Title: Exploring a potential energy surface by machine learning for characterizing atomic transport,
Abstract: We propose a machine-learning method for evaluating the potential barrier
governing atomic transport based on the preferential selection of dominant
points for the atomic transport. The proposed method generates numerous random
samples of the entire potential energy surface (PES) from a probabilistic
Gaussian process model of the PES, which enables defining the likelihood of the
dominant points. The robustness and efficiency of the method are demonstrated
on a dozen model cases for proton diffusion in oxides, in comparison with a
conventional nudge elastic band method.