pele.transition_states.NEBDriver

class pele.transition_states.NEBDriver(potential, coords1, coords2, k=100.0, max_images=50, image_density=10.0, iter_density=10.0, verbose=0, factor=1.05, NEBquenchParams=None, adjustk_freq=0, adjustk_tol=0.1, adjustk_factor=1.05, dneb=True, reinterpolate_tol=0.1, reinterpolate=0, adaptive_nimages=False, adaptive_niter=False, interpolator=<function interpolate_linear at 0x323ad70>, distance=<function distance_cart at 0x37850c8>, **kwargs)

driver class for NEB

The NEBDriver wraps calls for NEB from LocalConnect. The driver class is responsible for setting up the initial interpolation and optimizing the band.

Parameters :

potential :

the potential object

coords1, coords2 : array

the structures to connect with the band

k : float, optional

the elastic band spring constant

max_images : int

the maximum number of NEB images

image_density : float

how many NEB images per unit distance to use.

iter_density : float

how many optimization iterations per unit distance to use.

adjustk_freq : integer

frequency to adjust k, set to zero to disable

adjustk_tol : float

tolerance for adjusting k up or down

adjustk_factor : float

the multiplicative factor used to adjust k

dneb : bool

use DNEB (Doubly-Nudged Elastic Band) rather than NEB

reinterpolate : integer

reinterpolate the path to achieve equidistant spacing every so many steps

reinterpolate_tol : float

tolerance for reinterpolation, only reinterpolate if relative change in nimages or distance variation are above tolerance

adaptive_nimages : bool

adjust number of images on reinterpolate to match image density

adaptive_niter : bool

adjust number of iterations if nimages is adjusted

factor : float

The number of images is multiplied by this factor. If the number of images is already at it’s maximum, then the number of iterations is multiplied by this factor instead

verbose : integer

interpolator : callable, optional

the function used to do the path interpolation for the NEB

NEBquenchParams : dict

parameters passed to the minimizer

kwargs : keyword options

additional options are passed to the NEB class

See also

NEB, InterpolatedPath

Methods

generate_path(coords1, coords2)
params([obj])
prepare([path])
run()