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<p>High-entropy layered oxides are promising sodium-ion battery (SIB) cathodes, yet the fundamental role of conformational entropy in stacking phase preference remains unclear. Here, we combine density functional theory (DFT), ab initio molecular dynamics (AIMD), and a fine-tuned CHGNet machine-learning interatomic potential (MLIP) to investigate representative high-entropy (Na<sub>0.8</sub>Ni<sub>0.2</sub>Fe<sub>0.2</sub>Co<sub>0.2</sub>Mn<sub>0.2</sub>Ti<sub>0.2</sub>O<sub>2</sub>) and low-entropy (Na<sub>0.8</sub>Mn<sub>0.6</sub>Co<sub>0.4</sub>O<sub>2</sub>) layered oxides in both O3 and P2 phases. A three-stage Monte Carlo sampling strategy was developed to explore transition-metal arrangements, Na/vacancy distributions, and representative low-energy conformations. The fine-tuned CHGNet achieved near-DFT accuracy while enabling large-scale sampling at orders of magnitude lower cost. Our analyses reveal that high-entropy oxides exhibit stronger Na–TMO<sub>2</sub> interactions, broader O–TM bond length distributions, and smaller interlayer distance ratios compared with their low-entropy counterparts. These structural features favor O3-phase stabilization in cases where conventional ionic-potential descriptors are insufficient to clearly distinguish between O3- and P2-type layered oxides. Bond-length analyses further indicate that Jahn–Teller distortions in Mn are mitigated in high-entropy oxides, contributing to enhanced structural stability. This study establishes conformational entropy as a decisive factor, alongside Na ionic and cationic potentials, in governing stacking phase stability, and highlights the power of MLIP-accelerated modeling for exploring high-entropy materials and guiding the rational design of next-generation SIB cathodes.</p>

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Probing entropic control of stacking phase preference in layered oxide cathodes for sodium-ion batteries via machine-learning potentials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Liang-Ting Wu,
  • Zhong-Lun Li,
  • Shih-Ying Yen,
  • Payam Kaghazchi,
  • Jyh-Chiang Jiang

摘要

High-entropy layered oxides are promising sodium-ion battery (SIB) cathodes, yet the fundamental role of conformational entropy in stacking phase preference remains unclear. Here, we combine density functional theory (DFT), ab initio molecular dynamics (AIMD), and a fine-tuned CHGNet machine-learning interatomic potential (MLIP) to investigate representative high-entropy (Na0.8Ni0.2Fe0.2Co0.2Mn0.2Ti0.2O2) and low-entropy (Na0.8Mn0.6Co0.4O2) layered oxides in both O3 and P2 phases. A three-stage Monte Carlo sampling strategy was developed to explore transition-metal arrangements, Na/vacancy distributions, and representative low-energy conformations. The fine-tuned CHGNet achieved near-DFT accuracy while enabling large-scale sampling at orders of magnitude lower cost. Our analyses reveal that high-entropy oxides exhibit stronger Na–TMO2 interactions, broader O–TM bond length distributions, and smaller interlayer distance ratios compared with their low-entropy counterparts. These structural features favor O3-phase stabilization in cases where conventional ionic-potential descriptors are insufficient to clearly distinguish between O3- and P2-type layered oxides. Bond-length analyses further indicate that Jahn–Teller distortions in Mn are mitigated in high-entropy oxides, contributing to enhanced structural stability. This study establishes conformational entropy as a decisive factor, alongside Na ionic and cationic potentials, in governing stacking phase stability, and highlights the power of MLIP-accelerated modeling for exploring high-entropy materials and guiding the rational design of next-generation SIB cathodes.