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

N Ran and CB Li and QW Cui and DZ Xue and JJ Liu, NPJ COMPUTATIONAL MATERIALS, 11, 208 (2025).

DOI: 10.1038/s41524-025-01714-2

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/& Aring; 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.

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