A Deep Neural Network Interface Potential for Li‐Cu Systems. Issue 27 (26th August 2022)

• Record Type: Journal Article
• Authors: Lai, Genming; Jiao, Junyu; Fang, Chi; Zhang, Ruiqi; Xu, Xianqi; Sheng, Liyuan; Jiang, Yao; Ouyang, Chuying; Zheng, Jiaxin
• Is Part Of: Advanced materials interfaces. Volume 9:Issue 27(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-26
• Subjects: deep neural network -- Li metal batteries -- Li–Cu interface -- molecular dynamic simulation -- potential; Materials science -- Periodicals; 620.11
• DOI: 10.1002/admi.202201346
• Languages: English
• ISSNs: 2196-7350

Abstract

Abstract: Copper foil is one of the most commonly used current collector materials in Li metal batteries. However, many problems on the Li–Cu interface have not been effectively solved due to the lack of a fundamental understanding of Li–Cu interaction at the atomic scale. In this work, a deep neural network interface potential for Li‐Cu systems using neural networks combined with active learning strategies is developed. The potential shows excellent performances on the energy and force calculations, physical properties predictions, and structure explorations. Moreover, the study of the Li adsorption behaviors on the Cu surface demonstrates the accuracy of this potential in the investigation of the Li–Cu interface. This potential for the Li‐Cu systems provides an important opportunity to advance the understanding of interface problems in Li metal batteries. Abstract : A machine‐learning‐based Li‐Cu potential model with quantum‐mechanical computational accuracy is developed, which shows excellent performances in many aspects. It can act as an effective tool to investigate a series of problems in the Li metal battery with high accuracy and efficiency, such as the dynamic processes of Li deposition and growth.