agedi.api.prediction
====================

.. py:module:: agedi.api.prediction

.. autoapi-nested-parse::

   Force-field prediction using a trained regressor.



Functions
---------

.. autoapisummary::

   agedi.api.prediction.predict


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

.. py:function:: predict(diffusion: Agedi, structures: Sequence[ase.Atoms], *, batch_size: int = 64, cutoff: Optional[float] = None) -> List[ase.Atoms]

   Predict energies and forces for input structures using a trained force-field.

   The model must have been trained with ``force_field=True`` (i.e. it must
   have a ``regressor_model`` attached).  The predicted energy and forces are
   attached to the returned :class:`~ase.Atoms` objects via an
   :class:`~ase.calculators.singlepoint.SinglePointCalculator`.

   :param diffusion: A trained :class:`~agedi.Agedi` model with a force-field
                     regressor (trained with ``--force_field``).
   :param structures: Input ASE :class:`~ase.Atoms` objects to run predictions on.
   :param batch_size: Number of structures per inference batch.  Defaults to ``64``.
   :param cutoff: Neighbour-list cutoff in Å.  When ``None`` (default), the cutoff is
                  read from the model's representation automatically.

   :returns: The input structures with a
             :class:`~ase.calculators.singlepoint.SinglePointCalculator` attached
             containing the predicted energy and/or forces.
   :rtype: List[Atoms]

   :raises ValueError: If the model does not have a force-field regressor.