Issue 10, 2023

Origin of performance degradation in high-delithiation LixCoO2: insights from direct atomic simulations using global neural network potentials

Abstract

LixCoO2 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, we provide direct theoretical understandings by performing long-time and large-size atomic simulations using the global neural network potential (GNNP) developed by ourselves. We propose a self-consistent picture as follows: (i) CoO2 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; (iii) accumulated strains cause the rupture of Li diffusion channels and result in the 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. While our molecular dynamics (MD) under strain effects provides direct evidence for the above findings, we note that the uniform distribution of Li ions can effectively improve the cyclicality and safety issues but is very challenging to realize especially at high-delithiation states. Correspondingly, a more practically feasible strategy is suppressing accumulations of strains by controlling charge and discharge conditions as well as suppressing the gliding, i.e., by inserting some strongly-bonded ions between the CoO2 layers and/or by coating the LixCoO2 grains, which will be helpful for improving the performance of lithium-ion batteries (LIBs). The current work demonstrates the feasibility and necessity of atomic simulations with a global perspective to provide more and critical insights into LIBs, thus opening new doors in this field.

Graphical abstract: Origin of performance degradation in high-delithiation LixCoO2: insights from direct atomic simulations using global neural network potentials

Supplementary files

Article information

Article type
Paper
Submitted
11 Dec 2022
Accepted
13 Feb 2023
First published
15 Feb 2023

J. Mater. Chem. A, 2023,11, 5370-5379

Origin of performance degradation in high-delithiation LixCoO2: insights from direct atomic simulations using global neural network potentials

P. Zhang, C. Shang, Z. Liu, J. Yang and X. Gong, J. Mater. Chem. A, 2023, 11, 5370 DOI: 10.1039/D2TA09633A

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