def format_currentstate_for_input(func, name, allButMol=False, **kwargs):
"""Function to return an input file in preprocessed psithon.
Captures memory, molecule, options, function, method, and kwargs.
Used to write distributed (sow/reap) input files.
"""
warnings.warn(
"Using `psi4.driver.p4util.format_currentstate_for_input` is deprecated, and in 1.4 it will stop working\n",
category=FutureWarning,
stacklevel=2)
commands = """\n# This is a psi4 input file auto-generated from the %s() wrapper.\n\n""" % (
inspect.stack()[1][3])
commands += """memory %d mb\n\n""" % (int(0.000001 * core.get_memory()))
if not allButMol:
molecule = core.get_active_molecule()
molecule.update_geometry()
commands += format_molecule_for_input(molecule)
commands += '\n'
commands += prepare_options_for_modules(changedOnly=True,
commandsInsteadDict=True)
commands += """\n%s('%s', """ % (func.__name__, name.lower())
for key in kwargs.keys():
commands += """%s=%r, """ % (key, kwargs[key])
commands += ')\n\n'
return commands
def format_currentstate_for_input(func, name, allButMol=False, **kwargs):
"""Function to return an input file in preprocessed psithon.
Captures memory, molecule, options, function, method, and kwargs.
Used to write distributed (sow/reap) input files.
"""
warnings.warn(
"Using `psi4.driver.p4util.format_currentstate_for_input` is deprecated, and in 1.4 it will stop working\n",
category=FutureWarning,
stacklevel=2)
commands = """\n# This is a psi4 input file auto-generated from the %s() wrapper.\n\n""" % (inspect.stack()[1][3])
commands += """memory %d mb\n\n""" % (int(0.000001 * core.get_memory()))
if not allButMol:
molecule = core.get_active_molecule()
molecule.update_geometry()
commands += format_molecule_for_input(molecule)
commands += '\n'
commands += prepare_options_for_modules(changedOnly=True, commandsInsteadDict=True)
commands += """\n%s('%s', """ % (func.__name__, name.lower())
for key in kwargs.keys():
commands += """%s=%r, """ % (key, kwargs[key])
commands += ')\n\n'
return commands
def state_to_atomicinput(*, driver, method, basis=None, molecule=None, function_kwargs=None) -> "AtomicInput":
"""Form a QCSchema for job input from the current state of Psi4 settings."""
if molecule is None:
molecule = core.get_active_molecule()
keywords = {k.lower(): v for k, v in prepare_options_for_set_options().items()}
if function_kwargs is not None:
keywords["function_kwargs"] = function_kwargs
kw_basis = keywords.pop("basis", None)
basis = basis or kw_basis
resi = qcel.models.AtomicInput(
**{
"driver": driver,
"extras": {
"wfn_qcvars_only": True,
},
"model": {
"method": method,
"basis": basis,
},
"keywords": keywords,
"molecule": molecule.to_schema(dtype=2),
"provenance": provenance_stamp(__name__),
})
return resi
def initialize_database(database,
name,
prop,
properties_array,
additional_kwargs=None):
"""
Initialize the database for computation of some property
using distributed finite differences driver
database: (database) the database object passed from the caller
name: (string) name as passed to calling driver
prop: (string) the property being computed, used to add xxx_computed flag
to database
prop_array: (list of strings) properties to go in
properties kwarg of the property() cmd in each sub-dir
additional_kwargs: (list of strings) *optional*
any additional kwargs that should go in the call to the
property() driver method in each subdir
Returns: nothing
Throws: nothing
"""
database['inputs_generated'] = False
database['jobs_complete'] = False
prop_cmd = "property('{0}',".format(name)
prop_cmd += "properties=[ '{}' ".format(properties_array[0])
if len(properties_array) > 1:
for element in properties_array[1:]:
prop_cmd += ",'{}'".format(element)
prop_cmd += "]"
if additional_kwargs is not None:
for arg in additional_kwargs:
prop_cmd += ", {}".format(arg)
prop_cmd += ")"
database['prop_cmd'] = prop_cmd
database['job_status'] = collections.OrderedDict()
# Populate the job_status dict
molecule = core.get_active_molecule()
natom = molecule.natom()
coordinates = ['x', 'y', 'z']
#step_direction = ['p', 'm'] changing due to change in findif atomic_displacements
step_direction = ['m', 'p']
for atom in range(1, natom + 1):
for coord in coordinates:
for step in step_direction:
job_name = '{}_{}_{}'.format(atom, coord, step)
database['job_status'].update({job_name: 'not_started'})
database['{}_computed'.format(prop)] = False
def generate_inputs(db,name):
"""
Generates the input files in each sub-directory of the
distributed finite differences property calculation.
name: ( string ) method name passed to calling driver,
db: (database) The database object associated with this property
calculation. On exit this db['inputs_generated'] has been set True
Returns: nothing
Throws: Exception if the number of atomic displacements is not correct.
"""
molecule = core.get_active_molecule()
natom = molecule.natom()
# get list of displacements
displacement_geoms = core.atomic_displacements(molecule)
# Sanity Check
# there should be 3 cords * natoms *2 directions (+/-)
if not (6 * natom) == len(displacement_geoms):
raise Exception('The number of atomic displacements should be 6 times'
' the number of atoms!')
displacement_names = db['job_status'].keys()
for n, entry in enumerate(displacement_names):
if not os.path.exists(entry):
os.makedirs(entry)
# Setup up input file string
inp_template = 'molecule {molname}_{disp}'
inp_template += ' {{\n{molecule_info}\n}}\n{options}\n{jobspec}\n'
molecule.set_geometry(displacement_geoms[n])
molecule.fix_orientation(True)
molecule.fix_com(True)
inputfile = open('{0}/input.dat'.format(entry), 'w')
inputfile.write("# This is a psi4 input file auto-generated for"
"computing properties by finite differences.\n\n")
inputfile.write(
inp_template.format(
molname=molecule.name(),
disp=entry,
molecule_info=molecule.create_psi4_string_from_molecule(),
options=p4util.format_options_for_input(),
jobspec=db['prop_cmd']))
inputfile.close()
db['inputs_generated'] = True
def generate_inputs(db, name):
"""
Generates the input files in each sub-directory of the
distributed finite differences property calculation.
name: ( string ) method name passed to calling driver,
db: (database) The database object associated with this property
calculation. On exit this db['inputs_generated'] has been set True
Returns: nothing
Throws: Exception if the number of atomic displacements is not correct.
"""
molecule = core.get_active_molecule()
natom = molecule.natom()
# get list of displacements
displacement_geoms = core.atomic_displacements(molecule)
# Sanity Check
# there should be 3 cords * natoms *2 directions (+/-)
if not (6 * natom) == len(displacement_geoms):
raise Exception('The number of atomic displacements should be 6 times'
' the number of atoms!')
displacement_names = db['job_status'].keys()
for n, entry in enumerate(displacement_names):
if not os.path.exists(entry):
os.makedirs(entry)
# Setup up input file string
inp_template = 'molecule {molname}_{disp}'
inp_template += ' {{\n{molecule_info}\n}}\n{options}\n{jobspec}\n'
molecule.set_geometry(displacement_geoms[n])
molecule.fix_orientation(True)
molecule.fix_com(True)
inputfile = open('{0}/input.dat'.format(entry), 'w')
inputfile.write("# This is a psi4 input file auto-generated for"
"computing properties by finite differences.\n\n")
inputfile.write(
inp_template.format(
molname=molecule.name(),
disp=entry,
molecule_info=molecule.create_psi4_string_from_molecule(),
options=p4util.format_options_for_input(),
jobspec=db['prop_cmd']))
inputfile.close()
db['inputs_generated'] = True
def initialize_database(database, name, prop, properties_array, additional_kwargs=None):
"""
Initialize the database for computation of some property
using distributed finite differences driver
database: (database) the database object passed from the caller
name: (string) name as passed to calling driver
prop: (string) the property being computed, used to add xxx_computed flag
to database
prop_array: (list of strings) properties to go in
properties kwarg of the property() cmd in each sub-dir
additional_kwargs: (list of strings) *optional*
any additional kwargs that should go in the call to the
property() driver method in each subdir
Returns: nothing
Throws: nothing
"""
database['inputs_generated'] = False
database['jobs_complete'] = False
prop_cmd ="property('{0}',".format(name)
prop_cmd += "properties=[ '{}' ".format(properties_array[0])
if len(properties_array) > 1:
for element in properties_array[1:]:
prop_cmd += ",'{}'".format(element)
prop_cmd += "]"
if additional_kwargs is not None:
for arg in additional_kwargs:
prop_cmd += ", {}".format(arg)
prop_cmd += ")"
database['prop_cmd'] = prop_cmd
database['job_status'] = collections.OrderedDict()
# Populate the job_status dict
molecule = core.get_active_molecule()
natom = molecule.natom()
coordinates = ['x', 'y', 'z']
#step_direction = ['p', 'm'] changing due to change in findif atomic_displacements
step_direction = ['m', 'p']
for atom in range(1, natom + 1):
for coord in coordinates:
for step in step_direction:
job_name = '{}_{}_{}'.format(atom, coord, step)
database['job_status'].update({job_name: 'not_started'})
database['{}_computed'.format(prop)] = False
def auto_fragments(
molecule: Optional[core.Molecule] = None,
seed_atoms: Optional[List[List[int]]] = None,
) -> core.Molecule:
r"""Detects fragments in unfragmented molecule using BFS algorithm.
Currently only used for the WebMO implementation of SAPT.
Parameters
----------
molecule : :ref:`molecule <op_py_molecule>`, optional
The target molecule, if not the last molecule defined.
seed_atoms
List of lists of atoms (0-indexed) belonging to independent fragments.
Useful to prompt algorithm or to define intramolecular fragments through
border atoms. Example: `[[1, 0], [2]]`
Returns
-------
:py:class:`~psi4.core.Molecule` |w--w| fragmented molecule in
Cartesian, fixed-geom (no variable values), no dummy-atom format.
Examples
--------
>>> # [1] prepare unfragmented (and non-adjacent-atom) HHFF into (HF)_2 molecule ready for SAPT
>>> molecule mol {\nH 0.0 0.0 0.0\nH 2.0 0.0 0.0\nF 0.0 1.0 0.0\nF 2.0 1.0 0.0\n}
>>> print mol.nfragments() # 1
>>> fragmol = auto_fragments()
>>> print fragmol.nfragments() # 2
"""
# Make sure the molecule the user provided is the active one
if molecule is None:
molecule = core.get_active_molecule()
molecule.update_geometry()
molname = molecule.name()
frag, bmol = molecule.BFS(seed_atoms=seed_atoms, return_molecule=True)
bmol.set_name(molname)
bmol.print_cluster()
core.print_out(""" Exiting auto_fragments\n""")
return bmol
def auto_fragments(**kwargs):
r"""Detects fragments in unfragmented molecule using BFS algorithm.
Currently only used for the WebMO implementation of SAPT.
Parameters
----------
molecule : :ref:`molecule <op_py_molecule>`, optional
The target molecule, if not the last molecule defined.
seed_atoms : list, optional
List of lists of atoms (0-indexed) belonging to independent fragments.
Useful to prompt algorithm or to define intramolecular fragments through
border atoms. Example: `[[1, 0], [2]]`
Returns
-------
:py:class:`~psi4.core.Molecule` |w--w| fragmented molecule in
Cartesian, fixed-geom (no variable values), no dummy-atom format.
Examples
--------
>>> # [1] prepare unfragmented (and non-adjacent-atom) HHFF into (HF)_2 molecule ready for SAPT
>>> molecule mol {\nH 0.0 0.0 0.0\nH 2.0 0.0 0.0\nF 0.0 1.0 0.0\nF 2.0 1.0 0.0\n}
>>> print mol.nfragments() # 1
>>> fragmol = auto_fragments()
>>> print fragmol.nfragments() # 2
"""
# Make sure the molecule the user provided is the active one
molecule = kwargs.pop('molecule', core.get_active_molecule())
seeds = kwargs.pop('seed_atoms', None)
molecule.update_geometry()
molname = molecule.name()
frag, bmol = molecule.BFS(seed_atoms=seeds, return_molecule=True)
bmol.set_name(molname)
bmol.print_cluster()
core.print_out(""" Exiting auto_fragments\n""")
return bmol
def auto_fragments(**kwargs):
r"""Detects fragments if the user does not supply them.
Currently only used for the WebMO implementation of SAPT.
:returns: :py:class:`~psi4.core.Molecule`) |w--w| fragmented molecule.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:examples:
>>> # [1] replicates with cbs() the simple model chemistry scf/cc-pVDZ: set basis cc-pVDZ energy('scf')
>>> molecule mol {\nH 0.0 0.0 0.0\nH 2.0 0.0 0.0\nF 0.0 1.0 0.0\nF 2.0 1.0 0.0\n}
>>> print mol.nfragments() # 1
>>> fragmol = auto_fragments()
>>> print fragmol.nfragments() # 2
"""
# Make sure the molecule the user provided is the active one
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
molname = molecule.name()
geom = molecule.save_string_xyz()
numatoms = molecule.natom()
VdW = [1.2, 1.7, 1.5, 1.55, 1.52, 1.9, 1.85, 1.8]
symbol = list(range(numatoms))
X = [0.0] * numatoms
Y = [0.0] * numatoms
Z = [0.0] * numatoms
Queue = []
White = []
Black = []
F = geom.split('\n')
for f in range(numatoms):
A = F[f + 1].split()
symbol[f] = A[0]
X[f] = float(A[1])
Y[f] = float(A[2])
Z[f] = float(A[3])
White.append(f)
Fragment = [[] for i in range(numatoms)] # stores fragments
start = 0 # starts with the first atom in the list
Queue.append(start)
White.remove(start)
frag = 0
while((len(White) > 0) or (len(Queue) > 0)): # Iterates to the next fragment
while(len(Queue) > 0): # BFS within a fragment
for u in Queue: # find all nearest Neighbors
# (still coloured white) to vertex u
for i in White:
Distance = math.sqrt((X[i] - X[u]) * (X[i] - X[u]) +
(Y[i] - Y[u]) * (Y[i] - Y[u]) +
(Z[i] - Z[u]) * (Z[i] - Z[u]))
if Distance < _autofragment_convert(u, symbol) + _autofragment_convert(i, symbol):
Queue.append(i) # if you find you, put it in the que
White.remove(i) # and remove it from the untouched list
Queue.remove(u) # remove focus from Queue
Black.append(u)
Fragment[frag].append(int(u + 1)) # add to group (adding 1 to start
# list at one instead of zero)
if(len(White) != 0): # cant move White->Queue if no more exist
Queue.append(White[0])
White.remove(White[0])
frag += 1
new_geom = """\n"""
for i in Fragment[0]:
new_geom = new_geom + F[i].lstrip() + """\n"""
new_geom = new_geom + """--\n"""
for j in Fragment[1]:
new_geom = new_geom + F[j].lstrip() + """\n"""
new_geom = new_geom + """units angstrom\n"""
moleculenew = core.Molecule.create_molecule_from_string(new_geom)
moleculenew.set_name(molname)
moleculenew.update_geometry()
moleculenew.print_cluster()
core.print_out(""" Exiting auto_fragments\n""")
return moleculenew
def frac_nuke(name, **kwargs):
"""Pull all the electrons out, one at a time"""
optstash = p4util.OptionsState(
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
["SCF", "FRAC_START"],
["SCF", "FRAC_RENORMALIZE"],
# NYI ["SCF", "FRAC_LOAD"],
["SCF", "FRAC_OCC"],
["SCF", "FRAC_VAL"],
["SCF", "FRAC_DIIS"])
kwargs = p4util.kwargs_lower(kwargs)
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError("""frac_nuke requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(""" Requested procedure `frac_nuke` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
# By default, we start the frac procedure on the 25th iteration
# when not reading a previous guess
frac_start = kwargs.get('frac_start', 25)
# By default, we occupy by tenths of electrons
foccs = kwargs.get('foccs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
# By default, H**O and LUMO are both in alpha
N = 0
for A in range(molecule.natom()):
N += molecule.Z(A)
N -= charge0
N = int(N)
Nb = int((N - mult0 + 1) / 2)
Na = int(N - Nb)
charge = charge0
mult = mult0
# By default, nuke all the electrons
Nmin = 0
if 'nmax' in kwargs:
Nmin = N - int(kwargs['nmax'])
# By default, DIIS in FRAC (1.0 occupation is always DIIS'd)
frac_diis = kwargs.get('frac_diis', True)
# By default, drop the files to the molecule's name
root = kwargs.get('filename', molecule.name())
traverse_filename = root + '.traverse.dat'
stats_filename = root + '.stats.dat'
# => Traverse <= #
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
Ns = []
energies = []
potentials = []
convs = []
stats = []
# Run one SCF to burn things in
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule, **kwargs)
# Determine H**O
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
eps_a.print_out()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.get(int(Na - 1))
E_b = eps_b.get(int(Nb - 1))
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
stats.append(""" %6d %6d %6d %6d %6d %6d\n""" % (N, Na, Nb, charge, mult, H**O))
if H**O > 0:
Na -= 1
else:
Nb -= 1
charge += 1
mult = Na - Nb + 1
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
# Nuke 'em Rico!
for Nintegral in range(N, Nmin, -1):
# Nuke the current H**O
for occ in foccs:
core.set_local_option("SCF", "FRAC_OCC", [H**O])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule, **kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if H**O > 0:
eps = wfn.epsilon_a()
potentials.append(eps.np[H**O - 1])
else:
eps = wfn.epsilon_b()
potentials.append(eps.np[-H**O - 1])
Ns.append(Nintegral + occ - 1.0)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
# NYI core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "GUESS", "READ")
# Set the next charge/mult
molecule.set_molecular_charge(charge)
molecule.set_multiplicity(mult)
# Determine H**O
print('DGAS: What ref should this point to?')
#ref = core.legacy_wavefunction()
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
stats.append(""" %6d %6d %6d %6d %6d %6d\n""" % (Nintegral-1, Na, Nb, charge, mult, H**O))
if H**O > 0:
Na -= 1
else:
Nb -= 1
charge += 1
mult = Na - Nb + 1
core.set_local_option("SCF", "DF_INTS_IO", "NONE")
# => Print the results out <= #
E = {}
core.print_out("""\n ==> Fractional Occupation Nuke Results <==\n\n""")
core.print_out(""" %-11s %-24s %-24s %11s\n""" % ('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(Ns)):
core.print_out(""" %11.3E %24.16E %24.16E %11d\n""" % (Ns[k], energies[k], potentials[k], convs[k]))
E[Ns[k]] = energies[k]
core.print_out('\n')
core.print_out(""" %6s %6s %6s %6s %6s %6s\n""" % ('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
for line in stats:
core.print_out(line)
core.print_out('\n "You shoot a nuke down a bug hole, you got a lot of dead bugs"\n')
core.print_out(' -Starship Troopers\n')
# Drop the files out
with open(traverse_filename, 'w') as fh:
fh.write(""" %-11s %-24s %-24s %11s\n""" % ('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(Ns)):
fh.write(""" %11.3E %24.16E %24.16E %11d\n""" % (Ns[k], energies[k], potentials[k], convs[k]))
with open(stats_filename, 'w') as fh:
fh.write(""" %6s %6s %6s %6s %6s %6s\n""" % ('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
for line in stats:
fh.write(line)
optstash.restore()
return E
def run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF_TYPE'], ['SCF', 'REFERENCE'],
['SCF', 'DFT_GRAC_SHIFT'],
['SCF', 'SAVE_JK'])
core.tstart()
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DF')
core.prepare_options_for_module("SAPT")
# Get the molecule of interest
ref_wfn = kwargs.get('ref_wfn', None)
if ref_wfn is None:
sapt_dimer = kwargs.pop('molecule', core.get_active_molecule())
else:
core.print_out(
'Warning! SAPT argument "ref_wfn" is only able to use molecule information.'
)
sapt_dimer = ref_wfn.molecule()
sapt_dimer, monomerA, monomerB = proc_util.prepare_sapt_molecule(
sapt_dimer, "dimer")
# Grab overall settings
mon_a_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_A")
mon_b_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_B")
do_delta_hf = core.get_option("SAPT", "SAPT_DFT_DO_DHF")
sapt_dft_functional = core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL")
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " + "SAPT(DFT) Procedure".center(58) + "\n")
core.print_out("\n")
core.print_out(" " + "by Daniel G. A. Smith".center(58) + "\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(
" !!! WARNING: SAPT(DFT) capability is in beta. Please use with caution. !!!\n\n"
)
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" %
str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" %
("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" %
core.get_global_option("SCF_TYPE"))
core.print_out("\n")
core.print_out(" Required computations:\n")
if (do_delta_hf):
core.print_out(" HF (Dimer)\n")
core.print_out(" HF (Monomer A)\n")
core.print_out(" HF (Monomer B)\n")
core.print_out(" DFT (Monomer A)\n")
core.print_out(" DFT (Monomer B)\n")
core.print_out("\n")
if (sapt_dft_functional != "HF") and ((mon_a_shift == 0.0) or
(mon_b_shift == 0.0)):
raise ValidationError(
'SAPT(DFT): must set both "SAPT_DFT_GRAC_SHIFT_A" and "B".')
if (core.get_option('SCF', 'REFERENCE') != 'RHF'):
raise ValidationError(
'SAPT(DFT) currently only supports restricted references.')
core.IO.set_default_namespace('dimer')
data = {}
if (core.get_global_option('SCF_TYPE') == 'DF'):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.set_global_option('DF_INTS_IO', 'SAVE')
# # Compute dimer wavefunction
hf_wfn_dimer = None
if do_delta_hf:
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.set_global_option('DF_INTS_IO', 'SAVE')
core.timer_on("SAPT(DFT): Dimer SCF")
hf_data = {}
hf_wfn_dimer = scf_helper("SCF",
molecule=sapt_dimer,
banner="SAPT(DFT): delta HF Dimer",
**kwargs)
hf_data["HF DIMER"] = core.variable("CURRENT ENERGY")
core.timer_off("SAPT(DFT): Dimer SCF")
core.timer_on("SAPT(DFT): Monomer A SCF")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
hf_wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SAPT(DFT): delta HF Monomer A",
**kwargs)
hf_data["HF MONOMER A"] = core.variable("CURRENT ENERGY")
core.timer_off("SAPT(DFT): Monomer A SCF")
core.timer_on("SAPT(DFT): Monomer B SCF")
core.set_global_option("SAVE_JK", True)
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
hf_wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SAPT(DFT): delta HF Monomer B",
**kwargs)
hf_data["HF MONOMER B"] = core.variable("CURRENT ENERGY")
core.set_global_option("SAVE_JK", False)
core.timer_off("SAPT(DFT): Monomer B SCF")
# Grab JK object and set to A (so we do not save many JK objects)
sapt_jk = hf_wfn_B.jk()
hf_wfn_A.set_jk(sapt_jk)
core.set_global_option("SAVE_JK", False)
# Move it back to monomer A
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerB', 'dimer')
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out(" " + "SAPT(DFT): delta HF Segment".center(58) +
"\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Rob Parrish".center(58) +
"\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out("\n")
# Build cache
hf_cache = sapt_jk_terms.build_sapt_jk_cache(hf_wfn_A, hf_wfn_B,
sapt_jk, True)
# Electrostatics
core.timer_on("SAPT(DFT):SAPT:elst")
elst = sapt_jk_terms.electrostatics(hf_cache, True)
hf_data.update(elst)
core.timer_off("SAPT(DFT):SAPT:elst")
# Exchange
core.timer_on("SAPT(DFT):SAPT:exch")
exch = sapt_jk_terms.exchange(hf_cache, sapt_jk, True)
hf_data.update(exch)
core.timer_off("SAPT(DFT):SAPT:exch")
# Induction
core.timer_on("SAPT(DFT):SAPT:ind")
ind = sapt_jk_terms.induction(
hf_cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT", "D_CONVERGENCE"),
Sinf=core.get_option("SAPT", "DO_IND_EXCH_SINF"))
hf_data.update(ind)
core.timer_off("SAPT(DFT):SAPT:ind")
dhf_value = hf_data["HF DIMER"] - hf_data["HF MONOMER A"] - hf_data[
"HF MONOMER B"]
core.print_out("\n")
core.print_out(
print_sapt_hf_summary(hf_data, "SAPT(HF)", delta_hf=dhf_value))
data["Delta HF Correction"] = core.variable("SAPT(DFT) Delta HF")
sapt_jk.finalize()
del hf_wfn_A, hf_wfn_B, sapt_jk
if hf_wfn_dimer is None:
dimer_wfn = core.Wavefunction.build(sapt_dimer,
core.get_global_option("BASIS"))
else:
dimer_wfn = hf_wfn_dimer
# Set the primary functional
core.set_local_option('SCF', 'REFERENCE', 'RKS')
# Compute Monomer A wavefunction
core.timer_on("SAPT(DFT): Monomer A DFT")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
if mon_a_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_a_shift)
core.IO.set_default_namespace('monomerA')
wfn_A = scf_helper(sapt_dft_functional,
post_scf=False,
molecule=monomerA,
banner="SAPT(DFT): DFT Monomer A",
**kwargs)
data["DFT MONOMERA"] = core.variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
core.timer_off("SAPT(DFT): Monomer A DFT")
# Compute Monomer B wavefunction
core.timer_on("SAPT(DFT): Monomer B DFT")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
if mon_b_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_b_shift)
core.set_global_option("SAVE_JK", True)
core.IO.set_default_namespace('monomerB')
wfn_B = scf_helper(sapt_dft_functional,
post_scf=False,
molecule=monomerB,
banner="SAPT(DFT): DFT Monomer B",
**kwargs)
data["DFT MONOMERB"] = core.variable("CURRENT ENERGY")
# Save JK object
sapt_jk = wfn_B.jk()
wfn_A.set_jk(sapt_jk)
core.set_global_option("SAVE_JK", False)
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
core.timer_off("SAPT(DFT): Monomer B DFT")
# Write out header
scf_alg = core.get_global_option("SCF_TYPE")
sapt_dft_header(sapt_dft_functional, mon_a_shift, mon_b_shift,
bool(do_delta_hf), scf_alg)
# Call SAPT(DFT)
sapt_jk = wfn_B.jk()
sapt_dft(dimer_wfn,
wfn_A,
wfn_B,
sapt_jk=sapt_jk,
data=data,
print_header=False)
# Copy data back into globals
for k, v in data.items():
core.set_variable(k, v)
core.tstop()
return dimer_wfn
def frac_traverse(name, **kwargs):
"""Scan electron occupancy from +1 electron to -1.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
cation_mult : int, optional
Multiplicity of cation, if not neutral multiplicity + 1.
anion_mult : int, optional
Multiplicity of anion, if not neutral multiplicity + 1.
frac_start : int, optional
Iteration at which to start frac procedure when not reading previous
guess. Defaults to 25.
HOMO_occs : list, optional
Occupations to step through for cation, by default `[1 - 0.1 * x for x in range(11)]`.
LUMO_occs : list, optional
Occupations to step through for anion, by default `[1 - 0.1 * x for x in range(11)]`.
H**O : int, optional
Index of H**O.
LUMO : int, optional
Index of LUMO.
frac_diis : bool, optional
Do use DIIS for non-1.0-occupied points?
neutral_guess : bool, optional
Do use neutral orbitals as guess for the anion?
hf_guess: bool, optional
Do use UHF guess before UKS?
continuous_guess : bool, optional
Do carry along guess rather than reguessing at each occupation?
filename : str, optional
Result filename, if not name of molecule.
Returns
-------
dict
Dictionary associating SCF energies with occupations.
"""
optstash = p4util.OptionsState(
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
['SCF', 'REFERENCE'],
["SCF", "FRAC_START"],
["SCF", "FRAC_RENORMALIZE"],
#["SCF", "FRAC_LOAD"],
["SCF", "FRAC_OCC"],
["SCF", "FRAC_VAL"],
["SCF", "FRAC_DIIS"])
kwargs = p4util.kwargs_lower(kwargs)
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError(
"""frac_traverse requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(
""" Requested procedure `frac_traverse` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
chargep = charge0 + 1
chargem = charge0 - 1
multp = kwargs.get('cation_mult', mult0 + 1)
multm = kwargs.get('anion_mult', mult0 + 1)
# By default, we start the frac procedure on the 25th iteration
# when not reading a previous guess
frac_start = kwargs.get('frac_start', 25)
# By default, we occupy by tenths of electrons
HOMO_occs = kwargs.get(
'HOMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
LUMO_occs = kwargs.get(
'LUMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
# By default, H**O and LUMO are both in alpha
Z = 0
for A in range(molecule.natom()):
Z += molecule.Z(A)
Z -= charge0
H**O = kwargs.get('H**O', (Z / 2 + 1 if (Z % 2) else Z / 2))
LUMO = kwargs.get('LUMO', H**O + 1)
# By default, DIIS in FRAC (1.0 occupation is always DIIS'd)
frac_diis = kwargs.get('frac_diis', True)
# By default, use the neutral orbitals as a guess for the anion
neutral_guess = kwargs.get('neutral_guess', True)
# By default, burn-in with UHF first, if UKS
hf_guess = False
if core.get_local_option('SCF', 'REFERENCE') == 'UKS':
hf_guess = kwargs.get('hf_guess', True)
# By default, re-guess at each N
continuous_guess = kwargs.get('continuous_guess', False)
# By default, drop the files to the molecule's name
root = kwargs.get('filename', molecule.name())
traverse_filename = root + '.traverse.dat'
# => Traverse <= #
occs = []
energies = []
potentials = []
convs = []
# => Run the neutral for its orbitals, if requested <= #
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
old_guess = core.get_local_option("SCF", "GUESS")
if (neutral_guess):
if (hf_guess):
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the anion first <= #
molecule.set_molecular_charge(chargem)
molecule.set_multiplicity(multm)
# => Burn the anion in with hf, if requested <= #
if hf_guess:
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
for occ in LUMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [LUMO])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
**kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(LUMO) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(LUMO) - 1))
occs.append(occ)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
#core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the neutral next <= #
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
# Burn the neutral in with hf, if requested <= #
if not continuous_guess:
core.set_local_option("SCF", "GUESS", old_guess)
if hf_guess:
core.set_local_option("SCF", "FRAC_START", 0)
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf',
dft_functional=name,
molecule=molecule,
**kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
for occ in HOMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [H**O])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
**kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(H**O) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(H**O) - 1))
occs.append(occ - 1.0)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
# NYI core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Print the results out <= #
E = {}
core.print_out(
"""\n ==> Fractional Occupation Traverse Results <==\n\n""")
core.print_out(""" %-11s %-24s %-24s %11s\n""" %
('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
core.print_out(""" %11.3E %24.16E %24.16E %11d\n""" %
(occs[k], energies[k], potentials[k], convs[k]))
E[occs[k]] = energies[k]
core.print_out("""
You trying to be a hero Watkins?
Just trying to kill some bugs sir!
-Starship Troopers""")
# Drop the files out
with open(traverse_filename, 'w') as fh:
fh.write(""" %-11s %-24s %-24s %11s\n""" %
('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
fh.write(""" %11.3E %24.16E %24.16E %11d\n""" %
(occs[k], energies[k], potentials[k], convs[k]))
optstash.restore()
return E
def run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF', 'SCF_TYPE'], ['SCF', 'REFERENCE'],
['SCF', 'DFT_FUNCTIONAL'],
['SCF', 'DFT_GRAC_SHIFT'],
['SCF', 'SAVE_JK'])
core.tstart()
# Alter default algorithm
if not core.has_option_changed('SCF', 'SCF_TYPE'):
core.set_local_option('SCF', 'SCF_TYPE', 'DF')
core.prepare_options_for_module("SAPT")
# Get the molecule of interest
ref_wfn = kwargs.get('ref_wfn', None)
if ref_wfn is None:
sapt_dimer = kwargs.pop('molecule', core.get_active_molecule())
else:
core.print_out(
'Warning! SAPT argument "ref_wfn" is only able to use molecule information.'
)
sapt_dimer = ref_wfn.molecule()
# Shifting to C1 so we need to copy the active molecule
if sapt_dimer.schoenflies_symbol() != 'c1':
core.print_out(
' SAPT does not make use of molecular symmetry, further calculations in C1 point group.\n'
)
# Make sure the geometry doesnt shift or rotate
sapt_dimer = sapt_dimer.clone()
sapt_dimer.reset_point_group('c1')
sapt_dimer.fix_orientation(True)
sapt_dimer.fix_com(True)
sapt_dimer.update_geometry()
# Grab overall settings
mon_a_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_A")
mon_b_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_B")
do_delta_hf = core.get_option("SAPT", "SAPT_DFT_DO_DHF")
sapt_dft_functional = core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL")
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " + "SAPT(DFT) Procedure".center(58) + "\n")
core.print_out("\n")
core.print_out(" " + "by Daniel G. A. Smith".center(58) + "\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" %
str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" %
("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" %
core.get_option("SCF", "SCF_TYPE"))
core.print_out("\n")
core.print_out(" Required computations:\n")
if (do_delta_hf):
core.print_out(" HF (Dimer)\n")
core.print_out(" HF (Monomer A)\n")
core.print_out(" HF (Monomer B)\n")
core.print_out(" DFT (Monomer A)\n")
core.print_out(" DFT (Monomer B)\n")
core.print_out("\n")
if (mon_a_shift == 0.0) or (mon_b_shift == 0.0):
raise ValidationError(
'SAPT(DFT): must set both "SAPT_DFT_GRAC_SHIFT_A" and "B".')
if (core.get_option('SCF', 'REFERENCE') != 'RHF'):
raise ValidationError(
'SAPT(DFT) currently only supports restricted references.')
nfrag = sapt_dimer.nfragments()
if nfrag != 2:
raise ValidationError(
'SAPT requires active molecule to have 2 fragments, not %s.' %
(nfrag))
monomerA = sapt_dimer.extract_subsets(1, 2)
monomerA.set_name('monomerA')
monomerB = sapt_dimer.extract_subsets(2, 1)
monomerB.set_name('monomerB')
core.IO.set_default_namespace('dimer')
data = {}
core.set_global_option("SAVE_JK", True)
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.set_global_option('DF_INTS_IO', 'SAVE')
# # Compute dimer wavefunction
hf_cache = {}
hf_wfn_dimer = None
if do_delta_hf:
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.set_global_option('DF_INTS_IO', 'SAVE')
hf_data = {}
hf_wfn_dimer = scf_helper("SCF",
molecule=sapt_dimer,
banner="SAPT(DFT): delta HF Dimer",
**kwargs)
hf_data["HF DIMER"] = core.get_variable("CURRENT ENERGY")
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
hf_wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SAPT(DFT): delta HF Monomer A",
**kwargs)
hf_data["HF MONOMER A"] = core.get_variable("CURRENT ENERGY")
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
hf_wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SAPT(DFT): delta HF Monomer B",
**kwargs)
hf_data["HF MONOMER B"] = core.get_variable("CURRENT ENERGY")
# Move it back to monomer A
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerB', 'dimer')
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out(" " +
"SAPT(DFT): delta HF Segement".center(58) + "\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Rob Parrish".center(58) +
"\n")
core.print_out(
" ---------------------------------------------------------\n"
)
core.print_out("\n")
# Build cache and JK
sapt_jk = hf_wfn_B.jk()
hf_cache = sapt_jk_terms.build_sapt_jk_cache(hf_wfn_A, hf_wfn_B,
sapt_jk, True)
# Electostatics
elst = sapt_jk_terms.electrostatics(hf_cache, True)
hf_data.update(elst)
# Exchange
exch = sapt_jk_terms.exchange(hf_cache, sapt_jk, True)
hf_data.update(exch)
# Induction
ind = sapt_jk_terms.induction(
hf_cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT", "D_CONVERGENCE"))
hf_data.update(ind)
dhf_value = hf_data["HF DIMER"] - hf_data["HF MONOMER A"] - hf_data[
"HF MONOMER B"]
core.print_out("\n")
core.print_out(
print_sapt_hf_summary(hf_data, "SAPT(HF)", delta_hf=dhf_value))
data["Delta HF Correction"] = core.get_variable("SAPT(DFT) Delta HF")
if hf_wfn_dimer is None:
dimer_wfn = core.Wavefunction.build(sapt_dimer,
core.get_global_option("BASIS"))
else:
dimer_wfn = hf_wfn_dimer
# Set the primary functional
core.set_global_option("DFT_FUNCTIONAL",
core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL"))
core.set_local_option('SCF', 'REFERENCE', 'RKS')
# Compute Monomer A wavefunction
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
if mon_a_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_a_shift)
# Save the JK object
core.IO.set_default_namespace('monomerA')
wfn_A = scf_helper("SCF",
molecule=monomerA,
banner="SAPT(DFT): DFT Monomer A",
**kwargs)
data["DFT MONOMERA"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Compute Monomer B wavefunction
if (core.get_option('SCF', 'SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
if mon_b_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_b_shift)
core.IO.set_default_namespace('monomerB')
wfn_B = scf_helper("SCF",
molecule=monomerB,
banner="SAPT(DFT): DFT Monomer B",
**kwargs)
data["DFT MONOMERB"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Print out the title and some information
core.print_out("\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out(" " +
"SAPT(DFT): Intermolecular Interaction Segment".center(58) +
"\n")
core.print_out("\n")
core.print_out(" " +
"by Daniel G. A. Smith and Rob Parrish".center(58) + "\n")
core.print_out(
" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" %
str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" %
("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" %
core.get_option("SCF", "SCF_TYPE"))
# Build cache and JK
sapt_jk = wfn_B.jk()
cache = sapt_jk_terms.build_sapt_jk_cache(wfn_A, wfn_B, sapt_jk, True)
# Electostatics
elst = sapt_jk_terms.electrostatics(cache, True)
data.update(elst)
# Exchange
exch = sapt_jk_terms.exchange(cache, sapt_jk, True)
data.update(exch)
# Induction
ind = sapt_jk_terms.induction(cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT",
"D_CONVERGENCE"))
data.update(ind)
# Dispersion
primary_basis = wfn_A.basisset()
core.print_out("\n")
aux_basis = core.BasisSet.build(sapt_dimer, "DF_BASIS_MP2",
core.get_option("DFMP2", "DF_BASIS_MP2"),
"RIFIT", core.get_global_option('BASIS'))
fdds_disp = sapt_mp2_terms.df_fdds_dispersion(primary_basis, aux_basis,
cache)
data.update(fdds_disp)
if core.get_option("SAPT", "SAPT_DFT_MP2_DISP_ALG") == "FISAPT":
mp2_disp = sapt_mp2_terms.df_mp2_fisapt_dispersion(wfn_A,
primary_basis,
aux_basis,
cache,
do_print=True)
else:
mp2_disp = sapt_mp2_terms.df_mp2_sapt_dispersion(dimer_wfn,
wfn_A,
wfn_B,
primary_basis,
aux_basis,
cache,
do_print=True)
data.update(mp2_disp)
# Print out final data
core.print_out("\n")
core.print_out(print_sapt_dft_summary(data, "SAPT(DFT)"))
core.tstop()
return dimer_wfn
def nbody_gufunc(func: Union[str, Callable], method_string: str, **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: Callable
:param func: ``energy`` || etc.
Python function that accepts method_string and a molecule. Returns a
energy, gradient, or Hessian as requested.
:type method_string: str
: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: str 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: str
: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: str
: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: str
: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['ptype'] == 'gradient':
core.set_variable("CURRENT GRADIENT", nbody_results['ret_ptype'])
elif metadata['ptype'] == 'hessian':
core.set_variable("CURRENT HESSIAN", nbody_results['ret_ptype'])
if metadata['return_wfn']:
return (nbody_results['ret_ptype'], wfn)
else:
return nbody_results['ret_ptype']
def anharmonicity(rvals, energies, mol = None):
"""Generates spectroscopic constants for a diatomic molecules.
Fits a diatomic potential energy curve using either a 5 or 9 point Legendre fit, locates the minimum
energy point, and then applies second order vibrational perturbation theory to obtain spectroscopic
constants. The r values provided must bracket the minimum energy point, or an error will result.
A dictionary with the following keys, which correspond to spectroscopic constants, is returned:
:type rvals: list
:param rvals: The bond lengths (in Angstrom) for which energies are
provided of length either 5 or 9 but must be the same length as
the energies array
:type energies: list
:param energies: The energies (Eh) computed at the bond lengths in the rvals list
:returns: (*dict*) Keys: "re", "r0", "we", "wexe", "nu", "ZPVE(harmonic)", "ZPVE(anharmonic)", "Be", "B0", "ae", "De"
corresponding to the spectroscopic constants in cm-1
"""
angstrom_to_bohr = 1.0 / p4const.psi_bohr2angstroms
angstrom_to_meter = 10e-10;
if len(rvals) != len(energies):
raise Exception("The number of energies must match the number of distances")
npoints = len(rvals)
if npoints != 5 and npoints != 9:
raise Exception("Only 5- or 9-point fits are implemented right now")
core.print_out("\n\nPerforming a %d-point fit\n" % npoints)
core.print_out("\nOptimizing geometry based on current surface:\n\n");
if (npoints == 5):
optx = rvals[2]
elif (npoints == 9):
optx = rvals[4]
# Make sure the molecule the user provided is the active one
molecule = mol if mol is not None else core.get_active_molecule()
molecule.update_geometry()
natoms = molecule.natom()
if natoms != 2:
raise Exception("The current molecule must be a diatomic for this code to work!")
m1 = molecule.mass(0)
m2 = molecule.mass(1)
maxit = 30
thres = 1.0e-9
for i in range(maxit):
if (npoints == 5):
grad= first_deriv_5pt(rvals, energies, optx)
secd = second_deriv_5pt(rvals, energies, optx)
energy = function_5pt(rvals, energies, optx)
elif (npoints == 9):
grad = first_deriv_9pt(rvals, energies, optx)
secd = second_deriv_9pt(rvals, energies, optx)
energy = function_9pt(rvals, energies, optx)
core.print_out(" E = %20.14f, x = %14.7f, grad = %20.14f\n" % (energy, optx, grad))
if abs(grad) < thres:
break
optx -= grad / secd;
core.print_out(" Final E = %20.14f, x = %14.7f, grad = %20.14f\n" % (function_5pt(rvals, energies, optx), optx, grad));
if optx < min(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a lower range of r values.")
if optx > max(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a higher range of r values.")
if (npoints == 5):
energy = function_5pt(rvals, energies, optx)
first = first_deriv_5pt(rvals, energies, optx)
secd = second_deriv_5pt(rvals, energies, optx) * p4const.psi_hartree2aJ
third = third_deriv_5pt(rvals, energies, optx) * p4const.psi_hartree2aJ
fourth = fourth_deriv_5pt(rvals, energies, optx) * p4const.psi_hartree2aJ
elif (npoints == 9):
energy = function_9pt(rvals, energies, optx)
first = first_deriv_9pt(rvals, energies, optx)
secd = second_deriv_9pt(rvals, energies, optx) * p4const.psi_hartree2aJ
third = third_deriv_9pt(rvals, energies, optx) * p4const.psi_hartree2aJ
fourth = fourth_deriv_9pt(rvals, energies, optx) * p4const.psi_hartree2aJ
core.print_out("\nEquilibrium Energy %20.14f Hartrees\n" % energy)
core.print_out("Gradient %20.14f\n" % first)
core.print_out("Quadratic Force Constant %14.7f MDYNE/A\n" % secd)
core.print_out("Cubic Force Constant %14.7f MDYNE/A**2\n" % third)
core.print_out("Quartic Force Constant %14.7f MDYNE/A**3\n" % fourth)
hbar = p4const.psi_h / (2.0 * pi)
mu = ((m1*m2)/(m1+m2))*p4const.psi_amu2kg
we = 5.3088375e-11*sqrt(secd/mu)
wexe = (1.2415491e-6)*(we/secd)**2 * ((5.0*third*third)/(3.0*secd)-fourth)
# Rotational constant: Be
I = ((m1*m2)/(m1+m2)) * p4const.psi_amu2kg * (optx * angstrom_to_meter)**2
B = p4const.psi_h / (8.0 * pi**2 * p4const.psi_c * I)
# alpha_e and quartic centrifugal distortion constant
ae = -(6.0 * B**2 / we) * ((1.05052209e-3*we*third)/(sqrt(B * secd**3))+1.0)
de = 4.0*B**3 / we**2
# B0 and r0 (plus re check using Be)
B0 = B - ae / 2.0
r0 = sqrt(p4const.psi_h / (8.0 * pi**2 * mu * p4const.psi_c * B0))
recheck = sqrt(p4const.psi_h / (8.0 * pi**2 * mu * p4const.psi_c * B))
r0 /= angstrom_to_meter;
recheck /= angstrom_to_meter;
# Fundamental frequency nu
nu = we - 2.0 * wexe;
zpve_nu = 0.5 * we - 0.25 * wexe;
core.print_out("\nre = %10.6f A check: %10.6f\n" % (optx, recheck))
core.print_out("r0 = %10.6f A\n" % r0)
core.print_out("we = %10.4f cm-1\n" % we)
core.print_out("wexe = %10.4f cm-1\n" % wexe)
core.print_out("nu = %10.4f cm-1\n" % nu)
core.print_out("ZPVE(nu) = %10.4f cm-1\n" % zpve_nu)
core.print_out("Be = %10.4f cm-1\n" % B)
core.print_out("B0 = %10.4f cm-1\n" % B0)
core.print_out("ae = %10.4f cm-1\n" % ae)
core.print_out("De = %10.7f cm-1\n" % de)
results = {
"re" : optx,
"r0" : r0,
"we" : we,
"wexe" : wexe,
"nu" : nu,
"ZPVE(harmonic)" : zpve_nu,
"ZPVE(anharmonic)" : zpve_nu,
"Be" : B,
"B0" : B0,
"ae" : ae,
"De" : de
}
return results
def anharmonicity(rvals, energies, plot_fit='', mol = None):
"""Generates spectroscopic constants for a diatomic molecules.
Fits a diatomic potential energy curve using a weighted least squares approach
(c.f. http://dx.doi.org/10.1063/1.4862157, particularly eqn. 7), locates the minimum
energy point, and then applies second order vibrational perturbation theory to obtain spectroscopic
constants. Any number of points greater than 4 may be provided, and they should bracket the minimum.
The data need not be evenly spaced, and can be provided in any order. The data are weighted such that
those closest to the minimum have highest impact.
A dictionary with the following keys, which correspond to spectroscopic constants, is returned:
:type rvals: list
:param rvals: The bond lengths (in Angstrom) for which energies are
provided, of length at least 5 and equal to the length of the energies array
:type energies: list
:param energies: The energies (Eh) computed at the bond lengths in the rvals list
:type plot_fit: string
:param plot_fit: A string describing where to save a plot of the harmonic and anharmonic fits, the
inputted data points, re, r0 and the first few energy levels, if matplotlib
is available. Set to 'screen' to generate an interactive plot on the screen instead. If a filename is
provided, the image type is determined by the extension; see matplotlib for supported file types.
:returns: (*dict*) Keys: "re", "r0", "we", "wexe", "nu", "ZPVE(harmonic)", "ZPVE(anharmonic)", "Be", "B0", "ae", "De"
corresponding to the spectroscopic constants in cm-1
"""
angstrom_to_bohr = 1.0 / constants.bohr2angstroms
angstrom_to_meter = 10e-10;
# Make sure the input is valid
if len(rvals) != len(energies):
raise ValidationError("The number of energies must match the number of distances")
npoints = len(rvals)
if npoints < 5:
raise ValidationError("At least 5 data points must be provided to compute anharmonicity")
core.print_out("\n\nPerforming a fit to %d data points\n" % npoints)
# Make sure the molecule the user provided is the active one
molecule = mol if mol is not None else core.get_active_molecule()
molecule.update_geometry()
natoms = molecule.natom()
if natoms != 2:
raise Exception("The current molecule must be a diatomic for this code to work!")
m1 = molecule.mass(0)
m2 = molecule.mass(1)
# Optimize the geometry, refitting the surface around each new geometry
core.print_out("\nOptimizing geometry based on current surface:\n\n");
re = np.mean(rvals)
maxit = 30
thres = 1.0e-9
for i in range(maxit):
derivs = least_squares_fit_polynomial(rvals,energies,localization_point=re)
e,g,H = derivs[0:3]
core.print_out(" E = %20.14f, x = %14.7f, grad = %20.14f\n" % (e, re, g))
if abs(g) < thres:
break
re -= g/H;
if i == maxit-1:
raise ConvergenceError("diatomic geometry optimization", maxit)
core.print_out(" Final E = %20.14f, x = %14.7f, grad = %20.14f\n" % (e, re, g));
if re < min(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a lower range of r values.")
if re > max(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a higher range of r values.")
# Convert to convenient units, and compute spectroscopic constants
d0,d1,d2,d3,d4 = derivs*constants.hartree2aJ
core.print_out("\nEquilibrium Energy %20.14f Hartrees\n" % e)
core.print_out("Gradient %20.14f\n" % g)
core.print_out("Quadratic Force Constant %14.7f MDYNE/A\n" % d2)
core.print_out("Cubic Force Constant %14.7f MDYNE/A**2\n" % d3)
core.print_out("Quartic Force Constant %14.7f MDYNE/A**3\n" % d4)
hbar = constants.h / (2.0 * np.pi)
mu = ((m1*m2)/(m1+m2))*constants.amu2kg
we = 5.3088375e-11 * np.sqrt(d2/mu)
wexe = (1.2415491e-6)*(we/d2)**2 * ((5.0*d3*d3)/(3.0*d2)-d4)
# Rotational constant: Be
I = ((m1*m2)/(m1+m2)) * constants.amu2kg * (re * angstrom_to_meter)**2
B = constants.h / (8.0 * np.pi**2 * constants.c * I)
# alpha_e and quartic centrifugal distortion constant
ae = -(6.0 * B**2 / we) * ((1.05052209e-3*we*d3)/(np.sqrt(B * d2**3))+1.0)
de = 4.0*B**3 / we**2
# B0 and r0 (plus re check using Be)
B0 = B - ae / 2.0
r0 = np.sqrt(constants.h / (8.0 * np.pi**2 * mu * constants.c * B0))
recheck = np.sqrt(constants.h / (8.0 * np.pi**2 * mu * constants.c * B))
r0 /= angstrom_to_meter;
recheck /= angstrom_to_meter;
# Fundamental frequency nu
nu = we - 2.0 * wexe;
zpve_nu = 0.5 * we - 0.25 * wexe;
# Generate pretty pictures, if requested
if(plot_fit):
try:
import matplotlib.pyplot as plt
except ImportError:
msg = "\n\tPlot not generated; matplotlib is not installed on this machine.\n\n"
print(msg)
core.print_out(msg)
# Correct the derivatives for the missing factorial prefactors
dvals = np.zeros(5)
dvals[0:5] = derivs[0:5]
dvals[2] /= 2
dvals[3] /= 6
dvals[4] /= 24
# Default plot range, before considering energy levels
minE = np.min(energies)
maxE = np.max(energies)
minR = np.min(rvals)
maxR = np.max(rvals)
# Plot vibrational energy levels
we_au = we / constants.hartree2wavenumbers
wexe_au = wexe / constants.hartree2wavenumbers
coefs2 = [ dvals[2], dvals[1], dvals[0] ]
coefs4 = [ dvals[4], dvals[3], dvals[2], dvals[1], dvals[0] ]
for n in range(3):
Eharm = we_au*(n+0.5)
Evpt2 = Eharm - wexe_au*(n+0.5)**2
coefs2[-1] = -Eharm
coefs4[-1] = -Evpt2
roots2 = np.roots(coefs2)
roots4 = np.roots(coefs4)
xvals2 = roots2 + re
xvals4 = np.choose(np.where(np.isreal(roots4)), roots4)[0].real + re
Eharm += dvals[0]
Evpt2 += dvals[0]
plt.plot(xvals2, [Eharm, Eharm], 'b', linewidth=1)
plt.plot(xvals4, [Evpt2, Evpt2], 'g', linewidth=1)
maxE = Eharm
maxR = np.max([xvals2,xvals4])
minR = np.min([xvals2,xvals4])
# Find ranges for the plot
dE = maxE - minE
minE -= 0.2*dE
maxE += 0.4*dE
dR = maxR - minR
minR -= 0.2*dR
maxR += 0.2*dR
# Generate the fitted PES
xpts = np.linspace(minR, maxR, 1000)
xrel = xpts - re
xpows = xrel[:, None] ** range(5)
fit2 = np.einsum('xd,d', xpows[:,0:3], dvals[0:3])
fit4 = np.einsum('xd,d', xpows, dvals)
# Make / display the plot
plt.plot(xpts, fit2, 'b', linewidth=2.5, label='Harmonic (quadratic) fit')
plt.plot(xpts, fit4, 'g', linewidth=2.5, label='Anharmonic (quartic) fit')
plt.plot([re, re], [minE, maxE], 'b--', linewidth=0.5)
plt.plot([r0, r0], [minE, maxE], 'g--', linewidth=0.5)
plt.scatter(rvals, energies, c='Black', linewidth=3, label='Input Data')
plt.legend()
plt.xlabel('Bond length (Angstroms)')
plt.ylabel('Energy (Eh)')
plt.xlim(minR, maxR)
plt.ylim(minE, maxE)
if plot_fit == 'screen':
plt.show()
else:
plt.savefig(plot_fit)
core.print_out("\n\tPES fit saved to %s.\n\n" % plot_fit)
core.print_out("\nre = %10.6f A check: %10.6f\n" % (re, recheck))
core.print_out("r0 = %10.6f A\n" % r0)
core.print_out("we = %10.4f cm-1\n" % we)
core.print_out("wexe = %10.4f cm-1\n" % wexe)
core.print_out("nu = %10.4f cm-1\n" % nu)
core.print_out("ZPVE(nu) = %10.4f cm-1\n" % zpve_nu)
core.print_out("Be = %10.4f cm-1\n" % B)
core.print_out("B0 = %10.4f cm-1\n" % B0)
core.print_out("ae = %10.4f cm-1\n" % ae)
core.print_out("De = %10.7f cm-1\n" % de)
results = {
"re" : re,
"r0" : r0,
"we" : we,
"wexe" : wexe,
"nu" : nu,
"ZPVE(harmonic)" : zpve_nu,
"ZPVE(anharmonic)" : zpve_nu,
"Be" : B,
"B0" : B0,
"ae" : ae,
"De" : de
}
return results
def run_sapt_dft(name, **kwargs):
optstash = p4util.OptionsState(['SCF_TYPE'], ['SCF', 'REFERENCE'], ['SCF', 'DFT_GRAC_SHIFT'],
['SCF', 'SAVE_JK'])
core.tstart()
# Alter default algorithm
if not core.has_global_option_changed('SCF_TYPE'):
core.set_global_option('SCF_TYPE', 'DF')
core.prepare_options_for_module("SAPT")
# Get the molecule of interest
ref_wfn = kwargs.get('ref_wfn', None)
if ref_wfn is None:
sapt_dimer = kwargs.pop('molecule', core.get_active_molecule())
else:
core.print_out('Warning! SAPT argument "ref_wfn" is only able to use molecule information.')
sapt_dimer = ref_wfn.molecule()
sapt_dimer, monomerA, monomerB = proc_util.prepare_sapt_molecule(sapt_dimer, "dimer")
# Grab overall settings
mon_a_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_A")
mon_b_shift = core.get_option("SAPT", "SAPT_DFT_GRAC_SHIFT_B")
do_delta_hf = core.get_option("SAPT", "SAPT_DFT_DO_DHF")
sapt_dft_functional = core.get_option("SAPT", "SAPT_DFT_FUNCTIONAL")
# Print out the title and some information
core.print_out("\n")
core.print_out(" ---------------------------------------------------------\n")
core.print_out(" " + "SAPT(DFT) Procedure".center(58) + "\n")
core.print_out("\n")
core.print_out(" " + "by Daniel G. A. Smith".center(58) + "\n")
core.print_out(" ---------------------------------------------------------\n")
core.print_out("\n")
core.print_out(" !!! WARNING: SAPT(DFT) capability is in beta. Please use with caution. !!!\n\n")
core.print_out(" ==> Algorithm <==\n\n")
core.print_out(" SAPT DFT Functional %12s\n" % str(sapt_dft_functional))
core.print_out(" Monomer A GRAC Shift %12.6f\n" % mon_a_shift)
core.print_out(" Monomer B GRAC Shift %12.6f\n" % mon_b_shift)
core.print_out(" Delta HF %12s\n" % ("True" if do_delta_hf else "False"))
core.print_out(" JK Algorithm %12s\n" % core.get_global_option("SCF_TYPE"))
core.print_out("\n")
core.print_out(" Required computations:\n")
if (do_delta_hf):
core.print_out(" HF (Dimer)\n")
core.print_out(" HF (Monomer A)\n")
core.print_out(" HF (Monomer B)\n")
core.print_out(" DFT (Monomer A)\n")
core.print_out(" DFT (Monomer B)\n")
core.print_out("\n")
if (sapt_dft_functional != "HF") and ((mon_a_shift == 0.0) or (mon_b_shift == 0.0)):
raise ValidationError('SAPT(DFT): must set both "SAPT_DFT_GRAC_SHIFT_A" and "B".')
if (core.get_option('SCF', 'REFERENCE') != 'RHF'):
raise ValidationError('SAPT(DFT) currently only supports restricted references.')
core.IO.set_default_namespace('dimer')
data = {}
if (core.get_global_option('SCF_TYPE') == 'DF'):
# core.set_global_option('DF_INTS_IO', 'LOAD')
core.set_global_option('DF_INTS_IO', 'SAVE')
# # Compute dimer wavefunction
hf_wfn_dimer = None
if do_delta_hf:
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.set_global_option('DF_INTS_IO', 'SAVE')
hf_data = {}
hf_wfn_dimer = scf_helper("SCF", molecule=sapt_dimer, banner="SAPT(DFT): delta HF Dimer", **kwargs)
hf_data["HF DIMER"] = core.get_variable("CURRENT ENERGY")
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
hf_wfn_A = scf_helper("SCF", molecule=monomerA, banner="SAPT(DFT): delta HF Monomer A", **kwargs)
hf_data["HF MONOMER A"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("SAVE_JK", True)
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
hf_wfn_B = scf_helper("SCF", molecule=monomerB, banner="SAPT(DFT): delta HF Monomer B", **kwargs)
hf_data["HF MONOMER B"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("SAVE_JK", False)
# Grab JK object and set to A (so we do not save many JK objects)
sapt_jk = hf_wfn_B.jk()
hf_wfn_A.set_jk(sapt_jk)
core.set_global_option("SAVE_JK", False)
# Move it back to monomer A
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerB', 'dimer')
core.print_out("\n")
core.print_out(" ---------------------------------------------------------\n")
core.print_out(" " + "SAPT(DFT): delta HF Segement".center(58) + "\n")
core.print_out("\n")
core.print_out(" " + "by Daniel G. A. Smith and Rob Parrish".center(58) + "\n")
core.print_out(" ---------------------------------------------------------\n")
core.print_out("\n")
# Build cache
hf_cache = sapt_jk_terms.build_sapt_jk_cache(hf_wfn_A, hf_wfn_B, sapt_jk, True)
# Electostatics
elst = sapt_jk_terms.electrostatics(hf_cache, True)
hf_data.update(elst)
# Exchange
exch = sapt_jk_terms.exchange(hf_cache, sapt_jk, True)
hf_data.update(exch)
# Induction
ind = sapt_jk_terms.induction(
hf_cache,
sapt_jk,
True,
maxiter=core.get_option("SAPT", "MAXITER"),
conv=core.get_option("SAPT", "D_CONVERGENCE"),
Sinf=core.get_option("SAPT", "DO_IND_EXCH_SINF"))
hf_data.update(ind)
dhf_value = hf_data["HF DIMER"] - hf_data["HF MONOMER A"] - hf_data["HF MONOMER B"]
core.print_out("\n")
core.print_out(print_sapt_hf_summary(hf_data, "SAPT(HF)", delta_hf=dhf_value))
data["Delta HF Correction"] = core.get_variable("SAPT(DFT) Delta HF")
sapt_jk.finalize()
del hf_wfn_A, hf_wfn_B, sapt_jk
if hf_wfn_dimer is None:
dimer_wfn = core.Wavefunction.build(sapt_dimer, core.get_global_option("BASIS"))
else:
dimer_wfn = hf_wfn_dimer
# Set the primary functional
core.set_local_option('SCF', 'REFERENCE', 'RKS')
# Compute Monomer A wavefunction
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'dimer', 'monomerA')
if mon_a_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_a_shift)
# Save the JK object
core.IO.set_default_namespace('monomerA')
wfn_A = scf_helper(
sapt_dft_functional, post_scf=False, molecule=monomerA, banner="SAPT(DFT): DFT Monomer A", **kwargs)
data["DFT MONOMERA"] = core.get_variable("CURRENT ENERGY")
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Compute Monomer B wavefunction
if (core.get_global_option('SCF_TYPE') == 'DF'):
core.IO.change_file_namespace(97, 'monomerA', 'monomerB')
if mon_b_shift:
core.set_global_option("DFT_GRAC_SHIFT", mon_b_shift)
core.set_global_option("SAVE_JK", True)
core.IO.set_default_namespace('monomerB')
wfn_B = scf_helper(
sapt_dft_functional, post_scf=False, molecule=monomerB, banner="SAPT(DFT): DFT Monomer B", **kwargs)
data["DFT MONOMERB"] = core.get_variable("CURRENT ENERGY")
# Save JK object
sapt_jk = wfn_B.jk()
wfn_A.set_jk(sapt_jk)
core.set_global_option("SAVE_JK", False)
core.set_global_option("DFT_GRAC_SHIFT", 0.0)
# Write out header
scf_alg = core.get_global_option("SCF_TYPE")
sapt_dft_header(sapt_dft_functional, mon_a_shift, mon_b_shift, bool(do_delta_hf), scf_alg)
# Call SAPT(DFT)
sapt_jk = wfn_B.jk()
sapt_dft(dimer_wfn, wfn_A, wfn_B, sapt_jk=sapt_jk, data=data, print_header=False)
# Copy data back into globals
for k, v in data.items():
core.set_variable(k, v)
core.tstop()
return dimer_wfn
def run_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 ip_fitting(name, omega_l=0.05, omega_r=2.5, omega_convergence=1.0e-3, maxiter=20, **kwargs):
"""Optimize DFT omega parameter for molecular system.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
omega_l : float, optional
Minimum omega to be considered during fitting.
omega_r : float, optional
Maximum omega to be considered during fitting.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
omega_convergence : float, optional
Threshold below which to consider omega converged. (formerly omega_tolerance)
maxiter : int, optional
Maximum number of iterations towards omega convergence.
Returns
-------
float
Optimal omega parameter.
"""
optstash = p4util.OptionsState(
['SCF', 'REFERENCE'],
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
['SCF', 'DFT_OMEGA'],
['DOCC'],
['SOCC'])
kwargs = p4util.kwargs_lower(kwargs)
# By default, do not read previous 180 orbitals file
read = False
read180 = ''
if 'read' in kwargs:
read = True
read180 = kwargs['read']
if core.get_option('SCF', 'REFERENCE') != 'UKS':
core.print_out(""" Requested procedure `ip_fitting` runs further calculations with UKS reference.\n""")
core.set_local_option('SCF', 'REFERENCE', 'UKS')
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError("""IP Fitting requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(""" Requested procedure `ip_fitting` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
# How many electrons are there?
N = 0
for A in range(molecule.natom()):
N += molecule.Z(A)
N -= charge0
N = int(N)
Nb = int((N - mult0 + 1) / 2)
Na = int(N - Nb)
# Work in the ot namespace for this procedure
core.IO.set_default_namespace("ot")
# Burn in to determine orbital eigenvalues
if read:
core.set_local_option("SCF", "GUESS", "READ")
copy_file_to_scratch(read180, 'psi', 'ot', 180)
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Burn-in', **kwargs)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
if not wfn.functional().is_x_lrc():
raise ValidationError("""Not sensible to optimize omega for non-long-range-correction functional.""")
# Determine H**O, to determine mult1
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
Na1 = Na
Nb1 = Nb
if H**O > 0:
Na1 -= 1
else:
Nb1 -= 1
charge1 = charge0 + 1
mult1 = Na1 - Nb1 + 1
omegas = []
E0s = []
E1s = []
kIPs = []
IPs = []
types = []
# Right endpoint
core.set_local_option('SCF', 'DFT_OMEGA', omega_r)
# Neutral
if read:
core.set_local_option("SCF", "GUESS", "READ")
p4util.copy_file_to_scratch(read180, 'psi', 'ot', 180)
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
E0r, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Neutral, Right Endpoint', **kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
E_HOMOr = E_HOMO
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
if read:
core.set_local_option("SCF", "GUESS", "READ")
p4util.copy_file_to_scratch(read180, 'psi', 'ot', 180)
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
E1r = driver.energy('scf', dft_functional=name, molecule=molecule,
banner='IP Fitting SCF: Cation, Right Endpoint', **kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IPr = E1r - E0r
kIPr = -E_HOMOr
delta_r = IPr - kIPr
if IPr > kIPr:
raise ValidationError("""\n***IP Fitting Error: Right Omega limit should have kIP > IP: {} !> {}""".format(kIPr, IPr))
omegas.append(omega_r)
types.append('Right Limit')
E0s.append(E0r)
E1s.append(E1r)
IPs.append(IPr)
kIPs.append(kIPr)
# Use previous orbitals from here out
core.set_local_option("SCF", "GUESS", "READ")
# Left endpoint
core.set_local_option('SCF', 'DFT_OMEGA', omega_l)
# Neutral
core.IO.change_file_namespace(180, "neutral", "ot")
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
core.set_global_option("DOCC", [Nb])
core.set_global_option("SOCC", [Na - Nb])
E0l, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Neutral, Left Endpoint', **kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
E_HOMOl = E_HOMO
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
core.IO.change_file_namespace(180, "cation", "ot")
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
core.set_global_option("DOCC", [Nb1])
core.set_global_option("SOCC", [Na1 - Nb1])
E1l = driver.energy('scf', dft_functional=name, molecule=molecule,
banner='IP Fitting SCF: Cation, Left Endpoint', **kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IPl = E1l - E0l
kIPl = -E_HOMOl
delta_l = IPl - kIPl
if IPl < kIPl:
raise ValidationError("""\n***IP Fitting Error: Left Omega limit should have kIP < IP: {} !< {}""".format(kIPl, IPl))
omegas.append(omega_l)
types.append('Left Limit')
E0s.append(E0l)
E1s.append(E1l)
IPs.append(IPl)
kIPs.append(kIPl)
converged = False
repeat_l = 0
repeat_r = 0
for step in range(maxiter):
# Regula Falsi (modified)
if repeat_l > 1:
delta_l /= 2.0
if repeat_r > 1:
delta_r /= 2.0
omega = - (omega_r - omega_l) / (delta_r - delta_l) * delta_l + omega_l
core.set_local_option('SCF', 'DFT_OMEGA', omega)
# Neutral
core.IO.change_file_namespace(180, "neutral", "ot")
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
core.set_global_option("DOCC", [Nb])
core.set_global_option("SOCC", [Na - Nb])
E0, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule,
banner='IP Fitting SCF: Neutral, Omega = {:11.3E}'.format(omega), **kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
core.IO.change_file_namespace(180, "cation", "ot")
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
core.set_global_option("DOCC", [Nb1])
core.set_global_option("SOCC", [Na1 - Nb1])
E1 = driver.energy('scf', dft_functional=name, molecule=molecule,
banner='IP Fitting SCF: Cation, Omega = {:11.3E}'.format(omega), **kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IP = E1 - E0
kIP = -E_HOMO
delta = IP - kIP
if kIP > IP:
omega_r = omega
E0r = E0
E1r = E1
IPr = IP
kIPr = kIP
delta_r = delta
repeat_r = 0
repeat_l += 1
else:
omega_l = omega
E0l = E0
E1l = E1
IPl = IP
kIPl = kIP
delta_l = delta
repeat_l = 0
repeat_r += 1
omegas.append(omega)
types.append('Regula-Falsi')
E0s.append(E0)
E1s.append(E1)
IPs.append(IP)
kIPs.append(kIP)
# Termination
if abs(omega_l - omega_r) < omega_convergence:
converged = True
break
core.IO.set_default_namespace("")
core.print_out("""\n ==> IP Fitting Results <==\n\n""")
core.print_out(""" => Occupation Determination <= \n\n""")
core.print_out(""" %6s %6s %6s %6s %6s %6s\n""" % ('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
core.print_out(""" Neutral: %6d %6d %6d %6d %6d %6d\n""" % (N, Na, Nb, charge0, mult0, H**O))
core.print_out(""" Cation: %6d %6d %6d %6d %6d\n\n""" % (N - 1, Na1, Nb1, charge1, mult1))
core.print_out(""" => Regula Falsi Iterations <=\n\n""")
core.print_out(""" %3s %11s %14s %14s %14s %s\n""" % ('N','Omega','IP','kIP','Delta','Type'))
for k in range(len(omegas)):
core.print_out(""" %3d %11.3E %14.6E %14.6E %14.6E %s\n""" %
(k + 1, omegas[k], IPs[k], kIPs[k], IPs[k] - kIPs[k], types[k]))
optstash.restore()
if converged:
core.print_out("""\n IP Fitting Converged\n""")
core.print_out(""" Final omega = %14.6E\n""" % ((omega_l + omega_r) / 2))
core.print_out("""\n "M,I. does the dying. Fleet just does the flying."\n""")
core.print_out(""" -Starship Troopers\n""")
else:
raise ConvergenceError("""IP Fitting """, step + 1)
return ((omega_l + omega_r) / 2)
def frac_traverse(name, **kwargs):
"""Scan electron occupancy from +1 electron to -1.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
cation_mult : int, optional
Multiplicity of cation, if not neutral multiplicity + 1.
anion_mult : int, optional
Multiplicity of anion, if not neutral multiplicity + 1.
frac_start : int, optional
Iteration at which to start frac procedure when not reading previous
guess. Defaults to 25.
HOMO_occs : list, optional
Occupations to step through for cation, by default `[1 - 0.1 * x for x in range(11)]`.
LUMO_occs : list, optional
Occupations to step through for anion, by default `[1 - 0.1 * x for x in range(11)]`.
H**O : int, optional
Index of H**O.
LUMO : int, optional
Index of LUMO.
frac_diis : bool, optional
Do use DIIS for non-1.0-occupied points?
neutral_guess : bool, optional
Do use neutral orbitals as guess for the anion?
hf_guess: bool, optional
Do use UHF guess before UKS?
continuous_guess : bool, optional
Do carry along guess rather than reguessing at each occupation?
filename : str, optional
Result filename, if not name of molecule.
Returns
-------
dict
Dictionary associating SCF energies with occupations.
"""
optstash = p4util.OptionsState(
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
['SCF', 'REFERENCE'],
["SCF", "FRAC_START"],
["SCF", "FRAC_RENORMALIZE"],
#["SCF", "FRAC_LOAD"],
["SCF", "FRAC_OCC"],
["SCF", "FRAC_VAL"],
["SCF", "FRAC_DIIS"])
kwargs = p4util.kwargs_lower(kwargs)
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError("""frac_traverse requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(""" Requested procedure `frac_traverse` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
chargep = charge0 + 1
chargem = charge0 - 1
multp = kwargs.get('cation_mult', mult0 + 1)
multm = kwargs.get('anion_mult', mult0 + 1)
# By default, we start the frac procedure on the 25th iteration
# when not reading a previous guess
frac_start = kwargs.get('frac_start', 25)
# By default, we occupy by tenths of electrons
HOMO_occs = kwargs.get('HOMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
LUMO_occs = kwargs.get('LUMO_occs', [1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
# By default, H**O and LUMO are both in alpha
Z = 0
for A in range(molecule.natom()):
Z += molecule.Z(A)
Z -= charge0
H**O = kwargs.get('H**O', (Z / 2 + 1 if (Z % 2) else Z / 2))
LUMO = kwargs.get('LUMO', H**O + 1)
# By default, DIIS in FRAC (1.0 occupation is always DIIS'd)
frac_diis = kwargs.get('frac_diis', True)
# By default, use the neutral orbitals as a guess for the anion
neutral_guess = kwargs.get('neutral_guess', True)
# By default, burn-in with UHF first, if UKS
hf_guess = False
if core.get_local_option('SCF', 'REFERENCE') == 'UKS':
hf_guess = kwargs.get('hf_guess', True)
# By default, re-guess at each N
continuous_guess = kwargs.get('continuous_guess', False)
# By default, drop the files to the molecule's name
root = kwargs.get('filename', molecule.name())
traverse_filename = root + '.traverse.dat'
# => Traverse <= #
occs = []
energies = []
potentials = []
convs = []
# => Run the neutral for its orbitals, if requested <= #
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
old_guess = core.get_local_option("SCF", "GUESS")
if (neutral_guess):
if (hf_guess):
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the anion first <= #
molecule.set_molecular_charge(chargem)
molecule.set_multiplicity(multm)
# => Burn the anion in with hf, if requested <= #
if hf_guess:
core.set_local_option("SCF", "REFERENCE","UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
for occ in LUMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [LUMO])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule, **kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(LUMO) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(LUMO) - 1))
occs.append(occ)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
#core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Run the neutral next <= #
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
# Burn the neutral in with hf, if requested <= #
if not continuous_guess:
core.set_local_option("SCF", "GUESS", old_guess)
if hf_guess:
core.set_local_option("SCF", "FRAC_START", 0)
core.set_local_option("SCF", "REFERENCE", "UHF")
driver.energy('scf', dft_functional=name, molecule=molecule, **kwargs)
core.set_local_option("SCF", "REFERENCE", "UKS")
core.set_local_option("SCF", "GUESS", "READ")
# NYI core.set_local_option("SCF", "FRAC_LOAD", False)
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
for occ in HOMO_occs:
core.set_local_option("SCF", "FRAC_OCC", [H**O])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf', dft_functional=name, return_wfn=True, molecule=molecule, **kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if LUMO > 0:
eps = wfn.epsilon_a()
potentials.append(eps.get(int(H**O) - 1))
else:
eps = wfn.epsilon_b()
potentials.append(eps.get(-int(H**O) - 1))
occs.append(occ - 1.0)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
# NYI core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "GUESS", "READ")
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
# => Print the results out <= #
E = {}
core.print_out("""\n ==> Fractional Occupation Traverse Results <==\n\n""")
core.print_out(""" %-11s %-24s %-24s %11s\n""" % ('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
core.print_out(""" %11.3E %24.16E %24.16E %11d\n""" % (occs[k], energies[k], potentials[k], convs[k]))
E[occs[k]] = energies[k]
core.print_out("""
You trying to be a hero Watkins?
Just trying to kill some bugs sir!
-Starship Troopers""")
# Drop the files out
with open(traverse_filename, 'w') as fh:
fh.write(""" %-11s %-24s %-24s %11s\n""" % ('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(occs)):
fh.write(""" %11.3E %24.16E %24.16E %11d\n""" % (occs[k], energies[k], potentials[k], convs[k]))
optstash.restore()
return E
def 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_roa(name, **kwargs):
"""
Main driver for managing Raman Optical activity computations with
CC response theory.
Uses distributed finite differences approach -->
1. Sets up a database to keep track of running/finished/waiting
computations.
2. Generates separate input files for displaced geometries.
3. When all displacements are run, collects the necessary information
from each displaced computation, and computes final result.
"""
# Get list of omega values -> Make sure we only have one wavelength
# Catch this now before any real work gets done
omega = core.get_option('CCRESPONSE', 'OMEGA')
if len(omega) > 2:
raise Exception('ROA scattering can only be performed for one wavelength.')
else:
pass
core.print_out(
'Running ROA computation. Subdirectories for each '
'required displaced geometry have been created.\n\n')
dbno = 0
# Initialize database
db = shelve.open('database', writeback=True)
# Check if final result is in here
# ->if we have already computed roa, back up the dict
# ->copy it setting this flag to false and continue
if ('roa_computed' in db) and ( db['roa_computed'] ):
db2 = shelve.open('.database.bak{}'.format(dbno), writeback=True)
dbno += 1
for key,value in db.items():
db2[key]=value
db2.close()
db['roa_computed'] = False
else:
db['roa_computed'] = False
if 'inputs_generated' not in db:
findif_response_utils.initialize_database(db,name,"roa", ["roa_tensor"])
# Generate input files
if not db['inputs_generated']:
findif_response_utils.generate_inputs(db,name)
# handled by helper db['inputs_generated'] = True
# Check job status
if db['inputs_generated'] and not db['jobs_complete']:
print('Checking status')
findif_response_utils.stat(db)
for job, status in db['job_status'].items():
print("{} --> {}".format(job, status))
# Compute ROA Scattering
if db['jobs_complete']:
mygauge = core.get_option('CCRESPONSE', 'GAUGE')
consider_gauge = {
'LENGTH': ['Length Gauge'],
'VELOCITY': ['Modified Velocity Gauge'],
'BOTH': ['Length Gauge', 'Modified Velocity Gauge']
}
gauge_list = ["{} Results".format(x) for x in consider_gauge[mygauge]]
# Gather data
dip_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, 'Dipole Polarizability', 3)
opt_rot_list = [
x for x in (
findif_response_utils.collect_displaced_matrix_data(
db,
"Optical Rotation Tensor ({})".format(gauge),
3
)
for gauge in consider_gauge[mygauge]
)
]
dip_quad_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, "Electric-Dipole/Quadrupole Polarizability", 9)
# Compute Scattering
# Run new function (src/bin/ccresponse/scatter.cc)
core.print_out('Running scatter function')
step = core.get_local_option('FINDIF', 'DISP_SIZE')
for g_idx, gauge in enumerate(opt_rot_list):
print('\n\n----------------------------------------------------------------------')
print('\t%%%%%%%%%% {} %%%%%%%%%%'.format(gauge_list[g_idx]))
print('----------------------------------------------------------------------\n\n')
core.print_out('\n\n----------------------------------------------------------------------\n')
core.print_out('\t%%%%%%%%%% {} %%%%%%%%%%\n'.format(gauge_list[g_idx]))
core.print_out('----------------------------------------------------------------------\n\n')
print('roa.py:85 I am not being passed a molecule, grabbing from global :(')
core.scatter(core.get_active_molecule(), step, dip_polar_list, gauge, dip_quad_polar_list)
db['roa_computed'] = True
db.close()
def 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 anharmonicity(rvals, energies, mol = None):
"""Generates spectroscopic constants for a diatomic molecules.
Fits a diatomic potential energy curve using either a 5 or 9 point Legendre fit, locates the minimum
energy point, and then applies second order vibrational perturbation theory to obtain spectroscopic
constants. The r values provided must bracket the minimum energy point, or an error will result.
A dictionary with the following keys, which correspond to spectroscopic constants, is returned:
:type rvals: list
:param rvals: The bond lengths (in Angstrom) for which energies are
provided of length either 5 or 9 but must be the same length as
the energies array
:type energies: list
:param energies: The energies (Eh) computed at the bond lengths in the rvals list
:returns: (*dict*) Keys: "re", "r0", "we", "wexe", "nu", "ZPVE(harmonic)", "ZPVE(anharmonic)", "Be", "B0", "ae", "De"
corresponding to the spectroscopic constants in cm-1
"""
angstrom_to_bohr = 1.0 / p4const.psi_bohr2angstroms
angstrom_to_meter = 10e-10;
if len(rvals) != len(energies):
raise Exception("The number of energies must match the number of distances")
npoints = len(rvals)
if npoints != 5 and npoints != 9:
raise Exception("Only 5- or 9-point fits are implemented right now")
core.print_out("\n\nPerforming a %d-point fit\n" % npoints)
core.print_out("\nOptimizing geometry based on current surface:\n\n");
if (npoints == 5):
optx = rvals[2]
elif (npoints == 9):
optx = rvals[4]
# Make sure the molecule the user provided is the active one
molecule = mol if mol is not None else core.get_active_molecule()
molecule.update_geometry()
natoms = molecule.natom()
if natoms != 2:
raise Exception("The current molecule must be a diatomic for this code to work!")
m1 = molecule.mass(0)
m2 = molecule.mass(1)
maxit = 30
thres = 1.0e-9
for i in range(maxit):
if (npoints == 5):
grad= first_deriv_5pt(rvals, energies, optx)
secd = second_deriv_5pt(rvals, energies, optx)
energy = function_5pt(rvals, energies, optx)
elif (npoints == 9):
grad = first_deriv_9pt(rvals, energies, optx)
secd = second_deriv_9pt(rvals, energies, optx)
energy = function_9pt(rvals, energies, optx)
core.print_out(" E = %20.14f, x = %14.7f, grad = %20.14f\n" % (energy, optx, grad))
if abs(grad) < thres:
break
optx -= grad / secd;
core.print_out(" Final E = %20.14f, x = %14.7f, grad = %20.14f\n" % (function_5pt(rvals, energies, optx), optx, grad));
if optx < min(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a lower range of r values.")
if optx > max(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a higher range of r values.")
if (npoints == 5):
energy = function_5pt(rvals, energies, optx)
first = first_deriv_5pt(rvals, energies, optx)
secd = second_deriv_5pt(rvals, energies, optx) * p4const.psi_hartree2aJ
third = third_deriv_5pt(rvals, energies, optx) * p4const.psi_hartree2aJ
fourth = fourth_deriv_5pt(rvals, energies, optx) * p4const.psi_hartree2aJ
elif (npoints == 9):
energy = function_9pt(rvals, energies, optx)
first = first_deriv_9pt(rvals, energies, optx)
secd = second_deriv_9pt(rvals, energies, optx) * p4const.psi_hartree2aJ
third = third_deriv_9pt(rvals, energies, optx) * p4const.psi_hartree2aJ
fourth = fourth_deriv_9pt(rvals, energies, optx) * p4const.psi_hartree2aJ
core.print_out("\nEquilibrium Energy %20.14f Hartrees\n" % energy)
core.print_out("Gradient %20.14f\n" % first)
core.print_out("Quadratic Force Constant %14.7f MDYNE/A\n" % secd)
core.print_out("Cubic Force Constant %14.7f MDYNE/A**2\n" % third)
core.print_out("Quartic Force Constant %14.7f MDYNE/A**3\n" % fourth)
hbar = p4const.psi_h / (2.0 * pi)
mu = ((m1*m2)/(m1+m2))*p4const.psi_amu2kg
we = 5.3088375e-11*sqrt(secd/mu)
wexe = (1.2415491e-6)*(we/secd)**2 * ((5.0*third*third)/(3.0*secd)-fourth)
# Rotational constant: Be
I = ((m1*m2)/(m1+m2)) * p4const.psi_amu2kg * (optx * angstrom_to_meter)**2
B = p4const.psi_h / (8.0 * pi**2 * p4const.psi_c * I)
# alpha_e and quartic centrifugal distortion constant
ae = -(6.0 * B**2 / we) * ((1.05052209e-3*we*third)/(sqrt(B * secd**3))+1.0)
de = 4.0*B**3 / we**2
# B0 and r0 (plus re check using Be)
B0 = B - ae / 2.0
r0 = sqrt(p4const.psi_h / (8.0 * pi**2 * mu * p4const.psi_c * B0))
recheck = sqrt(p4const.psi_h / (8.0 * pi**2 * mu * p4const.psi_c * B))
r0 /= angstrom_to_meter;
recheck /= angstrom_to_meter;
# Fundamental frequency nu
nu = we - 2.0 * wexe;
zpve_nu = 0.5 * we - 0.25 * wexe;
core.print_out("\nre = %10.6f A check: %10.6f\n" % (optx, recheck))
core.print_out("r0 = %10.6f A\n" % r0)
core.print_out("we = %10.4f cm-1\n" % we)
core.print_out("wexe = %10.4f cm-1\n" % wexe)
core.print_out("nu = %10.4f cm-1\n" % nu)
core.print_out("ZPVE(nu) = %10.4f cm-1\n" % zpve_nu)
core.print_out("Be = %10.4f cm-1\n" % B)
core.print_out("B0 = %10.4f cm-1\n" % B0)
core.print_out("ae = %10.4f cm-1\n" % ae)
core.print_out("De = %10.7f cm-1\n" % de)
results = {
"re" : optx,
"r0" : r0,
"we" : we,
"wexe" : wexe,
"nu" : nu,
"ZPVE(harmonic)" : zpve_nu,
"ZPVE(anharmonic)" : zpve_nu,
"Be" : B,
"B0" : B0,
"ae" : ae,
"De" : de
}
return results
def run_roa(name, **kwargs):
"""
Main driver for managing Raman Optical activity computations with
CC response theory.
Uses distributed finite differences approach -->
1. Sets up a database to keep track of running/finished/waiting
computations.
2. Generates separate input files for displaced geometries.
3. When all displacements are run, collects the necessary information
from each displaced computation, and computes final result.
"""
# Get list of omega values -> Make sure we only have one wavelength
# Catch this now before any real work gets done
omega = core.get_option('CCRESPONSE', 'OMEGA')
if len(omega) > 2:
raise Exception('ROA scattering can only be performed for one wavelength.')
else:
pass
core.print_out(
'Running ROA computation. Subdirectories for each '
'required displaced geometry have been created.\n\n')
dbno = 0
# Initialize database
db = shelve.open('database', writeback=True)
# Check if final result is in here
# ->if we have already computed roa, back up the dict
# ->copy it setting this flag to false and continue
if ('roa_computed' in db) and ( db['roa_computed'] ):
db2 = shelve.open('.database.bak{}'.format(dbno), writeback=True)
dbno += 1
for key,value in db.items():
db2[key]=value
db2.close()
db['roa_computed'] = False
else:
db['roa_computed'] = False
if 'inputs_generated' not in db:
findif_response_utils.initialize_database(db,name,"roa", ["roa_tensor"])
# Generate input files
if not db['inputs_generated']:
findif_response_utils.generate_inputs(db,name)
# handled by helper db['inputs_generated'] = True
# Check job status
if db['inputs_generated'] and not db['jobs_complete']:
print('Checking status')
findif_response_utils.stat(db)
for job, status in db['job_status'].items():
print("{} --> {}".format(job, status))
# Compute ROA Scattering
if db['jobs_complete']:
mygauge = core.get_option('CCRESPONSE', 'GAUGE')
consider_gauge = {
'LENGTH': ['Length Gauge'],
'VELOCITY': ['Modified Velocity Gauge'],
'BOTH': ['Length Gauge', 'Modified Velocity Gauge']
}
gauge_list = ["{} Results".format(x) for x in consider_gauge[mygauge]]
# Gather data
dip_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, 'Dipole Polarizability', 3)
opt_rot_list = [
x for x in (
findif_response_utils.collect_displaced_matrix_data(
db,
"Optical Rotation Tensor ({})".format(gauge),
3
)
for gauge in consider_gauge[mygauge]
)
]
dip_quad_polar_list = findif_response_utils.collect_displaced_matrix_data(
db, "Electric-Dipole/Quadrupole Polarizability", 9)
# Compute Scattering
# Run new function (src/bin/ccresponse/scatter.cc)
core.print_out('Running scatter function')
step = core.get_local_option('FINDIF', 'DISP_SIZE')
for g_idx, gauge in enumerate(opt_rot_list):
print('\n\n----------------------------------------------------------------------')
print('\t%%%%%%%%%% {} %%%%%%%%%%'.format(gauge_list[g_idx]))
print('----------------------------------------------------------------------\n\n')
core.print_out('\n\n----------------------------------------------------------------------\n')
core.print_out('\t%%%%%%%%%% {} %%%%%%%%%%\n'.format(gauge_list[g_idx]))
core.print_out('----------------------------------------------------------------------\n\n')
print('roa.py:85 I am not being passed a molecule, grabbing from global :(')
core.scatter(core.get_active_molecule(), step, dip_polar_list, gauge, dip_quad_polar_list)
db['roa_computed'] = True
db.close()
def run_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 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 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 frac_nuke(name, **kwargs):
"""Pull all the electrons out, one at a time"""
optstash = p4util.OptionsState(
['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'],
["SCF", "FRAC_START"],
["SCF", "FRAC_RENORMALIZE"],
# NYI ["SCF", "FRAC_LOAD"],
["SCF", "FRAC_OCC"],
["SCF", "FRAC_VAL"],
["SCF", "FRAC_DIIS"])
kwargs = p4util.kwargs_lower(kwargs)
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError(
"""frac_nuke requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(
""" Requested procedure `frac_nuke` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
# By default, we start the frac procedure on the 25th iteration
# when not reading a previous guess
frac_start = kwargs.get('frac_start', 25)
# By default, we occupy by tenths of electrons
foccs = kwargs.get('foccs',
[1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0])
# By default, H**O and LUMO are both in alpha
N = 0
for A in range(molecule.natom()):
N += molecule.Z(A)
N -= charge0
N = int(N)
Nb = int((N - mult0 + 1) / 2)
Na = int(N - Nb)
charge = charge0
mult = mult0
# By default, nuke all the electrons
Nmin = 0
if 'nmax' in kwargs:
Nmin = N - int(kwargs['nmax'])
# By default, DIIS in FRAC (1.0 occupation is always DIIS'd)
frac_diis = kwargs.get('frac_diis', True)
# By default, drop the files to the molecule's name
root = kwargs.get('filename', molecule.name())
traverse_filename = root + '.traverse.dat'
stats_filename = root + '.stats.dat'
# => Traverse <= #
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
Ns = []
energies = []
potentials = []
convs = []
stats = []
# Run one SCF to burn things in
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
**kwargs)
# Determine H**O
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
eps_a.print_out()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.get(int(Na - 1))
E_b = eps_b.get(int(Nb - 1))
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
stats.append(""" %6d %6d %6d %6d %6d %6d\n""" %
(N, Na, Nb, charge, mult, H**O))
if H**O > 0:
Na -= 1
else:
Nb -= 1
charge += 1
mult = Na - Nb + 1
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
core.set_local_option("SCF", "FRAC_START", frac_start)
core.set_local_option("SCF", "FRAC_RENORMALIZE", True)
# Nuke 'em Rico!
for Nintegral in range(N, Nmin, -1):
# Nuke the current H**O
for occ in foccs:
core.set_local_option("SCF", "FRAC_OCC", [H**O])
core.set_local_option("SCF", "FRAC_VAL", [occ])
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
**kwargs)
C = 1
if E == 0.0:
E = core.variable('SCF ITERATION ENERGY')
C = 0
if H**O > 0:
eps = wfn.epsilon_a()
potentials.append(eps.np[H**O - 1])
else:
eps = wfn.epsilon_b()
potentials.append(eps.np[-H**O - 1])
Ns.append(Nintegral + occ - 1.0)
energies.append(E)
convs.append(C)
core.set_local_option("SCF", "FRAC_START", 2)
# NYI core.set_local_option("SCF", "FRAC_LOAD", True)
core.set_local_option("SCF", "FRAC_DIIS", frac_diis)
core.set_local_option("SCF", "GUESS", "READ")
# Set the next charge/mult
molecule.set_molecular_charge(charge)
molecule.set_multiplicity(mult)
# Determine H**O
print('DGAS: What ref should this point to?')
#ref = core.legacy_wavefunction()
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
stats.append(""" %6d %6d %6d %6d %6d %6d\n""" %
(Nintegral - 1, Na, Nb, charge, mult, H**O))
if H**O > 0:
Na -= 1
else:
Nb -= 1
charge += 1
mult = Na - Nb + 1
core.set_local_option("SCF", "DF_INTS_IO", "NONE")
# => Print the results out <= #
E = {}
core.print_out("""\n ==> Fractional Occupation Nuke Results <==\n\n""")
core.print_out(""" %-11s %-24s %-24s %11s\n""" %
('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(Ns)):
core.print_out(""" %11.3E %24.16E %24.16E %11d\n""" %
(Ns[k], energies[k], potentials[k], convs[k]))
E[Ns[k]] = energies[k]
core.print_out('\n')
core.print_out(""" %6s %6s %6s %6s %6s %6s\n""" %
('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
for line in stats:
core.print_out(line)
core.print_out(
'\n "You shoot a nuke down a bug hole, you got a lot of dead bugs"\n'
)
core.print_out(' -Starship Troopers\n')
# Drop the files out
with open(traverse_filename, 'w') as fh:
fh.write(""" %-11s %-24s %-24s %11s\n""" %
('N', 'Energy', 'H**O Energy', 'Converged'))
for k in range(len(Ns)):
fh.write(""" %11.3E %24.16E %24.16E %11d\n""" %
(Ns[k], energies[k], potentials[k], convs[k]))
with open(stats_filename, 'w') as fh:
fh.write(""" %6s %6s %6s %6s %6s %6s\n""" %
('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
for line in stats:
fh.write(line)
optstash.restore()
return E
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 ip_fitting(name,
omega_l=0.05,
omega_r=2.5,
omega_convergence=1.0e-3,
maxiter=20,
**kwargs):
"""Optimize DFT omega parameter for molecular system.
Parameters
----------
name : string or function
DFT functional string name or function defining functional
whose omega is to be optimized.
omega_l : float, optional
Minimum omega to be considered during fitting.
omega_r : float, optional
Maximum omega to be considered during fitting.
molecule : :ref:`molecule <op_py_molecule>`, optional
Target molecule (neutral) for which omega is to be tuned, if not last defined.
omega_convergence : float, optional
Threshold below which to consider omega converged. (formerly omega_tolerance)
maxiter : int, optional
Maximum number of iterations towards omega convergence.
Returns
-------
float
Optimal omega parameter.
"""
optstash = p4util.OptionsState(['SCF', 'REFERENCE'], ['SCF', 'GUESS'],
['SCF', 'DF_INTS_IO'], ['SCF', 'DFT_OMEGA'],
['DOCC'], ['SOCC'])
kwargs = p4util.kwargs_lower(kwargs)
# By default, do not read previous 180 orbitals file
read = False
read180 = ''
if 'read' in kwargs:
read = True
read180 = kwargs['read']
if core.get_option('SCF', 'REFERENCE') != 'UKS':
core.print_out(
""" Requested procedure `ip_fitting` runs further calculations with UKS reference.\n"""
)
core.set_local_option('SCF', 'REFERENCE', 'UKS')
# Make sure the molecule the user provided is the active one, and neutral
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
if molecule.molecular_charge() != 0:
raise ValidationError(
"""IP Fitting requires neutral molecule to start.""")
if molecule.schoenflies_symbol() != 'c1':
core.print_out(
""" Requested procedure `ip_fitting` does not make use of molecular symmetry: """
"""further calculations in C1 point group.\n""")
molecule = molecule.clone()
molecule.reset_point_group('c1')
molecule.update_geometry()
charge0 = molecule.molecular_charge()
mult0 = molecule.multiplicity()
# How many electrons are there?
N = 0
for A in range(molecule.natom()):
N += molecule.Z(A)
N -= charge0
N = int(N)
Nb = int((N - mult0 + 1) / 2)
Na = int(N - Nb)
# Work in the ot namespace for this procedure
core.IO.set_default_namespace("ot")
# Burn in to determine orbital eigenvalues
if read:
core.set_local_option("SCF", "GUESS", "READ")
copy_file_to_scratch(read180, 'psi', 'ot', 180)
core.set_local_option("SCF", "DF_INTS_IO", "SAVE")
E, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
banner='IP Fitting SCF: Burn-in',
**kwargs)
core.set_local_option("SCF", "DF_INTS_IO", "LOAD")
if not wfn.functional().is_x_lrc():
raise ValidationError(
"""Not sensible to optimize omega for non-long-range-correction functional."""
)
# Determine H**O, to determine mult1
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Na == Nb:
H**O = -Nb
elif Nb == 0:
H**O = Na
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
if E_a >= E_b:
H**O = Na
else:
H**O = -Nb
Na1 = Na
Nb1 = Nb
if H**O > 0:
Na1 -= 1
else:
Nb1 -= 1
charge1 = charge0 + 1
mult1 = Na1 - Nb1 + 1
omegas = []
E0s = []
E1s = []
kIPs = []
IPs = []
types = []
# Right endpoint
core.set_local_option('SCF', 'DFT_OMEGA', omega_r)
# Neutral
if read:
core.set_local_option("SCF", "GUESS", "READ")
p4util.copy_file_to_scratch(read180, 'psi', 'ot', 180)
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
E0r, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
banner='IP Fitting SCF: Neutral, Right Endpoint',
**kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
E_HOMOr = E_HOMO
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
if read:
core.set_local_option("SCF", "GUESS", "READ")
p4util.copy_file_to_scratch(read180, 'psi', 'ot', 180)
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
E1r = driver.energy('scf',
dft_functional=name,
molecule=molecule,
banner='IP Fitting SCF: Cation, Right Endpoint',
**kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IPr = E1r - E0r
kIPr = -E_HOMOr
delta_r = IPr - kIPr
if IPr > kIPr:
raise ValidationError(
"""\n***IP Fitting Error: Right Omega limit should have kIP > IP: {} !> {}"""
.format(kIPr, IPr))
omegas.append(omega_r)
types.append('Right Limit')
E0s.append(E0r)
E1s.append(E1r)
IPs.append(IPr)
kIPs.append(kIPr)
# Use previous orbitals from here out
core.set_local_option("SCF", "GUESS", "READ")
# Left endpoint
core.set_local_option('SCF', 'DFT_OMEGA', omega_l)
# Neutral
core.IO.change_file_namespace(180, "neutral", "ot")
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
core.set_global_option("DOCC", [Nb])
core.set_global_option("SOCC", [Na - Nb])
E0l, wfn = driver.energy('scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
banner='IP Fitting SCF: Neutral, Left Endpoint',
**kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
E_HOMOl = E_HOMO
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
core.IO.change_file_namespace(180, "cation", "ot")
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
core.set_global_option("DOCC", [Nb1])
core.set_global_option("SOCC", [Na1 - Nb1])
E1l = driver.energy('scf',
dft_functional=name,
molecule=molecule,
banner='IP Fitting SCF: Cation, Left Endpoint',
**kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IPl = E1l - E0l
kIPl = -E_HOMOl
delta_l = IPl - kIPl
if IPl < kIPl:
raise ValidationError(
"""\n***IP Fitting Error: Left Omega limit should have kIP < IP: {} !< {}"""
.format(kIPl, IPl))
omegas.append(omega_l)
types.append('Left Limit')
E0s.append(E0l)
E1s.append(E1l)
IPs.append(IPl)
kIPs.append(kIPl)
converged = False
repeat_l = 0
repeat_r = 0
for step in range(maxiter):
# Regula Falsi (modified)
if repeat_l > 1:
delta_l /= 2.0
if repeat_r > 1:
delta_r /= 2.0
omega = -(omega_r - omega_l) / (delta_r - delta_l) * delta_l + omega_l
core.set_local_option('SCF', 'DFT_OMEGA', omega)
# Neutral
core.IO.change_file_namespace(180, "neutral", "ot")
molecule.set_molecular_charge(charge0)
molecule.set_multiplicity(mult0)
core.set_global_option("DOCC", [Nb])
core.set_global_option("SOCC", [Na - Nb])
E0, wfn = driver.energy(
'scf',
dft_functional=name,
return_wfn=True,
molecule=molecule,
banner='IP Fitting SCF: Neutral, Omega = {:11.3E}'.format(omega),
**kwargs)
eps_a = wfn.epsilon_a()
eps_b = wfn.epsilon_b()
if Nb == 0:
E_HOMO = eps_a.np[int(Na - 1)]
else:
E_a = eps_a.np[int(Na - 1)]
E_b = eps_b.np[int(Nb - 1)]
E_HOMO = max(E_a, E_b)
core.IO.change_file_namespace(180, "ot", "neutral")
# Cation
core.IO.change_file_namespace(180, "cation", "ot")
molecule.set_molecular_charge(charge1)
molecule.set_multiplicity(mult1)
core.set_global_option("DOCC", [Nb1])
core.set_global_option("SOCC", [Na1 - Nb1])
E1 = driver.energy(
'scf',
dft_functional=name,
molecule=molecule,
banner='IP Fitting SCF: Cation, Omega = {:11.3E}'.format(omega),
**kwargs)
core.IO.change_file_namespace(180, "ot", "cation")
IP = E1 - E0
kIP = -E_HOMO
delta = IP - kIP
if kIP > IP:
omega_r = omega
E0r = E0
E1r = E1
IPr = IP
kIPr = kIP
delta_r = delta
repeat_r = 0
repeat_l += 1
else:
omega_l = omega
E0l = E0
E1l = E1
IPl = IP
kIPl = kIP
delta_l = delta
repeat_l = 0
repeat_r += 1
omegas.append(omega)
types.append('Regula-Falsi')
E0s.append(E0)
E1s.append(E1)
IPs.append(IP)
kIPs.append(kIP)
# Termination
if abs(omega_l - omega_r) < omega_convergence:
converged = True
break
core.IO.set_default_namespace("")
core.print_out("""\n ==> IP Fitting Results <==\n\n""")
core.print_out(""" => Occupation Determination <= \n\n""")
core.print_out(""" %6s %6s %6s %6s %6s %6s\n""" %
('N', 'Na', 'Nb', 'Charge', 'Mult', 'H**O'))
core.print_out(""" Neutral: %6d %6d %6d %6d %6d %6d\n""" %
(N, Na, Nb, charge0, mult0, H**O))
core.print_out(""" Cation: %6d %6d %6d %6d %6d\n\n""" %
(N - 1, Na1, Nb1, charge1, mult1))
core.print_out(""" => Regula Falsi Iterations <=\n\n""")
core.print_out(""" %3s %11s %14s %14s %14s %s\n""" %
('N', 'Omega', 'IP', 'kIP', 'Delta', 'Type'))
for k in range(len(omegas)):
core.print_out(
""" %3d %11.3E %14.6E %14.6E %14.6E %s\n""" %
(k + 1, omegas[k], IPs[k], kIPs[k], IPs[k] - kIPs[k], types[k]))
optstash.restore()
if converged:
core.print_out("""\n IP Fitting Converged\n""")
core.print_out(""" Final omega = %14.6E\n""" %
((omega_l + omega_r) / 2))
core.print_out(
"""\n "M,I. does the dying. Fleet just does the flying."\n""")
core.print_out(""" -Starship Troopers\n""")
else:
raise ConvergenceError("""IP Fitting """, step + 1)
return ((omega_l + omega_r) / 2)
def 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*, :ref:`Wavefunction<sec:psimod_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 :ref:`Wavefunction<sec:psimod_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)
print("\n ==> N-Body: Now computing %d-body complexes <==\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.cdict["nbody_energy"] = energies_dict
wfn.cdict["nbody_ptype"] = ptype_dict
wfn.cdict["nbody_body_energy"] = energy_body_dict
wfn.cdict["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_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 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 anharmonicity(rvals: List, energies: List, plot_fit: str = '', mol = None) -> Dict:
"""Generates spectroscopic constants for a diatomic molecules.
Fits a diatomic potential energy curve using a weighted least squares approach
(c.f. https://doi.org/10.1063/1.4862157, particularly eqn. 7), locates the minimum
energy point, and then applies second order vibrational perturbation theory to obtain spectroscopic
constants. Any number of points greater than 4 may be provided, and they should bracket the minimum.
The data need not be evenly spaced, and can be provided in any order. The data are weighted such that
those closest to the minimum have highest impact.
A dictionary with the following keys, which correspond to spectroscopic constants, is returned:
:param rvals: The bond lengths (in Angstrom) for which energies are
provided, of length at least 5 and equal to the length of the energies array
:param energies: The energies (Eh) computed at the bond lengths in the rvals list
:param plot_fit: A string describing where to save a plot of the harmonic and anharmonic fits, the
inputted data points, re, r0 and the first few energy levels, if matplotlib
is available. Set to 'screen' to generate an interactive plot on the screen instead. If a filename is
provided, the image type is determined by the extension; see matplotlib for supported file types.
:returns: (*dict*) Keys: "re", "r0", "we", "wexe", "nu", "ZPVE(harmonic)", "ZPVE(anharmonic)", "Be", "B0", "ae", "De"
corresponding to the spectroscopic constants in cm-1
"""
angstrom_to_bohr = 1.0 / constants.bohr2angstroms
angstrom_to_meter = 10e-10
# Make sure the input is valid
if len(rvals) != len(energies):
raise ValidationError("The number of energies must match the number of distances")
npoints = len(rvals)
if npoints < 5:
raise ValidationError("At least 5 data points must be provided to compute anharmonicity")
core.print_out("\n\nPerforming a fit to %d data points\n" % npoints)
# Sort radii and values first from lowest to highest radius
indices = np.argsort(rvals)
rvals = np.array(rvals)[indices]
energies = np.array(energies)[indices]
# Make sure the molecule the user provided is the active one
molecule = mol or core.get_active_molecule()
molecule.update_geometry()
natoms = molecule.natom()
if natoms != 2:
raise Exception("The current molecule must be a diatomic for this code to work!")
m1 = molecule.mass(0)
m2 = molecule.mass(1)
# Find rval of the minimum of energies, check number of points left and right
min_index = np.argmin(energies)
if min_index < 3 :
core.print_out("\nWarning: fewer than 3 points provided with a r < r(min(E))!\n")
if min_index >= len(energies) - 3:
core.print_out("\nWarning: fewer than 3 points provided with a r > r(min(E))!\n")
# Optimize the geometry, refitting the surface around each new geometry
core.print_out("\nOptimizing geometry based on current surface:\n\n")
re = rvals[min_index]
maxit = 30
thres = 1.0e-9
for i in range(maxit):
derivs = least_squares_fit_polynomial(rvals,energies,localization_point=re)
e,g,H = derivs[0:3]
core.print_out(" E = %20.14f, x = %14.7f, grad = %20.14f\n" % (e, re, g))
if abs(g) < thres:
break
re -= g/H
if i == maxit-1:
raise ConvergenceError("diatomic geometry optimization", maxit)
core.print_out(" Final E = %20.14f, x = %14.7f, grad = %20.14f\n" % (e, re, g))
if re < min(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a lower range of r values.")
if re > max(rvals):
raise Exception("Minimum energy point is outside range of points provided. Use a higher range of r values.")
# Convert to convenient units, and compute spectroscopic constants
d0,d1,d2,d3,d4 = derivs*constants.hartree2aJ
core.print_out("\nEquilibrium Energy %20.14f Hartrees\n" % e)
core.print_out("Gradient %20.14f\n" % g)
core.print_out("Quadratic Force Constant %14.7f MDYNE/A\n" % d2)
core.print_out("Cubic Force Constant %14.7f MDYNE/A**2\n" % d3)
core.print_out("Quartic Force Constant %14.7f MDYNE/A**3\n" % d4)
hbar = constants.h / (2.0 * np.pi)
mu = ((m1*m2)/(m1+m2))*constants.amu2kg
we = 5.3088375e-11 * np.sqrt(d2/mu)
wexe = (1.2415491e-6)*(we/d2)**2 * ((5.0*d3*d3)/(3.0*d2)-d4)
# Rotational constant: Be
I = ((m1*m2)/(m1+m2)) * constants.amu2kg * (re * angstrom_to_meter)**2
B = constants.h / (8.0 * np.pi**2 * constants.c * I)
# alpha_e and quartic centrifugal distortion constant
ae = -(6.0 * B**2 / we) * ((1.05052209e-3*we*d3)/(np.sqrt(B * d2**3))+1.0)
de = 4.0*B**3 / we**2
# B0 and r0 (plus re check using Be)
B0 = B - ae / 2.0
r0 = np.sqrt(constants.h / (8.0 * np.pi**2 * mu * constants.c * B0))
recheck = np.sqrt(constants.h / (8.0 * np.pi**2 * mu * constants.c * B))
r0 /= angstrom_to_meter
recheck /= angstrom_to_meter
# Fundamental frequency nu
nu = we - 2.0 * wexe
zpve_nu = 0.5 * we - 0.25 * wexe
zpve_we = 0.5 * we
# Generate pretty pictures, if requested
if(plot_fit):
try:
import matplotlib.pyplot as plt
except ImportError:
msg = "\n\tPlot not generated; matplotlib is not installed on this machine.\n\n"
print(msg)
core.print_out(msg)
# Correct the derivatives for the missing factorial prefactors
dvals = np.zeros(5)
dvals[0:5] = derivs[0:5]
dvals[2] /= 2
dvals[3] /= 6
dvals[4] /= 24
# Default plot range, before considering energy levels
minE = np.min(energies)
maxE = np.max(energies)
minR = np.min(rvals)
maxR = np.max(rvals)
# Plot vibrational energy levels
we_au = we / constants.hartree2wavenumbers
wexe_au = wexe / constants.hartree2wavenumbers
coefs2 = [ dvals[2], dvals[1], dvals[0] ]
coefs4 = [ dvals[4], dvals[3], dvals[2], dvals[1], dvals[0] ]
for n in range(3):
Eharm = we_au*(n+0.5)
Evpt2 = Eharm - wexe_au*(n+0.5)**2
coefs2[-1] = -Eharm
coefs4[-1] = -Evpt2
roots2 = np.roots(coefs2)
roots4 = np.roots(coefs4)
xvals2 = roots2 + re
xvals4 = np.choose(np.where(np.isreal(roots4)), roots4)[0].real + re
Eharm += dvals[0]
Evpt2 += dvals[0]
plt.plot(xvals2, [Eharm, Eharm], 'b', linewidth=1)
plt.plot(xvals4, [Evpt2, Evpt2], 'g', linewidth=1)
maxE = Eharm
maxR = np.max([xvals2,xvals4])
minR = np.min([xvals2,xvals4])
# Find ranges for the plot
dE = maxE - minE
minE -= 0.2*dE
maxE += 0.4*dE
dR = maxR - minR
minR -= 0.2*dR
maxR += 0.2*dR
# Generate the fitted PES
xpts = np.linspace(minR, maxR, 1000)
xrel = xpts - re
xpows = xrel[:, None] ** range(5)
fit2 = np.einsum('xd,d', xpows[:,0:3], dvals[0:3])
fit4 = np.einsum('xd,d', xpows, dvals)
# Make / display the plot
plt.plot(xpts, fit2, 'b', linewidth=2.5, label='Harmonic (quadratic) fit')
plt.plot(xpts, fit4, 'g', linewidth=2.5, label='Anharmonic (quartic) fit')
plt.plot([re, re], [minE, maxE], 'b--', linewidth=0.5)
plt.plot([r0, r0], [minE, maxE], 'g--', linewidth=0.5)
plt.scatter(rvals, energies, c='Black', linewidth=3, label='Input Data')
plt.legend()
plt.xlabel('Bond length (Angstroms)')
plt.ylabel('Energy (Eh)')
plt.xlim(minR, maxR)
plt.ylim(minE, maxE)
if plot_fit == 'screen':
plt.show()
else:
plt.savefig(plot_fit)
core.print_out("\n\tPES fit saved to %s.\n\n" % plot_fit)
core.print_out("\nre = %10.6f A check: %10.6f\n" % (re, recheck))
core.print_out("r0 = %10.6f A\n" % r0)
core.print_out("E at re = %17.10f Eh\n" % e)
core.print_out("we = %10.4f cm-1\n" % we)
core.print_out("wexe = %10.4f cm-1\n" % wexe)
core.print_out("nu = %10.4f cm-1\n" % nu)
core.print_out("ZPVE(we) = %10.4f cm-1\n" % zpve_we)
core.print_out("ZPVE(nu) = %10.4f cm-1\n" % zpve_nu)
core.print_out("Be = %10.4f cm-1\n" % B)
core.print_out("B0 = %10.4f cm-1\n" % B0)
core.print_out("ae = %10.4f cm-1\n" % ae)
core.print_out("De = %10.7f cm-1\n" % de)
results = {
"re" : re,
"r0" : r0,
"we" : we,
"wexe" : wexe,
"nu" : nu,
"E(re)" : e,
"ZPVE(harmonic)" : zpve_we,
"ZPVE(anharmonic)" : zpve_nu,
"Be" : B,
"B0" : B0,
"ae" : ae,
"De" : de
}
return results
def auto_fragments(**kwargs):
r"""Detects fragments if the user does not supply them.
Currently only used for the WebMO implementation of SAPT.
:returns: :py:class:`~psi4.core.Molecule`) |w--w| fragmented molecule.
:type molecule: :ref:`molecule <op_py_molecule>`
:param molecule: ``h2o`` || etc.
The target molecule, if not the last molecule defined.
:examples:
>>> # [1] replicates with cbs() the simple model chemistry scf/cc-pVDZ: set basis cc-pVDZ energy('scf')
>>> molecule mol {\nH 0.0 0.0 0.0\nH 2.0 0.0 0.0\nF 0.0 1.0 0.0\nF 2.0 1.0 0.0\n}
>>> print mol.nfragments() # 1
>>> fragmol = auto_fragments()
>>> print fragmol.nfragments() # 2
"""
# Make sure the molecule the user provided is the active one
molecule = kwargs.pop('molecule', core.get_active_molecule())
molecule.update_geometry()
molname = molecule.name()
geom = molecule.save_string_xyz()
numatoms = molecule.natom()
VdW = [1.2, 1.7, 1.5, 1.55, 1.52, 1.9, 1.85, 1.8]
symbol = list(range(numatoms))
X = [0.0] * numatoms
Y = [0.0] * numatoms
Z = [0.0] * numatoms
Queue = []
White = []
Black = []
F = geom.split('\n')
for f in range(numatoms):
A = F[f + 1].split()
symbol[f] = A[0]
X[f] = float(A[1])
Y[f] = float(A[2])
Z[f] = float(A[3])
White.append(f)
Fragment = [[] for i in range(numatoms)] # stores fragments
start = 0 # starts with the first atom in the list
Queue.append(start)
White.remove(start)
frag = 0
while ((len(White) > 0)
or (len(Queue) > 0)): # Iterates to the next fragment
while (len(Queue) > 0): # BFS within a fragment
for u in Queue: # find all nearest Neighbors
# (still coloured white) to vertex u
for i in White:
Distance = math.sqrt((X[i] - X[u]) * (X[i] - X[u]) +
(Y[i] - Y[u]) * (Y[i] - Y[u]) +
(Z[i] - Z[u]) * (Z[i] - Z[u]))
if Distance < _autofragment_convert(
u, symbol) + _autofragment_convert(i, symbol):
Queue.append(i) # if you find you, put it in the que
White.remove(
i) # and remove it from the untouched list
Queue.remove(u) # remove focus from Queue
Black.append(u)
Fragment[frag].append(int(u +
1)) # add to group (adding 1 to start
# list at one instead of zero)
if (len(White) != 0): # cant move White->Queue if no more exist
Queue.append(White[0])
White.remove(White[0])
frag += 1
new_geom = """\n"""
for i in Fragment[0]:
new_geom = new_geom + F[i].lstrip() + """\n"""
new_geom = new_geom + """--\n"""
for j in Fragment[1]:
new_geom = new_geom + F[j].lstrip() + """\n"""
new_geom = new_geom + """units angstrom\n"""
moleculenew = core.Molecule.create_molecule_from_string(new_geom)
moleculenew.set_name(molname)
moleculenew.update_geometry()
moleculenew.print_cluster()
core.print_out(""" Exiting auto_fragments\n""")
return moleculenew
