It is possible to draw a firm conclusion as to how well the NEB represents the pathway when the corresponding stationary points and steepest-descent paths are already known. We therefore base our criterion for the effectiveness of an NEB calculation on whether we obtain good estimates of all the transition states. By considering several systems of increasing complexity we hope to obtain comparisons that are not specific to a particular pathway.

Connections between two minima are defined by calculating an approximation to the two steepest-descent paths that lead downhill from each transition state, and two transition states are considered connected if they are linked to the same minimum via a steepest-descent path. We will say that minima are `connected' if there exists a path consisting of one or more transition states and intermediate minima linking them. Permutational isomers of the same minimum are distinguished in these calculations. We refer to the chain of images produced by the NEB calculation as `connected' if going downhill from each transition state using steepest-descent minimisation yields a set of minima that contains the endpoints linked together.

For NEB/SQVV calculations we used the NEB formulation defined in Reference henkelmanj00. DNEB is different from the above method because it includes an additional component in the NEB gradient, as described by Equation 2.39 and Equation 2.40. In addition, for the following DNEB calculations we did not remove overall rotation and translation (ORT), because we believe it is unnecessary when our gradient modification is used. To converge transition state candidates tightly we employed EF optimisation, limiting the maximum number of EF iterations to five with an RMS force convergence tolerance of . (Standard reduced units for the Lennard-Jones potential are used throughout this work.) Initial guesses for all the following calculations were obtained by linear interpolation between the endpoints. To prevent `atom-crashing' from causing overflow in the initial guess we simply perturbed such images slightly using random atomic displacements of order reduced units.

In each case we first minimised the Euclidean distance between the endpoints with respect to overall rotation and translation using the method described in Reference rhee00.[1] Structure alignment methods that are based on finding a pseudorotation matrix that satisfies Eckart axis conditions might equally well be used for this purpose [147,145,143,144,146]. SQVV minimisation was performed with a time step of and a maximum step size per degree of freedom of . This limit on the step size prevents the band from becoming `discontinuous' initially and plays an important role only during the first or so iterations. The limit was necessary because for the cases when the endpoints are permutational isomers linear interpolation usually yields bands with large gradients, and it is better to refrain from taking excessive steps at this stage. We did not try to select low energy initial guesses for each rearrangement individually, since one of our primary concerns was to automate this process. For the same reason, all the L-BFGS optimisations were started from guesses preoptimised using SQVV until the RMS force dropped below .

Table 2.1 shows the minimum number of images and gradient calls required to produce a connected pathway using the DNEB/L-BFGS and DNEB/SQVV methods. These calculations were run assuming no prior knowledge of the path. Normally there is no initial information available on the integrated path length or the number of intermediate minima between the endpoints, and it takes some experimentation to select an appropriate number of images. Our strategy is therefore to gradually increase the number of images to make the problem as computationally inexpensive as possible. Hence we increment the number of images and maximum number of NEB iterations in each calculation until a connected path is produced, in the sense defined above. The permitted image range was and the maximum number of NEB iterations ranged from . We were unable to obtain connected pathways for any of the four LJ rearrangements using the NEB/SQVV approach.

Method | 1-2 | 2-3 | 3-4 | 4-5 |

DNEB/L-BFGS | 5(1720) | 18(30276) | 11(2486) | 18(8010) |

DNEB/SQVV | 16(21648) | - | 10(14310) | - |

Table 2.2 presents the results of analogous calculations where we keep the number of images fixed to 50. Unlike the performance comparison where the number of images is kept to a minimum (Table 2.1), these results should provide insight into the performance of the DNEB approach when there are sufficient images to resolve all the transition states. All the optimisations for a particular rearrangement converged to the same or an enantiomeric pathway unless stated otherwise. The energy profiles that correspond to these rearrangements are shown in Figure 2.8.

Method | 1-2 | 2-3 | 3-4 | 4-5 |

DNEB/L-BFGS | 131 | 493 | 171 | 326 |

DNEB/SQVV | 1130 | 15178 | 2777 | 23405 |

NEB/SQVV | 11088 | - | 30627 | - |

The number of iterations is the sum of the SQVV preoptimisation steps (100 on average) and the actual number of iterations needed by L-BFGS minimiser. This value is not directly comparable since DNEB converged to a different path that contains more intermediate minima. Dashes signify cases where we were unable to obtain a connected pathway.

From Table 2.1 and Table 2.2 we conclude that in all cases the DNEB/L-BFGS approach is more than an order of magnitude faster than DNEB/SQVV. It is also noteworthy that the DNEB/SQVV approach is faster than NEB/SQVV because overall rotation and translation are not removed. Allowing the images to rotate or translate freely can lead to numerical problems, namely a vanishing norm for the tangent vector, when the image density is very large or the spring force constant is too small. However, when overall rotation and translation are not allowed there is less scope for improving a bad initial guess, because the images are more constrained. This constraint usually means that more images are needed or a better initial guess is required. Our experience is that such constraints usually slow down convergence, depending on which degrees of freedom are frozen: if these are active degrees of freedom (see above) the whole cluster must move instead, which is usually a slow, concerted multi-step process.