Problems converging a NEB run Quote

本文转自：
http://theory.cm.utexas.edu/forum/viewtopic.php?f=2&t=470&p=1497&hilit=velocity+neb#p1497

For a hard to converge paths I would pre-converge using quick-min (QM) as opposed to FIRE. FIRE tends to be more aggressive and will sometimes blow past a minimum into a separate basin.

You should always use LCLIMB = .TRUE.

• unless for some reason you really don’t want to know the saddle exactly (all method tend to converge better because the path in essence gets pinned at the saddle and the two sides are decoupled making convergence faster)

To pre-converge in a conservative fashion use QM

IOPT=3
TIMESTEP = 0.01 (but can go as low as 0.001 for really high initial forces)
MAXMOVE = 0.2 (you could also control radical change by lowering this but I find it more effective to control the TIMESTEP)

I typically take the view that if an optimizer need to be restricted with maxmove the optimizer/parameters is not well suited for the minimization.

If you path is highly curved in xyz space (ie. rotation around a bond) it would be better to start with a reasonable guess for the saddle oppose to the default linear interpolation. (Note: NEB converges to saddle closest to the initial path not always the lowest saddle)

Once a path seems to be in a reasonable local minima the time step for QM can be increased to speed the optimization. Or you may switch to a more aggressive optimizer like LBFGS

I consider the default parameters for this to be pretty conservative

LBFGS is typically the fastest optimizer but it can have issues/instabilities with tangential movement.
LBFGS optimizer (IOPT=1)
MAXMOVE = 0.2 Maximum allowed step size for translation
ILBFGSMEM = 20 Number of steps saved when building the inverse Hessian matrix
LGLOBAL = .TRUE. Optimize the NEB globally instead of image-by-image
INVCURV = 0.01 Initial inverse curvature, used to construct the inverse Hessian matrix

I typically use a more aggressive INVCURV = 0.05 (should not be used with strong bonds like Oxygen-Oxygen)

Again I feel the best way to control this optimizer is to adjust INVCURV but MAXMOVE can be lowered to 0.05, any lower than that and you might as well return to using QM

I cannot recommended using the line based version of this optimizer (CG would be a better choice if you want to do fd steps and calculate curvatures )
*LLINEOPT = .FALSE. Use a force based line minimizer for translation
*FDSTEP = 5E-3 Finite difference step size for line optimizer

LBFGS biggest instability comes from tangential movements where the NEB is trying to become evenly spaced. The GLOBAL option fixed most of these issues however they can creep back in for a path with many images.

The NEB method works best for 3-8 interior images.