def _run_statmech(self, arkane_spc, arkane_file, output_path=None, use_bac=False, kinetics=False, plot=False):
"""
A helper function for running an Arkane statmech job.
Args:
arkane_spc (str): An Arkane species() function representor.
arkane_file (str): The path to the Arkane species file (either in .py or YAML form).
output_path (str): The path to the folder containing the Arkane output.py file.
use_bac (bool): A flag indicating whether or not to use bond additivity corrections (True to use).
kinetics (bool) A flag indicating whether this specie is part of a kinetics job.
plot (bool): A flag indicating whether to plot a PDF of the calculated thermo properties (True to plot)
Returns:
bool: Whether the job was successful (True for successful).
"""
success = True
stat_mech_job = StatMechJob(arkane_spc, arkane_file)
stat_mech_job.applyBondEnergyCorrections = use_bac and not kinetics and self.sp_level
if not kinetics or (kinetics and self.sp_level):
# currently we have to use a model chemistry for thermo
stat_mech_job.modelChemistry = self.sp_level
else:
# if this is a kinetics computation and we don't have a valid model chemistry, don't bother about it
stat_mech_job.applyAtomEnergyCorrections = False
# Use the scaling factor if given, else try determining it from Arkane
# (defaults to 1 and prints a warning if not found)
stat_mech_job.frequencyScaleFactor = self.freq_scale_factor or assign_frequency_scale_factor(self.freq_level)
try:
stat_mech_job.execute(output_directory=os.path.join(output_path, 'output.py'), plot=plot)
except Exception:
success = False
return success
def _run_statmech(self,
arkane_spc,
arkane_file,
output_file_path=None,
use_bac=False,
kinetics=False,
plot=False):
"""
A helper function for running an Arkane statmech job
`arkane_spc` is the species() function from Arkane's input.py
`arkane_file` is the Arkane species file (either .py or YAML form)
`output_file_path` is a path to the Arkane output.py file
`use_bac` is a bool flag indicating whether or not to use bond additivity corrections
`kinetics` is a bool flag indicating whether this specie sis part of a kinetics job, in which case..??
`plot` is a bool flag indicating whether or not to plot a PDF of the calculated thermo properties
"""
success = True
stat_mech_job = StatMechJob(arkane_spc, arkane_file)
stat_mech_job.applyBondEnergyCorrections = use_bac and not kinetics and self.model_chemistry
if not kinetics or kinetics and self.model_chemistry:
# currently we have to use a model chemistry for thermo
stat_mech_job.modelChemistry = self.model_chemistry
else:
# if this is a klinetics computation and we don't have a valid model chemistry, don't bother about it
stat_mech_job.applyAtomEnergyCorrections = False
stat_mech_job.frequencyScaleFactor = assign_frequency_scale_factor(
self.model_chemistry)
try:
stat_mech_job.execute(outputFile=output_file_path, plot=plot)
except Exception:
success = False
return success
def run_statmech(
self,
arkane_species: Type[Species],
arkane_file_path: str,
arkane_output_path: str = None,
bac_type: Optional[str] = None,
sp_level: Optional[Level] = None,
plot: bool = False,
) -> bool:
"""
A helper function for running an Arkane statmech job.
Args:
arkane_species (arkane_input_species): An instance of an Arkane species() object.
arkane_file_path (str): The path to the Arkane species file (either in .py or YAML form).
arkane_output_path (str): The path to the folder in which the Arkane output.py file will be saved.
bac_type (str, optional): The bond additivity correction type. 'p' for Petersson- or 'm' for Melius-type BAC.
``None`` to not use BAC.
sp_level (Level, optional): The level of theory used for energy corrections.
plot (bool): A flag indicating whether to plot a PDF of the calculated thermo properties (True to plot)
Returns:
bool: Whether the statmech job was successful.
"""
success = True
stat_mech_job = StatMechJob(arkane_species, arkane_file_path)
stat_mech_job.applyBondEnergyCorrections = bac_type is not None and sp_level is not None
if bac_type is not None:
stat_mech_job.bondEnergyCorrectionType = bac_type
if sp_level is None:
# if this is a kinetics computation and we don't have a valid model chemistry, don't bother about it
stat_mech_job.applyAtomEnergyCorrections = False
else:
stat_mech_job.level_of_theory = sp_level.to_arkane_level_of_theory(
)
stat_mech_job.frequencyScaleFactor = self.freq_scale_factor
try:
stat_mech_job.execute(output_directory=arkane_output_path,
plot=plot)
except Exception as e:
logger.error(
f'Arkane statmech job for species {arkane_species.label} failed with the error message:\n{e}'
)
if stat_mech_job.applyBondEnergyCorrections \
and 'missing' in str(e).lower() and 'bac parameters for model chemistry' in str(e).lower():
# try executing Arkane w/o BACs
logger.warning('Trying to run Arkane without BACs')
stat_mech_job.applyBondEnergyCorrections = False
try:
stat_mech_job.execute(output_directory=arkane_output_path,
plot=plot)
except Exception as e:
logger.error(
f'Arkane statmech job for {arkane_species.label} failed with the error message:\n{e}'
)
success = False
else:
success = False
return success
def process(self):
"""Process ARC outputs and generate thermo and kinetics"""
# Thermo:
species_list_for_thermo_parity = list()
species_for_thermo_lib = list()
for species in self.species_dict.values():
if not species.is_ts and 'ALL converged' in self.output[
species.label]['status']:
species_for_thermo_lib.append(species)
output_file_path = self._generate_arkane_species_file(species)
arkane_spc = arkane_species(str(species.label),
species.arkane_file)
if species.mol_list:
arkane_spc.molecule = species.mol_list
stat_mech_job = StatMechJob(arkane_spc, species.arkane_file)
stat_mech_job.applyBondEnergyCorrections = self.use_bac
stat_mech_job.modelChemistry = self.model_chemistry
stat_mech_job.frequencyScaleFactor = assign_frequency_scale_factor(
self.model_chemistry)
stat_mech_job.execute(outputFile=output_file_path, plot=False)
if species.generate_thermo:
thermo_job = ThermoJob(arkane_spc, 'NASA')
thermo_job.execute(outputFile=output_file_path, plot=False)
species.thermo = arkane_spc.getThermoData()
plotter.log_thermo(species.label, path=output_file_path)
species.rmg_species = Species(molecule=[species.mol])
species.rmg_species.reactive = True
if species.mol_list:
species.rmg_species.molecule = species.mol_list # add resonance structures for thermo determination
try:
species.rmg_thermo = self.rmgdb.thermo.getThermoData(
species.rmg_species)
except ValueError:
logging.info(
'Could not retrieve RMG thermo for species {0}, possibly due to missing 2D structure '
'(bond orders). Not including this species in the parity plots.'
.format(species.label))
else:
if species.generate_thermo:
species_list_for_thermo_parity.append(species)
# Kinetics:
rxn_list_for_kinetics_plots = list()
arkane_spc_dict = dict() # a dictionary with all species and the TSs
for rxn in self.rxn_list:
logging.info('\n\n')
species = self.species_dict[rxn.ts_label] # The TS
if 'ALL converged' in self.output[
species.label]['status'] and rxn.check_ts():
self.copy_freq_output_for_ts(species.label)
success = True
rxn_list_for_kinetics_plots.append(rxn)
output_file_path = self._generate_arkane_species_file(species)
arkane_ts = arkane_transition_state(str(species.label),
species.arkane_file)
arkane_spc_dict[species.label] = arkane_ts
stat_mech_job = StatMechJob(arkane_ts, species.arkane_file)
stat_mech_job.applyBondEnergyCorrections = False
if not self.model_chemistry:
stat_mech_job.modelChemistry = self.model_chemistry
else:
stat_mech_job.applyAtomEnergyCorrections = False
stat_mech_job.frequencyScaleFactor = assign_frequency_scale_factor(
self.model_chemistry)
stat_mech_job.execute(outputFile=None, plot=False)
for spc in rxn.r_species + rxn.p_species:
if spc.label not in arkane_spc_dict.keys():
# add an extra character to the arkane_species label to distinguish between species calculated
# for thermo and species calculated for kinetics (where we don't want to use BAC)
arkane_spc = arkane_species(str(spc.label + '_'),
spc.arkane_file)
stat_mech_job = StatMechJob(arkane_spc,
spc.arkane_file)
arkane_spc_dict[spc.label] = arkane_spc
stat_mech_job.applyBondEnergyCorrections = False
if not self.model_chemistry:
stat_mech_job.modelChemistry = self.model_chemistry
else:
stat_mech_job.applyAtomEnergyCorrections = False
stat_mech_job.frequencyScaleFactor = assign_frequency_scale_factor(
self.model_chemistry)
stat_mech_job.execute(outputFile=None, plot=False)
# thermo_job = ThermoJob(arkane_spc, 'NASA')
# thermo_job.execute(outputFile=None, plot=False)
# arkane_spc.thermo = arkane_spc.getThermoData()
rxn.dh_rxn298 = sum([product.thermo.getEnthalpy(298) for product in arkane_spc_dict.values()
if product.label in rxn.products])\
- sum([reactant.thermo.getEnthalpy(298) for reactant in arkane_spc_dict.values()
if reactant.label in rxn.reactants])
arkane_rxn = arkane_reaction(
label=str(rxn.label),
reactants=[
str(label + '_') for label in arkane_spc_dict.keys()
if label in rxn.reactants
],
products=[
str(label + '_') for label in arkane_spc_dict.keys()
if label in rxn.products
],
transitionState=rxn.ts_label,
tunneling='Eckart')
kinetics_job = KineticsJob(reaction=arkane_rxn,
Tmin=self.t_min,
Tmax=self.t_max,
Tcount=self.t_count)
logging.info('Calculating rate for reaction {0}'.format(
rxn.label))
try:
kinetics_job.execute(outputFile=output_file_path,
plot=False)
except ValueError as e:
"""
ValueError: One or both of the barrier heights of -9.35259 and 62.6834 kJ/mol encountered in Eckart
method are invalid.
"""
logging.error(
'Failed to generate kinetics for {0} with message:\n{1}'
.format(rxn.label, e))
success = False
if success:
rxn.kinetics = kinetics_job.reaction.kinetics
plotter.log_kinetics(species.label, path=output_file_path)
rxn.rmg_reactions = rmgdb.determine_rmg_kinetics(
rmgdb=self.rmgdb,
reaction=rxn.rmg_reaction,
dh_rxn298=rxn.dh_rxn298)
logging.info('\n\n')
output_dir = os.path.join(self.project_directory, 'output')
if species_list_for_thermo_parity:
plotter.draw_thermo_parity_plots(species_list_for_thermo_parity,
path=output_dir)
libraries_path = os.path.join(output_dir, 'RMG libraries')
# species_list = [spc for spc in self.species_dict.values()]
plotter.save_thermo_lib(species_for_thermo_lib,
path=libraries_path,
name=self.project,
lib_long_desc=self.lib_long_desc)
if rxn_list_for_kinetics_plots:
plotter.draw_kinetics_plots(rxn_list_for_kinetics_plots,
path=output_dir,
t_min=self.t_min,
t_max=self.t_max,
t_count=self.t_count)
libraries_path = os.path.join(output_dir, 'RMG libraries')
plotter.save_kinetics_lib(rxn_list=rxn_list_for_kinetics_plots,
path=libraries_path,
name=self.project,
lib_long_desc=self.lib_long_desc)
self.clean_output_directory()