| Abstract Scope |
Li-rich transition metal oxides are promising as next-generation cathode materials, offering the potential to nearly double energy storage capacity. This significant enhancement in capacity originates from oxygen redox activity. However, these materials experience capacity and voltage degradation due to irreversible structural changes, including transition metal migration, oxygen evolution, and surface densification. To investigate these phenomena, we developed a Machine Learning Interatomic Potential (MLIP) based on the NequIP framework, achieving energy, force, and stress prediction accuracies within 11 meV/atom, 65 meV/Å, and 4 meV/Å3of DFT, respectively. Using this potential, we explored structural transitions across lithiation levels. Our studies show lithium-ion migration from octahedral to tetrahedral sites and the formation of oxygen dimers at low lithiation levels. We also observe that oxygen dimers with longer O–O bonds become more stable as lithiation increases. These findings offer valuable insights into the structural changes occurring in Li-rich cathodes and help explain their performance. |