pele.landscape.local_connect#

class pele.landscape.local_connect.LocalConnect[source]

Source

class LocalConnect:

a class to do a single local connect run, i.e. NEB + transition state search

Parameters:
potpotential object

the potential

mindistcallable

the function which returns the optimized minimum distance between two structures

tsSearchParams: dict

parameters passed to the transition state search algorithm

NEBparamsdict

NEB setup parameters. Use NEBquenchParams for parameters related to the optimization of the band.

nrefine_maxint

the maximum number of NEB transition state candidates to refine

reoptimize_climbingint

the number of iterations to use for re-optimizing the climbing images after the NEB is done.

pushoff_paramsint

parameters for detemining how to find the minima on either side of a transition state

verbosityint

this controls how many status messages are printed. (not really implemented yet)

See also

DoubleEndedConnect

the routine from which local connect is generally called

pele.transition_states.NEB

one of the core routines

pele.transition_states.NEBDriver

the wrapper which sets up NEB

pele.transition_states.findTransitionState

one of the core routine

Notes

this class takes two minima as input, does an NEB run to find transition state candidates, then refines those candidates into transition states. Finally, we fall off either side of the transition states to fine the minima on either side.

This is the core routine of DoubleEndedConnect. It is separated out in order to make parallelization easier. This class intentionally has no knowledge of the global landscape (database, graph, etc.).

LocalConnect.connect()[source]

Source

def connect(self, min1, min2):

  1. NEB to find transition state candidates.

for each transition state candidate:

  1. refine the transition state candidates

3) if successful, fall off either side of the transition state to find the minima the transition state connects. Add the new transition state and minima to the graph