gt4sd.properties.crystals.core module¶

Summary¶

Classes:

`AbsoluteEnergy`
`AbsoluteEnergyParameters`
`BandGap`
`BandGapParameters`
`BulkModuli`
`BulkModuliParameters`
`CGCNNParameters`
`FermiEnergy`
`FermiEnergyParameters`
`FormationEnergy`
`FormationEnergyParameters`
`MetalNonMetalClassifier`	Metal/non-metal classifier class.
`MetalNonMetalClassifierParameters`
`MetalSemiconductorClassifier`
`MetalSemiconductorClassifierParameters`
`PoissonRatio`
`PoissonRatioParameters`
`S3ParametersCrystals`
`ShearModuli`
`ShearModuliParameters`

Reference¶

class S3ParametersCrystals(**data)[source]¶

Bases: S3Parameters

domain: DomainSubmodule¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': <enum 'DomainSubmodule'>, 'model_config': 'ClassVar[ConfigDict]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>, 'config': {'title': 'S3ParametersCrystals'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>>]}, 'ref': 'gt4sd.properties.crystals.core.S3ParametersCrystals:94818316748064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_updates': {'examples': ['Tox21']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_updates': {'description': 'Name of the algorithm', 'examples': ['MCA']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'S3ParametersCrystals', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=True, description='Name of the algorithm', examples=['MCA']), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>)}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c168520, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c168520, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c168520, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "S3ParametersCrystals", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="S3ParametersCrystals", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507d7600, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "S3ParametersCrystals", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c168520, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "S3ParametersCrystals", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str, algorithm_version: str, algorithm_application: str) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class CGCNNParameters(**data)[source]¶

Bases: S3ParametersCrystals

algorithm_name: str¶

batch_size: int¶

workers: int¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': <class 'int'>, 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': <class 'int'>}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.CGCNNParameters'>, 'config': {'title': 'CGCNNParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.CGCNNParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.CGCNNParameters:94818316753280', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_updates': {'examples': ['Tox21']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'CGCNNParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=True, examples=['Tox21']), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c169980, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c169980, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c169980, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CGCNNParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="CGCNNParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "properties": 1, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507ece00, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "CGCNNParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c169980, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "CGCNNParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str, batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class MetalNonMetalClassifierParameters(**data)[source]¶

Bases: S3ParametersCrystals

algorithm_name: str¶

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>, 'config': {'title': 'MetalNonMetalClassifierParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters:94818316758800', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'MetalNonMetalClassifier', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'RFC', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MetalNonMetalClassifierParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='MetalNonMetalClassifier'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='RFC'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>)}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16af10, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16af10, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c16af10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222e6297b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509bfd70, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 5, }, ), has_extra: false, root_model: false, name: "MetalNonMetalClassifierParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MetalNonMetalClassifierParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "properties": 1, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507ee000, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222e6297b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509bfd70, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "MetalNonMetalClassifierParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c16af10, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "MetalNonMetalClassifierParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'RFC', algorithm_version: str, algorithm_application: str = 'MetalNonMetalClassifier') -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class FormationEnergyParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>, 'config': {'title': 'FormationEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.FormationEnergyParameters:94818316764016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'FormationEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'FormationEnergyParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='FormationEnergy'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16c370, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16c370, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c16c370, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509c2830, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "FormationEnergyParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="FormationEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507eef80, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509c2830, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "FormationEnergyParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c16c370, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "FormationEnergyParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'FormationEnergy', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class AbsoluteEnergyParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>, 'config': {'title': 'AbsoluteEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters:94818316769536', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'AbsoluteEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'AbsoluteEnergyParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='AbsoluteEnergy'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16d900, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16d900, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c16d900, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509c1fb0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "AbsoluteEnergyParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="AbsoluteEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "crystals": 2, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507fc240, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509c1fb0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "AbsoluteEnergyParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c16d900, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "AbsoluteEnergyParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'AbsoluteEnergy', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class BandGapParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.BandGapParameters'>, 'config': {'title': 'BandGapParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.BandGapParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.BandGapParameters:94818316775056', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'BandGap', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'BandGapParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='BandGap'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16ee90, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c16ee90, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c16ee90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509c1df0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "BandGapParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="BandGapParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "crystals": 2, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507fd540, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509c1df0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "BandGapParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c16ee90, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "BandGapParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'BandGap', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class FermiEnergyParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>, 'config': {'title': 'FermiEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.FermiEnergyParameters:94818316780576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'FermiEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'FermiEnergyParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='FermiEnergy'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c170420, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c170420, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c170420, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509c26f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "FermiEnergyParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="FermiEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "molecules": 0, "properties": 1, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507fe800, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509c26f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "FermiEnergyParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c170420, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "FermiEnergyParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'FermiEnergy', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class BulkModuliParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.BulkModuliParameters'>, 'config': {'title': 'BulkModuliParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.BulkModuliParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.BulkModuliParameters:94818316786096', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'BulkModuli', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'BulkModuliParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='BulkModuli'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c1719b0, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c1719b0, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c1719b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509c2530, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "BulkModuliParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="BulkModuliParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f21507ffa40, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509c2530, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "BulkModuliParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c1719b0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "BulkModuliParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'BulkModuli', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class ShearModuliParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.ShearModuliParameters'>, 'config': {'title': 'ShearModuliParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.ShearModuliParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.ShearModuliParameters:94818316791616', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'ShearModuli', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ShearModuliParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='ShearModuli'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c172f40, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c172f40, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c172f40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507d5ff0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "ShearModuliParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ShearModuliParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "crystals": 2, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f2150808dc0, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507d5ff0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "ShearModuliParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c172f40, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "ShearModuliParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'ShearModuli', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class PoissonRatioParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>, 'config': {'title': 'PoissonRatioParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.PoissonRatioParameters:94818316797136', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'PoissonRatio', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PoissonRatioParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='PoissonRatio'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c1744d0, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c1744d0, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c1744d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507d5f70, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "PoissonRatioParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="PoissonRatioParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "crystals": 2, "properties": 1, "molecules": 0, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f2150809fc0, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507d5f70, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "PoissonRatioParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c1744d0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "PoissonRatioParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'PoissonRatio', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class MetalSemiconductorClassifierParameters(**data)[source]¶

Bases: CGCNNParameters

algorithm_application: str¶

__dict__¶

__pydantic_fields_set__: set[str]¶: The names of fields explicitly set during instantiation.

__pydantic_extra__: Dict[str, Any] | None¶: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_private__: Dict[str, Any] | None¶: Values of private attributes set on the model instance.

__abstractmethods__ = frozenset({})¶

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_computed_fields__': 'ClassVar[Dict[str, ComputedFieldInfo]]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'Dict[str, Any] | None', '__pydantic_extra_info__': 'ClassVar[PydanticExtraInfo | None]', '__pydantic_fields__': 'ClassVar[Dict[str, FieldInfo]]', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'Dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_setattr_handlers__': 'ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'batch_size': 'int', 'domain': 'DomainSubmodule', 'model_config': 'ClassVar[ConfigDict]', 'workers': 'int'}¶

__class_vars__: ClassVar[set[str]] = {}¶: The names of the class variables defined on the model.

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}¶: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True¶: Whether model building is completed, or if there are still undefined fields.

__pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding [ComputedFieldInfo][pydantic.fields.ComputedFieldInfo] objects.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>, 'config': {'title': 'MetalSemiconductorClassifierParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_functions': [<bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters:94818316802656', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {}, 'schema': {'default': 'MetalSemiconductorClassifier', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_updates': {'description': 'Version of the algorithm', 'examples': ['v0']}}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_updates': {'description': 'Prediction batch size'}}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {}, 'schema': {'default': DomainSubmodule.crystals, 'schema': {'cls': <enum 'DomainSubmodule'>, 'members': [<DomainSubmodule.molecules: 'molecules'>, <DomainSubmodule.properties: 'properties'>, <DomainSubmodule.crystals: 'crystals'>], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'gt4sd.properties.core.DomainSubmodule:94818316728160', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_updates': {'description': 'Number of data loading workers'}}, 'schema': {'default': 0, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'MetalSemiconductorClassifierParameters', 'type': 'model-fields'}, 'type': 'model'}¶: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False¶: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})¶: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] = None¶

A wrapper around the __pydantic_extra__ annotation, if explicitly annotated on a model.

This is a private attribute, not meant to be used outside Pydantic.

__pydantic_fields__: ClassVar[Dict[str, FieldInfo]] = {'algorithm_application': FieldInfo(annotation=str, required=False, default='MetalSemiconductorClassifier'), 'algorithm_name': FieldInfo(annotation=str, required=False, default='cgcnn'), 'algorithm_type': FieldInfo(annotation=str, required=False, default='prediction'), 'algorithm_version': FieldInfo(annotation=str, required=True, description='Version of the algorithm', examples=['v0']), 'batch_size': FieldInfo(annotation=int, required=False, default=256, description='Prediction batch size'), 'domain': FieldInfo(annotation=DomainSubmodule, required=False, default=<DomainSubmodule.crystals: 'crystals'>), 'workers': FieldInfo(annotation=int, required=False, default=0, description='Number of data loading workers')}¶: A dictionary of field names and their corresponding [FieldInfo][pydantic.fields.FieldInfo] objects. This replaces Model.__fields__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}¶

A dictionary containing metadata about generic Pydantic models.

The origin and args items map to the [__origin__][genericalias.__origin__] and [__args__][genericalias.__args__] attributes of [generic aliases][types-genericalias], and the parameter item maps to the __parameter__ attribute of generic classes.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None¶: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None¶: The name of the post-init method for the model, if defined.

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c175a60, ), serializer: PolymorphismTrampoline( PolymorphismTrampoline { class: Py( 0x0000563c9c175a60, ), serializer: Model( ModelSerializer { class: Py( 0x0000563c9c175a60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key: "algorithm_version", alias: None, serializer: Some( Str( StrSerializer, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_application": SerField { key: "algorithm_application", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21509bfdc0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_name": SerField { key: "algorithm_name", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f220e1071f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "algorithm_type": SerField { key: "algorithm_type", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f2177cc1330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "domain": SerField { key: "domain", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f21507c4890, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000563c9c163760, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "batch_size": SerField { key: "batch_size", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, "workers": SerField { key: "workers", alias: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f222f3000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, serialize_by_alias: None, serialization_exclude_if: None, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "MetalSemiconductorClassifierParameters", }, ), enabled_from_config: false, }, ), enabled_from_config: false, }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] = {}¶: __setattr__ handlers. Memoizing the handlers leads to a dramatic performance improvement in __setattr__

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MetalSemiconductorClassifierParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_type", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f2177cc1330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "domain", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "domain", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21507c4890, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000563c9c163760, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "molecules": 0, "properties": 1, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f215080b240, ), ), values: [ Py( 0x00007f21507c47b0, ), Py( 0x00007f21507c4820, ), Py( 0x00007f21507c4890, ), ], }, missing: None, expected_repr: "'molecules', 'properties' or 'crystals'", strict: false, class_repr: "DomainSubmodule", name: "str-enum[DomainSubmodule]", }, ), validate_default: false, copy_default: false, name: "default[str-enum[DomainSubmodule]]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_name", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f220e1071f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_version", ), rest: [], }, by_alias: [], }, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "algorithm_application", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f21509bfdc0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "batch_size", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, Field { name: "workers", lookup_path_collection: LookupPathCollection { by_name: LookupPath { first_item: PathItemString( "workers", ), rest: [], }, by_alias: [], }, validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f222f3000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f222cf13af0, ), }, ), frozen: false, }, ], model_name: "MetalSemiconductorClassifierParameters", extra_behavior: Ignore, extras_validator: None, extras_keys_validator: None, strict: false, from_attributes: false, loc_by_alias: true, lookup: LookupTree { inner: { PathItemString( "algorithm_type", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 0, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_name", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 2, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_version", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 3, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "algorithm_application", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 4, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "batch_size", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 5, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "workers", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 6, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, PathItemString( "domain", ): LookupTreeNode { fields: [ LookupFieldInfo { field_index: 1, lookup_priority: LookupFieldPriority { lookup_type: Both, alias_index: 0, }, }, ], map: {}, list: {}, }, }, }, validate_by_alias: None, validate_by_name: None, }, ), class: Py( 0x0000563c9c175a60, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f222cf13af0, ), name: "MetalSemiconductorClassifierParameters", }, ), definitions=[], cache_strings=True)¶: The pydantic-core SchemaValidator used to validate instances of the model.

__signature__: ClassVar[Signature] = <Signature (*, algorithm_type: str = 'prediction', domain: gt4sd.properties.core.DomainSubmodule = <DomainSubmodule.crystals: 'crystals'>, algorithm_name: str = 'cgcnn', algorithm_version: str, algorithm_application: str = 'MetalSemiconductorClassifier', batch_size: int = 256, workers: int = 0) -> None>¶: The synthesized __init__ [Signature][inspect.Signature] of the model.

_abc_impl = <_abc._abc_data object>¶

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

class MetalNonMetalClassifier(parameters)[source]¶

Bases: PredictorAlgorithm

Metal/non-metal classifier class.

__init__(parameters)[source]¶

Targeted or untargeted generation.

Parameters: configuration – application specific helper that allows to setup the generator.

get_model(resources_path)[source]¶

Instantiate the actual model.

Parameters: resources_path (str) – local path to model files.
Returns: the model.
Return type: Predictor

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = 'Metal/non-metal classifier class.'¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class FormationEnergy(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class AbsoluteEnergy(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class BandGap(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class FermiEnergy(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class BulkModuli(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class ShearModuli(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class PoissonRatio(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶

class MetalSemiconductorClassifier(parameters)[source]¶

Bases: _CGCNN

classmethod get_description()[source]¶

Return type: str

__abstractmethods__ = frozenset({})¶

__annotations__ = {'max_runtime': 'int'}¶

__doc__ = None¶

__module__ = 'gt4sd.properties.crystals.core'¶

__parameters__ = ()¶

_abc_impl = <_abc._abc_data object>¶