  We present the incorporation of a surrogate Gaussian Process Regression (GPR)
atomistic model to greatly accelerate the rate of convergence of classical
Nudged Elastic Band (NEB) calculations. In our surrogate model approach, the
cost of converging the elastic band no longer scales with the number of moving
images on the path. This provides a far more efficient and robust transition
state search. In contrast to a conventional NEB calculation, the algorithm
presented here eliminates any need for manipulating the number of images to
obtain a converged result. This is achieved by inventing a new convergence
criteria that exploits the probabilistic nature of the GPR to use uncertainty
estimates of all images in combination with the force of the transition state
in the analytic potential. Our method is an order of magnitude faster in terms
of function evaluations than the conventional NEB method with no accuracy loss
for the converged energy barrier values.
