Dynamic oxygen-redox evolution of cathode reactions based on the multistate equilibrium potential model

Abstract

Understanding the mechanisms of oxygen anion electrochemical reactions within crystals has long perplexed electrochemical scientists and hindered the structural design and composition optimization of Li-ion cathode materials. Machine learning interatomic potentials (MLIP) are transforming the landscape by enabling high-accuracy atomistic modeling on a large scale in materials science and chemistry. The diversity and comprehensiveness of the dataset are fundamental to building a high-accuracy MLIP. Here, we constructed a Li1.2–xMn0.6Ni0.2O2 (x = 0–1.04) dataset that includes over 15,000 chemical non-equilibrium and chemical equilibrium structures. Using this dataset, we trained an MLIP model (multistate equilibrium potential, named MSEP) with test accuracies of 0.008 eV/atom and 0.153 eV/Å for energy and force, respectively. Through MSEP-MD simulations, we identify a kinetically viable O-redox mechanism in which the formation of transient interlayer O22−, O2− or O3− intermediates drives out-of-plane Mn and Ni migration, resulting in O2 molecules forming within the bulk structure. O3− intermediates have a certain ability to capture O2, which may help alleviate the formation of lattice O2.