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

• Published in: Nature communications Vol. 10; no. 1; pp. 2667 - 9
• Main Authors: Xie, Tian, France-Lanord, Arthur, Wang, Yanming, Shao-Horn, Yang, Grossman, Jeffrey C.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.06.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301; 639/301/1034/1037; 639/705/117; Amorphous materials; Approximation; Automation; Deep learning; Electrolytes; Functional materials; Humanities and Social Sciences; Interfaces; Lithium; Lithium ions; MATERIALS SCIENCE; Molecular dynamics; multidisciplinary; Neural networks; Science; Science & Technology - Other Topics; Science (multidisciplinary); Simulation; Unsupervised learning; Water chemistry

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed ph...