`descent.targets.thermo`

Train against thermodynamic properties.

Functions

`create_dataset`(*rows)	Create a dataset from a list of existing data points.
`create_from_evaluator`(dataset_file)	Create a dataset from an evaluator PhysicalPropertyDataSet
`default_closure`(trainable, topologies, dataset)	Return a default closure function for training against thermodynamic properties.
`default_config`(phase, temperature, pressure)	Return a default simulation configuration for the specified phase.
`extract_smiles`(dataset)	Return a list of unique SMILES strings in the dataset.
`predict`(dataset, force_field, topologies, ...)	Predict the properties in a dataset using molecular simulation, or by reweighting previous simulation data.
`select_config`(phase, temperature, pressure)	A helper method to choose the simulation config based on the phase

Classes

`DataEntry`	Represents a single experimental data point.
`SimulationConfig`(*, max_mols, gen_coords[, ...])	Configuration for a simulation to run.
`SimulationKey`(smiles, counts, temperature, ...)	A key used to identify a simulation.

class descent.targets.thermo.DataEntry[source]

Represents a single experimental data point.

type: Literal['density', 'hvap', 'hmix']: The type of data point.

smiles_a: str: The SMILES definition of the first component.

x_a: float | None: The mole fraction of the first component. This must be set to 1.0 if the data

smiles_b: str | None: The SMILES definition of the second component if present.

x_b: float | None: The mole fraction of the second component if present.

temperature: float: The temperature at which the data point was measured.

pressure: float: The pressure at which the data point was measured.

value: float: The value of the data point.

std: float | None: The standard deviation of the data point if available.

units: str: The units of the data point.

source: str: The source of the data point.

clear() → None. Remove all items from D.

copy() → a shallow copy of D

fromkeys(value=None, /): Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /): Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

pop(k[, d]) → v, remove specified key and return the corresponding value.: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from mapping/iterable E and F.: If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values

class descent.targets.thermo.SimulationKey(smiles: tuple[str, ...], counts: tuple[int, ...], temperature: float, pressure: float | None)[source]

A key used to identify a simulation.

smiles: tuple[str, ...]: The SMILES definitions of the components present in the system.

counts: tuple[int, ...]: The number of copies of each component present in the system.

temperature: float: The temperature [K] at which the simulation was run.

pressure: float | None: The pressure [atm] at which the simulation was run.

count(value, /): Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

class descent.targets.thermo.SimulationConfig(*, max_mols: int, gen_coords: GenerateCoordsConfig, apply_hmr: bool = False, equilibrate: list[smee.mm._config.MinimizationConfig | smee.mm._config.SimulationConfig], production: SimulationConfig, production_frequency: int)[source]

Configuration for a simulation to run.

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Args:: include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:: A copy of the model with included, excluded and updated fields as specified.

model_config: ClassVar[ConfigDict] = {}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Args:

_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,: this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”: [model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Args:

update: Values to change/add in the new model. Note: the data is not validated: before creating the new model. You should trust this data.

deep: Set to True to make a deep copy of the model.

Returns:

New model instance.

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args:

mode: The mode in which to_python should run.: If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,: a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Args:

indent: Indentation to use in the JSON output. If None is passed, the output will be compact. include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,: a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = GenerateJsonSchema, mode: Literal['validation', 'serialization'] = 'validation') → dict[str, Any]

Generates a JSON schema for a model class.

Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. schema_generator: To override the logic used to generate the JSON schema, as a subclass of

GenerateJsonSchema with your desired modifications

mode: The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Args:

params: Tuple of types of the class. Given a generic class: Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None: Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Args:: force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to None.
Returns:: Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

Validate a pydantic model instance.

Args:: obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.
Raises:: ValidationError: If the object could not be validated.
Returns:: The validated model instance.

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Args:: json_data: The JSON data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.
Returns:: The validated Pydantic model.
Raises:: ValidationError: If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Args:: obj: The object containing string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.
Returns:: The validated Pydantic model.

descent.targets.thermo.create_dataset(*rows: DataEntry) → Dataset[source]

Create a dataset from a list of existing data points.

Args:: rows: The data points to create the dataset from.
Returns:: The created dataset.

descent.targets.thermo.create_from_evaluator(dataset_file: Path) → Dataset[source]

Create a dataset from an evaluator PhysicalPropertyDataSet

Args:: dataset_file: The path to the evaluator dataset
Returns:: The created dataset

descent.targets.thermo.extract_smiles(dataset: Dataset) → list[str][source]

Return a list of unique SMILES strings in the dataset.

Args:: dataset: The dataset to extract the SMILES strings from.
Returns:: The unique SMILES strings with full atom mapping.

descent.targets.thermo.default_config(phase: Literal['bulk', 'vacuum'], temperature: float, pressure: float | None) → SimulationConfig[source]

Return a default simulation configuration for the specified phase.

Args:: phase: The phase to return the default configuration for. temperature: The temperature [K] at which to run the simulation. pressure: The pressure [atm] at which to run the simulation.
Returns:: The default simulation configuration.

descent.targets.thermo.select_config(phase: Literal['bulk', 'vacuum'], temperature: float, pressure: float | None, custom_config: dict[str, SimulationConfig] | None = None) → SimulationConfig[source]

A helper method to choose the simulation config based on the phase: with the desired temperature and pressure.

If a custom configuration is not available the default will be used.

Args:: phase: The phase of the simulation. temperature: The temperature [K] at which to run the simulation. pressure: The pressure [atm] at which to run the simulation custom_config: The custom simulation configuration for each phase.
Returns:: The simulation configuration for the given phase.

descent.targets.thermo.predict(dataset: Dataset, force_field: TensorForceField, topologies: dict[str, smee._models.TensorTopology], output_dir: Path, cached_dir: Path | None = None, per_type_scales: dict[Literal['density', 'hvap', 'hmix'], float] | None = None, verbose: bool = False, simulation_config: dict[str, SimulationConfig] | None = None) → tuple[Tensor, Tensor, Tensor, Tensor][source]

Predict the properties in a dataset using molecular simulation, or by reweighting previous simulation data.

Args:

dataset: The dataset to predict the properties of. force_field: The force field to use. topologies: The topologies of the molecules present in the dataset, with keys

of mapped SMILES patterns.

output_dir: The directory to write the simulation trajectories to. cached_dir: The (optional) directory to read cached simulation trajectories

from.

per_type_scales: The scale factor to apply to each data type. A default of 1.0: will be used for any data type not specified.

verbose: Whether to log additional information. simulation_config: The (optional) simulation configuration, should contain

a config for each phase if not provided the default will be used.

descent.targets.thermo.default_closure(trainable: Trainable, topologies: dict[str, smee._models.TensorTopology], dataset: Dataset, per_type_scales: dict[Literal['density', 'hvap', 'hmix'], float] | None = None, verbose: bool = False, simulation_config: dict[str, SimulationConfig] | None = None) → Callable[[Tensor, bool, bool], tuple[Tensor, Tensor | None, Tensor | None]][source]

Return a default closure function for training against thermodynamic properties.

Args:

trainable: The wrapper around trainable parameters. topologies: The topologies of the molecules present in the dataset, with keys

of mapped SMILES patterns.

dataset: The dataset to train against. per_type_scales: The scale factor to apply to each data type. verbose: Whether to log additional information about predictions. simulation_config: The (optional) simulation configuration, should contain

a config for each phase if not provided the default will be used.

Returns:

The default closure function.