self.get_PCM().compute_PCM_terms(Dt, calc_type)
self.push_back_external_potential(Vpcm)
# Properties
# Comments so that autodoc utility will find these PSI variables
# Process::environment.globals["SCF DIPOLE X"] =
# Process::environment.globals["SCF DIPOLE Y"] =
# Process::environment.globals["SCF DIPOLE Z"] =
# Process::environment.globals["SCF QUADRUPOLE XX"] =
# Process::environment.globals["SCF QUADRUPOLE XY"] =
# Process::environment.globals["SCF QUADRUPOLE XZ"] =
# Process::environment.globals["SCF QUADRUPOLE YY"] =
# Process::environment.globals["SCF QUADRUPOLE YZ"] =
# Process::environment.globals["SCF QUADRUPOLE ZZ"] =
# Orbitals are always saved, in case an MO guess is requested later
# save_orbitals()
# Shove variables into global space
for k, v in self.variables().items():
core.set_variable(k, v)
self.finalize()
core.print_out("\nComputation Completed\n")
return energy
def run_gcp(self, func=None, dertype=None, verbose=False): # dashlvl=None, dashparam=None
"""Function to call Grimme's dftd3 program (http://toc.uni-muenster.de/DFTD3/)
to compute the -D correction of level *dashlvl* using parameters for
the functional *func*. The dictionary *dashparam* can be used to supply
a full set of dispersion parameters in the absense of *func* or to supply
individual overrides in the presence of *func*. Returns energy if *dertype* is 0,
gradient if *dertype* is 1, else tuple of energy and gradient if *dertype*
unspecified. The dftd3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
*self* may be either a qcdb.Molecule (sensibly) or a psi4.Molecule
(works b/c psi4.Molecule has been extended by this method py-side and
only public interface fns used) or a string that can be instantiated
into a qcdb.Molecule.
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(self, Molecule):
# called on a qcdb.Molecule
pass
elif isinstance(self, core.Molecule):
# called on a python export of a psi4.core.Molecule (py-side through Psi4's driver)
self.create_psi4_string_from_molecule()
elif isinstance(self, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
self = Molecule(self)
else:
raise ValidationError("""Argument mol must be psi4string or qcdb.Molecule""")
self.update_geometry()
# # Validate arguments
# dashlvl = dashlvl.lower()
# dashlvl = dash_alias['-' + dashlvl][1:] if ('-' + dashlvl) in dash_alias.keys() else dashlvl
# if dashlvl not in dashcoeff.keys():
# raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
# elif der2nd.match(str(dertype)):
# raise ValidationError('Requested derivative level \'dertype\' %s not valid for run_dftd3.' % (dertype))
else:
raise ValidationError('Requested derivative level \'dertype\' %s not valid for run_dftd3.' % (dertype))
# if func is None:
# if dashparam is None:
# # defunct case
# raise ValidationError("""Parameters for -D correction missing. Provide a func or a dashparam kwarg.""")
# else:
# # case where all param read from dashparam dict (which must have all correct keys)
# func = 'custom'
# dashcoeff[dashlvl][func] = {}
# dashparam = dict((k.lower(), v) for k, v in dashparam.iteritems())
# for key in dashcoeff[dashlvl]['b3lyp'].keys():
# if key in dashparam.keys():
# dashcoeff[dashlvl][func][key] = dashparam[key]
# else:
# raise ValidationError("""Parameter %s is missing from dashparam dict %s.""" % (key, dashparam))
# else:
# func = func.lower()
# if func not in dashcoeff[dashlvl].keys():
# raise ValidationError("""Functional %s is not available for -D level %s.""" % (func, dashlvl))
# if dashparam is None:
# # (normal) case where all param taken from dashcoeff above
# pass
# else:
# # case where items in dashparam dict can override param taken from dashcoeff above
# dashparam = dict((k.lower(), v) for k, v in dashparam.iteritems())
# for key in dashcoeff[dashlvl]['b3lyp'].keys():
# if key in dashparam.keys():
# dashcoeff[dashlvl][func][key] = dashparam[key]
# TODO temp until figure out paramfile
allowed_funcs = ['HF/MINIS', 'DFT/MINIS', 'HF/MINIX', 'DFT/MINIX',
'HF/SV', 'DFT/SV', 'HF/def2-SV(P)', 'DFT/def2-SV(P)', 'HF/def2-SVP',
'DFT/def2-SVP', 'HF/DZP', 'DFT/DZP', 'HF/def-TZVP', 'DFT/def-TZVP',
'HF/def2-TZVP', 'DFT/def2-TZVP', 'HF/631Gd', 'DFT/631Gd',
'HF/def2-TZVP', 'DFT/def2-TZVP', 'HF/cc-pVDZ', 'DFT/cc-pVDZ',
'HF/aug-cc-pVDZ', 'DFT/aug-cc-pVDZ', 'DFT/SV(P/h,c)', 'DFT/LANL',
'DFT/pobTZVP', 'TPSS/def2-SVP', 'PW6B95/def2-SVP',
# specials
'hf3c', 'pbeh3c']
allowed_funcs = [f.lower() for f in allowed_funcs]
if func.lower() not in allowed_funcs:
raise Dftd3Error("""bad gCP func: %s. need one of: %r""" % (func, allowed_funcs))
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser('~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
try:
import psi4
except ImportError as err:
isP4regime = False
else:
isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
gcp_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.gcp.' + str(uuid.uuid4())[:8]
else:
gcp_tmpdir = os.environ['HOME'] + os.sep + 'gcp_' + str(uuid.uuid4())[:8]
if os.path.exists(gcp_tmpdir) is False:
os.mkdir(gcp_tmpdir)
os.chdir(gcp_tmpdir)
# Write gcp_parameters file that governs cp correction
# paramcontents = gcp_server(func, dashlvl, 'dftd3')
# paramfile1 = 'dftd3_parameters' # older patched name
# with open(paramfile1, 'w') as handle:
# handle.write(paramcontents)
# paramfile2 = '.gcppar'
# with open(paramfile2, 'w') as handle:
# handle.write(paramcontents)
###Two kinds of parameter files can be read in: A short and an extended version. Both are read from
###$HOME/.gcppar.$HOSTNAME by default. If the option -local is specified the file is read in from
###the current working directory: .gcppar
###The short version reads in: basis-keywo
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = self.natom()
geom = self.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './gcp_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call gcp program
command = ['gcp', geomfile]
command.extend(['-level', func])
if dertype != 0:
command.append('-grad')
try:
#print('command', command)
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program gcp not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Egcp:', line):
sline = line.split()
dashd = float(sline[1])
if re.match(' normal termination of gCP', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful gCP run.""")
# Parse grad output
if dertype != 0:
derivfile = './gcp_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([float(x.replace('D', 'E')) for x in dfile.readline().split()])
dfile.close()
if len(dashdderiv) != self.natom():
raise ValidationError('Program gcp gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), self.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('GCP CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> GCP Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# # Clean up files and remove scratch directory
# os.unlink(paramfile1)
# os.unlink(paramfile2)
# os.unlink(geomfile)
# if dertype != 0:
# os.unlink(derivfile)
# if defmoved is True:
# os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
# try:
# shutil.rmtree(dftd3_tmpdir)
# except OSError as e:
# ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def nbody_gufunc(func, method_string, **kwargs):
"""
Computes the nbody interaction energy, gradient, or Hessian depending on input.
This is a generalized univeral function for computing interaction and total quantities.
:returns: *return type of func* |w--w| The data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
:type func: function
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: string
:param method_string: ``'scf'`` || ``'mp2'`` || ``'ci5'`` || etc.
First argument, lowercase and usually unlabeled. Indicates the computational
method to be passed to func.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:type return_wfn: :ref:`boolean <op_py_boolean>`
:param return_wfn: ``'on'`` || |dl| ``'off'`` |dr|
Indicate to additionally return the :py:class:`~psi4.core.Wavefunction`
calculation result as the second element of a tuple.
:type bsse_type: string or list
:param bsse_type: ``'cp'`` || ``['nocp', 'vmfc']`` || |dl| ``None`` |dr| || etc.
Type of BSSE correction to compute: CP, NoCP, or VMFC. The first in this
list is returned by this function. By default, this function is not called.
:type max_nbody: int
:param max_nbody: ``3`` || etc.
Maximum n-body to compute, cannot exceed the number of fragments in the moleucle.
:type ptype: string
:param ptype: ``'energy'`` || ``'gradient'`` || ``'hessian'``
Type of the procedure passed in.
:type return_total_data: :ref:`boolean <op_py_boolean>`
:param return_total_data: ``'on'`` || |dl| ``'off'`` |dr|
If True returns the total data (energy/gradient/etc) of the system,
otherwise returns interaction data.
:type levels: dict
:param levels: ``{1: 'ccsd(t)', 2: 'mp2', 'supersystem': 'scf'}`` || ``{1: 2, 2: 'ccsd(t)', 3: 'mp2'}`` || etc
Dictionary of different levels of theory for different levels of expansion
Note that method_string is not used in this case.
supersystem computes all higher order n-body effects up to nfragments.
:type embedding_charges: dict
:param embedding_charges: ``{1: [-0.834, 0.417, 0.417], ..}``
Dictionary of atom-centered point charges. keys: 1-based index of fragment, values: list of charges for each fragment.
:type charge_method: string
:param charge_method: ``scf/6-31g`` || ``b3lyp/6-31g*`` || etc
Method to compute point charges for monomers. Overridden by embedding_charges if both are provided.
:type charge_type: string
:param charge_type: ``MULLIKEN_CHARGES`` || ``LOWDIN_CHARGES``
Default is ``MULLIKEN_CHARGES``
"""
# Initialize dictionaries for easy data passing
metadata, component_results, nbody_results = {}, {}, {}
# Parse some kwargs
kwargs = p4util.kwargs_lower(kwargs)
if kwargs.get('levels', False):
return driver_nbody_helper.multi_level(func, **kwargs)
metadata['ptype'] = kwargs.pop('ptype', None)
metadata['return_wfn'] = kwargs.pop('return_wfn', False)
metadata['return_total_data'] = kwargs.pop('return_total_data', False)
metadata['molecule'] = kwargs.pop('molecule', core.get_active_molecule())
metadata['molecule'].update_geometry()
metadata['molecule'].fix_com(True)
metadata['molecule'].fix_orientation(True)
metadata['embedding_charges'] = kwargs.get('embedding_charges', False)
metadata['kwargs'] = kwargs
core.clean_variables()
if metadata['ptype'] not in ['energy', 'gradient', 'hessian']:
raise ValidationError("""N-Body driver: The ptype '%s' is not regonized.""" % metadata['ptype'])
# Parse bsse_type, raise exception if not provided or unrecognized
metadata['bsse_type_list'] = kwargs.pop('bsse_type')
if metadata['bsse_type_list'] is None:
raise ValidationError("N-Body GUFunc: Must pass a bsse_type")
if not isinstance(metadata['bsse_type_list'], list):
metadata['bsse_type_list'] = [metadata['bsse_type_list']]
for num, btype in enumerate(metadata['bsse_type_list']):
metadata['bsse_type_list'][num] = btype.lower()
if btype.lower() not in ['cp', 'nocp', 'vmfc']:
raise ValidationError("N-Body GUFunc: bsse_type '%s' is not recognized" % btype.lower())
metadata['max_nbody'] = kwargs.get('max_nbody', -1)
metadata['max_frag'] = metadata['molecule'].nfragments()
if metadata['max_nbody'] == -1:
metadata['max_nbody'] = metadata['molecule'].nfragments()
else:
metadata['max_nbody'] = min(metadata['max_nbody'], metadata['max_frag'])
# Flip this off for now, needs more testing
# If we are doing CP lets save them integrals
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# # Set to save RI integrals for repeated full-basis computations
# ri_ints_io = core.get_global_option('DF_INTS_IO')
# # inquire if above at all applies to dfmp2 or just scf
# core.set_global_option('DF_INTS_IO', 'SAVE')
# psioh = core.IOManager.shared_object()
# psioh.set_specific_retention(97, True)
bsse_str = metadata['bsse_type_list'][0]
if len(metadata['bsse_type_list']) > 1:
bsse_str = str(metadata['bsse_type_list'])
core.print_out("\n\n")
core.print_out(" ===> N-Body Interaction Abacus <===\n")
core.print_out(" BSSE Treatment: %s\n" % bsse_str)
# Get compute list
metadata = build_nbody_compute_list(metadata)
# Compute N-Body components
component_results = compute_nbody_components(func, method_string, metadata)
# Assemble N-Body quantities
nbody_results = assemble_nbody_components(metadata, component_results)
# Build wfn and bind variables
wfn = core.Wavefunction.build(metadata['molecule'], 'def2-svp')
dicts = [
'energies', 'ptype', 'intermediates', 'energy_body_dict', 'gradient_body_dict', 'hessian_body_dict', 'nbody',
'cp_energy_body_dict', 'nocp_energy_body_dict', 'vmfc_energy_body_dict'
]
if metadata['ptype'] == 'gradient':
wfn.set_gradient(nbody_results['ret_ptype'])
nbody_results['gradient_body_dict'] = nbody_results['ptype_body_dict']
elif metadata['ptype'] == 'hessian':
nbody_results['hessian_body_dict'] = nbody_results['ptype_body_dict']
wfn.set_hessian(nbody_results['ret_ptype'])
component_results_gradient = component_results.copy()
component_results_gradient['ptype'] = component_results_gradient['gradients']
metadata['ptype'] = 'gradient'
nbody_results_gradient = assemble_nbody_components(metadata, component_results_gradient)
wfn.set_gradient(nbody_results_gradient['ret_ptype'])
nbody_results['gradient_body_dict'] = nbody_results_gradient['ptype_body_dict']
for r in [component_results, nbody_results]:
for d in r:
if d in dicts:
for var, value in r[d].items():
try:
wfn.set_scalar_variable(str(var), value)
core.set_scalar_variable(str(var), value)
except:
wfn.set_array_variable(d.split('_')[0].upper() + ' ' + str(var), core.Matrix.from_array(value))
core.set_variable("CURRENT ENERGY", nbody_results['ret_energy'])
wfn.set_variable("CURRENT ENERGY", nbody_results['ret_energy'])
if metadata['return_wfn']:
return (nbody_results['ret_ptype'], wfn)
else:
return nbody_results['ret_ptype']
def print_sapt_dft_summary(data, name, short=False):
ret = " %s Results\n" % name
ret += " " + "-" * 105 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst1,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
# Induction
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
if "Delta HF Correction" in list(data):
ind += data["Delta HF Correction"]
ret += print_sapt_var("Induction", ind) + "\n"
ret += print_sapt_var(" Ind2,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind2,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba) + "\n"
if "Delta HF Correction" in list(data):
ret += print_sapt_var(" delta HF,r (2)",
data["Delta HF Correction"]) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
# Exchange-dispersion scaling
exch_disp_scheme = core.get_option("SAPT",
"SAPT_DFT_EXCH_DISP_SCALE_SCHEME")
if exch_disp_scheme == "NONE":
data["Exch-Disp20,r"] = data["Exch-Disp20,u"]
elif exch_disp_scheme == "FIXED":
exch_disp_scale = core.get_option("SAPT",
"SAPT_DFT_EXCH_DISP_FIXED_SCALE")
data["Exch-Disp20,r"] = exch_disp_scale * data["Exch-Disp20,u"]
elif exch_disp_scheme == "DISP":
exch_disp_scale = data["Disp20"] / data["Disp20,u"]
data["Exch-Disp20,r"] = exch_disp_scale * data["Exch-Disp20,u"]
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,r"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp2,r", data["Disp20"]) + "\n"
ret += print_sapt_var(" Disp2,u", data["Disp20,u"]) + "\n"
if exch_disp_scheme != "NONE":
ret += print_sapt_var(" Est. Exch-Disp2,r",
data["Exch-Disp20,r"]) + "\n"
ret += print_sapt_var(" Exch-Disp2,u", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-17s" % name, total,
start_spacer=" ") + "\n"
core.set_variable("SAPT(DFT) TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 105 + "\n"
return ret
def print_sapt_hf_summary(data, name, short=False, delta_hf=False):
ret = " %s Results\n" % name
ret += " " + "-" * 97 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst10,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch10", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch10(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
ret += print_sapt_var("Induction", ind) + "\n"
ret += print_sapt_var(" Ind20,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind20,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
if delta_hf:
total_sapt = (data["Elst10,r"] + data["Exch10"] + ind)
sapt_hf_delta = delta_hf - total_sapt
core.set_variable("SAPT(DFT) Delta HF", sapt_hf_delta)
ret += print_sapt_var(
"%-21s" % "Total SAPT", total_sapt, start_spacer=" ") + "\n"
ret += print_sapt_var(
"%-21s" % "Total HF", delta_hf, start_spacer=" ") + "\n"
ret += print_sapt_var(
"%-21s" % "Delta HF", sapt_hf_delta, start_spacer=" ") + "\n"
ret += " " + "-" * 97 + "\n"
return ret
else:
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,u"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp20", data["Disp20,u"]) + "\n"
ret += print_sapt_var(" Exch-Disp20", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-15s" % name, total,
start_spacer=" ") + "\n"
core.set_variable("SAPT0 TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 97 + "\n"
return ret
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 nbody_gufunc(func, method_string, **kwargs):
"""
Computes the nbody interaction energy, gradient, or Hessian depending on input.
This is a generalized univeral function for computing interaction and total quantities.
:returns: *return type of func* |w--w| The data.
:returns: (*float*, :py:class:`~psi4.core.Wavefunction`) |w--w| data and wavefunction with energy/gradient/hessian set appropriately when **return_wfn** specified.
:type func: function
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: string
:param method_string: ``'scf'`` || ``'mp2'`` || ``'ci5'`` || etc.
First argument, lowercase and usually unlabeled. Indicates the computational
method to be passed to func.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:type return_wfn: :ref:`boolean <op_py_boolean>`
:param return_wfn: ``'on'`` || |dl| ``'off'`` |dr|
Indicate to additionally return the :py:class:`~psi4.core.Wavefunction`
calculation result as the second element of a tuple.
:type bsse_type: string or list
:param bsse_type: ``'cp'`` || ``['nocp', 'vmfc']`` || |dl| ``None`` |dr| || etc.
Type of BSSE correction to compute: CP, NoCP, or VMFC. The first in this
list is returned by this function. By default, this function is not called.
:type max_nbody: int
:param max_nbody: ``3`` || etc.
Maximum n-body to compute, cannot exceed the number of fragments in the moleucle.
:type ptype: string
:param ptype: ``'energy'`` || ``'gradient'`` || ``'hessian'``
Type of the procedure passed in.
:type return_total_data: :ref:`boolean <op_py_boolean>`
:param return_total_data: ``'on'`` || |dl| ``'off'`` |dr|
If True returns the total data (energy/gradient/etc) of the system,
otherwise returns interaction data.
:type levels: dict
:param levels: ``{1: 'ccsd(t)', 2: 'mp2', 'supersystem': 'scf'}`` || ``{1: 2, 2: 'ccsd(t)', 3: 'mp2'}`` || etc
Dictionary of different levels of theory for different levels of expansion
Note that method_string is not used in this case.
supersystem computes all higher order n-body effects up to nfragments.
:type embedding_charges: dict
:param embedding_charges: ``{1: [-0.834, 0.417, 0.417], ..}``
Dictionary of atom-centered point charges. keys: 1-based index of fragment, values: list of charges for each fragment.
:type charge_method: string
:param charge_method: ``scf/6-31g`` || ``b3lyp/6-31g*`` || etc
Method to compute point charges for monomers. Overridden by embedding_charges if both are provided.
:type charge_type: string
:param charge_type: ``MULLIKEN_CHARGES`` || ``LOWDIN_CHARGES``
Default is ``MULLIKEN_CHARGES``
"""
# Initialize dictionaries for easy data passing
metadata, component_results, nbody_results = {}, {}, {}
# Parse some kwargs
kwargs = p4util.kwargs_lower(kwargs)
if kwargs.get('levels', False):
return driver_nbody_helper.multi_level(func, **kwargs)
metadata['ptype'] = kwargs.pop('ptype', None)
metadata['return_wfn'] = kwargs.pop('return_wfn', False)
metadata['return_total_data'] = kwargs.pop('return_total_data', False)
metadata['molecule'] = kwargs.pop('molecule', core.get_active_molecule())
metadata['molecule'].update_geometry()
metadata['molecule'].fix_com(True)
metadata['molecule'].fix_orientation(True)
metadata['embedding_charges'] = kwargs.get('embedding_charges', False)
metadata['kwargs'] = kwargs
core.clean_variables()
if metadata['ptype'] not in ['energy', 'gradient', 'hessian']:
raise ValidationError(
"""N-Body driver: The ptype '%s' is not regonized.""" %
metadata['ptype'])
# Parse bsse_type, raise exception if not provided or unrecognized
metadata['bsse_type_list'] = kwargs.pop('bsse_type')
if metadata['bsse_type_list'] is None:
raise ValidationError("N-Body GUFunc: Must pass a bsse_type")
if not isinstance(metadata['bsse_type_list'], list):
metadata['bsse_type_list'] = [metadata['bsse_type_list']]
for num, btype in enumerate(metadata['bsse_type_list']):
metadata['bsse_type_list'][num] = btype.lower()
if btype.lower() not in ['cp', 'nocp', 'vmfc']:
raise ValidationError(
"N-Body GUFunc: bsse_type '%s' is not recognized" %
btype.lower())
metadata['max_nbody'] = kwargs.get('max_nbody', -1)
if metadata['molecule'].nfragments() == 1:
raise ValidationError(
"N-Body requires active molecule to have more than 1 fragment.")
metadata['max_frag'] = metadata['molecule'].nfragments()
if metadata['max_nbody'] == -1:
metadata['max_nbody'] = metadata['molecule'].nfragments()
else:
metadata['max_nbody'] = min(metadata['max_nbody'],
metadata['max_frag'])
# Flip this off for now, needs more testing
# If we are doing CP lets save them integrals
#if 'cp' in bsse_type_list and (len(bsse_type_list) == 1):
# # Set to save RI integrals for repeated full-basis computations
# ri_ints_io = core.get_global_option('DF_INTS_IO')
# # inquire if above at all applies to dfmp2 or just scf
# core.set_global_option('DF_INTS_IO', 'SAVE')
# psioh = core.IOManager.shared_object()
# psioh.set_specific_retention(97, True)
bsse_str = metadata['bsse_type_list'][0]
if len(metadata['bsse_type_list']) > 1:
bsse_str = str(metadata['bsse_type_list'])
core.print_out("\n\n")
core.print_out(" ===> N-Body Interaction Abacus <===\n")
core.print_out(" BSSE Treatment: %s\n" %
bsse_str)
# Get compute list
metadata = build_nbody_compute_list(metadata)
# Compute N-Body components
component_results = compute_nbody_components(func, method_string, metadata)
# Assemble N-Body quantities
nbody_results = assemble_nbody_components(metadata, component_results)
# Build wfn and bind variables
wfn = core.Wavefunction.build(metadata['molecule'], 'def2-svp')
dicts = [
'energies', 'ptype', 'intermediates', 'energy_body_dict',
'gradient_body_dict', 'hessian_body_dict', 'nbody',
'cp_energy_body_dict', 'nocp_energy_body_dict', 'vmfc_energy_body_dict'
]
if metadata['ptype'] == 'gradient':
wfn.set_gradient(nbody_results['ret_ptype'])
nbody_results['gradient_body_dict'] = nbody_results['ptype_body_dict']
elif metadata['ptype'] == 'hessian':
nbody_results['hessian_body_dict'] = nbody_results['ptype_body_dict']
wfn.set_hessian(nbody_results['ret_ptype'])
component_results_gradient = component_results.copy()
component_results_gradient['ptype'] = component_results_gradient[
'gradients']
metadata['ptype'] = 'gradient'
nbody_results_gradient = assemble_nbody_components(
metadata, component_results_gradient)
wfn.set_gradient(nbody_results_gradient['ret_ptype'])
nbody_results['gradient_body_dict'] = nbody_results_gradient[
'ptype_body_dict']
for r in [component_results, nbody_results]:
for d in r:
if d in dicts:
for var, value in r[d].items():
try:
wfn.set_scalar_variable(str(var), value)
core.set_scalar_variable(str(var), value)
except:
wfn.set_array_variable(
d.split('_')[0].upper() + ' ' + str(var),
core.Matrix.from_array(value))
core.set_variable("CURRENT ENERGY", nbody_results['ret_energy'])
wfn.set_variable("CURRENT ENERGY", nbody_results['ret_energy'])
if metadata['return_wfn']:
return (nbody_results['ret_ptype'], wfn)
else:
return nbody_results['ret_ptype']
def run_gaussian_2(name, **kwargs):
# throw an exception for open-shells
if (core.get_option('SCF','REFERENCE') != 'RHF' ):
raise ValidationError("""g2 computations require "reference rhf".""")
# stash user options:
optstash = p4util.OptionsState(
['FNOCC','COMPUTE_TRIPLES'],
['FNOCC','COMPUTE_MP4_TRIPLES'],
['FREEZE_CORE'],
['MP2_TYPE'],
['SCF','SCF_TYPE'])
# override default scf_type
core.set_local_option('SCF','SCF_TYPE','PK')
# optimize geometry at scf level
core.clean()
core.set_global_option('BASIS',"6-31G(D)")
driver.optimize('scf')
core.clean()
# scf frequencies for zpe
# NOTE This line should not be needed, but without it there's a seg fault
scf_e, ref = driver.frequency('scf', return_wfn=True)
# thermodynamic properties
du = core.get_variable('INTERNAL ENERGY CORRECTION')
dh = core.get_variable('ENTHALPY CORRECTION')
dg = core.get_variable('GIBBS FREE ENERGY CORRECTION')
freqs = ref.frequencies()
nfreq = freqs.dim(0)
freqsum = 0.0
for i in range(0, nfreq):
freqsum += freqs.get(i)
zpe = freqsum / p4const.psi_hartree2wavenumbers * 0.8929 * 0.5
core.clean()
# optimize geometry at mp2 (no frozen core) level
# note: freeze_core isn't an option in MP2
core.set_global_option('FREEZE_CORE',"FALSE")
core.set_global_option('MP2_TYPE', 'CONV')
driver.optimize('mp2')
core.clean()
# qcisd(t)
core.set_local_option('FNOCC','COMPUTE_MP4_TRIPLES',"TRUE")
core.set_global_option('FREEZE_CORE',"TRUE")
core.set_global_option('BASIS',"6-311G(D_P)")
ref = driver.proc.run_fnocc('qcisd(t)', return_wfn=True, **kwargs)
# HLC: high-level correction based on number of valence electrons
nirrep = ref.nirrep()
frzcpi = ref.frzcpi()
nfzc = 0
for i in range (0,nirrep):
nfzc += frzcpi[i]
nalpha = ref.nalpha() - nfzc
nbeta = ref.nbeta() - nfzc
# hlc of gaussian-2
hlc = -0.00481 * nalpha -0.00019 * nbeta
# hlc of gaussian-1
hlc1 = -0.00614 * nalpha
eqci_6311gdp = core.get_variable("QCISD(T) TOTAL ENERGY")
emp4_6311gd = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311gd = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# correction for diffuse functions
core.set_global_option('BASIS',"6-311+G(D_P)")
driver.energy('mp4')
emp4_6311pg_dp = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311pg_dp = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# correction for polarization functions
core.set_global_option('BASIS',"6-311G(2DF_P)")
driver.energy('mp4')
emp4_6311g2dfp = core.get_variable("MP4 TOTAL ENERGY")
emp2_6311g2dfp = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
# big basis mp2
core.set_global_option('BASIS',"6-311+G(3DF_2P)")
#run_fnocc('_mp2',**kwargs)
driver.energy('mp2')
emp2_big = core.get_variable("MP2 TOTAL ENERGY")
core.clean()
eqci = eqci_6311gdp
e_delta_g2 = emp2_big + emp2_6311gd - emp2_6311g2dfp - emp2_6311pg_dp
e_plus = emp4_6311pg_dp - emp4_6311gd
e_2df = emp4_6311g2dfp - emp4_6311gd
eg2 = eqci + e_delta_g2 + e_plus + e_2df
eg2_mp2_0k = eqci + (emp2_big - emp2_6311gd) + hlc + zpe
core.print_out('\n')
core.print_out(' ==> G1/G2 Energy Components <==\n')
core.print_out('\n')
core.print_out(' QCISD(T): %20.12lf\n' % eqci)
core.print_out(' E(Delta): %20.12lf\n' % e_delta_g2)
core.print_out(' E(2DF): %20.12lf\n' % e_2df)
core.print_out(' E(+): %20.12lf\n' % e_plus)
core.print_out(' E(G1 HLC): %20.12lf\n' % hlc1)
core.print_out(' E(G2 HLC): %20.12lf\n' % hlc)
core.print_out(' E(ZPE): %20.12lf\n' % zpe)
core.print_out('\n')
core.print_out(' ==> 0 Kelvin Results <==\n')
core.print_out('\n')
eg2_0k = eg2 + zpe + hlc
core.print_out(' G1: %20.12lf\n' % (eqci + e_plus + e_2df + hlc1 + zpe))
core.print_out(' G2(MP2): %20.12lf\n' % eg2_mp2_0k)
core.print_out(' G2: %20.12lf\n' % eg2_0k)
core.set_variable("G1 TOTAL ENERGY",eqci + e_plus + e_2df + hlc1 + zpe)
core.set_variable("G2 TOTAL ENERGY",eg2_0k)
core.set_variable("G2(MP2) TOTAL ENERGY",eg2_mp2_0k)
core.print_out('\n')
T = core.get_global_option('T')
core.print_out(' ==> %3.0lf Kelvin Results <==\n'% T)
core.print_out('\n')
internal_energy = eg2_mp2_0k + du - zpe / 0.8929
enthalpy = eg2_mp2_0k + dh - zpe / 0.8929
gibbs = eg2_mp2_0k + dg - zpe / 0.8929
core.print_out(' G2(MP2) energy: %20.12lf\n' % internal_energy )
core.print_out(' G2(MP2) enthalpy: %20.12lf\n' % enthalpy)
core.print_out(' G2(MP2) free energy: %20.12lf\n' % gibbs)
core.print_out('\n')
core.set_variable("G2(MP2) INTERNAL ENERGY",internal_energy)
core.set_variable("G2(MP2) ENTHALPY",enthalpy)
core.set_variable("G2(MP2) FREE ENERGY",gibbs)
internal_energy = eg2_0k + du - zpe / 0.8929
enthalpy = eg2_0k + dh - zpe / 0.8929
gibbs = eg2_0k + dg - zpe / 0.8929
core.print_out(' G2 energy: %20.12lf\n' % internal_energy )
core.print_out(' G2 enthalpy: %20.12lf\n' % enthalpy)
core.print_out(' G2 free energy: %20.12lf\n' % gibbs)
core.set_variable("CURRENT ENERGY",eg2_0k)
core.set_variable("G2 INTERNAL ENERGY",internal_energy)
core.set_variable("G2 ENTHALPY",enthalpy)
core.set_variable("G2 FREE ENERGY",gibbs)
core.clean()
optstash.restore()
# return 0K g2 results
return eg2_0k
def exchange(cache, jk, do_print=True):
"""
Computes the E10 exchange (S^2 and S^inf) from a build_sapt_jk_cache datacache.
"""
if do_print:
core.print_out("\n ==> E10 Exchange <== \n\n")
# Build potenitals
h_A = cache["V_A"].clone()
h_A.axpy(2.0, cache["J_A"])
h_A.axpy(-1.0, cache["K_A"])
h_B = cache["V_B"].clone()
h_B.axpy(2.0, cache["J_B"])
h_B.axpy(-1.0, cache["K_B"])
w_A = cache["V_A"].clone()
w_A.axpy(2.0, cache["J_A"])
w_B = cache["V_B"].clone()
w_B.axpy(2.0, cache["J_B"])
# Build inverse exchange metric
nocc_A = cache["Cocc_A"].shape[1]
nocc_B = cache["Cocc_B"].shape[1]
SAB = core.triplet(cache["Cocc_A"], cache["S"], cache["Cocc_B"], True,
False, False)
num_occ = nocc_A + nocc_B
Sab = core.Matrix(num_occ, num_occ)
Sab.np[:nocc_A, nocc_A:] = SAB.np
Sab.np[nocc_A:, :nocc_A] = SAB.np.T
Sab.np[np.diag_indices_from(Sab.np)] += 1
Sab.power(-1.0, 1.e-14)
Sab.np[np.diag_indices_from(Sab.np)] -= 1.0
Tmo_AA = core.Matrix.from_array(Sab.np[:nocc_A, :nocc_A])
Tmo_BB = core.Matrix.from_array(Sab.np[nocc_A:, nocc_A:])
Tmo_AB = core.Matrix.from_array(Sab.np[:nocc_A, nocc_A:])
T_A = np.dot(cache["Cocc_A"], Tmo_AA).dot(cache["Cocc_A"].np.T)
T_B = np.dot(cache["Cocc_B"], Tmo_BB).dot(cache["Cocc_B"].np.T)
T_AB = np.dot(cache["Cocc_A"], Tmo_AB).dot(cache["Cocc_B"].np.T)
S = cache["S"]
D_A = cache["D_A"]
P_A = cache["P_A"]
D_B = cache["D_B"]
P_B = cache["P_B"]
# Compute the J and K matrices
jk.C_clear()
jk.C_left_add(cache["Cocc_A"])
jk.C_right_add(core.doublet(cache["Cocc_A"], Tmo_AA, False, False))
jk.C_left_add(cache["Cocc_B"])
jk.C_right_add(core.doublet(cache["Cocc_A"], Tmo_AB, False, False))
jk.C_left_add(cache["Cocc_A"])
jk.C_right_add(core.Matrix.chain_dot(P_B, S, cache["Cocc_A"]))
jk.compute()
JT_A, JT_AB, Jij = jk.J()
KT_A, KT_AB, Kij = jk.K()
# Start S^2
Exch_s2 = 0.0
tmp = core.Matrix.chain_dot(D_A, S, D_B, S, P_A)
Exch_s2 -= 2.0 * w_B.vector_dot(tmp)
tmp = core.Matrix.chain_dot(D_B, S, D_A, S, P_B)
Exch_s2 -= 2.0 * w_A.vector_dot(tmp)
tmp = core.Matrix.chain_dot(P_A, S, D_B)
Exch_s2 -= 2.0 * Kij.vector_dot(tmp)
if do_print:
core.print_out(print_sapt_var("Exch10(S^2) ", Exch_s2, short=True))
core.print_out("\n")
# Start Sinf
Exch10 = 0.0
Exch10 -= 2.0 * np.vdot(cache["D_A"], cache["K_B"])
Exch10 += 2.0 * np.vdot(T_A, h_B.np)
Exch10 += 2.0 * np.vdot(T_B, h_A.np)
Exch10 += 2.0 * np.vdot(T_AB, h_A.np + h_B.np)
Exch10 += 4.0 * np.vdot(T_B, JT_AB.np - 0.5 * KT_AB.np)
Exch10 += 4.0 * np.vdot(T_A, JT_AB.np - 0.5 * KT_AB.np)
Exch10 += 4.0 * np.vdot(T_B, JT_A.np - 0.5 * KT_A.np)
Exch10 += 4.0 * np.vdot(T_AB, JT_AB.np - 0.5 * KT_AB.np.T)
if do_print:
core.set_variable("Exch10", Exch10)
core.print_out(print_sapt_var("Exch10", Exch10, short=True))
core.print_out("\n")
return {"Exch10(S^2)": Exch_s2, "Exch10": Exch10}
def run_dftd3(mol, func=None, dashlvl=None, dashparam=None, dertype=None, verbose=False):
"""Compute dispersion correction using Grimme's DFTD3 executable.
Function to call Grimme's dftd3 program to compute the -D correction
of level `dashlvl` using parameters for the functional `func`.
`dashparam` can supply a full set of dispersion parameters in the
absence of `func` or individual overrides in the presence of `func`.
The DFTD3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
Parameters
----------
mol : qcdb.Molecule or psi4.core.Molecule or str
Molecule on which to run dispersion calculation. Both qcdb and
psi4.core Molecule classes have been extended by this method, so
either allowed. Alternately, a string that can be instantiated
into a qcdb.Molecule.
func : str or None
Density functional (Psi4, not Turbomole, names) for which to
load parameters from dashcoeff[dashlvl][func]. This is not
passed to DFTD3 and thus may be a dummy or `None`. Any or all
parameters initialized can be overwritten via `dashparam`.
dashlvl : {'d2p4', 'd2gr', 'd3zero', 'd3bj', 'd3mzero', d3mbj', 'd', 'd2', 'd3', 'd3m'}
Flavor of a posteriori dispersion correction for which to load
parameters and call procedure in DFTD3. Must be a keys in
dashcoeff dict (or a key in dashalias that resolves to one).
dashparam : dict, optional
Dictionary of the same keys as dashcoeff[dashlvl] used to
override any or all values initialized by
dashcoeff[dashlvl][func].
dertype : {None, 0, 'none', 'energy', 1, 'first', 'gradient'}, optional
Maximum derivative level at which to run DFTD3. For large
`mol`, energy-only calculations can be significantly more
efficient. Also controls return values, see below.
verbose : bool, optional
When `True`, additionally include DFTD3 output in output.
Returns
-------
energy : float, optional
When `dertype` is 0, energy [Eh].
gradient : list of lists of floats or psi4.core.Matrix, optional
When `dertype` is 1, (nat, 3) gradient [Eh/a0].
(energy, gradient) : float and list of lists of floats or psi4.core.Matrix, optional
When `dertype` is unspecified, both energy [Eh] and (nat, 3) gradient [Eh/a0].
Notes
-----
research site: https://www.chemie.uni-bonn.de/pctc/mulliken-center/software/dft-d3
Psi4 mode: When `psi4` the python module is importable at `import qcdb`
time, Psi4 mode is activated, with the following alterations:
* output goes to output file
* gradient returned as psi4.core.Matrix, not list o'lists
* scratch is written to randomly named subdirectory of psi scratch
* psivar "DISPERSION CORRECTION ENERGY" is set
* `verbose` triggered when PRINT keywork of SCF module >=3
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(mol, (Molecule, core.Molecule)):
# 1st: called on a qcdb.Molecule
# 2nd: called on a python export of a psi4.Molecule (py-side through Psi4's driver)
pass
elif isinstance(mol, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
mol = Molecule(mol)
else:
raise ValidationError("""Argument mol must be psi4string or qcdb.Molecule""")
mol.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError("""Requested derivative level 'dertype' %s not valid for run_dftd3.""" % (dertype))
else:
raise ValidationError("""Requested derivative level 'dertype' %s not valid for run_dftd3.""" % (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = get_dispersion_aliases()[dashlvl] if dashlvl in get_dispersion_aliases() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError("""-D correction level %s is not available. Choose among %s.""" % (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser('~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(uuid.uuid4())[:8]
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(uuid.uuid4())[:8]
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = mol.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
os.chdir(current_directory)
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = mol.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error("""Unsuccessful run. Possibly -D variant not available in dftd3 version.""")
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([float(x.replace('D', 'E')) for x in dfile.readline().split()])
dfile.close()
if len(dashdderiv) != mol.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), mol.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix.from_list(dashdderiv)
# Print program output to file if verbose
if not verbose and isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out.decode('utf-8')
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def run_dftd3(self,
func=None,
dashlvl=None,
dashparam=None,
dertype=None,
verbose=False):
"""Function to call Grimme's dftd3 program (http://toc.uni-muenster.de/DFTD3/)
to compute the -D correction of level *dashlvl* using parameters for
the functional *func*. The dictionary *dashparam* can be used to supply
a full set of dispersion parameters in the absense of *func* or to supply
individual overrides in the presence of *func*. Returns energy if *dertype* is 0,
gradient if *dertype* is 1, else tuple of energy and gradient if *dertype*
unspecified. The dftd3 executable must be independently compiled and found in
:envvar:`PATH` or :envvar:`PSIPATH`.
*self* may be either a qcdb.Molecule (sensibly) or a psi4.Molecule
(works b/c psi4.Molecule has been extended by this method py-side and
only public interface fns used) or a string that can be instantiated
into a qcdb.Molecule.
func - functional alias or None
dashlvl - functional type d2gr/d3zero/d3bj/d3mzero/d3mbj
dashparam - dictionary
dertype = derivative level
"""
# Create (if necessary) and update qcdb.Molecule
if isinstance(self, Molecule):
# called on a qcdb.Molecule
pass
elif isinstance(self, core.Molecule):
# called on a python export of a psi4.Molecule (py-side through Psi4's driver)
self.create_psi4_string_from_molecule()
elif isinstance(self, basestring):
# called on a string representation of a psi4.Molecule (c-side through psi4.Dispersion)
self = Molecule(self)
else:
raise ValidationError(
"""Argument mol must be psi4string or qcdb.Molecule""")
self.update_geometry()
# Validate arguments
if dertype is None:
dertype = -1
elif der0th.match(str(dertype)):
dertype = 0
elif der1st.match(str(dertype)):
dertype = 1
elif der2nd.match(str(dertype)):
raise ValidationError(
'Requested derivative level \'dertype\' %s not valid for run_dftd3.'
% (dertype))
else:
raise ValidationError(
'Requested derivative level \'dertype\' %s not valid for run_dftd3.'
% (dertype))
if dashlvl is not None:
dashlvl = dashlvl.lower()
dashlvl = dash_alias['-' + dashlvl][1:] if (
'-' + dashlvl) in dash_alias.keys() else dashlvl
if dashlvl not in dashcoeff.keys():
raise ValidationError(
"""-D correction level %s is not available. Choose among %s."""
% (dashlvl, dashcoeff.keys()))
else:
raise ValidationError("""Must specify a dashlvl""")
if func is not None:
dftd3_params = dash_server(func, dashlvl)
else:
dftd3_params = {}
if dashparam is not None:
dftd3_params.update(dashparam)
# Move ~/.dftd3par.<hostname> out of the way so it won't interfere
defaultfile = os.path.expanduser(
'~') + '/.dftd3par.' + socket.gethostname()
defmoved = False
if os.path.isfile(defaultfile):
os.rename(defaultfile, defaultfile + '_hide')
defmoved = True
# Find environment by merging PSIPATH and PATH environment variables
lenv = {
'PATH': ':'.join([os.path.abspath(x) for x in os.environ.get('PSIPATH', '').split(':') if x != '']) + \
':' + os.environ.get('PATH'),
'LD_LIBRARY_PATH': os.environ.get('LD_LIBRARY_PATH')
}
# Filter out None values as subprocess will fault on them
lenv = {k: v for k, v in lenv.items() if v is not None}
# Find out if running from Psi4 for scratch details and such
# try:
# import psi4
# except ImportError as err:
# isP4regime = False
# else:
# isP4regime = True
# Setup unique scratch directory and move in
current_directory = os.getcwd()
if isP4regime:
psioh = core.IOManager.shared_object()
psio = core.IO.shared_object()
os.chdir(psioh.get_default_path())
dftd3_tmpdir = 'psi.' + str(os.getpid()) + '.' + psio.get_default_namespace() + \
'.dftd3.' + str(random.randint(0, 99999))
else:
dftd3_tmpdir = os.environ['HOME'] + os.sep + 'dftd3_' + str(
random.randint(0, 99999))
if os.path.exists(dftd3_tmpdir) is False:
os.mkdir(dftd3_tmpdir)
os.chdir(dftd3_tmpdir)
# Write dftd3_parameters file that governs dispersion calc
paramcontents = dftd3_coeff_formatter(dashlvl, dftd3_params)
paramfile1 = 'dftd3_parameters' # older patched name
with open(paramfile1, 'w') as handle:
handle.write(paramcontents)
paramfile2 = '.dftd3par.local' # new mainline name
with open(paramfile2, 'w') as handle:
handle.write(paramcontents)
# Write dftd3_geometry file that supplies geometry to dispersion calc
numAtoms = self.natom()
# We seem to have a problem with one atom, force the correct result
if numAtoms == 1:
dashd = 0.0
dashdderiv = core.Matrix(1, 3)
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return dashdderiv
geom = self.save_string_xyz()
reals = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
numAtoms -= 1
else:
reals.append(line)
geomtext = str(numAtoms) + '\n\n'
for line in reals:
geomtext += line.strip() + '\n'
geomfile = './dftd3_geometry.xyz'
with open(geomfile, 'w') as handle:
handle.write(geomtext)
# TODO somehow the variations on save_string_xyz and
# whether natom and chgmult does or doesn't get written
# have gotten all tangled. I fear this doesn't work
# the same btwn libmints and qcdb or for ghosts
# Call dftd3 program
command = ['dftd3', geomfile]
if dertype != 0:
command.append('-grad')
try:
dashout = subprocess.Popen(command, stdout=subprocess.PIPE, env=lenv)
except OSError as e:
raise ValidationError('Program dftd3 not found in path. %s' % e)
out, err = dashout.communicate()
# Parse output (could go further and break into E6, E8, E10 and Cn coeff)
success = False
for line in out.splitlines():
line = line.decode('utf-8')
if re.match(' Edisp /kcal,au', line):
sline = line.split()
dashd = float(sline[3])
if re.match(' normal termination of dftd3', line):
success = True
if not success:
os.chdir(current_directory)
raise Dftd3Error(
"""Unsuccessful run. Possibly -D variant not available in dftd3 version."""
)
# Parse grad output
if dertype != 0:
derivfile = './dftd3_gradient'
dfile = open(derivfile, 'r')
dashdderiv = []
for line in geom.splitlines():
lline = line.split()
if len(lline) != 4:
continue
if lline[0] == 'Gh':
dashdderiv.append([0.0, 0.0, 0.0])
else:
dashdderiv.append([
float(x.replace('D', 'E'))
for x in dfile.readline().split()
])
dfile.close()
if len(dashdderiv) != self.natom():
raise ValidationError('Program dftd3 gradient file has %d atoms- %d expected.' % \
(len(dashdderiv), self.natom()))
# Prepare results for Psi4
if isP4regime and dertype != 0:
core.set_variable('DISPERSION CORRECTION ENERGY', dashd)
psi_dashdderiv = core.Matrix(self.natom(), 3)
psi_dashdderiv.set(dashdderiv)
# Print program output to file if verbose
if isP4regime:
verbose = True if core.get_option('SCF', 'PRINT') >= 3 else False
if verbose:
text = '\n ==> DFTD3 Output <==\n'
text += out
if dertype != 0:
with open(derivfile, 'r') as handle:
text += handle.read().replace('D', 'E')
text += '\n'
if isP4regime:
core.print_out(text)
else:
print(text)
# Clean up files and remove scratch directory
os.unlink(paramfile1)
os.unlink(paramfile2)
os.unlink(geomfile)
if dertype != 0:
os.unlink(derivfile)
if defmoved is True:
os.rename(defaultfile + '_hide', defaultfile)
os.chdir('..')
try:
shutil.rmtree(dftd3_tmpdir)
except OSError as e:
ValidationError('Unable to remove dftd3 temporary directory %s' % e)
os.chdir(current_directory)
# return -D & d(-D)/dx
if dertype == -1:
return dashd, dashdderiv
elif dertype == 0:
return dashd
elif dertype == 1:
return psi_dashdderiv
def mcscf_solver(ref_wfn):
# Build CIWavefunction
core.prepare_options_for_module("DETCI")
ciwfn = core.CIWavefunction(ref_wfn)
# Hush a lot of CI output
ciwfn.set_print(0)
# Begin with a normal two-step
step_type = 'Initial CI'
total_step = core.Matrix("Total step", ciwfn.get_dimension('OA'), ciwfn.get_dimension('AV'))
start_orbs = ciwfn.get_orbitals("ROT").clone()
ciwfn.set_orbitals("ROT", start_orbs)
# Grab da options
mcscf_orb_grad_conv = core.get_option("DETCI", "MCSCF_R_CONVERGENCE")
mcscf_e_conv = core.get_option("DETCI", "MCSCF_E_CONVERGENCE")
mcscf_max_macroiteration = core.get_option("DETCI", "MCSCF_MAXITER")
mcscf_type = core.get_option("DETCI", "MCSCF_TYPE")
mcscf_d_file = core.get_option("DETCI", "CI_FILE_START") + 3
mcscf_nroots = core.get_option("DETCI", "NUM_ROOTS")
mcscf_wavefunction_type = core.get_option("DETCI", "WFN")
mcscf_ndet = ciwfn.ndet()
mcscf_nuclear_energy = ciwfn.molecule().nuclear_repulsion_energy()
mcscf_steplimit = core.get_option("DETCI", "MCSCF_MAX_ROT")
mcscf_rotate = core.get_option("DETCI", "MCSCF_ROTATE")
# DIIS info
mcscf_diis_start = core.get_option("DETCI", "MCSCF_DIIS_START")
mcscf_diis_freq = core.get_option("DETCI", "MCSCF_DIIS_FREQ")
mcscf_diis_error_type = core.get_option("DETCI", "MCSCF_DIIS_ERROR_TYPE")
mcscf_diis_max_vecs = core.get_option("DETCI", "MCSCF_DIIS_MAX_VECS")
# One-step info
mcscf_target_conv_type = core.get_option("DETCI", "MCSCF_ALGORITHM")
mcscf_so_start_grad = core.get_option("DETCI", "MCSCF_SO_START_GRAD")
mcscf_so_start_e = core.get_option("DETCI", "MCSCF_SO_START_E")
mcscf_current_step_type = 'Initial CI'
# Start with SCF energy and other params
scf_energy = core.get_variable("HF TOTAL ENERGY")
eold = scf_energy
norb_iter = 1
converged = False
ah_step = False
qc_step = False
approx_integrals_only = True
# Fake info to start with the inital diagonalization
ediff = 1.e-4
orb_grad_rms = 1.e-3
# Grab needed objects
diis_obj = solvers.DIIS(mcscf_diis_max_vecs)
mcscf_obj = ciwfn.mcscf_object()
# Execute the rotate command
for rot in mcscf_rotate:
if len(rot) != 4:
raise p4util.PsiException("Each element of the MCSCF rotate command requires 4 arguements (irrep, orb1, orb2, theta).")
irrep, orb1, orb2, theta = rot
if irrep > ciwfn.Ca().nirrep():
raise p4util.PsiException("MCSCF_ROTATE: Expression %s irrep number is larger than the number of irreps" %
(str(rot)))
if max(orb1, orb2) > ciwfn.Ca().coldim()[irrep]:
raise p4util.PsiException("MCSCF_ROTATE: Expression %s orbital number exceeds number of orbitals in irrep" %
(str(rot)))
theta = np.deg2rad(theta)
x = ciwfn.Ca().nph[irrep][:, orb1].copy()
y = ciwfn.Ca().nph[irrep][:, orb2].copy()
xp = np.cos(theta) * x - np.sin(theta) * y
yp = np.sin(theta) * x + np.cos(theta) * y
ciwfn.Ca().nph[irrep][:, orb1] = xp
ciwfn.Ca().nph[irrep][:, orb2] = yp
# Limited RAS functionality
if core.get_local_option("DETCI", "WFN") == "RASSCF" and mcscf_target_conv_type != "TS":
core.print_out("\n Warning! Only the TS algorithm for RASSCF wavefunction is currently supported.\n")
core.print_out(" Switching to the TS algorithm.\n\n")
mcscf_target_conv_type = "TS"
# Print out headers
if mcscf_type == "CONV":
mtype = " @MCSCF"
core.print_out("\n ==> Starting MCSCF iterations <==\n\n")
core.print_out(" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n")
elif mcscf_type == "DF":
mtype = " @DF-MCSCF"
core.print_out("\n ==> Starting DF-MCSCF iterations <==\n\n")
core.print_out(" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n")
else:
mtype = " @AO-MCSCF"
core.print_out("\n ==> Starting AO-MCSCF iterations <==\n\n")
core.print_out(" Iter Total Energy Delta E Orb RMS CI RMS NCI NORB\n")
# Iterate !
for mcscf_iter in range(1, mcscf_max_macroiteration + 1):
# Transform integrals, diagonalize H
ciwfn.transform_mcscf_integrals(approx_integrals_only)
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
# After the first diag we need to switch to READ
ciwfn.set_ci_guess("DFILE")
ciwfn.form_opdm()
ciwfn.form_tpdm()
ci_grad_rms = core.get_variable("DETCI AVG DVEC NORM")
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
current_energy = core.get_variable("MCSCF TOTAL ENERGY")
orb_grad_rms = mcscf_obj.gradient_rms()
ediff = current_energy - eold
# Print iterations
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms, ci_grad_rms,
nci_iter, norb_iter, mcscf_current_step_type)
eold = current_energy
if mcscf_current_step_type == 'Initial CI':
mcscf_current_step_type = 'TS'
# Check convergence
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) < abs(mcscf_e_conv)) and\
(mcscf_iter > 3) and not qc_step:
core.print_out("\n %s has converged!\n\n" % mtype);
converged = True
break
# Which orbital convergence are we doing?
if ah_step:
converged, norb_iter, step = ah_iteration(mcscf_obj, print_micro=False)
norb_iter += 1
if converged:
mcscf_current_step_type = 'AH'
else:
core.print_out(" !Warning. Augmented Hessian did not converge. Taking an approx step.\n")
step = mcscf_obj.approx_solve()
mcscf_current_step_type = 'TS, AH failure'
else:
step = mcscf_obj.approx_solve()
step_type = 'TS'
maxstep = step.absmax()
if maxstep > mcscf_steplimit:
core.print_out(' Warning! Maxstep = %4.2f, scaling to %4.2f\n' % (maxstep, mcscf_steplimit))
step.scale(mcscf_steplimit / maxstep)
xstep = total_step.clone()
total_step.add(step)
# Do or add DIIS
if (mcscf_iter >= mcscf_diis_start) and ("TS" in mcscf_current_step_type):
# Figure out DIIS error vector
if mcscf_diis_error_type == "GRAD":
error = core.Matrix.triplet(ciwfn.get_orbitals("OA"),
mcscf_obj.gradient(),
ciwfn.get_orbitals("AV"),
False, False, True)
else:
error = step
diis_obj.add(total_step, error)
if not (mcscf_iter % mcscf_diis_freq):
total_step = diis_obj.extrapolate()
mcscf_current_step_type = 'TS, DIIS'
# Build the rotation by continuous updates
if mcscf_iter == 1:
totalU = mcscf_obj.form_rotation_matrix(total_step)
else:
xstep.axpy(-1.0, total_step)
xstep.scale(-1.0)
Ustep = mcscf_obj.form_rotation_matrix(xstep)
totalU = core.Matrix.doublet(totalU, Ustep, False, False)
# Build the rotation directly (not recommended)
# orbs_mat = mcscf_obj.Ck(start_orbs, total_step)
# Finally rotate and set orbitals
orbs_mat = core.Matrix.doublet(start_orbs, totalU, False, False)
ciwfn.set_orbitals("ROT", orbs_mat)
# Figure out what the next step should be
if (orb_grad_rms < mcscf_so_start_grad) and (abs(ediff) < abs(mcscf_so_start_e)) and\
(mcscf_iter >= 2):
if mcscf_target_conv_type == 'AH':
approx_integrals_only = False
ah_step = True
elif mcscf_target_conv_type == 'OS':
approx_integrals_only = False
mcscf_current_step_type = 'OS, Prep'
break
else:
continue
#raise p4util.PsiException("")
# If we converged do not do onestep
if converged or (mcscf_target_conv_type != 'OS'):
one_step_iters = []
# If we are not converged load in Dvec and build iters array
else:
one_step_iters = range(mcscf_iter + 1, mcscf_max_macroiteration + 1)
dvec = ciwfn.D_vector()
dvec.init_io_files(True)
dvec.read(0, 0)
dvec.symnormalize(1.0, 0)
ci_grad = ciwfn.new_civector(1, mcscf_d_file + 1, True, True)
ci_grad.set_nvec(1)
ci_grad.init_io_files(True)
# Loop for onestep
for mcscf_iter in one_step_iters:
# Transform integrals and update the MCSCF object
ciwfn.transform_mcscf_integrals(ciwfn.H(), False)
ciwfn.form_opdm()
ciwfn.form_tpdm()
# Update MCSCF object
Cocc = ciwfn.get_orbitals("DOCC")
Cact = ciwfn.get_orbitals("ACT")
Cvir = ciwfn.get_orbitals("VIR")
opdm = ciwfn.get_opdm(-1, -1, "SUM", False)
tpdm = ciwfn.get_tpdm("SUM", True)
mcscf_obj.update(Cocc, Cact, Cvir, opdm, tpdm)
orb_grad_rms = mcscf_obj.gradient_rms()
# Warning! Does not work for SA-MCSCF
current_energy = mcscf_obj.current_total_energy()
current_energy += mcscf_nuclear_energy
core.set_variable("CI ROOT %d TOTAL ENERGY" % 1, current_energy)
core.set_variable("CURRENT ENERGY", current_energy)
docc_energy = mcscf_obj.current_docc_energy()
ci_energy = mcscf_obj.current_ci_energy()
# Compute CI gradient
ciwfn.sigma(dvec, ci_grad, 0, 0)
ci_grad.scale(2.0, 0)
ci_grad.axpy(-2.0 * ci_energy, dvec, 0, 0)
ci_grad_rms = ci_grad.norm(0)
orb_grad_rms = mcscf_obj.gradient().rms()
ediff = current_energy - eold
print_iteration(mtype, mcscf_iter, current_energy, ediff, orb_grad_rms, ci_grad_rms,
nci_iter, norb_iter, mcscf_current_step_type)
mcscf_current_step_type = 'OS'
eold = current_energy
if (orb_grad_rms < mcscf_orb_grad_conv) and (abs(ediff) < abs(mcscf_e_conv)):
core.print_out("\n %s has converged!\n\n" % mtype);
converged = True
break
# Take a step
converged, norb_iter, nci_iter, step = qc_iteration(dvec, ci_grad, ciwfn, mcscf_obj)
# Rotate integrals to new frame
total_step.add(step)
orbs_mat = mcscf_obj.Ck(ciwfn.get_orbitals("ROT"), step)
ciwfn.set_orbitals("ROT", orbs_mat)
core.print_out(mtype + " Final Energy: %20.15f\n" % current_energy)
# Die if we did not converge
if (not converged):
if core.get_global_option("DIE_IF_NOT_CONVERGED"):
raise p4util.PsiException("MCSCF: Iterations did not converge!")
else:
core.print_out("\nWarning! MCSCF iterations did not converge!\n\n")
# Print out CI vector information
if mcscf_target_conv_type == 'OS':
dvec.close_io_files()
ci_grad.close_io_files()
# For orbital invariant methods we transform the orbitals to the natural or
# semicanonical basis. Frozen doubly occupied and virtual orbitals are not
# modified.
if core.get_option("DETCI", "WFN") == "CASSCF":
# Do we diagonalize the opdm?
if core.get_option("DETCI", "NAT_ORBS"):
ciwfn.ci_nat_orbs()
else:
ciwfn.semicanonical_orbs()
# Retransform intragrals and update CI coeffs., OPDM, and TPDM
ciwfn.transform_mcscf_integrals(approx_integrals_only)
nci_iter = ciwfn.diag_h(abs(ediff) * 1.e-2, orb_grad_rms * 1.e-3)
ciwfn.set_ci_guess("DFILE")
ciwfn.form_opdm()
ciwfn.form_tpdm()
proc_util.print_ci_results(ciwfn, "MCSCF", scf_energy, current_energy, print_opdm_no=True)
# Set final energy
core.set_variable("CURRENT ENERGY", core.get_variable("MCSCF TOTAL ENERGY"))
# What do we need to cleanup?
if core.get_option("DETCI", "MCSCF_CI_CLEANUP"):
ciwfn.cleanup_ci()
if core.get_option("DETCI", "MCSCF_DPD_CLEANUP"):
ciwfn.cleanup_dpd()
del diis_obj
del mcscf_obj
return ciwfn
def print_sapt_dft_summary(data, name, short=False):
ret = " %s Results\n" % name
ret += " " + "-" * 97 + "\n"
# Elst
ret += print_sapt_var("Electrostatics", data["Elst10,r"]) + "\n"
ret += print_sapt_var(" Elst1,r", data["Elst10,r"]) + "\n"
ret += "\n"
core.set_variable("SAPT ELST ENERGY", data["Elst10,r"])
# Exchange
ret += print_sapt_var("Exchange", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1", data["Exch10"]) + "\n"
ret += print_sapt_var(" Exch1(S^2)", data["Exch10(S^2)"]) + "\n"
ret += "\n"
core.set_variable("SAPT EXCH ENERGY", data["Exch10"])
# Induction
ind = data["Ind20,r"] + data["Exch-Ind20,r"]
ind_ab = data["Ind20,r (A<-B)"] + data["Exch-Ind20,r (A<-B)"]
ind_ba = data["Ind20,r (A->B)"] + data["Exch-Ind20,r (A->B)"]
if "Delta HF Correction" in list(data):
ind += data["Delta HF Correction"]
ret += print_sapt_var("Induction", ind) + "\n"
ret += print_sapt_var(" Ind2,r", data["Ind20,r"]) + "\n"
ret += print_sapt_var(" Exch-Ind2,r", data["Exch-Ind20,r"]) + "\n"
ret += print_sapt_var(" Induction (A<-B)", ind_ab) + "\n"
ret += print_sapt_var(" Induction (A->B)", ind_ba) + "\n"
if "Delta HF Correction" in list(data):
ret += print_sapt_var(" delta HF,r (2)",
data["Delta HF Correction"]) + "\n"
ret += "\n"
core.set_variable("SAPT IND ENERGY", ind)
# Dispersion
disp = data["Disp20"] + data["Exch-Disp20,u"]
ret += print_sapt_var("Dispersion", disp) + "\n"
ret += print_sapt_var(" Disp2,r", data["Disp20"]) + "\n"
ret += print_sapt_var(" Disp2,u", data["Disp20,u"]) + "\n"
ret += print_sapt_var(" Exch-Disp2,u", data["Exch-Disp20,u"]) + "\n"
ret += "\n"
core.set_variable("SAPT DISP ENERGY", disp)
# Total energy
total = data["Elst10,r"] + data["Exch10"] + ind + disp
ret += print_sapt_var("Total %-15s" % name, total,
start_spacer=" ") + "\n"
core.set_variable("SAPT(DFT) TOTAL ENERGY", total)
core.set_variable("SAPT TOTAL ENERGY", total)
core.set_variable("CURRENT ENERGY", total)
ret += " " + "-" * 97 + "\n"
return ret
