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<p>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 high-accuracy MLIP. Here, we constructed a Li<sub>1.2-x</sub>Mn<sub>0.6</sub>Ni<sub>0.2</sub>O<sub>2</sub> (<i>x</i> = 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 O<sub>2</sub><sup>2-</sup>, O<sub>2</sub><sup>-</sup> or O<sub>3</sub><sup>-</sup> intermediates drives out-of-plane Mn and Ni migration, resulting in O<sub>2</sub> molecules forming within the bulk structure. O<sub>3</sub><sup>-</sup> intermediates has a certain ability to capture O<sub>2</sub>, which may help alleviate the formation of lattice O<sub>2</sub>.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Nian Ran,
  • Chengbo Li,
  • Qinwen Cui,
  • Dezhen Xue,
  • Jianjun Liu

摘要

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 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 has a certain ability to capture O2, which may help alleviate the formation of lattice O2.