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

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.