deepmd.tf.infer.deep_polar
==========================

.. py:module:: deepmd.tf.infer.deep_polar


Classes
-------

.. autoapisummary::

   deepmd.tf.infer.deep_polar.DeepGlobalPolar
   deepmd.tf.infer.deep_polar.DeepPolar


Module Contents
---------------

.. py:class:: DeepGlobalPolar(model_file: str, *args: Any, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = True, neighbor_list: Optional[ase.neighborlist.NewPrimitiveNeighborList] = None, **kwargs: Any)

   Bases: :py:obj:`deepmd.infer.deep_tensor.OldDeepTensor`


   
   Old tensor models from v1, which has no gradient output.
















   ..
       !! processed by numpydoc !!

   .. py:property:: output_tensor_name
      :type: str


      
      The name of the tensor.
















      ..
          !! processed by numpydoc !!


   .. py:method:: eval(coords: numpy.ndarray, cells: Optional[numpy.ndarray], atom_types: Union[list[int], numpy.ndarray], atomic: bool = False, fparam: Optional[numpy.ndarray] = None, aparam: Optional[numpy.ndarray] = None, mixed_type: bool = False, **kwargs) -> numpy.ndarray

      
      Evaluate the model.


      :Parameters:

          **coords**
              The coordinates of atoms.
              The array should be of size nframes x natoms x 3

          **cells**
              The cell of the region.
              If None then non-PBC is assumed, otherwise using PBC.
              The array should be of size nframes x 9

          **atom_types** : :class:`python:list`\[:class:`python:int`] :obj:`or` :obj:`np.ndarray <numpy.ndarray>`
              The atom types
              The list should contain natoms ints

          **atomic**
              If True (default), return the atomic tensor
              Otherwise return the global tensor

          **fparam**
              Not used in this model

          **aparam**
              Not used in this model

          **mixed_type**
              Whether to perform the mixed_type mode.
              If True, the input data has the mixed_type format (see doc/model/train_se_atten.md),
              in which frames in a system may have different natoms_vec(s), with the same nloc.



      :Returns:

          :obj:`tensor`
              The returned tensor
              If atomic == False then of size nframes x output_dim
              else of size nframes x natoms x output_dim











      ..
          !! processed by numpydoc !!


   .. py:property:: output_def
      :type: deepmd.dpmodel.output_def.ModelOutputDef


      
      Get the output definition of this model.
















      ..
          !! processed by numpydoc !!


.. py:class:: DeepPolar(model_file: str, *args: Any, auto_batch_size: Union[bool, int, deepmd.utils.batch_size.AutoBatchSize] = True, neighbor_list: Optional[ase.neighborlist.NewPrimitiveNeighborList] = None, **kwargs: Any)

   Bases: :py:obj:`deepmd.infer.deep_tensor.DeepTensor`


   
   Deep polar model.


   :Parameters:

       **model_file** : :obj:`Path`
           The name of the frozen model file.

       **\*args** : :class:`python:list`
           Positional arguments.

       **auto_batch_size** : :ref:`bool <python:bltin-boolean-values>` or :class:`python:int` or :obj:`AutoBatchSize`, default: :data:`python:True`
           If True, automatic batch size will be used. If int, it will be used
           as the initial batch size.

       **neighbor_list** : :obj:`ase.neighborlist.NewPrimitiveNeighborList`, :obj:`optional`
           The ASE neighbor list class to produce the neighbor list. If None, the
           neighbor list will be built natively in the model.

       **\*\*kwargs** : :class:`python:dict`
           Keyword arguments.














   ..
       !! processed by numpydoc !!

   .. py:property:: output_tensor_name
      :type: str


      
      The name of the tensor.
















      ..
          !! processed by numpydoc !!