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_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_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': 'str', 'algorithm_name': 'str', 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': <enum 'DomainSubmodule'>, 'model_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]'}¶

__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:94427930798528', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b745a9c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="S3ParametersCrystals", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc26ba030, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc26b9ff0, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc26ba070, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc26ba0b0, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc26b9f80, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc26ba0f0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc26ba130, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b4ad0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b4b20, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b4a80, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b4b70, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "S3ParametersCrystals", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b745a9c0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930804336', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b745c070, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="CGCNNParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc26bb1f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc26bb1b0, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc26bb230, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc26bb270, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc26bb100, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc26bb2b0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc26bb2f0, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b5430, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b5480, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b53e0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b54d0, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc26bb330, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc26bb370, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc26bb3b0, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc26bb3f0, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "CGCNNParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b745c070, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'algorithm_application': <class 'str'>, 'algorithm_name': <class 'str'>, 'algorithm_type': 'str', 'algorithm_version': 'str', 'domain': 'DomainSubmodule', 'model_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]'}¶

__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:94427930814768', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b745e930, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc26975f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9c9e7770, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MetalNonMetalClassifierParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc26c88b0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc26c8870, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc26c88f0, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc26c8930, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "molecules": 0, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f9dc26c8800, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc26c8970, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc26c89b0, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9c9e7770, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dce26e1f0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b57a0, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b5750, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b5700, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc26975f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "MetalNonMetalClassifierParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b745e930, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930824528', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b7460f50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc268ed30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="FormationEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc26ca530, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc26ca4f0, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc26ca570, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc26ca5b0, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc26ca480, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc26ca5f0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc26ca630, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b5a20, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b5a70, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b59d0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b5ac0, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc268ed30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc26ca670, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc26ca6b0, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc26ca6f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc26ca730, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "FormationEnergyParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b7460f50, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930836080', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b7463c70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc268e4b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="AbsoluteEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc24d81f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc24d81b0, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc24d8230, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc24d8270, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc24d8140, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc24d82b0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc24d82f0, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc6673820, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b5d90, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b5d40, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b5cf0, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc268e4b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc24d8330, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc24d8370, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc24d83b0, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc24d83f0, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "AbsoluteEnergyParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b7463c70, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930850624', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b7467540, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc268e2f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="BandGapParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc24d9e70, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc24d9e30, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc24d9eb0, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc24d9ef0, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "properties": 1, "molecules": 0, "crystals": 2, }, ), expected_py_dict: None, expected_py_values: None, expected_py_primitives: Some( Py( 0x00007f9dc24d9dc0, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc24d9f30, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc24d9f70, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b6010, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b6060, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b5fc0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b60b0, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc268e2f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc24d9fb0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc24d9ff0, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc24da030, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc24da070, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "BandGapParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b7467540, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930865168', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b746ae10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc268ebf0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="FermiEnergyParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc26cb770, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc26cb6f0, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc26cb530, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc26caf70, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc26cb7c0, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc26cb630, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc26cb4f0, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b5ca0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b5930, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b5c50, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b5980, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc268ebf0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc26c94b0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc26cb570, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc26cb5f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc26c9270, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "FermiEnergyParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b746ae10, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930879152', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b746e4b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc268ea30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="BulkModuliParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc24dad70, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc24dad30, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc24dadb0, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc24dadf0, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc24dacc0, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc24dae30, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc24dae70, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b6380, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b63d0, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b6330, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b6420, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc268ea30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc24daeb0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc24daef0, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc24daf30, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc24daf70, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "BulkModuliParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b746e4b0, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930893696', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b7471d80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc26a6d30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="ShearModuliParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc24e0a70, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc24e0a30, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc24e0ab0, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc24e0af0, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc24e09c0, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc24e0b30, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc24e0b70, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b66a0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b66f0, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b6650, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b6740, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc26a6d30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc24e0bb0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc24e0bf0, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc24e0c30, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc24e0c70, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "ShearModuliParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b7471d80, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930910304', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b7475e60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc26a7430, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="PoissonRatioParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc24e26f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc24e26b0, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc24e2730, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc24e2770, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc24e2640, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc24e27b0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc24e27f0, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b69c0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b6a10, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b6970, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b6a60, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc26a7430, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc24e2830, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc24e2870, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc24e28b0, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc24e28f0, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "PoissonRatioParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b7475e60, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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_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_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_computed_fields': 'ClassVar[dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[dict[str, FieldInfo]]', '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:94427930924848', '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:94427930770672', '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_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': ()}¶: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__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=Model( ModelSerializer { class: Py( 0x000055e1b7479730, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key_py: Py( 0x00007f9e9adb0230, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5000d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f9e996500b0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e9d5020d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f9e99651bb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9de91c2db0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f9e99653ab0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f9e9963bc80, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f9e9d0cd1f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc25fec00, ), ), serializer: Enum( EnumSerializer { class: Py( 0x000055e1b7453cf0, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f9dc60dec40, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f9dc2697640, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, }, 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", }, ), definitions=[])¶: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="MetalSemiconductorClassifierParameters", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "algorithm_type", lookup_key: Simple { key: "algorithm_type", py_key: Py( 0x00007f9dc24ec370, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f9dc24ec330, ), ), ], ), }, name_py: Py( 0x00007f9e99651bb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9de91c2db0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f9dc24ec3b0, ), path: LookupPath( [ S( "domain", Py( 0x00007f9dc24ec3f0, ), ), ], ), }, name_py: Py( 0x00007f9e9d0cd1f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc25fec00, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055e1b7453cf0, ), 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( 0x00007f9dc24ec2c0, ), ), values: [ Py( 0x00007f9dc25feb20, ), Py( 0x00007f9dc25feb90, ), Py( 0x00007f9dc25fec00, ), ], }, 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( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f9dc24ec430, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f9dc24ec470, ), ), ], ), }, name_py: Py( 0x00007f9e99653ab0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e7a8e8ab0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f9dc26b6ce0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f9dc26b6d30, ), ), ], ), }, name_py: Py( 0x00007f9e9963bc80, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "algorithm_application", lookup_key: Simple { key: "algorithm_application", py_key: Py( 0x00007f9dc26b6c90, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f9dc26b6d80, ), ), ], ), }, name_py: Py( 0x00007f9dc60dec40, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9dc2697640, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f9dc24ec4b0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f9dc24ec4f0, ), ), ], ), }, name_py: Py( 0x00007f9e996500b0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5020d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f9dc24ec530, ), path: LookupPath( [ S( "workers", Py( 0x00007f9dc24ec570, ), ), ], ), }, name_py: Py( 0x00007f9e9adb0230, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f9e9d5000d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f9e9b5139a0, ), }, ), frozen: false, }, ], model_name: "MetalSemiconductorClassifierParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055e1b7479730, ), generic_origin: None, post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f9e9b5139a0, ), 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>¶