Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.
TL;DR
Smart computing is steering layered cathode development from empirical trial-and-error toward demand-oriented rational design, highlighting how the "physics-constrained modeling- data-driven prediction-experimental feedback" loop addresses high-voltage stability, phase-transition control, and kinetic limitations, enabling targeted cathode design.
Abstract
Sodium-ion batteries are promising for large-scale energy storage due to resource abundance and low cost. Layered NaxTMO2 cathodes offer high specific capacity and tunable compositions but face challenges in structure-property correlations, sluggish kinetics, and phase instability. First-principles calculations elucidate sodium-storage mechanisms and thermodynamic stability yet struggle with high-dimensional design, finite-temperature effects, and long-timescale dynamics. Artificial intelligence (AI) now enables efficient property prediction, composition screening, and mechanism analysis. Integrating physics-based models with AI establishes an intelligent computing framework-combining mechanistic interpretability and data-driven efficiency-that shifts research from isolated computations to high-throughput, multiscale, closed-loop optimization. This review summarizes advances in theoretical calculations, AI applications, and their convergence toward this paradigm. It highlights how the "physics-constrained modeling-data-driven prediction-experimental feedback" loop addresses high-voltage stability, phase-transition control, and kinetic limitations, enabling targeted cathode design. Future directions include active learning, generative inverse design, and automated experimentation. Overall, intelligent computing is steering layered cathode development from empirical trial-and-error toward demand-oriented rational design.