"""
A collection of density physical property definitions.
"""
import copy
from openff.evaluator import unit
from openff.evaluator.attributes import UNDEFINED, PlaceholderValue
from openff.evaluator.datasets import PhysicalProperty, PropertyPhase
from openff.evaluator.datasets.thermoml import thermoml_property
from openff.evaluator.layers import register_calculation_schema
from openff.evaluator.layers.reweighting import ReweightingLayer, ReweightingSchema
from openff.evaluator.layers.simulation import SimulationLayer, SimulationSchema
from openff.evaluator.protocols import analysis, miscellaneous, reweighting
from openff.evaluator.protocols.utils import (
generate_base_reweighting_protocols,
generate_base_simulation_protocols,
generate_gradient_protocol_group,
)
from openff.evaluator.storage.query import SimulationDataQuery, SubstanceQuery
from openff.evaluator.utils.statistics import ObservableType
from openff.evaluator.workflow.schemas import ProtocolReplicator, WorkflowSchema
from openff.evaluator.workflow.utils import ProtocolPath, ReplicatorValue
[docs]@thermoml_property("Mass density, kg/m3", supported_phases=PropertyPhase.Liquid)
class Density(PhysicalProperty):
"""A class representation of a density property"""
[docs] @classmethod
def default_unit(cls):
return unit.gram / unit.millilitre
[docs] @staticmethod
def default_simulation_schema(
absolute_tolerance=UNDEFINED, relative_tolerance=UNDEFINED, n_molecules=1000
):
"""Returns the default calculation schema to use when estimating
this class of property from direct simulations.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_molecules: int
The number of molecules to use in the simulation.
Returns
-------
SimulationSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = SimulationSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
use_target_uncertainty = (
absolute_tolerance != UNDEFINED or relative_tolerance != UNDEFINED
)
# Define the protocol which will extract the average density from
# the results of a simulation.
extract_density = analysis.ExtractAverageStatistic("extract_density")
extract_density.statistics_type = ObservableType.Density
# Define the protocols which will run the simulation itself.
protocols, value_source, output_to_store = generate_base_simulation_protocols(
extract_density, use_target_uncertainty, n_molecules=n_molecules,
)
# Set up the gradient calculations
coordinate_source = ProtocolPath(
"output_coordinate_file", protocols.equilibration_simulation.id
)
trajectory_source = ProtocolPath(
"trajectory_file_path",
protocols.converge_uncertainty.id,
protocols.production_simulation.id,
)
statistics_source = ProtocolPath(
"statistics_file_path",
protocols.converge_uncertainty.id,
protocols.production_simulation.id,
)
reweight_density_template = reweighting.ReweightStatistics("")
reweight_density_template.statistics_type = ObservableType.Density
reweight_density_template.statistics_paths = statistics_source
reweight_density_template.reference_reduced_potentials = statistics_source
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_density_template,
ProtocolPath("force_field_path", "global"),
coordinate_source,
trajectory_source,
statistics_source,
)
# Build the workflow schema.
schema = WorkflowSchema()
schema.protocol_schemas = [
protocols.build_coordinates.schema,
protocols.assign_parameters.schema,
protocols.energy_minimisation.schema,
protocols.equilibration_simulation.schema,
protocols.converge_uncertainty.schema,
protocols.extract_uncorrelated_trajectory.schema,
protocols.extract_uncorrelated_statistics.schema,
gradient_group.schema,
]
schema.protocol_replicators = [gradient_replicator]
schema.outputs_to_store = {"full_system": output_to_store}
schema.gradients_sources = [gradient_source]
schema.final_value_source = value_source
calculation_schema.workflow_schema = schema
return calculation_schema
[docs] @staticmethod
def default_reweighting_schema(
absolute_tolerance=UNDEFINED,
relative_tolerance=UNDEFINED,
n_effective_samples=50,
):
"""Returns the default calculation schema to use when estimating
this property by reweighting existing data.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_effective_samples: int
The minimum number of effective samples to require when
reweighting the cached simulation data.
Returns
-------
ReweightingSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = ReweightingSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
data_replicator_id = "data_replicator"
# The protocol which will be used to calculate the densities from
# the existing data.
density_calculation = analysis.ExtractAverageStatistic(
f"calc_density_$({data_replicator_id})"
)
density_calculation.statistics_type = ObservableType.Density
reweight_density = reweighting.ReweightStatistics("reweight_density")
reweight_density.statistics_type = ObservableType.Density
reweight_density.required_effective_samples = n_effective_samples
protocols, data_replicator = generate_base_reweighting_protocols(
density_calculation, reweight_density, data_replicator_id
)
# Set up the gradient calculations
coordinate_path = ProtocolPath(
"output_coordinate_path", protocols.concatenate_trajectories.id
)
trajectory_path = ProtocolPath(
"output_trajectory_path", protocols.concatenate_trajectories.id
)
statistics_path = ProtocolPath(
"statistics_file_path", protocols.reduced_target_potential.id
)
reweight_density_template = copy.deepcopy(reweight_density)
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_density_template,
ProtocolPath("force_field_path", "global"),
coordinate_path,
trajectory_path,
statistics_path,
replicator_id="grad",
effective_sample_indices=ProtocolPath(
"effective_sample_indices", protocols.mbar_protocol.id
),
)
schema = WorkflowSchema()
schema.protocol_schemas = [
*(x.schema for x in protocols),
gradient_group.schema,
]
schema.protocol_replicators = [data_replicator, gradient_replicator]
schema.gradients_sources = [gradient_source]
schema.final_value_source = ProtocolPath("value", protocols.mbar_protocol.id)
calculation_schema.workflow_schema = schema
return calculation_schema
[docs]@thermoml_property("Excess molar volume, m3/mol", supported_phases=PropertyPhase.Liquid)
class ExcessMolarVolume(PhysicalProperty):
"""A class representation of an excess molar volume property"""
[docs] @classmethod
def default_unit(cls):
return unit.centimeter ** 3 / unit.mole
@staticmethod
def _get_simulation_protocols(
id_suffix,
gradient_replicator_id,
replicator_id=None,
weight_by_mole_fraction=False,
component_substance_reference=None,
full_substance_reference=None,
use_target_uncertainty=False,
n_molecules=1000,
):
"""Returns the set of protocols which when combined in a workflow
will yield the molar volume of a substance.
Parameters
----------
id_suffix: str
A suffix to append to the id of each of the returned protocols.
gradient_replicator_id: str
The id of the replicator which will clone those protocols which will
estimate the gradient of the molar volume with respect to a given parameter.
replicator_id: str, optional
The id of the replicator which will be used to clone these protocols.
This will be appended to the id of each of the returned protocols if
set.
weight_by_mole_fraction: bool
If true, an extra protocol will be added to weight the calculated
molar volume by the mole fraction of the component.
component_substance_reference: ProtocolPath or PlaceholderValue, optional
An optional protocol path (or replicator reference) to the component substance
whose enthalpy is being estimated.
full_substance_reference: ProtocolPath or PlaceholderValue, optional
An optional protocol path (or replicator reference) to the full substance
whose enthalpy of mixing is being estimated. This cannot be `None` if
`weight_by_mole_fraction` is `True`.
use_target_uncertainty: bool
Whether to calculate the observable to within the target
uncertainty.
n_molecules: int
The number of molecules to use in the simulation.
Returns
-------
BaseSimulationProtocols
The protocols used to estimate the molar volume of a substance.
DivideValue
The protocol used to calculate the number of molar molecules in
the system.
ProtocolPath
A reference to the estimated molar volume.
WorkflowSimulationDataToStore
An object which describes the default data from a simulation to store,
such as the uncorrelated statistics and configurations.
ProtocolGroup
The group of protocols which will calculate the gradient of the reduced potential
with respect to a given property.
ProtocolReplicator
The protocol which will replicate the gradient group for every gradient to
estimate.
ProtocolPath
A reference to the value of the gradient.
"""
if replicator_id is not None:
id_suffix = f"{id_suffix}_$({replicator_id})"
if component_substance_reference is None:
component_substance_reference = ProtocolPath("substance", "global")
if weight_by_mole_fraction is True and full_substance_reference is None:
raise ValueError(
"The full substance reference must be set when weighting by"
"the mole fraction"
)
# Define the protocol which will extract the average molar volume from
# the results of a simulation.
extract_volume = analysis.ExtractAverageStatistic(f"extract_volume{id_suffix}")
extract_volume.statistics_type = ObservableType.Volume
# Define the protocols which will run the simulation itself.
(
simulation_protocols,
value_source,
output_to_store,
) = generate_base_simulation_protocols(
extract_volume, use_target_uncertainty, id_suffix, n_molecules=n_molecules
)
# Divide the volume by the number of molecules in the system
number_of_molecules = ProtocolPath(
"output_number_of_molecules", simulation_protocols.build_coordinates.id
)
built_substance = ProtocolPath(
"output_substance", simulation_protocols.build_coordinates.id
)
number_of_molar_molecules = miscellaneous.DivideValue(
f"number_of_molar_molecules{id_suffix}"
)
number_of_molar_molecules.value = number_of_molecules
number_of_molar_molecules.divisor = (1.0 * unit.avogadro_constant).to(
"mole**-1"
)
extract_volume.divisor = ProtocolPath("result", number_of_molar_molecules.id)
# Use the correct substance.
simulation_protocols.build_coordinates.substance = component_substance_reference
simulation_protocols.assign_parameters.substance = built_substance
output_to_store.substance = built_substance
conditional_group = simulation_protocols.converge_uncertainty
if weight_by_mole_fraction:
# The component workflows need an extra step to multiply their molar volumes by their
# relative mole fraction.
weight_by_mole_fraction = miscellaneous.WeightByMoleFraction(
f"weight_by_mole_fraction{id_suffix}"
)
weight_by_mole_fraction.value = ProtocolPath("value", extract_volume.id)
weight_by_mole_fraction.full_substance = full_substance_reference
weight_by_mole_fraction.component = component_substance_reference
conditional_group.add_protocols(weight_by_mole_fraction)
value_source = ProtocolPath(
"weighted_value", conditional_group.id, weight_by_mole_fraction.id
)
if use_target_uncertainty:
# Make sure the convergence criteria is set to use the per component
# uncertainty target.
conditional_group.conditions[0].right_hand_value = ProtocolPath(
"per_component_uncertainty", "global"
)
if weight_by_mole_fraction:
# Make sure the weighted uncertainty is being used in the conditional comparison.
conditional_group.conditions[0].left_hand_value = ProtocolPath(
"weighted_value.error",
conditional_group.id,
weight_by_mole_fraction.id,
)
# Set up the gradient calculations
coordinate_source = ProtocolPath(
"output_coordinate_file", simulation_protocols.equilibration_simulation.id
)
trajectory_source = ProtocolPath(
"trajectory_file_path",
simulation_protocols.converge_uncertainty.id,
simulation_protocols.production_simulation.id,
)
statistics_source = ProtocolPath(
"statistics_file_path",
simulation_protocols.converge_uncertainty.id,
simulation_protocols.production_simulation.id,
)
reweight_molar_volume_template = reweighting.ReweightStatistics("")
reweight_molar_volume_template.statistics_type = ObservableType.Volume
reweight_molar_volume_template.statistics_paths = statistics_source
reweight_molar_volume_template.reference_reduced_potentials = statistics_source
(
gradient_group,
gradient_replicator,
gradient_source,
) = generate_gradient_protocol_group(
reweight_molar_volume_template,
ProtocolPath("force_field_path", "global"),
coordinate_source,
trajectory_source,
statistics_source,
replicator_id=gradient_replicator_id,
substance_source=built_substance,
id_suffix=id_suffix,
)
# Remove the group id from the path.
gradient_source.pop_next_in_path()
if weight_by_mole_fraction:
# The component workflows need an extra step to multiply their gradients by their
# relative mole fraction.
weight_gradient = miscellaneous.WeightByMoleFraction(
f"weight_gradient_by_mole_fraction{id_suffix}"
)
weight_gradient.value = gradient_source
weight_gradient.full_substance = full_substance_reference
weight_gradient.component = component_substance_reference
gradient_group.add_protocols(weight_gradient)
gradient_source = ProtocolPath("weighted_value", weight_gradient.id)
scale_gradient = miscellaneous.DivideValue(f"scale_gradient{id_suffix}")
scale_gradient.value = gradient_source
scale_gradient.divisor = ProtocolPath("result", number_of_molar_molecules.id)
gradient_group.add_protocols(scale_gradient)
gradient_source = ProtocolPath("result", gradient_group.id, scale_gradient.id)
return (
simulation_protocols,
number_of_molar_molecules,
value_source,
output_to_store,
gradient_group,
gradient_replicator,
gradient_source,
)
@staticmethod
def _get_reweighting_protocols(
id_suffix,
gradient_replicator_id,
data_replicator_id,
replicator_id=None,
weight_by_mole_fraction=False,
substance_reference=None,
n_effective_samples=50,
):
"""Returns the set of protocols which when combined in a workflow
will yield the molar volume of a substance by reweighting cached data.
Parameters
----------
id_suffix: str
A suffix to append to the id of each of the returned protocols.
gradient_replicator_id: str
The id of the replicator which will clone those protocols which will
estimate the gradient of the molar volume with respect to a given parameter.
data_replicator_id: str
The id of the replicator which will be used to clone these protocols
for each cached simulation data.
replicator_id: str, optional
The optional id of the replicator which will be used to clone these
protocols, e.g. for each component in the system.
weight_by_mole_fraction: bool
If true, an extra protocol will be added to weight the calculated
molar volume by the mole fraction of the component.
substance_reference: ProtocolPath or PlaceholderValue, optional
An optional protocol path (or replicator reference) to the substance
whose molar volume is being estimated.
n_effective_samples: int
The minimum number of effective samples to require when
reweighting the cached simulation data.
Returns
-------
BaseReweightingProtocols
The protocols used to estimate the molar volume of a substance.
ProtocolPath
A reference to the estimated molar volume.
ProtocolReplicator
The replicator which will replicate each protocol for each
cached simulation datum.
ProtocolGroup
The group of protocols which will calculate the gradient of the reduced potential
with respect to a given property.
ProtocolPath
A reference to the value of the gradient.
"""
if replicator_id is not None:
id_suffix = f"{id_suffix}_$({replicator_id})"
full_id_suffix = id_suffix
if data_replicator_id is not None:
full_id_suffix = f"{id_suffix}_$({data_replicator_id})"
if substance_reference is None:
substance_reference = ProtocolPath("substance", "global")
extract_volume = analysis.ExtractAverageStatistic(
f"extract_volume{full_id_suffix}"
)
extract_volume.statistics_type = ObservableType.Volume
reweight_volume = reweighting.ReweightStatistics(f"reweight_volume{id_suffix}")
reweight_volume.statistics_type = ObservableType.Volume
reweight_volume.required_effective_samples = n_effective_samples
(protocols, data_replicator) = generate_base_reweighting_protocols(
analysis_protocol=extract_volume,
mbar_protocol=reweight_volume,
replicator_id=data_replicator_id,
id_suffix=id_suffix,
)
# Make sure to use the correct substance.
protocols.build_target_system.substance = substance_reference
value_source = ProtocolPath("value", protocols.mbar_protocol.id)
# Set up the protocols which will be responsible for adding together
# the component molar volumes, and subtracting these from the full system volume.
weight_volume = None
if weight_by_mole_fraction is True:
weight_volume = miscellaneous.WeightByMoleFraction(
f"weight_volume{id_suffix}"
)
weight_volume.value = ProtocolPath("value", protocols.mbar_protocol.id)
weight_volume.full_substance = ProtocolPath("substance", "global")
weight_volume.component = substance_reference
value_source = ProtocolPath("weighted_value", weight_volume.id)
# Divide by the component molar volumes by the number of molecules in the system
number_of_molecules = ProtocolPath(
"total_number_of_molecules",
protocols.unpack_stored_data.id.replace(f"$({data_replicator_id})", "0"),
)
number_of_molar_molecules = miscellaneous.MultiplyValue(
f"number_of_molar_molecules{id_suffix}"
)
number_of_molar_molecules.value = (1.0 / unit.avogadro_constant).to(unit.mole)
number_of_molar_molecules.multiplier = number_of_molecules
divide_by_molecules = miscellaneous.DivideValue(
f"divide_by_molecules{id_suffix}"
)
divide_by_molecules.value = value_source
divide_by_molecules.divisor = ProtocolPath(
"result", number_of_molar_molecules.id
)
value_source = ProtocolPath("result", divide_by_molecules.id)
# Set up the gradient calculations.
reweight_volume_template = copy.deepcopy(reweight_volume)
coordinate_path = ProtocolPath(
"output_coordinate_path", protocols.concatenate_trajectories.id
)
trajectory_path = ProtocolPath(
"output_trajectory_path", protocols.concatenate_trajectories.id
)
statistics_path = ProtocolPath(
"statistics_file_path", protocols.reduced_target_potential.id
)
gradient_group, _, gradient_source = generate_gradient_protocol_group(
reweight_volume_template,
ProtocolPath("force_field_path", "global"),
coordinate_path,
trajectory_path,
statistics_path,
replicator_id=gradient_replicator_id,
id_suffix=id_suffix,
substance_source=substance_reference,
effective_sample_indices=ProtocolPath(
"effective_sample_indices", protocols.mbar_protocol.id
),
)
# Remove the group id from the path.
gradient_source.pop_next_in_path()
if weight_by_mole_fraction is True:
# The component workflows need an extra step to multiply their gradients by their
# relative mole fraction.
weight_gradient = miscellaneous.WeightByMoleFraction(
f"weight_gradient_$({gradient_replicator_id})_"
f"by_mole_fraction{id_suffix}"
)
weight_gradient.value = gradient_source
weight_gradient.full_substance = ProtocolPath("substance", "global")
weight_gradient.component = substance_reference
gradient_group.add_protocols(weight_gradient)
gradient_source = ProtocolPath("weighted_value", weight_gradient.id)
scale_gradient = miscellaneous.DivideValue(
f"scale_gradient_$({gradient_replicator_id}){id_suffix}"
)
scale_gradient.value = gradient_source
scale_gradient.divisor = ProtocolPath("result", number_of_molar_molecules.id)
gradient_group.add_protocols(scale_gradient)
gradient_source = ProtocolPath("result", gradient_group.id, scale_gradient.id)
all_protocols = (*protocols, number_of_molar_molecules, divide_by_molecules)
if weight_volume is not None:
all_protocols = (*all_protocols, weight_volume)
return (
all_protocols,
value_source,
data_replicator,
gradient_group,
gradient_source,
)
@staticmethod
def _default_reweighting_storage_query():
"""Returns the default storage queries to use when
retrieving cached simulation data to reweight.
This will include one query (with the key `"full_system_data"`)
to return data for the full mixture system, and another query
(with the key `"component_data"`) which will include data for
each pure component in the system.
Returns
-------
dict of str and SimulationDataQuery
The dictionary of queries.
"""
mixture_data_query = SimulationDataQuery()
mixture_data_query.substance = PlaceholderValue()
mixture_data_query.property_phase = PropertyPhase.Liquid
# Set up a query which will return the data of each
# individual component in the system.
component_query = SubstanceQuery()
component_query.components_only = True
component_data_query = SimulationDataQuery()
component_data_query.property_phase = PropertyPhase.Liquid
component_data_query.substance = PlaceholderValue()
component_data_query.substance_query = component_query
return {
"full_system_data": mixture_data_query,
"component_data": component_data_query,
}
[docs] @staticmethod
def default_simulation_schema(
absolute_tolerance=UNDEFINED, relative_tolerance=UNDEFINED, n_molecules=1000
):
"""Returns the default calculation schema to use when estimating
this class of property from direct simulations.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_molecules: int
The number of molecules to use in the simulation.
Returns
-------
SimulationSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = SimulationSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
use_target_uncertainty = (
absolute_tolerance != UNDEFINED or relative_tolerance != UNDEFINED
)
# Define the id of the replicator which will clone the gradient protocols
# for each gradient key to be estimated.
gradient_replicator_id = "gradient_replicator"
# Set up a workflow to calculate the molar volume of the full, mixed system.
(
full_system_protocols,
full_system_molar_molecules,
full_system_volume,
full_output,
full_system_gradient_group,
full_system_gradient_replicator,
full_system_gradient,
) = ExcessMolarVolume._get_simulation_protocols(
"_full",
gradient_replicator_id,
use_target_uncertainty=use_target_uncertainty,
n_molecules=n_molecules,
)
# Set up a general workflow for calculating the molar volume of one of the system components.
component_replicator_id = "component_replicator"
component_substance = ReplicatorValue(component_replicator_id)
# Make sure to weight by the mole fractions of the actual full system as these may be slightly
# different to the mole fractions of the measure property due to rounding.
full_substance = ProtocolPath(
"output_substance", full_system_protocols.build_coordinates.id
)
(
component_protocols,
component_molar_molecules,
component_volumes,
component_output,
component_gradient_group,
component_gradient_replicator,
component_gradient,
) = ExcessMolarVolume._get_simulation_protocols(
"_component",
gradient_replicator_id,
replicator_id=component_replicator_id,
weight_by_mole_fraction=True,
component_substance_reference=component_substance,
full_substance_reference=full_substance,
use_target_uncertainty=use_target_uncertainty,
n_molecules=n_molecules,
)
# Finally, set up the protocols which will be responsible for adding together
# the component molar volumes, and subtracting these from the mixed system molar volume.
add_component_molar_volumes = miscellaneous.AddValues(
"add_component_molar_volumes"
)
add_component_molar_volumes.values = component_volumes
calculate_excess_volume = miscellaneous.SubtractValues(
"calculate_excess_volume"
)
calculate_excess_volume.value_b = full_system_volume
calculate_excess_volume.value_a = ProtocolPath(
"result", add_component_molar_volumes.id
)
# Create the replicator object which defines how the pure component
# molar volume estimation protocols will be replicated for each component.
component_replicator = ProtocolReplicator(replicator_id=component_replicator_id)
component_replicator.template_values = ProtocolPath("components", "global")
# Combine the gradients.
add_component_gradients = miscellaneous.AddValues(
f"add_component_gradients" f"_$({gradient_replicator_id})"
)
add_component_gradients.values = component_gradient
combine_gradients = miscellaneous.SubtractValues(
f"combine_gradients_$({gradient_replicator_id})"
)
combine_gradients.value_b = full_system_gradient
combine_gradients.value_a = ProtocolPath("result", add_component_gradients.id)
# Combine the gradient replicators.
gradient_replicator = ProtocolReplicator(replicator_id=gradient_replicator_id)
gradient_replicator.template_values = ProtocolPath(
"parameter_gradient_keys", "global"
)
# Build the final workflow schema
schema = WorkflowSchema()
schema.protocol_schemas = [
component_protocols.build_coordinates.schema,
component_protocols.assign_parameters.schema,
component_protocols.energy_minimisation.schema,
component_protocols.equilibration_simulation.schema,
component_protocols.converge_uncertainty.schema,
component_molar_molecules.schema,
full_system_protocols.build_coordinates.schema,
full_system_protocols.assign_parameters.schema,
full_system_protocols.energy_minimisation.schema,
full_system_protocols.equilibration_simulation.schema,
full_system_protocols.converge_uncertainty.schema,
full_system_molar_molecules.schema,
component_protocols.extract_uncorrelated_trajectory.schema,
component_protocols.extract_uncorrelated_statistics.schema,
full_system_protocols.extract_uncorrelated_trajectory.schema,
full_system_protocols.extract_uncorrelated_statistics.schema,
add_component_molar_volumes.schema,
calculate_excess_volume.schema,
component_gradient_group.schema,
full_system_gradient_group.schema,
add_component_gradients.schema,
combine_gradients.schema,
]
schema.protocol_replicators = [gradient_replicator, component_replicator]
# Finally, tell the schemas where to look for its final values.
schema.gradients_sources = [ProtocolPath("result", combine_gradients.id)]
schema.final_value_source = ProtocolPath("result", calculate_excess_volume.id)
schema.outputs_to_store = {
"full_system": full_output,
f"component_$({component_replicator_id})": component_output,
}
calculation_schema.workflow_schema = schema
return calculation_schema
[docs] @staticmethod
def default_reweighting_schema(
absolute_tolerance=UNDEFINED,
relative_tolerance=UNDEFINED,
n_effective_samples=50,
):
"""Returns the default calculation schema to use when estimating
this property by reweighting existing data.
Parameters
----------
absolute_tolerance: pint.Quantity, optional
The absolute tolerance to estimate the property to within.
relative_tolerance: float
The tolerance (as a fraction of the properties reported
uncertainty) to estimate the property to within.
n_effective_samples: int
The minimum number of effective samples to require when
reweighting the cached simulation data.
Returns
-------
ReweightingSchema
The schema to follow when estimating this property.
"""
assert absolute_tolerance == UNDEFINED or relative_tolerance == UNDEFINED
calculation_schema = ReweightingSchema()
calculation_schema.absolute_tolerance = absolute_tolerance
calculation_schema.relative_tolerance = relative_tolerance
# Set up the storage queries
calculation_schema.storage_queries = (
ExcessMolarVolume._default_reweighting_storage_query()
)
# Set up a replicator that will re-run the component reweighting workflow for each
# component in the system.
component_replicator = ProtocolReplicator(replicator_id="component_replicator")
component_replicator.template_values = ProtocolPath("components", "global")
gradient_replicator = ProtocolReplicator("gradient")
gradient_replicator.template_values = ProtocolPath(
"parameter_gradient_keys", "global"
)
# Set up the protocols which will reweight data for the full system.
full_data_replicator_id = "full_data_replicator"
(
full_protocols,
full_volume,
full_data_replicator,
full_gradient_group,
full_gradient_source,
) = ExcessMolarVolume._get_reweighting_protocols(
"_full",
gradient_replicator.id,
full_data_replicator_id,
n_effective_samples=n_effective_samples,
)
# Set up the protocols which will reweight data for each component.
component_data_replicator_id = (
f"component_{component_replicator.placeholder_id}_data_replicator"
)
(
component_protocols,
component_volumes,
component_data_replicator,
component_gradient_group,
component_gradient_source,
) = ExcessMolarVolume._get_reweighting_protocols(
"_component",
gradient_replicator.id,
component_data_replicator_id,
replicator_id=component_replicator.id,
weight_by_mole_fraction=True,
substance_reference=ReplicatorValue(component_replicator.id),
n_effective_samples=n_effective_samples,
)
# Make sure the replicator is only replicating over component data.
component_data_replicator.template_values = ProtocolPath(
f"component_data[$({component_replicator.id})]", "global"
)
add_component_molar_volumes = miscellaneous.AddValues(
"add_component_molar_volumes"
)
add_component_molar_volumes.values = component_volumes
calculate_excess_volume = miscellaneous.SubtractValues(
"calculate_excess_potential"
)
calculate_excess_volume.value_b = full_volume
calculate_excess_volume.value_a = ProtocolPath(
"result", add_component_molar_volumes.id
)
# Combine the gradients.
add_component_gradients = miscellaneous.AddValues(
f"add_component_gradients" f"_{gradient_replicator.placeholder_id}"
)
add_component_gradients.values = component_gradient_source
combine_gradients = miscellaneous.SubtractValues(
f"combine_gradients_{gradient_replicator.placeholder_id}"
)
combine_gradients.value_b = full_gradient_source
combine_gradients.value_a = ProtocolPath("result", add_component_gradients.id)
# Build the final workflow schema.
schema = WorkflowSchema()
schema.protocol_schemas = [
*(x.schema for x in full_protocols),
*(x.schema for x in component_protocols),
add_component_molar_volumes.schema,
calculate_excess_volume.schema,
full_gradient_group.schema,
component_gradient_group.schema,
add_component_gradients.schema,
combine_gradients.schema,
]
schema.protocol_replicators = [
full_data_replicator,
component_replicator,
component_data_replicator,
gradient_replicator,
]
schema.gradients_sources = [ProtocolPath("result", combine_gradients.id)]
schema.final_value_source = ProtocolPath("result", calculate_excess_volume.id)
calculation_schema.workflow_schema = schema
return calculation_schema
# Register the properties via the plugin system.
register_calculation_schema(Density, SimulationLayer, Density.default_simulation_schema)
register_calculation_schema(
Density, ReweightingLayer, Density.default_reweighting_schema
)
register_calculation_schema(
ExcessMolarVolume, SimulationLayer, ExcessMolarVolume.default_simulation_schema,
)
register_calculation_schema(
ExcessMolarVolume, ReweightingLayer, ExcessMolarVolume.default_reweighting_schema,
)