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Published on: September 17, 2021
Machine learning molecular dynamics simulations of coordination and diffusion behaviors in lithiated gallium
Qiuyi Fu1, Hao Yuan1, Haitang Wang1
1School of Chemical Engineering, College of Chemistry and Materials, Jiangxi Normal University, Nanchang, 330022, China. gbzhou@jxnu.edu.cn.
Machine learning force fields reveal how lithium-gallium alloy structures impact ion transport in flexible battery anodes. Understanding these dynamics is key for developing advanced energy storage solutions.
Area of Science:
- Materials Science
- Computational Chemistry
- Electrochemistry
Background:
- Liquid gallium (Ga) is a promising anode for flexible lithium-ion batteries due to its fluidity and self-healing properties.
- Limited understanding exists regarding the structure and ion transport in lithium-gallium alloy (LGA) phases during battery operation.
Purpose of the Study:
- To develop machine learning force fields (MLFFs) for four key Li-Ga alloy phases.
- To investigate the structural evolution and lithium diffusion behaviors within these LGA phases using molecular dynamics simulations.
Main Methods:
- Development of MLFFs for Li3Ga14, Li2Ga7, LiGa, and Li2Ga.
- Large-scale molecular dynamics simulations to analyze local coordination and diffusion.
- Application of polyhedral template matching and van Hove analysis for structural and dynamic insights.
Main Results:
- Lithiation alters Li local environments, transitioning from Ga-dominated to Li-rich networks.
- All LGA phases exhibit predominantly disordered structures with increasing short-range order upon lithiation.
- Lithium exhibits liquid-like mobility in Li3Ga14 and Li2Ga7, but solid-like dynamics in LiGa and Li2Ga, with Li3Ga14 showing the highest diffusion coefficient.
Conclusions:
- The study clarifies the structure-diffusion relationship across different Li-Ga alloy phases.
- Findings provide crucial theoretical insights into the structural evolution of Ga-based anodes during lithiation.
- Understanding these relationships is vital for optimizing flexible battery performance.
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