oqp.library.oqp_neb
===================

.. py:module:: oqp.library.oqp_neb

.. autoapi-nested-parse::

   .. code-block:: text

      OQP nudged elastic band (NEB) for OpenQP.

      Backend-agnostic (NumPy only) chain-of-states reaction-path optimizer.  The band
      of images is optimized in Cartesian coordinates with:

      * the improved tangent estimate (Henkelman & Jonsson, J. Chem. Phys. 113, 9978
        (2000));
      * a parallel spring force on the segment-length difference and the perpendicular
        component of the true force;
      * an optional climbing image (Henkelman, Uberuaga, Jonsson, J. Chem. Phys. 113,
        9901 (2000)) that drives the highest-energy image to the saddle; and
      * the FIRE optimizer (Bitzek et al., Phys. Rev. Lett. 97, 170201 (2006)), which
        is robust for the non-conservative NEB force where a quasi-Newton RFO step is
        not appropriate.

      The endpoints (reactant/product) are held fixed.  The caller supplies an
      ``energy_gradient(x_flat) -> (E, g_flat)`` callback (atomic units), so this
      module is testable on analytic potentials without the compiled OQP core.



Classes
-------

.. autoapisummary::

   oqp.library.oqp_neb.NEB


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

.. py:class:: NEB(images, k_spring=0.05, climbing=False, climb_fmax=0.05, logger=None)

   .. py:attribute:: images


   .. py:attribute:: nimage


   .. py:attribute:: k


   .. py:attribute:: climbing
      :value: False



   .. py:attribute:: climb_fmax


   .. py:attribute:: logger
      :value: None



   .. py:attribute:: energies


   .. py:attribute:: gradients


   .. py:method:: run(energy_gradient, fmax_tol=0.002, maxiter=200, dt=0.5, dt_max=1.0, maxmove=0.2, on_iteration=None, frms_tol=None)