  In this paper, we propose a selective sampling procedure to preferentially
evaluate a potential energy surface (PES) in a part of the configuration space
governing a physical property of interest. The proposed sampling procedure is
based on a machine learning method called the Gaussian process (GP), which is
used to construct a statistical model of the PES for identifying the region of
interest in the configuration space. We demonstrate the efficacy of the
proposed procedure for atomic diffusion and ionic conduction, specifically the
proton conduction in a well-studied proton-conducting oxide, barium zirconate
BaZrO3. The results of the demonstration study indicate that our procedure can
efficiently identify the low-energy region characterizing the proton conduction
in the host crystal lattice, and that the descriptors used for the statistical
PES model have a great influence on the performance.
