Observation of dendrite formation at Li metal-electrolyte interface by a machine-learning enhanced constant potential framework

Taiping Hu, Haichao Huang, Guobing Zhou, Xinyan Wang, Jiaxin Zhu, Zheng Cheng, Fangjia Fu, Xiaoxu Wang, Fuzhi Dai, Kuang Yu () and Shenzhen Xu ()
Additional contact information
Taiping Hu: Peking University
Haichao Huang: Tsinghua University
Guobing Zhou: Peking University
Xinyan Wang: DP Technology
Jiaxin Zhu: Xiamen University
Zheng Cheng: AI for Science Institute
Fangjia Fu: AI for Science Institute
Xiaoxu Wang: DP Technology
Fuzhi Dai: AI for Science Institute
Kuang Yu: Tsinghua University
Shenzhen Xu: Peking University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Uncontrollable dendrites growth during electrochemical cycles leads to low Coulombic efficiency and critical safety issues in Li metal batteries. Hence, a comprehensive understanding of the dendrite formation mechanism is essential for further enhancing the performance of Li metal batteries. Machine learning accelerated molecular dynamics simulations can provide atomic-scale resolution for various key processes at an ab-initio level accuracy. However, traditional molecular dynamics simulation tools hardly capture Li electrochemical depositions, due to lack of an electrochemical constant potential condition. In this work, we propose a constant potential approach that combines a machine learning force field with the charge equilibration method to reveal the dynamic process of dendrites nucleation at Li metal anode surfaces. Our simulations show that inhomogeneous Li depositions, following Li aggregations in amorphous inorganic components of solid electrolyte interphases, can initiate dendrites nucleation. Our study provides microscopic insights for Li dendrites formations in Li metal anodes. More importantly, we present an efficient and accurate simulation method for modeling realistic constant potential conditions, which holds considerable potential for broader applications in modeling complex electrochemical interfaces.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-62824-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62824-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-62824-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().