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Li$_x$CoO$_2$ based batteries have serious capacity degradation and safetyissues when cycling at high-delithiation states but full and consistentmechanisms are still poorly understood. Herein, a global neural networkpotential (GNNP) is developed to provide direct theoretical understandings byperforming long-time and large-size atomic simulations. We propose aself-consistent picture as follows: (i) CoO$_2$ layers are easier to glide withlonger distances at more highly delithiated states, resulting in structuraltransitions and structural inhomogeneity; (ii) at regions between differentphases with different Li distributions due to gliding, local strains areinduced and accumulate during cycling processes; (3) accumulated strains causethe rupture of Li diffusion channels and result in formation of oxygen dimersduring cycling especially when Li has inhomogeneous distributions, leading tocapacity degradations and safety issues. We find that large tensile strainscombined with inhomogeneous distributions of Li ions play critical roles in theformation processes of blocked Li diffusion channels and the oxygen dimers athigh-delithiation states, which could be the fundamental origins of capacitydegradations and safety issues. Correspondingly, suppressing accumulations ofstrains by controlling charge and discharge conditions as well as suppressingthe gliding will be helpful for improving the performance of lithium-ionbatteries (LIBs).

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