Abstract
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.
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Data availability
The datasets generated and analyzed during the current study are available in https://github.com/KrisZhongLunLi/CHGNet-based-MC-sampling-toolkit.
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Acknowledgements
This work has been financially supported by the National Science and Technology Council, Taiwan (NSTC 113-2113-M-011-003-MY3, 114-2639-E-011-001-ASP, 115-2927-I-011-502, and 114-2923-E-011-001) and Sustainable Electrochemical Energy Development (SEED) Center supported by the Ministry of Education (MOE), Taiwan. The authors thank the Taiwan National Center of High-Performance Computing (NCHC) for computing resources. We also acknowledge support from the UMC Fellowship.
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L.T.W., Z.L.L., and S.Y.Y. designed and performed the computations under the supervision of P.K. and J.C.J. All authors discussed the computational results. L.T.W. wrote the manuscript with support from Z.L.L. and S.Y.Y. P.K., and J.C.J. contributed to revising the manuscript. J.C.J. served as the lead principal investigator.
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Wu, LT., Li, ZL., Yen, SY. et al. Probing entropic control of stacking phase preference in layered oxide cathodes for sodium-ion batteries via machine-learning potentials. npj Comput Mater (2026). https://doi.org/10.1038/s41524-025-01954-2
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DOI: https://doi.org/10.1038/s41524-025-01954-2
