2019_EJRNL_PP_JOSE_A__GARRIDO_TORRES_1.pdf
Terbatas Ratnasari
» ITB
Terbatas Ratnasari
» ITB
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 in the saddle point in the target model 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.