Exploring a potential energy surface by machine learning for characterizing atomic transport
By Kenta Kanamori, K. Toyoura, Junya Honda, Kazuki Hattori, Atsuto Seko, Masayuki Karasuyama, Kazuki Shitara, Motoki Shiga, A. Kuwabara, Ichiro Takeuchi. 2017
22 citations recorded by the source corpus.
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.
Subfields: Electrical and Electronic Engineering, Materials Chemistry
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In Materials Chemistry
Citations in the corpus
- Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, background): “…system (Zielinski et al., 2017; Venkatasubramanian, 2019), and finding minima on complex potential energy surfaces (Chen et al., 2015; Chmiela et al., 2018; Kanamori et al., 2018; Xia and Kais, 2018; Hughes et al., 2019), such as those of large water clusters (Bose et al., 2018; Chan et al., 2019).”