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20172020
most citedExploring a potential energy surface by machine learning for characterizing atomic transport

37 citations · 43 across the 3 of their papers we have counts for

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cond-mat.mtrl-sci2020★ 6 cited

A Sampling Strategy in Efficient Potential Energy Surface Mapping for Predicting Atomic Diffusivity in Crystals by Machine Learning

Kazuaki Toyoura, Takeo Fujii, Kenta Kanamori +1

We propose a machine-learning-based (ML-based) method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier…