# This file is part of dftd4.
# SPDX-Identifier: LGPL-3.0-or-later
#
# dftd4 is free software: you can redistribute it and/or modify it under
# the terms of the Lesser GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# dftd4 is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# Lesser GNU General Public License for more details.
#
# You should have received a copy of the Lesser GNU General Public License
# along with dftd4. If not, see <https://www.gnu.org/licenses/>.
"""
QCSchema Support
----------------
Integration with the `QCArchive infrastructure <http://docs.qcarchive.molssi.org>`_.
This module provides a way to translate QCSchema or QCElemental Atomic Input
into a format understandable by the ``dftd4`` API which in turn provides the
calculation results in a QCSchema compatible format.
Supported keywords are
======================== =========== ============================================
Keyword Default Description
======================== =========== ============================================
level_hint None Dispersion correction level ("d4" or "d4s")
params_tweaks None Optional dict with the damping parameters
pair_resolved False Enable pairwise resolved dispersion energy
property False Evaluate dispersion related properties
======================== =========== ============================================
The params_tweaks dict contains the damping parameters, at least s8, a1 and a2
must be provided
======================== =========== ============================================
Tweakable parameter Default Description
======================== =========== ============================================
s6 1.0 Scaling of the dipole-dipole dispersion
s8 None Scaling of the dipole-quadrupole dispersion
s9 1.0 Scaling of the three-body dispersion energy
a1 None Scaling of the critical radii
a2 None Offset of the critical radii
alp 16.0 Exponent of the zero damping (ATM only)
ga 3.0 Charge scaling limiting value
gc 2.0 Charge scaling steepness
wf 6.0 Coordination number weighting
======================== =========== ============================================
Either method or s8, a1 and a2 must be provided, s9 can be used to overwrite
the ATM scaling if the method is provided in the model.
Disabling the three-body dispersion (s9=0.0) changes the internal selection rules
for damping parameters of a given method and prefers special two-body only
damping parameters if available!
.. note::
input_data.model.method with a full method name and input_data.keywords["params_tweaks"]
cannot be provided at the same time. It is an error to provide both options at the
same time.
Example
-------
>>> from dftd4.qcschema import run_qcschema
>>> import qcelemental as qcel
>>> atomic_input = qcel.models.AtomicInput(
... molecule = qcel.models.Molecule(
... symbols = ["O", "H", "H"],
... geometry = [
... 0.00000000000000, 0.00000000000000, -0.73578586109551,
... 1.44183152868459, 0.00000000000000, 0.36789293054775,
... -1.44183152868459, 0.00000000000000, 0.36789293054775
... ],
... ),
... driver = "energy",
... model = {
... "method": "TPSS-D4",
... },
... keywords = {},
... )
...
>>> atomic_result = run_qcschema(atomic_input)
>>> atomic_result.return_result
-0.0002667885779142513
"""
import sys
from typing import Union, overload
import numpy as np
from .interface import DampingParam, DispersionModel
from .library import get_api_version
if sys.version_info < (3, 14):
try:
import qcelemental.models.v1 as qcel_v1
except ModuleNotFoundError:
import qcelemental.models as qcel_v1
else:
qcel_v1 = None
try:
import qcelemental.models.v2 as qcel_v2
except ModuleNotFoundError:
qcel_v2 = None
if qcel_v1 is None and qcel_v2 is None:
raise ModuleNotFoundError(
"The qcelemental package is required for qcschema support. "
"Please install it with 'pip install qcelemental'."
)
_supported_drivers = [
"energy",
"gradient",
]
_available_levels = [
"d4",
"d4s",
]
_clean_dashlevel = str.maketrans("", "", "()")
if qcel_v1 is not None:
@overload
def run_qcschema(
input_data: Union[dict, "qcel_v1.AtomicInput"],
) -> "qcel_v1.AtomicResult": ...
if qcel_v2 is not None:
@overload
def run_qcschema(
input_data: Union[dict, "qcel_v2.AtomicInput"],
) -> "qcel_v2.AtomicResult": ...
[docs]
def run_qcschema(input_data):
"""Perform disperson correction based on an atomic inputmodel"""
if qcel_v2 is not None and isinstance(input_data, qcel_v2.AtomicInput):
atomic_input = input_data
elif qcel_v1 is not None and isinstance(input_data, qcel_v1.AtomicInput):
atomic_input = input_data
elif qcel_v2 is not None and input_data.get("specification"):
atomic_input = qcel_v2.AtomicInput(**input_data)
elif qcel_v1 is not None:
atomic_input = qcel_v1.AtomicInput(**input_data)
else:
raise ValueError(
"Input data is not a valid QCSchema AtomicInput for either v1 or v2."
)
schema_version = atomic_input.schema_version
if schema_version == 1:
ret_data = atomic_input.dict()
input_keywords = atomic_input.keywords
input_method = atomic_input.model.method
input_driver = atomic_input.driver
elif schema_version == 2:
ret_data = {
"input_data": atomic_input,
"extras": {},
"molecule": atomic_input.molecule,
}
input_keywords = atomic_input.specification.keywords
input_method = atomic_input.specification.model.method
input_driver = atomic_input.specification.driver
else:
raise ValueError(
f"Unsupported QCSchema version: {schema_version}. Only v1 and v2 are supported."
)
provenance = {
"creator": "dftd4",
"version": get_api_version(),
"routine": "dftd4.qcschema.run_qcschema",
}
success = False
return_result = 0.0
properties = {}
# Since it is a level hint we a forgiving if it is not present,
# we are much less forgiving if the wrong level is hinted here.
_level = input_keywords.get("level_hint", "d4")
if _level.lower() not in _available_levels:
error = dict(
error_type="input error",
error_message="Level '{}' is invalid for this dispersion correction".format(
_level
),
)
if schema_version == 1:
ret_data.update(
provenance=provenance,
success=success,
properties=properties,
return_result=return_result,
error=qcel_v1.ComputeError(**error),
)
return qcel_v1.AtomicResult(**ret_data)
elif schema_version == 2:
return qcel_v2.FailedOperation(
input_data=atomic_input, error=qcel_v2.ComputeError(**error)
)
# Check if the method is provided and strip the “dashlevel” from the method
_method = input_method.split("-")
if _method[-1].lower().translate(_clean_dashlevel) == _level.lower():
_method.pop()
_method = "-".join(_method)
if len(_method) == 0:
_method = None
# Obtain the parameters for the damping function
_input_param = input_keywords.get("params_tweaks", {"method": _method})
if _level.lower() == "d4s":
_model_param = {
key: _input_param.pop(key, default)
for key, default in (
("ga", 3.0),
("gc", 2.0),
)
}
else:
_model_param = {
key: _input_param.pop(key, default)
for key, default in (
("ga", 3.0),
("gc", 2.0),
("wf", 6.0),
)
}
try:
param = DampingParam(**_input_param)
disp = DispersionModel(
atomic_input.molecule.atomic_numbers[atomic_input.molecule.real],
atomic_input.molecule.geometry[atomic_input.molecule.real],
atomic_input.molecule.molecular_charge,
model=_level,
**_model_param,
)
driver = input_driver
res = disp.get_dispersion(
param=param,
grad=driver == "gradient",
)
if input_keywords.get("property", False):
res.update(**disp.get_properties())
extras = {"dftd4": res}
if driver == "gradient":
if all(atomic_input.molecule.real):
fullgrad = res.get("gradient")
else:
ireal = np.argwhere(atomic_input.molecule.real).reshape((-1))
fullgrad = np.zeros_like(atomic_input.molecule.geometry)
fullgrad[ireal, :] = res.get("gradient")
properties.update(return_energy=res.get("energy"))
if input_keywords.get("pair_resolved", False):
res = disp.get_pairwise_dispersion(param=param)
extras["dftd4"].update(res)
success = driver in _supported_drivers
if driver == "energy":
return_result = properties["return_energy"]
elif driver == "gradient":
return_result = fullgrad
else:
ret_data.update(
error=dict(
error_type="input error",
error_message="Calculation succeeded but invalid driver request provided",
),
)
ret_data["extras"].update(extras)
except (RuntimeError, TypeError) as e:
ret_data.update(
error=dict(error_type="input error", error_message=str(e)),
)
ret_data.update(
provenance=provenance,
success=success,
properties=properties,
return_result=return_result,
)
if schema_version == 1:
return qcel_v1.AtomicResult(**ret_data)
if "error" in ret_data:
return qcel_v2.FailedOperation(
input_data=atomic_input, error=ret_data["error"]
)
return qcel_v2.AtomicResult(**ret_data)
