Machine learning molecular dynamics simulations of coordination and diffusion behaviors in lithiated gallium electrodes

Abstract

Liquid gallium (Ga) has emerged as a promising anode material for flexible lithium-ion batteries owing to its exceptional fluidity, intrinsic self-healing capability, and high theoretical capacity. However, the understanding of structure and transport properties across diverse Li–Ga alloy (LGA) phases formed during lithiation remains limited. Here, we develop machine learning force fields (MLFFs) for four experimentally identified LGAs (Li₃Ga₁₄, Li₂Ga₇, LiGa, and Li₂Ga) and perform large-scale molecular dynamics simulations to investigate local coordination and diffusion behaviors. Our simulations reveal a lithiation-induced evolution of Li local environments from Ga-dominated coordination shells in Li₃Ga₁₄ and Li₂Ga₇ to Li-rich networks in LiGa and Li₂Ga. Polyhedral template matching further indicates that all four LGA phases remain predominantly disordered, while the fraction of short-range ordered motifs increases upon lithiation. Consistently, Li exhibits liquid-like mobility in Li₃Ga₁₄ and Li₂Ga₇, but strongly localized, solid-like dynamics in LiGa and Li₂Ga. The Li diffusion coefficient in Li₃Ga₁₄ (4.46 × 10⁻¹¹ m² s⁻¹) is nearly an order of magnitude higher than that in Li₂Ga₇ (3.14 × 10⁻¹² m² s⁻¹), primarily due to the weaker interactions between Li and the surrounding Li/Ga in the former system. Finally, van Hove analysis and trajectory visualizations uncover intermittent residence–jump (hopping-like) dynamics in Li₂Ga₇ and Li₂Ga. Overall, our findings clarify the structure–diffusion relationship across different LGAs and offer important theoretical insights into the structural evolution of Ga-based anodes during the lithiation process.