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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>, 'config': {'title': 'S3ParametersCrystals'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.S3ParametersCrystals'>>]}, 'ref': 'gt4sd.properties.crystals.core.S3ParametersCrystals:94662808900624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', '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_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( 0x0000561867191410, ), serializer: Fields( GeneralFieldsSerializer { fields: { "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), 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: "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( 0x00007f1dce8d2cb0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8d3c70, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8d3fb0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8d8070, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8d80b0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8d80f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), 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( 0x00007f1dce8ca420, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8c8bc0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8ca3d0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8c8c10, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), 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( 0x0000561867191410, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.CGCNNParameters'>, 'config': {'title': 'CGCNNParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.CGCNNParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.CGCNNParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.CGCNNParameters:94662808902144', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x0000561867191a00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), 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: 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( 0x00007f1dce8d9ff0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8d9fb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8d9f70, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8d9f30, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8da030, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8da070, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cad80, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cadd0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cad30, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cae20, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), 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( 0x00007f1dce8da0b0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce8da0f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce8da130, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce8da170, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "CGCNNParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867191a00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>, 'config': {'title': 'MetalNonMetalClassifierParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.MetalNonMetalClassifierParameters:94662808911872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MetalNonMetalClassifier', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'RFC', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', '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_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( 0x0000561867194000, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea86c64b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8c8fd0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), 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: "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( 0x00007f1dce8dbbb0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8dbb70, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8dbb30, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8dbaf0, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8dbbf0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8dbc30, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea86c64b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cb0a0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cb0f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cb050, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cb140, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8c8fd0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MetalNonMetalClassifierParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867194000, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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'>)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>, 'config': {'title': 'FormationEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.FormationEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.FormationEnergyParameters:94662808922384', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'FormationEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x0000561867196910, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc5b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), 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: "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( 0x00007f1dce8f22b0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8f2270, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8f2230, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8f21f0, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8f22f0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8f2330, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cb3c0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cb410, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cb370, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cb460, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc5b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce8f2370, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce8f23b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce8f23f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce8f2430, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "FormationEnergyParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867196910, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>, 'config': {'title': 'AbsoluteEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.AbsoluteEnergyParameters:94662808931600', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'AbsoluteEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x0000561867198d10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dceaa3b30, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), 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: "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( 0x00007f1dce8d3bb0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8d3370, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8d3c30, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8d3770, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8d2eb0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8d02f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cb320, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cafb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cb2d0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cb000, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dceaa3b30, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce8d27f0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce8d3970, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce8d2d70, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce8d3a70, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "AbsoluteEnergyParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x0000561867198d10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.BandGapParameters'>, 'config': {'title': 'BandGapParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.BandGapParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.BandGapParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.BandGapParameters:94662808943040', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'BandGap', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x000056186719b9c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc2b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), 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: "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( 0x00007f1dce8fc730, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8fc6f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8fc6b0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8fc670, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8fc770, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8fc7b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cb820, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cb870, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cb7d0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cb8c0, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc2b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce8fc7f0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce8fc830, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce8fc870, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce8fc8b0, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "BandGapParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186719b9c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>, 'config': {'title': 'FermiEnergyParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.FermiEnergyParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.FermiEnergyParameters:94662808956608', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'FermiEnergy', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x000056186719eec0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc470, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), 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: "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( 0x00007f1dce8fec30, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8febf0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8febb0, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8feb70, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8fec70, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce8fecb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cbb40, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cbb90, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cbaf0, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cbbe0, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc470, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce8fecf0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce8fed30, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce8fed70, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce8fedb0, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "FermiEnergyParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000056186719eec0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.BulkModuliParameters'>, 'config': {'title': 'BulkModuliParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.BulkModuliParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.BulkModuliParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.BulkModuliParameters:94662808969616', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'BulkModuli', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x00005618671a2190, ), serializer: Fields( GeneralFieldsSerializer { fields: { "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8bc670, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), 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: "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( 0x00007f1dce9051b0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce905170, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce905130, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce9050f0, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce9051f0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce905230, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cbe60, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cbeb0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cbe10, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cbf00, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8bc670, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce905270, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce9052b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce9052f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce905330, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "BulkModuliParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618671a2190, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.ShearModuliParameters'>, 'config': {'title': 'ShearModuliParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.ShearModuliParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.ShearModuliParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.ShearModuliParameters:94662808983184', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'ShearModuli', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x00005618671a5690, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8d1430, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), 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: "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( 0x00007f1dce8f3b30, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce8f3ab0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce8f2d30, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce8f3a30, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce8f3c70, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce904130, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce8cb5f0, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce8cb640, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce8cb780, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce8cbf50, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8d1430, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce904030, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce9053b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce9054f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce905430, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "ShearModuliParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618671a5690, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>, 'config': {'title': 'PoissonRatioParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.PoissonRatioParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.PoissonRatioParameters:94662808996752', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'PoissonRatio', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x00005618671a8b90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), serializer: Some( Str( StrSerializer, ), ), }, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8d14b0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), 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( 0x00007f1dce906fb0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce906f70, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce906f30, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce906ef0, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce906ff0, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce907030, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce908260, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce9082b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce908210, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce908300, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8d14b0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce907070, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce9070b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce9070f0, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce907130, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "PoissonRatioParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618671a8b90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__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_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>, 'config': {'title': 'MetalSemiconductorClassifierParameters'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters'>>]}, 'ref': 'gt4sd.properties.crystals.core.MetalSemiconductorClassifierParameters:94662809010320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'algorithm_application': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'MetalSemiconductorClassifier', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'cgcnn', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'prediction', 'schema': {'type': 'str'}, 'type': 'default'}, 'type': 'model-field'}, 'algorithm_version': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'batch_size': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 256, 'schema': {'type': 'int'}, 'type': 'default'}, 'type': 'model-field'}, 'domain': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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:94662808869760', 'sub_type': 'str', 'type': 'enum'}, 'type': 'default'}, 'type': 'model-field'}, 'workers': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, '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_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( 0x00005618671ac090, ), serializer: Fields( GeneralFieldsSerializer { fields: { "algorithm_version": SerField { key_py: Py( 0x00007f1ea52ed250, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, "algorithm_type": SerField { key_py: Py( 0x00007f1ea52cf4f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dea152330, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "algorithm_application": SerField { key_py: Py( 0x00007f1dcf4e0530, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dce8c9020, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "batch_size": SerField { key_py: Py( 0x00007f1ea52ccc70, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea946a0d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "algorithm_name": SerField { key_py: Py( 0x00007f1ea52ce770, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1e88bc31f0, ), ), serializer: Str( StrSerializer, ), }, ), ), required: true, }, "workers": SerField { key_py: Py( 0x00007f1ea6a99070, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1ea94680d0, ), ), serializer: Int( IntSerializer, ), }, ), ), required: true, }, "domain": SerField { key_py: Py( 0x00007f1ea8dd1270, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f1dcea99310, ), ), serializer: Enum( EnumSerializer { class: Py( 0x0000561867189b80, ), 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: "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( 0x00007f1dce9194f0, ), path: LookupPath( [ S( "algorithm_type", Py( 0x00007f1dce9194b0, ), ), ], ), }, name_py: Py( 0x00007f1ea52cf4f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dea152330, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "domain", lookup_key: Simple { key: "domain", py_key: Py( 0x00007f1dce919470, ), path: LookupPath( [ S( "domain", Py( 0x00007f1dce919430, ), ), ], ), }, name_py: Py( 0x00007f1ea8dd1270, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dcea99310, ), ), on_error: Raise, validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x0000561867189b80, ), 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, values: [ Py( 0x00007f1dcea99230, ), Py( 0x00007f1dcea992a0, ), Py( 0x00007f1dcea99310, ), ], }, 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( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_name", lookup_key: Simple { key: "algorithm_name", py_key: Py( 0x00007f1dce919530, ), path: LookupPath( [ S( "algorithm_name", Py( 0x00007f1dce919570, ), ), ], ), }, name_py: Py( 0x00007f1ea52ce770, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1e88bc31f0, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "algorithm_version", lookup_key: Simple { key: "algorithm_version", py_key: Py( 0x00007f1dce908580, ), path: LookupPath( [ S( "algorithm_version", Py( 0x00007f1dce9085d0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ed250, ), 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( 0x00007f1dce908530, ), path: LookupPath( [ S( "algorithm_application", Py( 0x00007f1dce908620, ), ), ], ), }, name_py: Py( 0x00007f1dcf4e0530, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1dce8c9020, ), ), on_error: Raise, validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), validate_default: false, copy_default: false, name: "default[str]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "batch_size", lookup_key: Simple { key: "batch_size", py_key: Py( 0x00007f1dce9195b0, ), path: LookupPath( [ S( "batch_size", Py( 0x00007f1dce9195f0, ), ), ], ), }, name_py: Py( 0x00007f1ea52ccc70, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea946a0d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, Field { name: "workers", lookup_key: Simple { key: "workers", py_key: Py( 0x00007f1dce919630, ), path: LookupPath( [ S( "workers", Py( 0x00007f1dce919670, ), ), ], ), }, name_py: Py( 0x00007f1ea6a99070, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f1ea94680d0, ), ), on_error: Raise, validator: Int( IntValidator { strict: false, }, ), validate_default: false, copy_default: false, name: "default[int]", undefined: Py( 0x00007f1ea71db950, ), }, ), frozen: false, }, ], model_name: "MetalSemiconductorClassifierParameters", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x00005618671ac090, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f1ea71db950, ), 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_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}¶: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

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

model_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')}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

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>¶