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

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.