def qd_opt(mol: Molecule,
jobs: Tuple[Optional[Type[Job]], ...],
settings: Tuple[Optional[Settings], ...],
use_ff: bool = False) -> None:
"""Perform an optimization of the quantum dot.
Performs an inplace update of **mol**.
Parameters
----------
mol : |plams.Molecule|_
The to-be optimized molecule.
job_recipe : |plams.Settings|_
A Settings instance containing all jon settings.
Expects 4 keys: ``"job1"``, ``"job2"``, ``"s1"``, ``"s2"``.
forcefield : bool
If ``True``, perform the job with CP2K with a user-specified forcefield.
"""
# Prepare the job settings
if use_ff:
qd_opt_ff(mol, jobs, settings)
return None
# Expand arguments
job1, job2 = jobs
s1, s2 = settings
# Extra options for AMSJob
if job1 is AMSJob:
s1 = Settings(s1)
s1.input.ams.constraints.atom = mol.properties.indices
if job2 is AMSJob:
s2 = Settings(s2)
s2.input.ams.constraints.atom = mol.properties.indices
# Run the first job and fix broken angles
mol.job_geometry_opt(job1, s1, name='QD_opt_part1')
fix_carboxyl(mol)
fix_h(mol)
mol.round_coords()
# Run the second job
if job2 is not None:
mol.job_geometry_opt(job2, s2, name='QD_opt_part2')
mol.round_coords()
return None
def _preoptimize(mol: Molecule) -> None:
"""Perform a constrained geometry optimization of **mol** with AMS UFF."""
s = get_template('qd.yaml')['UFF']
s.input.ams.constraints.atom = mol.properties.indices
s.input.ams.GeometryOptimization.coordinatetype = 'Cartesian'
mol.job_geometry_opt(AMSJob, s, name='E_XYn_preopt')
def get_bde_dE(tot: Molecule,
lig: Molecule,
core: Iterable[Molecule],
job: Type[Job],
s: Settings,
forcefield: bool = False) -> np.ndarray:
"""Calculate the bond dissociation energy: dE = dE(mopac) + (dG(uff) - dE(uff)).
Parameters
----------
tot : |plams.Molecule|_
The complete intact quantum dot.
lig : |plams.Molecule|_
A ligand dissociated from the surface of the quantum dot
core : |list|_ [|plams.Molecule|_]
A list with one or more quantum dots (*i.e.* **tot**) with **lig** removed.
job : |plams.Job|_
A :class:`.Job` subclass.
s : |plams.Settings|_
The settings for **job**.
"""
# Optimize XYn
len_core = len(core)
if job is AMSJob:
s_cp = Settings(s)
s_cp.input.ams.GeometryOptimization.coordinatetype = 'Cartesian'
lig.job_geometry_opt(job, s_cp, name='E_XYn_opt')
elif forcefield:
qd_opt_ff(lig, Settings({'job1': Cp2kJob, 's1': s}), name='E_XYn_opt')
else:
lig.job_geometry_opt(job, s, name='E_XYn_opt')
E_lig = lig.properties.energy.E
if E_lig in (None, np.nan):
logger.error(
'The BDE XYn geometry optimization failed, skipping further jobs')
return np.full(len_core, np.nan)
# Perform a single point on the full quantum dot
if forcefield:
qd_opt_ff(tot, Settings({'job1': Cp2kJob, 's1': s}), name='E_QD_opt')
else:
tot.job_single_point(job, s, name='E_QD_sp')
E_tot = tot.properties.energy.E
if E_tot in (None, np.nan):
logger.error(
'The BDE quantum dot single point failed, skipping further jobs')
return np.full(len_core, np.nan)
# Perform a single point on the quantum dot(s) - XYn
for mol in core:
if forcefield:
qd_opt_ff(mol,
Settings({
'job1': Cp2kJob,
's1': s
}),
name='E_QD-XYn_opt')
else:
mol.job_single_point(job, s, name='E_QD-XYn_sp')
E_core = np.fromiter([mol.properties.energy.E for mol in core],
count=len_core,
dtype=float)
# Calculate and return dE
dE = (E_lig + E_core) - E_tot
return dE
