Origin of performance degradation in high-delithiation Li$_x$CoO$_2$: insights from direct atomic simulations using global neural network potentials

Abstract: Li$_x$CoO$_2$ based batteries have serious capacity degradation and safety issues when cycling at high-delithiation states but full and consistent mechanisms are still poorly understood. Herein, a global neural network potential (GNNP) is developed to provide direct theoretical understandings by performing long-time and large-size atomic simulations. We propose a self-consistent picture as follows: (i) CoO$_2$ layers are easier to glide with longer distances at more highly delithiated states, resulting in structural transitions and structural inhomogeneity; (ii) at regions between different phases with different Li distributions due to gliding, local strains are induced and accumulate during cycling processes; (3) accumulated strains cause the rupture of Li diffusion channels and result in formation of oxygen dimers during cycling especially when Li has inhomogeneous distributions, leading to capacity degradations and safety issues. We find that large tensile strains combined with inhomogeneous distributions of Li ions play critical roles in the formation processes of blocked Li diffusion channels and the oxygen dimers at high-delithiation states, which could be the fundamental origins of capacity degradations and safety issues. Correspondingly, suppressing accumulations of strains by controlling charge and discharge conditions as well as suppressing the gliding will be helpful for improving the performance of lithium-ion batteries (LIBs).