Toward Predictive Modeling of Layered‐Oxide Cathodes in Na‐Ion Batteries

Layered Na x TMO 2 transition‐metal oxides are among the most promising cathodes for Na‐ion batteries, yet their predictive computational modeling remains intrinsically challenging. Their electrochemical behavior emerges from a delicate interplay between multivalent redox chemistry, structural flexibility, configurational disorder, and kinetic evolution during cycling. In this perspective, we provide a methodology‐centered overview of the main theoretical challenges associated with modeling these materials and discuss practical strategies to bridge electronic accuracy and structural/chemical complexity. We first examine the capabilities and limitations of density functional theory (DFT) in describing cationic and anionic redox processes, highlighting the role of self‐interaction error and the complementary use of DFT+U and hybrid functionals. We then address the explicit treatment of configurational disorder, emphasizing special quasi‐random structures (SQS) and antisite defect modeling as essential tools to capture entropy‐driven stabilization in realistic local environments. Finally, we discuss the interplay between thermodynamics and kinetics in Na intercalation, connecting voltage calculations, ion diffusion barriers and phase‐transition pathways described via NEB methods. Rather than advocating a single best technique, we argue that predictive modeling of Na‐ion cathodes requires integrated multiscale frameworks in which electronic‐structure methods, statistical sampling, and emerging machine‐learning potentials operate synergistically. In this evolving landscape, DFT‐based first‐principles calculations remain the indispensable foundation for transforming atomistic insights into rational materials discovery.