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arXiv · 1710.03468

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

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

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Kenta Kanamori, Kazuaki Toyoura, Junya Honda, Kazuki Hattori, Atsuto Seko, Masayuki Karasuyama, Kazuki Shitara, Motoki Shiga, Akihide Kuwabara, Ichiro Takeuchi. 2017-10-10. Exploring a potential energy surface by machine learning for characterizing atomic transport. https://doi.org/10.1103/physrevb.97.125124

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