from collections import OrderedDict
from scm.plams.core.basejob import MultiJob
from scm.plams.core.functions import add_to_instance
from scm.plams.core.results import Results
from scm.plams.interfaces.adfsuite.ams import AMSJob
__all__ = ["ReorganizationEnergyJob", "ReorganizationEnergyResults"]
# using this function to pass a molecule inside a MultiJob ensures proper parallel execution
def pass_molecule(source, target):
@add_to_instance(target)
def prerun(self): # noqa F811
self.molecule = source.results.get_main_molecule()
[docs]class ReorganizationEnergyResults(Results):
"""Results class for reorganization energy."""
def reorganization_energy(self, unit="au"):
energies = self.get_all_energies(unit)
reorganization_energy = (
energies["state B geo A"]
- energies["state A geo A"]
+ energies["state A geo B"]
- energies["state B geo B"]
)
return reorganization_energy
def get_all_energies(self, unit="au"):
energies = {}
energies["state A geo A"] = self.job.children["go_A"].results.get_energy(unit=unit)
energies["state B geo B"] = self.job.children["go_B"].results.get_energy(unit=unit)
energies["state A geo B"] = self.job.children["sp_A_for_geo_B"].results.get_energy(unit=unit)
energies["state B geo A"] = self.job.children["sp_B_for_geo_A"].results.get_energy(unit=unit)
return energies
[docs]class ReorganizationEnergyJob(MultiJob):
"""A class for calculating the reorganization energy using AMS.
Given two states, A and B, the reorganization energy is defined as follows:
reorganization energy =
E(state B at optimal geometry for state A) -
E(state A at optimal geometry for state A) +
E(state A at optimal geometry for state B) -
E(state B at optimal geometry for state B)
This job will run two geometry optimizations and two single point calculations.
"""
_result_type = ReorganizationEnergyResults
def __init__(self, molecule, common_settings, settings_state_A, settings_state_B, **kwargs):
"""
molecule: the molecule
common_settings: a setting object for an AMSJob containing the shared settings for all the calculations
settings_state_A: Setting object for an AMSJob containing exclusivelt the options defining the state A (e.g. charge and spin)
settings_state_B: Setting object for an AMSJob containing exclusivelt the options defining the state B (e.g. charge and spin)
kwargs: other options to be passed to the MultiJob constructor
"""
MultiJob.__init__(self, children=OrderedDict(), **kwargs)
go_settings = common_settings.copy()
go_settings.input.ams.task = "GeometryOptimization"
sp_settings = common_settings.copy()
sp_settings.input.ams.task = "SinglePoint"
# copy the settings so that we wont modify the ones provided as input by the user
settings_state_A = settings_state_A.copy()
settings_state_B = settings_state_B.copy()
# In case the charge key is not specified, excplicitely set the value to 0.
# This is to prevent the charge in molecule.properties.charge (set by get_main_molecule())
# to be used in case of neutral systems
for s in [settings_state_A, settings_state_B]:
if not "charge" in s.input.ams.system:
s.input.ams.system.charge = 0
self.children["go_A"] = AMSJob(molecule=molecule, settings=go_settings + settings_state_A, name="go_A")
self.children["go_B"] = AMSJob(molecule=molecule, settings=go_settings + settings_state_B, name="go_B")
self.children["sp_A_for_geo_B"] = AMSJob(settings=sp_settings + settings_state_A, name="sp_A_geo_B")
self.children["sp_B_for_geo_A"] = AMSJob(settings=sp_settings + settings_state_B, name="sp_B_geo_A")
pass_molecule(self.children["go_A"], self.children["sp_B_for_geo_A"])
pass_molecule(self.children["go_B"], self.children["sp_A_for_geo_B"])
