  A local optimization method based on Bayesian Gaussian Processes is developed
and applied to atomic structures. The method is applied to a variety of systems
including molecules, clusters, bulk materials, and molecules at surfaces. The
approach is seen to compare favorably to standard optimization algorithms like
conjugate gradient or BFGS in all cases. The method relies on prediction of
surrogate potential energy surfaces, which are fast to optimize, and which are
gradually improved as the calculation proceeds. The method includes a few
hyperparameters, the optimization of which may lead to further improvements of
the computational speed.
