通过机器学习探索表征原子输运的势能面
Exploring a potential energy surface by machine learning for characterizing atomic transport
摘要：
我们提出了一种机器学习方法，用于评估的基础上优先选择的优势点的原子运输的原子运输的势垒。所提出的方法产生大量的随机样本的整个势能面（PES）的概率高斯过程模型的PES，这使得定义的可能性的优势点。与传统的轻推弹性带方法相比，该方法的鲁棒性和效率被证明在十几个模型的情况下，质子在氧化物中的扩散。
英文摘要：
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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