def test_error_noargs():
thr = 1e-9
atomic_input = qcel.models.AtomicInput(
molecule={
"symbols":
"C C C C N C S H H H H H".split(),
"geometry": [
[-2.56745685564671, -0.02509985979910, 0.00000000000000],
[-1.39177582455797, +2.27696188880014, 0.00000000000000],
[+1.27784995624894, +2.45107479759386, 0.00000000000000],
[+2.62801937615793, +0.25927727028120, 0.00000000000000],
[+1.41097033661123, -1.99890996077412, 0.00000000000000],
[-1.17186102298849, -2.34220576284180, 0.00000000000000],
[-2.39505990368378, -5.22635838332362, 0.00000000000000],
[+2.41961980455457, -3.62158019253045, 0.00000000000000],
[-2.51744374846065, +3.98181713686746, 0.00000000000000],
[+2.24269048384775, +4.24389473203647, 0.00000000000000],
[+4.66488984573956, +0.17907568006409, 0.00000000000000],
[-4.60044244782237, -0.17794734637413, 0.00000000000000],
],
},
driver="energy",
model={"method": ""},
keywords={},
)
error = qcel.models.ComputeError(
error_type="input error",
error_message="Method name or complete damping parameter set required",
)
atomic_result = run_qcschema(atomic_input)
assert not atomic_result.success
assert atomic_result.error == error
def test_gradient_b97m_d4():
thr = 1e-9
atomic_input = qcel.models.AtomicInput(
molecule={
"symbols":
"C C C C C C I H H H H H S H C H H H".split(" "),
"geometry": [
[-1.42754169820131, -1.50508961850828, -1.93430551124333],
[+1.19860572924150, -1.66299114873979, -2.03189643761298],
[+2.65876001301880, +0.37736955363609, -1.23426391650599],
[+1.50963368042358, +2.57230374419743, -0.34128058818180],
[-1.12092277855371, +2.71045691257517, -0.25246348639234],
[-2.60071517756218, +0.67879949508239, -1.04550707592673],
[-2.86169588073340, +5.99660765711210, +1.08394899986031],
[+2.09930989272956, -3.36144811062374, -2.72237695164263],
[+2.64405246349916, +4.15317840474646, +0.27856972788526],
[+4.69864865613751, +0.26922271535391, -1.30274048619151],
[-4.63786461351839, +0.79856258572808, -0.96906659938432],
[-2.57447518692275, -3.08132039046931, -2.54875517521577],
[-5.88211879210329, 11.88491819358157, +2.31866455902233],
[-8.18022701418703, 10.95619984550779, +1.83940856333092],
[-5.08172874482867, 12.66714386256482, -0.92419491629867],
[-3.18311711399702, 13.44626574330220, -0.86977613647871],
[-5.07177399637298, 10.99164969235585, -2.10739192258756],
[-6.35955320518616, 14.08073002965080, -1.68204314084441],
],
},
driver="gradient",
model={
"method": "b97m-D4",
},
keywords={},
)
gradient = np.array([
[-2.44611355e-04, -5.62668284e-04, -2.23387025e-04],
[+2.55729172e-04, -4.81570822e-04, -1.97100251e-04],
[+6.28561864e-04, -1.60531557e-04, -7.38793802e-05],
[+5.07019846e-04, +1.84224291e-04, +6.97725810e-05],
[-1.48274928e-04, +2.74024495e-04, +1.28583321e-04],
[-4.66509227e-04, -2.63846497e-04, -9.72992491e-05],
[-1.45316573e-04, +1.14524656e-04, +5.68088686e-04],
[+8.73699092e-05, -1.64562363e-04, -6.71815237e-05],
[+1.99967459e-04, +1.12215186e-04, +4.54083544e-05],
[+2.04308124e-04, -1.82947927e-05, -1.01832111e-05],
[-2.17520641e-04, -8.08940986e-05, -2.67701158e-05],
[-1.08257557e-04, -1.62398104e-04, -6.37544043e-05],
[-3.95087002e-04, +5.42941863e-04, +2.98100565e-04],
[-8.67554935e-05, +4.46950178e-05, +9.10061596e-05],
[-4.95209003e-05, +3.52211029e-04, -2.37113109e-04],
[+2.26441621e-05, +9.26206419e-05, -3.41527716e-05],
[-3.29465221e-05, +1.14378254e-04, -1.11215443e-04],
[-1.08003368e-05, +6.29310823e-05, -5.89231846e-05],
])
atomic_result = run_qcschema(atomic_input)
assert atomic_result.success
assert approx(atomic_result.return_result, abs=thr) == gradient
def test_gradient_tpss_d4():
thr = 1.0e-9
atomic_input = qcel.models.AtomicInput(
molecule={
"symbols":
"O C C F O F H".split(),
"geometry": [
[+4.877023733, -3.909030492, +1.796260143],
[+6.112318716, -2.778558610, +0.091330457],
[+7.360520527, -4.445334728, -1.932830640],
[+7.978801077, -6.767751279, -1.031771494],
[+6.374499300, -0.460299457, -0.213142194],
[+5.637581753, -4.819746139, -3.831249370],
[+9.040657008, -3.585225944, -2.750722946],
],
"molecular_charge":
-1,
},
driver="gradient",
model={
"method": "",
},
keywords={
"params_tweaks": {
"s8": 1.76596355,
"a1": 0.42822303,
"a2": 4.54257102,
},
"pair_resolved": True,
},
)
gradient = np.array([
[-1.47959449e-04, +2.95411758e-05, +2.69548700e-04],
[-9.35127114e-06, +2.37170201e-05, +2.01896552e-05],
[+4.90329914e-05, -9.08636769e-06, -4.75377709e-05],
[+1.28728759e-04, -1.98165729e-04, -5.63375526e-05],
[-1.02103452e-05, +3.04524759e-04, +9.14084679e-05],
[-4.27301731e-06, -1.00071311e-04, -2.42775990e-04],
[-5.96766712e-06, -5.04595462e-05, -3.44955102e-05],
])
atomic_result = run_qcschema(atomic_input)
assert atomic_result.success
assert approx(atomic_result.return_result, abs=thr) == gradient
assert "energy" in atomic_result.extras["dftd4"]
assert "gradient" in atomic_result.extras["dftd4"]
assert "virial" in atomic_result.extras["dftd4"]
assert "additive pairwise energy" in atomic_result.extras["dftd4"]
assert "non-additive pairwise energy" in atomic_result.extras["dftd4"]
assert (
approx(atomic_result.extras["dftd4"]["energy"]) ==
atomic_result.extras["dftd4"]["additive pairwise energy"].sum() +
atomic_result.extras["dftd4"]["non-additive pairwise energy"].sum())
def test_energy_lh20t_d4():
thr = 1e-9
atomic_input = qcel.models.AtomicInput(
molecule={
"symbols":
"C C C C C C I H H H H H S H C H H H".split(" "),
"geometry": [
[-1.42754169820131, -1.50508961850828, -1.93430551124333],
[+1.19860572924150, -1.66299114873979, -2.03189643761298],
[+2.65876001301880, +0.37736955363609, -1.23426391650599],
[+1.50963368042358, +2.57230374419743, -0.34128058818180],
[-1.12092277855371, +2.71045691257517, -0.25246348639234],
[-2.60071517756218, +0.67879949508239, -1.04550707592673],
[-2.86169588073340, +5.99660765711210, +1.08394899986031],
[+2.09930989272956, -3.36144811062374, -2.72237695164263],
[+2.64405246349916, +4.15317840474646, +0.27856972788526],
[+4.69864865613751, +0.26922271535391, -1.30274048619151],
[-4.63786461351839, +0.79856258572808, -0.96906659938432],
[-2.57447518692275, -3.08132039046931, -2.54875517521577],
[-5.88211879210329, 11.88491819358157, +2.31866455902233],
[-8.18022701418703, 10.95619984550779, +1.83940856333092],
[-5.08172874482867, 12.66714386256482, -0.92419491629867],
[-3.18311711399702, 13.44626574330220, -0.86977613647871],
[-5.07177399637298, 10.99164969235585, -2.10739192258756],
[-6.35955320518616, 14.08073002965080, -1.68204314084441],
],
},
driver="energy",
model={"method": ""},
keywords={
"params_tweaks": {
"s8": 0.113,
"a1": 0.479,
"a2": 4.635,
},
},
)
atomic_result = run_qcschema(atomic_input)
assert atomic_result.success
assert approx(atomic_result.return_result,
abs=thr) == -0.010064263146257654
def test_ghost_pbe_d4():
thr = 1e-9
atomic_input = qcel.models.AtomicInput(
molecule={
"symbols":
"Pb H H H H Bi H H H".split(),
"geometry": [
[-0.00000020988889, -4.98043478877778, +0.00000000000000],
[+3.06964045311111, -6.06324400177778, +0.00000000000000],
[-1.53482054188889, -6.06324400177778, -2.65838526500000],
[-1.53482054188889, -6.06324400177778, +2.65838526500000],
[-0.00000020988889, -1.72196703577778, +0.00000000000000],
[-0.00000020988889, +4.77334244722222, +0.00000000000000],
[+1.35700257511111, +6.70626379422222, -2.35039772300000],
[-2.71400388988889, +6.70626379422222, +0.00000000000000],
[+1.35700257511111, +6.70626379422222, +2.35039772300000],
],
"real": [True] * 5 + [False] * 4,
},
driver="gradient",
model={
"method": "pbe",
},
)
gradient = np.array([
[+0.00000000e-0, +2.76351835e-7, +0.00000000e-0],
[+6.38066609e-5, -2.25449242e-5, +0.00000000e-0],
[-3.19033757e-5, -2.25449510e-5, -5.52582163e-5],
[-3.19033757e-5, -2.25449510e-5, +5.52582163e-5],
[+0.00000000e-0, +6.73584743e-5, +0.00000000e-0],
[+0.00000000e-0, +0.00000000e-0, +0.00000000e-0],
[+0.00000000e-0, +0.00000000e-0, +0.00000000e-0],
[+0.00000000e-0, +0.00000000e-0, +0.00000000e-0],
[+0.00000000e-0, +0.00000000e-0, +0.00000000e-0],
])
atomic_result = run_qcschema(atomic_input)
assert atomic_result.success
assert approx(atomic_result.return_result, abs=thr) == gradient
def compute(self, input_model: AtomicInput,
config: TaskConfig) -> AtomicResult:
"""
Actual interface to the dftd4 package. The compute function is just a thin
wrapper around the native QCSchema interface of the dftd4 Python-API.
"""
self.found(raise_error=True)
import dftd4
from dftd4.qcschema import run_qcschema
# strip engine hint
input_data = input_model.dict()
method = input_model.model.method
if method.startswith("d4-"):
method = method[3:]
input_data["model"]["method"] = method
qcvkey = method.upper() if method is not None else None
# send `from_arrays` the dftd4 behavior of functional specification overrides explicit parameters specification
# * differs from dftd3 harness behavior where parameters extend or override functional
# * stash the resolved plan in extras or, if errored, leave it for the proper dftd4 api to reject
param_tweaks = None if method else input_model.keywords.get(
"params_tweaks", None)
try:
planinfo = from_arrays(
verbose=1,
name_hint=method,
level_hint=input_model.keywords.get("level_hint", None),
param_tweaks=param_tweaks,
dashcoeff_supplement=input_model.keywords.get(
"dashcoeff_supplement", None),
)
except InputError:
pass
else:
input_data["extras"]["info"] = planinfo
# strip dispersion level from method
for alias, d4 in get_dispersion_aliases().items():
if d4 == "d4bjeeqatm" and method.lower().endswith(alias):
method = method[:-(len(alias) + 1)]
input_data["model"]["method"] = method
# consolidate dispersion level aliases
level_hint = input_model.keywords.get("level_hint", None)
if level_hint and get_dispersion_aliases()[
level_hint.lower()] == "d4bjeeqatm":
level_hint = "d4"
input_data["keywords"]["level_hint"] = level_hint
input_model = AtomicInput(**input_data)
# Run the Harness
output = run_qcschema(input_model)
if "info" in output.extras:
qcvkey = output.extras["info"]["fctldash"].upper()
calcinfo = {}
energy = output.properties.return_energy
calcinfo["CURRENT ENERGY"] = energy
calcinfo["DISPERSION CORRECTION ENERGY"] = energy
if qcvkey:
calcinfo[f"{qcvkey} DISPERSION CORRECTION ENERGY"] = energy
if output.driver == "gradient":
gradient = output.return_result
calcinfo["CURRENT GRADIENT"] = gradient
calcinfo["DISPERSION CORRECTION GRADIENT"] = gradient
if qcvkey:
calcinfo[f"{qcvkey} DISPERSION CORRECTION GRADIENT"] = gradient
if output.keywords.get("pair_resolved", False):
pw2 = output.extras["dftd4"]["additive pairwise energy"]
pw3 = output.extras["dftd4"]["non-additive pairwise energy"]
assert abs(pw2.sum() + pw3.sum() - energy
) < 1.0e-8, f"{pw2.sum()} + {pw3.sum()} != {energy}"
calcinfo["2-BODY DISPERSION CORRECTION ENERGY"] = pw2.sum()
calcinfo["3-BODY DISPERSION CORRECTION ENERGY"] = pw3.sum()
calcinfo["2-BODY PAIRWISE DISPERSION CORRECTION ANALYSIS"] = pw2
calcinfo["3-BODY PAIRWISE DISPERSION CORRECTION ANALYSIS"] = pw3
output.extras["qcvars"] = calcinfo
return output
def test_energy_r2scan_d4():
thr = 1e-6
atomic_input = qcel.models.AtomicInput(
molecule={
"symbols":
"Li Cl F H H Na B H C H H F C H H H".split(" "),
"geometry": [
[+2.06521084486823, +0.08218432748393, -3.31794862285397],
[-2.32042477766402, -5.46684392277772, -4.04262137940086],
[+2.26054697581965, -3.03018226193694, -4.64150015772052],
[-0.14839777550969, -0.43671669092912, +5.46349590128611],
[+1.25506764958846, +3.72255296450239, +3.12461655723367],
[-3.70871338035337, -1.70938913801338, -1.44451499871032],
[-0.50789889989427, -0.08663524018430, +3.25334078665520],
[+1.95889668026174, -0.69562876271128, +1.49780663184774],
[-0.13609934796341, +2.63877103476555, +2.01392332556491],
[-2.03651637484513, -1.53932035918944, +2.35235290216748],
[+1.01732693290435, +4.50598465234050, -1.41360090365614],
[-1.21371363315079, +1.45147299379122, -2.46002878669847],
[+0.99959759042027, +2.58862220349908, -0.61148816085282],
[+1.82755250562899, -1.68931361321449, +2.62013289657120],
[-1.90490242357524, +3.74722938063725, +2.11670736907658],
[+0.59246743346417, -4.08278756806328, -4.51067336050986],
],
},
driver="energy",
model={
"method": "m06l",
},
keywords={
"property": True,
},
)
charges = [
+6.4130068073040725e-1,
-2.8383857750967989e-1,
-4.4179022311738259e-1,
+7.1454451873716449e-2,
+9.1415362551287238e-2,
+6.9802661015384404e-1,
-3.9007049547150685e-1,
+2.9957268041964854e-2,
-1.7345919709167879e-1,
+2.4649274982777081e-2,
+8.2070988323221294e-2,
-3.3909452402278339e-1,
-2.6535793149085934e-1,
+3.6883214212240473e-2,
+8.3750568494545596e-2,
+1.3410252933988615e-1,
]
atomic_result = run_qcschema(atomic_input)
assert atomic_result.success
assert approx(atomic_result.return_result,
abs=thr) == -0.0013314656225517764
assert approx(atomic_result.extras["dftd4"]["partial charges"],
abs=thr) == charges
