• Corpus ID: 219401531

Automatic machine-learning potential generation scheme and simulation protocol for the LiGePS-type superionic conductors

@article{Huang2020AutomaticMP,
title={Automatic machine-learning potential generation scheme and simulation protocol for the LiGePS-type superionic conductors},
author={Jianxing Huang and Linfeng Zhang and Han Wang and Jinbao Zhao and Jun Cheng and E Weinan},
journal={arXiv: Computational Physics},
year={2020}
}
• Published 5 June 2020
• Materials Science
• arXiv: Computational Physics
It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost. This situation has changed drastically in recent years due to the advances in machine learning-based interatomic potentials. Here we implement the Deep Potential Generator scheme to \textit{automatically} generate interatomic potentials for LiGePS-type solid-state electrolyte materials. This…

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