Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Understanding local dynamical processes in materials is challenging due to the complexity of the local atomic environments. Here the authors propose a graph dynamical networks approach that is shown to learn the atomic scale dynamics in arbitrary phases and environments from molecular dynamics simul...

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Autores principales: Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey C. Grossman
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/e95ce383366d4ae098b08194f9e7596d
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