3 years ago

Exploring a potential energy surface by machine learning for characterizing atomic transport.

Kenta Kanamori, Ichiro Takeuchi, Atsuto Seko, Kazuki Hattori, Kazuaki Toyoura, Junya Honda, Motoki Shiga, Akihide Kuwabara, Kazuki Shitara, Masayuki Karasuyama

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.

Publisher URL: http://arxiv.org/abs/1710.03468

DOI: arXiv:1710.03468v2

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