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import collections
from typing import Dict, List, Union
import numpy as np
import qcengine as qcng
from qcelemental.models import AtomicInput
from psi4 import core
from .. import p4util
from ..p4util.exceptions import ValidationError
_engine_can_do = collections.OrderedDict([
# engine order establishes default for each disp
("libdisp", ["d1", "d2", "chg", "das2009", "das2010",]),
("s-dftd3", [ "d3zero2b", "d3bj2b", "d3mzero2b", "d3mbj2b", "d3zeroatm", "d3bjatm", "d3mzeroatm", "d3mbjatm", ]),
("dftd3", [ "d2", "d3zero2b", "d3bj2b", "d3mzero2b", "d3mbj2b", ]),
("nl", [ "nl", ]),
("mp2d", [ "dmp2", ]),
("dftd4", [ "d4bjeeqatm", ]),
("mctc-gcp", [ "3c", ]),
("gcp", [ "3c", ]),
]) # yapf: disable
def _capable_engines_for_disp()-> Dict[str, List[str]]:
"""Invert _engine_can_do dictionary and check program detection.
Returns a dictionary with keys all dispersion levels and values a list of all
capable engines, where the engine in the first element is available, if any are.
"""
try:
from qcengine.testing import _programs as _programs_qcng
except ModuleNotFoundError:
# _programs_qcng is up-to-date with current harnesses but it requires pytest present, so let's provide a workaround
from qcelemental.util import which, which_import
_programs_qcng = {
"dftd3": which("dftd3", return_bool=True),
"dftd4": which_import("dftd4", return_bool=True),
"s-dftd3": which_import("dftd3", return_bool=True),
"mctc-gcp": which("mctc-gcp", return_bool=True),
"gcp": which("gcp", return_bool=True),
"mp2d": which("mp2d", return_bool=True),
}
programs_disp = {k: v for k, v in _programs_qcng.items() if k in _engine_can_do}
programs_disp["libdisp"] = True
programs_disp["nl"] = True
capable = collections.defaultdict(list)
capable_sorted_by_available = collections.defaultdict(list)
for eng, disps in _engine_can_do.items():
for disp in disps:
capable[disp].append(eng)
for disp, engines in capable.items():
capable_sorted_by_available[disp] = sorted(engines, key=lambda x: (not programs_disp[x], x))
return capable_sorted_by_available
[docs]
class EmpiricalDispersion():
"""Lightweight unification of empirical dispersion calculation modes.
Attributes
----------
dashlevel : str
{"d1", "d2", "chg", "das2009", "das2010", "nl", "dmp2", "d3zero2b", "d3bj2b", "d3mzero2b", "d3mbj2b", "d3zeroatm", "d3bjatm", "d3mzeroatm", "d3mbjatm", "d4bjeeqatm"}
Name of dispersion correction to be applied. Resolved
from `name_hint` and/or `level_hint` into a key of
`empirical_dispersion_resources.dashcoeff`.
dashparams : dict
Complete set of parameter values defining the flexible parts
of :py:attr:`dashlevel`. Number and parameter names vary by
:py:attr:`dashlevel`. Resolved into a complete set (keys of
dashcoeff[dashlevel]['default']) from `name_hint` and/or
`dashcoeff_supplement` and/or user `param_tweaks`.
fctldash : str
If :py:attr:`dashparams` for :py:attr:`dashlevel` corresponds to a defined,
named, untweaked "functional-dashlevel" set, then that
functional. Otherwise, empty string.
description : str
Tagline for dispersion :py:attr:`dashlevel`.
dashlevel_citation : str
Literature reference for dispersion :py:attr:`dashlevel` in general,
*not necessarily* for :py:attr:`dashparams`.
dashparams_citation : str
Literature reference for dispersion parameters, if :py:attr:`dashparams`
corresponds to a defined, named, untweaked "functional-dashlevel"
set with a citation. Otherwise, empty string.
dashcoeff_supplement : dict
See description in `qcengine.programs.empirical_dispersion_resources.from_arrays`. Used
here to "bless" the dispersion definitions attached to
the procedures/dft/<rung>_functionals-defined dictionaries
as legit, non-custom, and of equal validity to
`qcengine.programs.empirical_dispersion_resources.dashcoeff` itself for purposes of
validating :py:attr:`fctldash`.
engine : str
{'libdisp', "s-dftd3", 'dftd3', 'nl', 'mp2d', "dftd4"}
Compute engine for dispersion. One of Psi4's internal libdisp
library, external Grimme or Beran projects, or nl.
disp : Dispersion
Only present for :py:attr:`engine` `=libdisp`. Psi4 class instance prepared
to compute dispersion.
ordered_params : list
Fixed-order list of relevant parameters for :py:attr:`dashlevel`. Matches
:rst:psivar:`DISPERSION CORRECTION ENERGY` ordering. Used for printing.
Parameters
----------
name_hint
Name of functional (func only, func & disp, or disp only) for
which to compute dispersion (e.g., blyp, BLYP-D2, blyp-d3bj,
blyp-d3(bj), hf+d). Any or all parameters initialized from
``dashcoeff[dashlevel][functional-without-dashlevel]`` or
``dashcoeff_supplement[dashlevel][functional-with-dashlevel]``
can be overwritten via `param_tweaks`.
level_hint
Name of dispersion correction to be applied (e.g., d, D2,
d3(bj), das2010). Must be key in `dashcoeff` or "alias" or
"formal" to one.
param_tweaks
Values for the same keys as `dashcoeff[dashlevel]['default']`
(and same order if list) used to override any or all values
initialized by `name_hint`. Extra parameters will error.
engine
Override which code computes dispersion. See above for allowed
values. Formerly (pre Nov 2022) only relevant for -D2, which can be computed by
libdisp or dftd3. Now (post Nov 2022) also relevant for -D3 variants,
which can be computed by dftd3 executable or simple-dftd3 Python module.
gcp_engine
Override which code computes the gcp correction. Now can use
classic gcp or mctc-gcp executables.
save_pairwise_disp
Whether to request atomic pairwise analysis.
"""
def __init__(self, *, name_hint: str = None, level_hint: str = None, param_tweaks: Union[Dict, List] = None, engine: str = None, gcp_engine: str = None, save_pairwise_disp: bool = False):
from .dft import dashcoeff_supplement
self.dashcoeff_supplement = dashcoeff_supplement
self.save_pairwise_disp = save_pairwise_disp
resolved = qcng.programs.empirical_dispersion_resources.from_arrays(
name_hint=name_hint,
level_hint=level_hint,
param_tweaks=param_tweaks,
dashcoeff_supplement=self.dashcoeff_supplement)
self.fctldash = resolved['fctldash']
self.dashlevel = resolved['dashlevel']
self.dashparams = resolved['dashparams']
self.description = qcng.programs.empirical_dispersion_resources.dashcoeff[self.dashlevel]['description']
self.ordered_params = qcng.programs.empirical_dispersion_resources.dashcoeff[self.dashlevel]['default'].keys()
self.dashlevel_citation = qcng.programs.empirical_dispersion_resources.dashcoeff[self.dashlevel]['citation']
self.dashparams_citation = resolved['dashparams_citation']
capable_engines_for_disp = _capable_engines_for_disp()
if engine is None:
self.engine = capable_engines_for_disp[self.dashlevel][0]
else:
if self.dashlevel in _engine_can_do[engine]:
self.engine = engine
else:
raise ValidationError(f"This little engine ({engine}) can't ({self.dashlevel})")
if self.engine == 'libdisp':
self.disp = core.Dispersion.build(self.dashlevel, **resolved['dashparams'])
if gcp_engine is None:
self.gcp_engine = capable_engines_for_disp["3c"][0]
else:
if "3c" in _engine_can_do[gcp_engine]:
self.gcp_engine = gcp_engine
else:
raise ValidationError(f"This little engine ({engine}) can't (3c)")
[docs]
def print_out(self):
"""Format dispersion parameters of `self` for output file."""
text = []
text.append(" => {}: Empirical Dispersion <=".format(
(self.fctldash.upper() if self.fctldash.upper() else 'Custom')))
text.append('')
text.append(self.description)
text.append(self.dashlevel_citation.rstrip())
if self.dashparams_citation:
text.append(" Parametrisation from:{}".format(self.dashparams_citation.rstrip()))
text.append('')
for op in self.ordered_params:
text.append(" %6s = %14.6f" % (op, self.dashparams[op]))
text.append('\n')
core.print_out('\n'.join(text))
[docs]
def compute_energy(self, molecule: core.Molecule, wfn: core.Wavefunction = None) -> float:
"""Compute dispersion energy based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
float
Dispersion energy [Eh].
Notes
-----
:psivar:`DISPERSION CORRECTION ENERGY`
Disp always set. Overridden in SCF finalization, but that only changes for "-3C" methods.
:psivar:`fctl DISPERSION CORRECTION ENERGY`
Set if :py:attr:`fctldash` nonempty.
"""
if self.engine in ["s-dftd3", 'dftd3', 'mp2d', "dftd4"]:
resi = AtomicInput(
**{
'driver': 'energy',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'pair_resolved': self.save_pairwise_disp,
'apply_qcengine_aliases': True, # for s-dftd3
'verbose': 1,
},
'molecule': molecule.to_schema(dtype=2),
'provenance': p4util.provenance_stamp(__name__),
})
jobrec = qcng.compute(
resi,
self.engine,
raise_error=True,
task_config={"scratch_directory": core.IOManager.shared_object().get_default_path(), "ncores": core.get_num_threads()})
dashd_part = float(jobrec.extras['qcvars']['DISPERSION CORRECTION ENERGY'])
if wfn is not None:
for k, qca in jobrec.extras['qcvars'].items():
if ("CURRENT" not in k) and ("PAIRWISE" not in k):
wfn.set_variable(k, float(qca) if isinstance(qca, str) else qca)
# Pass along the pairwise dispersion decomposition if we need it
if self.save_pairwise_disp is True:
wfn.set_variable("PAIRWISE DISPERSION CORRECTION ANALYSIS",
jobrec.extras['qcvars']["2-BODY PAIRWISE DISPERSION CORRECTION ANALYSIS"])
if self.fctldash in ['hf3c', 'pbeh3c']:
jobrec = qcng.compute(
resi,
self.gcp_engine,
raise_error=True,
task_config={"scratch_directory": core.IOManager.shared_object().get_default_path(), "ncores": core.get_num_threads()})
gcp_part = jobrec.return_result
dashd_part += gcp_part
return dashd_part
else:
ene = self.disp.compute_energy(molecule)
core.set_variable('DISPERSION CORRECTION ENERGY', ene)
if self.fctldash:
core.set_variable(f"{self.fctldash} DISPERSION CORRECTION ENERGY", ene)
return ene
[docs]
def compute_gradient(self,
molecule: core.Molecule,
wfn: core.Wavefunction = None) -> core.Matrix:
"""Compute dispersion gradient based on engine, dispersion level, and parameters in `self`.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
Matrix
(nat, 3) dispersion gradient [Eh/a0].
"""
if self.engine in ["s-dftd3", 'dftd3', 'mp2d', "dftd4"]:
resi = AtomicInput(
**{
'driver': 'gradient',
'model': {
'method': self.fctldash,
'basis': '(auto)',
},
'keywords': {
'level_hint': self.dashlevel,
'params_tweaks': self.dashparams,
'dashcoeff_supplement': self.dashcoeff_supplement,
'apply_qcengine_aliases': True, # for s-dftd3
'verbose': 1,
},
'molecule': molecule.to_schema(dtype=2),
'provenance': p4util.provenance_stamp(__name__),
})
jobrec = qcng.compute(
resi,
self.engine,
raise_error=True,
task_config={"scratch_directory": core.IOManager.shared_object().get_default_path(), "ncores": core.get_num_threads()})
dashd_part = core.Matrix.from_array(jobrec.extras['qcvars']['DISPERSION CORRECTION GRADIENT'])
if wfn is not None:
for k, qca in jobrec.extras['qcvars'].items():
if "CURRENT" not in k:
wfn.set_variable(k, float(qca) if isinstance(qca, str) else qca)
if self.fctldash in ['hf3c', 'pbeh3c']:
jobrec = qcng.compute(
resi,
self.gcp_engine,
raise_error=True,
task_config={"scratch_directory": core.IOManager.shared_object().get_default_path(), "ncores": core.get_num_threads()})
gcp_part = core.Matrix.from_array(jobrec.return_result)
dashd_part.add(gcp_part)
return dashd_part
else:
return self.disp.compute_gradient(molecule)
[docs]
def compute_hessian(self,
molecule: core.Molecule,
wfn: core.Wavefunction = None) -> core.Matrix:
"""Compute dispersion Hessian based on engine, dispersion level, and parameters in `self`.
Uses finite difference, as no dispersion engine has analytic second derivatives.
Parameters
----------
molecule
System for which to compute empirical dispersion correction.
wfn
Location to set QCVariables
Returns
-------
Matrix
(3*nat, 3*nat) dispersion Hessian [Eh/a0/a0].
"""
from psi4.driver.driver_findif import assemble_hessian_from_gradients, hessian_from_gradients_geometries
optstash = p4util.OptionsState(['PRINT'], ['PARENT_SYMMETRY'])
core.set_global_option('PRINT', 0)
core.print_out("\n\n Analytical Dispersion Hessians are not supported by any engine.\n")
core.print_out(" Computing the Hessian through finite difference of gradients.\n\n")
# Setup the molecule
molclone = molecule.clone()
molclone.reinterpret_coordentry(False)
molclone.fix_orientation(True)
molclone.fix_com(True)
# Record undisplaced symmetry for projection of diplaced point groups
core.set_global_option("PARENT_SYMMETRY", molecule.schoenflies_symbol())
findif_meta_dict = hessian_from_gradients_geometries(molclone, -1)
for displacement in findif_meta_dict["displacements"].values():
geom_array = np.reshape(displacement["geometry"], (-1, 3))
molclone.set_geometry(core.Matrix.from_array(geom_array))
molclone.update_geometry()
displacement["gradient"] = self.compute_gradient(molclone).np.ravel().tolist()
H = assemble_hessian_from_gradients(findif_meta_dict, -1)
if wfn is not None:
wfn.set_variable('DISPERSION CORRECTION HESSIAN', H)
optstash.restore()
return core.Matrix.from_array(H)
